REVIEW PAPER
Near Infrared Spectroscopy —Advanced Analytical Tool in Wheat Breeding,Trade,and Processing
Milica M.Poji ?&Jasna S.Mastilovi ?
Received:21February 2012/Accepted:14June 2012/Published online:23June 2012#Springer Science+Business Media,LLC 2012
Abstract Due to its suitability to be processed in wide range of final products,wheat has an upmost significance among all cereals.The global wheat marketing are becom-ing increasingly demanding with regard to all aspects of wheat quality.To fulfil these demands,each link in wheat production chain —breeding,trade,and processing should be supported by objective and reliable quality assessment tool such as the near infrared spectroscopy (NIRS)tech-nique.The aim of this paper is to summarize the recent advances in the applications of NIRS technique for wheat quality control applicable in wheat breeding,trade,and processing.Although heavily intertwined,each link is char-acterized by specific NIRS applications,with ultimate aim —the production of consumer-appealing and safe final products.
Keywords Near infrared spectroscopy .Wheat .
Composition .End-use quality .Safety .Functionality
Introduction
Product quality improvement and the rationalization of pro-duction in many branches of industry require a permanent quality control of intermediate and finished products and continuous monitoring of technological processes.Different techniques of vibrational spectroscopy (mid-and near infra-red and Raman)have proved its applicability for above-mentioned purposes,where the near infrared spectroscopy (NIRS)appeared to be particularly useful due to the
numerous advantages it provides.The application of the NIRS technique significantly reduces the test time and costs and does not require the use of chemicals and sample preparation,making it superior over traditional chemical analytical methods,especially in industrial quality control and process monitoring applications (Cen and He 2007;McClure 2007;Siesler 2008).The prerequisite for the use of the NIRS method is an appropriate calibration model developed to relate the compound (or property)of interest to the sample's spectral data.NIRS calibration model devel-opment is a very demanding task due to the complex com-position of analyzed samples which result in severe overlapping of interfering bands representing the compo-nent(s)(or properties)of interest.Due to the significant overlapping of NIR bands,the NIR spectral region for many years was mainly used for fundamental research in chemis-try and physics and had no analytical application.The initial application of the NIRS technique in routine analysis is related to the determination of moisture and protein in cereals,in the '60s (Massie and Norris 1965).Since then,the permanent development of the NIRS devices,chemo-metrics techniques,and computer technology has contribut-ed to the development of the NIRS technique that is currently used for testing a wide range of foodstuffs (Work-man 2008).The NIR analytical technique has developed to such an extent that nowadays it uses different spectral ranges,different ways of recording and processing of spec-tra,different sample presentations,and various statistical methods used for calibration model development (Osborne and Fearn 1986;Baeten and Dardenne 2002;Murray 2004;Poji ?2010).
Over the past decade,the NIRS measurements have been extended from determination of the certain constituents present in the sample to the determination of functional properties of samples.In this sense,the functional properties are defined as the ability of a commodity to manifest the
M.M.Poji ?(*):J.S.Mastilovi ?
Institute of Food Technology,University of Novi Sad,Bulevar cara Lazara 1,21000Novi Sad,Serbia
e-mail:milica.pojic@fins.uns.ac.rs
Food Bioprocess Technol (2013)6:330–352DOI 10.1007/s11947-012-0917-3
properties in accordance with intended purposes.Since the functionality of the commodity is strongly affected by its physico-chemical properties which do not manifest character-istics absorption in NIR spectral region,the use of NIRS to predict functionality is based on the relationship between physico-chemical properties and certain constituent having absorptions in NIR region(protein,oil,starch etc.)(Williams 2007).
The objective of this paper is to give an overview of current and most important applications of the NIRS technique in wheat technology.
The Specificity of NIRS Methodology
Principles of NIRS
NIRS is a spectroscopic technique that measures the absorp-tion of NIR radiation(from about800to2,500nm)by the sample.Detection of absorbed NIR radiation by the sample is conducted by measurement of transmittance,reflectance, interactance,and/or transflectance,where the suitable acqui-sition mode is dependent on the sample type and the con-stituent and/or property being analyzed.Transmittance mode involves the measurement of radiation that passes through a sample,whilst reflectance mode involves the measurement of radiation reflected from the sample surface. In transflectance measurement,the radiation is transmitted
through the sample,reflected from a diffusely reflecting plate and then again transmitted back through the sample. Interactance mode involves illumination and detection at laterally separated points on the sample's surface(Osborne 2000).
When sample is exposed to NIR radiation,the energy of chemical bonds involving hydrogen(CH,NH,OH,SH)is changed.Due to very complex composition of the analyzed samples,different organic bonds involving hydrogen absorb the NIR radiation.As a result,the molecular overtone and combination bands seen in the NIR spectral region are very broad,overlapped,and difficult to assign to specific chem-ical components(Fig.1).Consequently,the useful spectral information can only be extracted by applying modern che-mometrics methods in the calibration development process resulting in calibration model that enables the practical application of NIRS.In this regard,the calibration model defines the relationship between the obtained spectral data and the content of the compound or property of interest obtained by selected reference method.Being an indirect method,the performance of the NIRS method is highly dependent on the quality of the reference method used for basis in calibration development process. Therefore,the validation process should be as much comprehensive as possible to demonstrate all the necessary characteristics of the NIRS method for its fitness-for purpose,where accuracy is the characteristics of most concern(Poji?2006).The accuracy of the NIRS models is commonly described by the coefficient of determination(R2),standard error of calibration (SEC),standard error of cross validation,and/or stan-dard error of prediction(SEP).Moreover,as being an indirect method,standard error of reference laboratory method(SEL)has a great importance for interpretation of predictive ability of developed NIRS models,by expressing the ratio of SECV vs.SEL and/or SEP vs. SEL(Hruschka2001).
Chemometric Methods in NIRS
Due to the above-mentioned complexity of NIR spectrum, NIRS analysis requires extraction of relevant information from many variables which is achieved by application of chemometrics which involve the utilization of mathematics, statistics,and computational techniques to NIR spectral data.When applied to NIRS,chemometric techniques could be roughly divided into techniques used for spectral data preprocessing and multivariate techniques used in calibra-tion model development(Pasquini2003;Lin and Ying 2009;Agelet and Hurburgh2010
).
Fig.1NIR transmittance(850–1,050nm)(a)and NIR reflectance spectrum of wheat flour(1,100–2,500nm)(b)
Chemometric Techniques for Spectral Data Pre-processing
Spectral data pre-processing commonly precedes the cali-bration model development stage in order to reduce the spectral interferences and to emphasize the spectral effects related to the sample composition of functional properties. In general,spectral data pre-processing techniques can be divided into two different categories depending on the goal of correction:(1)normalization methods that are used for the correction of the baseline shifts and curvilinearity and for multiplicative interference as a result of scattering and (2)differention methods that are used for correction of peak overlapping and signal enhancement from the chemical information and constant or linear baseline drift(Zeaiter et al.2005;Ozaki et al.2007;Wang and Paliwal2007).
Most commonly used spectral normalization methods comprise the standard normal variate(SNV)transformation, robust normal variate(RNV)transformation,de-trend method (D),and multiplicative scatter correction(MSC).Savitzky and Golay(SG)and Fourier transform(FT)algorithms are com-monly used for data smoothing and differentiation.
The selection of certain pre-processing is not defined by general rule and is usually guided by experience requiring a trial-error process.The selection of adequate pre-processing technique is dependent on the type of NIR signal(i.e.,transmittance,reflectance),sample char-acteristics,instrument design,and ultimate aim(calibra-tion or discrimination).In general,the application of pre-processing techniques enhance the predictive ability of a future calibration model,except in the case when pre-processing excessively smoothes the signal which may adversely influences the model predicting ability.
Multivariate Techniques Used in Calibration Model Development
In general,multivariate techniques used in calibration model development process could be classified as qualitative(1) and quantitative methods(2).Qualitative methods could be classified as:
–Exploratory data analysis(principal component analysis (PCA),factor analysis(FA)),
–Unsupervised pattern recognition analysis(cluster analysis),
–Supervised classification analysis(linear discriminant analysis(LDA),quadratic discriminant analysis (QDA),categorical regression,K-nearest neighbours analysis(KNN),artificial neural networks(ANN))and –High-dimensional classification analysis(soft indepen-dent modeling of class analogy(SIMCA),regularized discriminant analysis(RDA),penalized discriminant analysis(PDA))(Wang and Paliwal2007).
Relatively new chemometric techique introduced for classification purposes is extended canonical variates anal-ysis(ECVA),which enables finding multivariate directions that separates groups and simultaneously classifying these, as well as provides separation of several groups along one direction(N?rgaard et al.2006).
It should be noted that classification techniques could be used for calibration population definition and calibration sample set structuring as previously described by Shenk and Westerhaus(1991a,b).Sample selection on the basis of their spectral characteristics proved to be practical method allow-ing the elimination of the samples with extreme spectra as well as spectrally similar samples.The statistics commonly used for sample selection comprised the Global Mahalanobis distance(GH)indicating the extent to which the spectra of a sample is different from the average spectra of all samples within the calibration set,and Neighborhood Mahalanobis distance(NH)which indicates the extent to which the spectra of a sample is similar to the spectra of neighboring samples within the calibration set.Widely accepted limit for GH is 3.0,while for NH,it is0.60for the spectral interval1,100–2,500nm or0.20for spectral interval850–1,050nm.Hence, the benefits from sample selection are twofold:the sample set is efficiently structured and the substantial savings in wet laboratory analysis are enabled without losing the predictive ability of the NIRS model(Shenk and Westerhaus1991b; Shenk et al.2008).
Quantitative multivariate techniques that proved their po-tential for wide range of NIRS applications are multiple linear regression(MLR),principal component regression(PCR), and partial least squares(PLS),which has appeared to be the most preferred technique.The utilization of listed techniques is based on the assumption that the compound or property of interest and absorbance are linearly related.However,instru-mental factors and/or the physico-chemical nature of the sam-ple could cause non-linearity between the spectral data and sample composition or property requiring non-linear calibra-tion methods,where the artificial neural networks(ANN)has the most dominant importance(Blanco and Villarroya2002; Agelet and Hurburgh2010).Support vector machine(SVM), as a relatively new chemometrics technique,is also used for non-linear modelling(Wang and Paliwal2007).
NIRS Instruments
Each NIRS instrument is designed to contain sample com-partment,light source,wavelength selection system and detector,where the technology used for wavelength selec-tion determines the classification of the modern NIRS instruments,distinguished as:
–Filter instruments(interference filter instruments and acousto-optic tunable filter instruments(AOTF)),
–Diode-based instruments,
–Dispersive instruments and
–Interferometric(Fourier Transform)instruments(Pasquini 2003;Wang and Paliwal2007;Agelet and Hurburgh 2010).
The first phase of the application of the NIRS technique was designated by the usage of simple interference filter instruments with3to more than20filters,representing the absorptions of the most popular applications,such as pro-tein,moisture,and oil in cereals.Nowadays,in the light of recent research innovations,filter instruments might be con-sidered as obsolete technology,but they are very useful for narrow and very specific purposes,especially for on-line and in-field applications(Osborne2000;Pasquini2003; Wang and Paliwal2007;Agelet and Hurburgh2010).
AOTF instruments due to their mechanical simplicity with no moving parts,high scan speed over a wide range of NIR spectral region,and wavelength stability could be a justified choice for different on-line applications.
Diode-based instruments,covering the spectral range of 400–1,700nm,are suitable for specific in-field applications due to its low-cost,portable size,high power efficiency,high sample throughput and long lifetime(Osborne2000;Pasquini 2003;Wang and Paliwal2007;Agelet and Hurburgh2010).
Dispersive instruments,based on diffraction gratings or grating monochromators,are most commonly used NIRS instruments in wheat quality control,used in visible and NIR spectral region.They are typically available as commer-cial stand-alone bench type instruments suitable for measur-ing the intensity of transmission of NIR radiation from the spectral range850–1,050nm or measuring the intensity of diffuse reflection of NIR radiation from the spectral range 1,000–1,400nm.For research and development purposes, requiring wider spectral ranges,stand-alone bench type instruments are available for spectral ranges400–2,500, 400–1,700,1,100–2,500,and/or1,000–2,600nm.
Although dispersive NIR instruments have sufficient throughput for most laboratory applications,lower price and very low noise,over the last10years,Fourier-Transform NIR instruments(FT-NIR)have been more and more used in wheat quality control(Manley et al.2002;Armstrong et al.2006; Foca et al.2011).However,FT-NIR instrument for prediction of selected quality parameters of wheat flour was firstly used by Sorvaniemi et al.(1993).FT-NIR instruments offer higher signal-to-noise ratios,high wavelength accuracy,faster spectral acquisition and higher resolutions compared to grating mono-chromator instruments(Armstrong et al.2006;Osborne2006).
The selection of appropriate NIRS instruments primarily depends on the purpose of application—research or com-mercial applications.Research applications require high versatility in terms of broad spectral range,high spectral resolution,and different sample presentation modes.However,the selection of proper commercial instrument should be guided by type of samples being analyzed,work-ing environment,and the level of expected accuracy(Wang and Paliwal2007;Agelet and Hurburgh2010). Application of NIRS in Wheat Quality Control
Due to its role in human nutrition and its flexibility to be processed in wide range of final products(e.g.bread,pastry, cookies,pasta),wheat has an upmost significance among all cereals.Production of each type of wheat-based final prod-uct requires specific end-use quality which represents the combination of physical,chemical,and rheological proper-ties(Pasha et al.2010).Labor-intensive and time-consuming nature of conventional methods for determina-tion of wheat properties have stimulated the rapid develop-ment of the NIRS method in wheat quality control.The NIRS technique for wheat quality control can be applied in two different ways:as an analytical method for accurate and rapid determination of composition as being applied in trade and as a screening method as being applied in wheat breeding and processing(Williams2007,2008).In the first stage of commercial application of the NIRS technique during the1970s,it was predominantly applicable for pre-diction of protein,moisture,and oil content in cereals while in the second stage during the1980s was predominantly related to the prediction of more complex constituents such as complex carbohydrates.The third stage of NIRS appli-cation in wheat technology is related to the prediction of its end-use properties,while the final stage of NIRS application is related to the prediction of functional and safety quality indicators.Due to the fact that the overall quality of wheat is dependent on its composition,functionality,and safety,all of them have an equal importance in wheat breeding,trade, and processing(Fig.2)(Williams2007,2008).
A number of reports on the potential of NIRS to be used as an analytical tool in wheat quality determination are available in the literature which involves either classification or calibration.In this regard,it should be noted that the ultimate goal of the application of the NIRS technique is not solely the best estimates of the reference values,but a discrimination of the samples according to their spectral prop-erties reflecting their functionality(Christy and Kvalheim 2007;Miralbés2008).The application of NIRS technique for classification purposes is based on fact that samples with different spectral responses are different in physical and/or chemical properties.Differences in the spectral responses are a consequence of genetics,growing conditions,and seasons. Reported NIRS applications for classification purposes are summarized in Table1.The first application of the NIRS technique for classification purposes was for identification of wheat varieties suitable for bread baking(Bertrand et al.
1985).Soon after,several classification applications for wheat were reported.Delwiche and Norris (1993)reported success-ful classification of ground samples of hard red winter (HRW)and hard red spring (HRS)wheat.Neural network classifica-tion of wheat classes was reported by Chen et al.(1995)and Song et al.(1995).Song et al.(1995)used the spectral range 850–1,050nm in transmittance mode,while Chen et al.(1995)used NIR diffuse reflectance spectra in the spectral range 1,100–2,500nm.Chen et al.(1995)indicated that the spectral information in the long NIR wavelength region of ground grain was more useful for differentiation of wheat classes than that in the short NIR wavelength region,while Song et al.(1995)proposed spectral range from 899to 1,049nm for rapid classification without losing accuracy.Delwiche and Massie (1996)reported successful classification of individual kernels of wheat classes by PLS or MLR models which was based on the color (551–750nm)and intrinsic properties (1,120–2,476nm),where its accuracy were depen-dent on the spectral region and the number of wheat classes included into a model.Dowell (2000)as well as Wang et al.(2002)reported the successful application of NIRS for classification of vitreous and non-vitreous wheat kernels sug-gesting that the scattering effects,grain hardness,and differ-ences in starch and protein content between vitreous and non-vitreous kernels contributed the more to the classification of vitreous and non-vitreous kernels.Cocchi et al.(2005)and Foca et al.(2009)demonstrated the successful NIRS applica-tion for distinction of bread wheats and flours of different baking quality.Miralbés (2008)demonstrated discrimination of European wheat varieties originated from two growing regions —Spain and France.Another application of the NIRS technique for classification purposes was reported by Dowell et al.(2009a )which was related to the selecting and sorting waxy wheat kernels by using an automated single kernel NIR sorting system.It was indicated that wavelengths 985,1,130,1,210,1,310and 1,432nm were the most important for the waxiness classification.Delwiche et al.(2011a )also reported the utilization of the NIRS technique to be used for differen-tiation of the starch waxy genotypes with accuracy 90–100%.It was suggested that the spectral differentiation of waxy and non-waxy samples was enabled due to the lipid –amylose complex which reduces with waxiness,physical
differences
Fig.2The relationship between different NIRS
applications in wheat quality control
in endosperm that affect light scatter,or changes in starch crystallinity.The classification models reported utilizing either PCA or ANN,where ANN models proved to perform better than PCA with Mahalanobis distance classifier.It should be noted that application of the NIRS technique for classification purposes is characterized by high complexity due to the fact that NIR spectral features reflect different aspects of wheat quality,where the contribution of certain relevant constituent and/or property can often be low or hidden.Moreover,NIR spectral classification offers new,challenging options in iden-tifying functional properties in breeding,single seed sorting, process control,and in food formulation and production (Jespersen and Munck2009).
NIRS in Wheat Breeding
End-use quality of wheat has always been an issue of a great concern for wheat breeders.End-use quality of wheat is determined on the basis of milling yield,kernel texture, empirical rheological parameters,while the parameters obtained by test baking and pasta preparation(loaf volume, product appearance etc.)are considered as the ultimate indicators of end-use quality of wheat(Mastilovi?et al. 2010).Due to insufficient quantities of sample available in the early stages of breeding process,a reliable and compre-hensive evaluation of wheat functionality is disabled.More-over,this problem is further aggravated by the need to assess the quality of huge number of lines.Therefore,the potential of the NIRS technique as being applied as an assistant tool in breeding programs is firstly recognized among the cereal breeders.Its importance among cereal breeders has been further increased due to its ability to predict real differences in quality among breeding materials and its non-destructive nature(Osborne2006).However,commercially available NIR instruments designed to be used for measuring wheat bulk samples proved to be inapplicable in cereal breeding programs due to the quantity of the sample required.Start-ing from this problem,modifications of standard measuring cells as well as development of new micro-cells has been introduced and a single seed sample accessories has been designed to be embedded into conventional bulk NIRS grain analyzer to enable single kernel analysis(Nielsen et al. 2003).Furthermore,NIR single kernel characterization sys-tem has been developed enabling its further applications for breeding purposes(Delwiche1995,1998;Delwiche and Hruschka2000;Shadow and Carrasco2000;Maghirang and Dowell2003;Bramble et al.2006;Dowell et al. 2006a;Wesley et al.2008;Dowell et al.2009b).Although the data on wheat quality obtained for the individual kernels are not sufficiently significant compared to data obtained for bulk samples,it was demonstrated that the accuracy of single kernel instruments were equivalent to its bulk coun-terpart.The most common single kernel NIR applications reported so far are related to the prediction of protein content (Delwiche1995;Delwiche and Hruschka2000;Pasikatan and Dowell2004;Bramble et al.2006),wheat grain hardness
Table1An overview of selected NIRS applications for classification purposes
Application Acquisition
mode Spectral
range
Type of sample Preprocessing Chemometrics
technique
Classification
performance
Reference
Baking quality Reflectance1,100–2,500Intact grain n/a PCA,MDA87%Bertrand et al.(1985)
Classification of wheat classes Reflectance1,100–2,500Ground meal n/a PCA,DA75–95%Delwiche and
Norris(1993)
Classification of
wheat classes
Reflectance1,100–2,500Ground meal Log1/R,2nd der.ANN95.1–97.0%Chen et al.(1995) Classification of
wheat classes
Transmittance850–1,050Intact kernels2nd der.ANN97–100%Song et al.(1995)
Classification of wheat classes Relectance537–993Intact kernel Log1/R PLS,MLR65–99%Delwiche and
Massie(1996) 1,100–2,500
Vitreousness Reflectance400–1,700Intact kernel Log1/R PLS73–100%Dowell(2000) Vitreousness Reflectance400–1,700Intact kernel Log1/R PLS91.1–100%Wang et al.(2002) Baking quality Reflectance400–2,500Flour Log1/R,SNV
1st,2nd der.
SIMCA0–100%Cocchi et al.(2005) Wheat varieties Reflectance400–2,500Intact kernel WMSC,2nd der.PCA,DA,PLS94–99.5%Miralbés(2008) Baking quality Reflectance400–2,500Flour n/a SIMCA89–100%Foca et al.(2009) Waxy wheat kernels Reflectance900–1,700Intact kernels n/a PLS71–97.6%Dowell et al.(2009a) Starch waxiness Reflectance400–2,500Intact kernels,
ground meal
1st,2nd der.PCA,LDA90–100%Delwiche et al.(2011a)
PCA Principal component analysis;MDA multiple discriminant analysis;DA discriminant analysis;ANN artificial neural network;PLS partial least square;MLR multiple linear regression;SIMCA soft independent modeling of class analogy;LDA linear discriminant analysis;WMSC weighted multiplicative scatter correction;1st der.first derivative;2nd der.second derivative
(Maghirang and Dowell2003;Delwiche et al.2006),as well
as color classification of wheat of different classes(Dowell
1998;Wang et al.1999).Moreover,the development of more
complex applications such as wheat protein composition,
which relate directly to end-use quality,was found to be
particularly important for cereal breeding purposes(Wesley
et al.2008).Differences in the functionality of proteins are
associated with differences in the amino acid composition,the
position of these amino acids in the protein molecules,and the
composition of storage proteins(glutenins and gliadins).First
attempt to predict the amino acid composition in wheat by
NIRS was made by Rubenthaler and Bruinsma(1978)who
demonstrated the possibility to determine lysine concentration
by modeling the ratio lysine/crude protein for several small
wheat populations(R200.72–0.96).Later research of Williams et al.(1984)indicated that second derivative of the log1/R of
ground wheat samples was the most effective in prediction of
lysine,threonine,tryptophan,and methionine content to
achieve the acceptable accuracy for screening purposes in
cereal breeding programs.Almost20years later,Fontaine et
al.(2002)demonstrated the NIRS application for fast and
accurate prediction of methionine(SECV00.009;R200.91), cystine(SECV00.012;R200.92),lysine(SECV00.015;R20 0.91),threonine(SECV00.010;R200.97),tryptophan (SECV00.008;R200.84),arginine(SECV00.027;R20 0.90),isoleucine(SECV00.012;R200.97),leucine(SECV0 0.018;R200.98),and valine(SECV00.019;R200.94)con-tents in wheat and other cereals.
One of the first attempts to develop NIRS calibration model
for prediction of wheat storage protein composition was made
by Delwiche et al.(1998).It was possible due to the fact that
the main fractions of gluten—glutenin and gliadin,exhibit
some differences in their NIR spectra which enable them to
be determined in mixtures with starch(Wesley et al.1999).
Total glutenin,insoluble glutenin,and gliadin contents can
also be measured in whole wheat kernel by NIR against HPLC
as a reference method(Delwiche et al.1998;Seabourn et al.
1998;Wesley et al.1999;2001;Bhandari et al.2000;Millar
2003;Dowell et al.2006b).An overview of the NIRS appli-
cations for prediction of wheat storage protein composition is
given in Table2.The results of Wesley et al.(1999)indicated
that robust calibrations for gliadin and glutenin content can be
developed using both curve-fitting and PLS calibration tech-
https://www.doczj.com/doc/d89243288.html,lar(2003)reported very comprehensive research
on the development of prediction models for high-(HMW)
and low-molecular weight(LMW)glutenins and gliadins in
whole wheat,ground wheat,and flour samples,comprising
the use of discrete wavelengths typical of filter instruments as
well as the visible and NIR spectral range.The models devel-
oped for the whole wheat samples were characterized by
poorer predictive ability in comparison to those developed
for the flour and ground wheat samples.Also,the models
developed by using a filter instrument approach were of unacceptably poor predictive ability in comparison to those developed for the whole visible and NIR spectral range. Moreover,Millar(2003)also tested the possibility of the development the robust calibration models on the basis of typical methods of two countries,UK and France,indicating improved potential of such models for general use.Liu et al. (2009)indicated that the protein bands around1,980,2,040, 2,200,2,260,and2,350nm could be associated with gluten proteins that influence the end-use quality of wheat flour.By summarizing the listed studies concerning the prediction of gliadin and glutenin content,the NIRS method could be designated with sufficient accuracy when applied for screen-ing purposes in breeding programs with small standard error of cross validation and with coefficient of determination higher than0.80.Also,works of(Dowell et al.2006b)showed that efficiency of NIRS calibration models for prediction of soluble glutenins and gliadins were dependent on the used NIR spectral range,form of samples(flour,kernel)and wheat variety,being more efficient when the model was developed for hard red winter wheat than for hard red spring wheat (Table2).Although some authors has recommended the use of instrument with a monochromator in reflectance mode over the range of2,000to2,300nm(Wesley et al.2001),it has been proven that use of instruments in transmittance mode with narrower spectral range below2,000nm could also be applicable for this application(Dowell et al.2006b;Scholz et al.2007).
Besides the functionality determined by the condition of protein–proteinase complex of wheat grain,the functionality of wheat depends on the condition of carbohydrate complex as well.Changes in the functionality of starches are associ-ated with differences in the amylose to amylopectin ratio (Delwiche et al.1998;Williams2007).Hence,flour paste viscosity and the quality of pasta and noodle products are highly influenced by the carbohydrate composition of the grain,in particular amylose and amylopectin fractions (Epstein et al.2002).The NIR spectra of amylose and amylopectin are very similar due to the fact that they consist of the same glucose unit.Therefore,very little progress has been made in estimating the quality of carbohydrate com-ponents in wheat(Epstein et al.2002),whereas several authors have reported on the application of NIR to the measurement of amylose in rice(Villareal et al.1994;Del-wiche et al.1995;Delwiche et al.1996;Shimizu et al.1998; Bao et al.2001).However,certain progress towards the estimation of condition of carbohydrate complex was made by Wesley and Blakeney(2001)who investigated starch gelatinization in starch–protein–water mixtures by means of dynamic NIRS.Their results indicated that wavelength of1,160nm was associated with the bonding of water molecules in the mixture,e.g.,with the gelatinization of the starch granules.The changes in the absorbance at 1,160nm also provided information on the interactions
Table2An overview of the NIRS applications for prediction of wheat storage protein composition
Application Acquisition
mode Spectral
range,nm
Type of sample Chemometrics Calibration
performance
Reference
Preprocessing Modeling R2SECV SEP
Gliadin content Reflectance1,100–2,500Flour MSC PLS0.93n/a n/a Delwiche
et al.(1998)
Reflectance1,100–2,500Intact grain,
flour n/a n/a0.76–0.79n/a n/a Seabourn
et al.(1998)
Reflectance400–2,500Flour2nd der.PLS0.84n/a n/a Wesley et al.
(1999)
Reflectance400–2,500Flour SNV+D,
2nd der.MPLS0.780.43n/a Wesley et al.
(2001)
Reflectance400–2,500Ground meal,
flour SNV+D,1st der.,
2nd der.
MPLS0.72–0.830.42–1.69n/a Millar(2003)
Reflectance400–2,500Intact grain SNV+D1st der.,
2nd der.
MPLS0.42–0.720.60–1.88n/a Millar(2003)
Soluble gliadins content Transmittance850–1,050Intact grain/flour1st der.PLS0.89/0.890.64/0.64n/a(Dowell
et al.2006b) Reflectance835–2,502Intact grain/flour1st der.PLS0.85/0.840.76/0.77n/a Dowell et al.
(2006b) Reflectance950–1,650Intact grain/flour1st der.PLS0.88/0.880.65/0.67n/a Dowell et al.
(2006b) Reflectance400–2,500Intact grain/flour1st der.PLS0.86/0.86 1.72/0.71n/a Dowell et al.
(2006b)
Glutenin content Reflectance1,100–2,500Flour MSC PLS0.91n/a n/a Delwiche et al.
(1998) Reflectance400–2,500Flour2nd der.PLS0.70n/a n/a Wesley et al.
(1999) Reflectance400–2,500Intact grain MSC,1st der.MPLS0.74 1.07n/a Bhandari et al.
(2000) Reflectance400–2,500Flour SNV+D,2nd der.MPLS0.830.38n/a Wesley et al.
(2001) Transmittance850–1,050Intact grain/flour1st der.PLS0.93/0.930.59/0.62n/a Dowell et al.
(2006b) Reflectance835–2,502Intact grain/flour1st der.PLS0.89/0.910.76/0.68n/a Dowell et al.
(2006b) Reflectance950–1,650Intact grain/flour1st der.PLS0.81/0.95 1.02/0.53n/a Dowell et al.
(2006b) Reflectance400–2,500Intact grain/flour1st der.PLS0.92/0.960.64/0.48n/a Dowell et al.
(2006b)
Soluble glutenins content Transmittance850–1,050Intact grain/flour1st der.PLS0.77/0.780.40/0.39n/a Dowell et al.
(2006b) Reflectance835–2,502Intact grain/flour1st der.PLS0.75/0.750.42/0.41n/a Dowell et al.
(2006b) Reflectance950–1,650Intact grain/flour1st der.PLS0.61/0.790.51/0.38n/a Dowell et al.
(2006b) Reflectance400–2,500Intact grain/flour1st der.PLS0.75–0.780.39–0.42n/a Dowell et al.
(2006b)
Insoluble glutenins content Transmittance850–1,050Intact grain/flour1st der.PLS0.85/0.830.65/0.68n/a Dowell et al.
(2006b) Reflectance835–2,502Whole grain/
flour
1st der.PLS0.85/0.820.64/0.71n/a Dowell et al.
(2006b) Reflectance950–1,650Whole grain/
flour
1st der.PLS0.84/0.860.67/0.63n/a Dowell et al.
(2006b) Reflectance400–2,500Intact grain/flour1st der.PLS0.84/0.860.66/0.63n/a Dowell et al.
(2006b)
HMW glutenins Reflectance400–2,500Intact grain MSC,1st der.MPLS0.880.60n/a Bhandari et al.
(2000)
Reflectance400–2,500Ground meal,
flour SNV+D,1st der.,
2nd der.
MPLS0.74–0.790.92–1.02n/a Millar(2003)
Reflectance400–2,500Intact grain SNV+D,1st der.,
2nd der.
MPLS0.48 1.27n/a Millar(2003)
among protein,starch,and water during heating(Wesley
and Blakeney2001).
Crosbie et al.(2007)demonstrated the promising results in
developing calibration models for determination of flour
swelling volume used in screening wheat breeding lines for
starch quality intended for noodle production(R200.56–0.86; SEP00.77–1.65).
Increasing consumer demand for wide range of safe and
nutritious food products has set up new demands for cereal
breeders.The concern for enhanced nutritional quality has
been brought into focus together with agronomic perfor-
mance and processing quality of newly breed lines.Hence,
the improvement of the content of phytochemicals and other
bioactive compounds in cereals is getting more and more
important.However,very limited research work has been
reported dealing with the application of NIRS technique for
assessment of phytochemicals and other bioactive com-
pounds in rice and corn,whereas no results has been
reported for wheat either for classification or calibration
purposes,assuming that it could be one of the challenges
for future NIRS applications(Brenna and Berardo2004;
Zhang et al.2008;Ward et al.2008;McGoverin et al.2010). NIRS in Wheat Trade
As previously stated,the first stage of commercial applica-
tion of the NIRS technique was predominantly related to the
adding value to cereal crops and associated with the predic-
tion of certain constituent in cereals(protein,moisture,oil,
starch,cellulose).One of the oldest and most important
applications of NIRS in food and agriculture sector is for
the measurement of protein content in grains,especially in
wheat(Williams2007).Measuring protein content in wheat
has been a successful application of NIRS due to strong and
broad absorption by the N–H bonds in the NIR spectral
region despite the substantial differences in the nature of
NIRS and traditional chemical methods for protein content
determination(Williams1987).Therefore,works that dem-
onstrate the potential of NIR methods for determination of protein are numerous(Williams1979;Williams et al.1985; Williams and Sobering1993;Cozzolino et al.2006;Mutlu et al.2011).The very first official commercial application of NIRS in1975is associated with the Canadian Grain Com-mission and change from wheat protein-marketing system based on the Kjeldahl method to the system based on NIRS method.Although different countries established their own systems for classifying their wheat on the basis of different parameters of end-use quality,wheat grading systems is commonly based on the basis of protein content(Váradi et al.1999;Hulasare et al.2003;Williams2007).Neverthe-less,the system of official inspection of certain countries requires identification of wheat classes which is related to the classification applications of NIRS.
Assurance of Confidence in NIRS Results
The application of the NIRS technique has been facing a bad publicity as a“black box”technique over a years,but the development of global ANN NIRS calibrations has signifi-cantly improved the reputation of this technique.The global calibrations concept has significantly facilitated the wheat quality control in trade due to the fact that they are capable to analyze from90%to95%of all samples of a product of interest.Global calibrations are developed to be used for broad range of samples originated from different locations and production years with acceptable accuracy(Shenk et al. 2008).Nevertheless,the acceptance of the NIRS method by the AACC,AOAC,ICC,and ISO confirmed its applicabil-ity for routine use(Váradi et al.1999).Before its adoption as an official analytical method,special attention had to be paid to the regulatory requirements for analytical procedures especially due to the fact that the NIRS technique heavily relies on the use of chemometrics calibration and statistical analysis of data(Mark et al.2002).For this reason,it is of upmost importance to verify the characteristics of the NIRS method to demonstrate its fitness for intended purpose (Mark et al.2002;Poji?et al.,2006,2008,2009).In order to demonstrate the analytical capability of the NIRS
Table2(continued)
Application Acquisition
mode Spectral
range,nm
Type of sample Chemometrics Calibration
performance
Reference
Preprocessing Modeling R2SECV SEP
LMW glutenins Reflectance400–2,500Ground meal,
flour SNV+D,1st der.,
2nd der.
MPLS0.64–0.65 1.17–1.18n/a Millar(2003)
Reflectance400–2,500Intact grain SNV+D,1st der.,
2nd der.
MPLS0.23 1.32n/a Millar(2003)
PLS partial least square;MPLS modified partial least square;MSC multiplicative scatter correction;SNV standard normal variate;D detrend;1st der. first derivative;2nd der.second derivative;R2coefficient of determination(R2values≥0.70shown in bold font);SECV standard error of cross validation;SEP standard error of prediction;n/a data not available
technique for its application in grain trade,accuracy and precision(e.g.,reproducibility)are the characteristics of upmost importance.The importance of accuracy is so high due to the fact that premiums of1.30–1.50$are paid for increments of0.1%in protein content per ton.Unlike precision,accuracy can be easily achieved by adjustment of calibration model.Precision of the NIRS method is affected by the instrument and calibration used and the environmental conditions in which the measurements are performed(Büchmann et al.2001).Apart from those char-acteristics,transferability of calibrations among different instruments is also very important especially in the case when single organization handles several instruments.The establishment of NIRS networks worldwide facilitate mon-itoring the performance of a large number of NIRS instru-ments and determination of the wheat value as well (Büchman1996;Büchmann et al.2001).
NIRS Networks
In order to provide independent measurements of required quality which are internationally equivalent,as well as to fully utilize the advantages of NIRS technology and keep the simplicity of the NIRS operation and maintenance,the measurement and technical infrastructures has been set up worldwide comprising the networks of NIRS instruments for measurements of protein content in wheat(Büchman 1996;Büchmann et al.2001;Büning-Pfaue2003;Majcen and Taylor2004;Tseng et al.2004;Poji?and Mastilovi?2005;Poji?et al.2008).NIRS network comprises from two up to several hundred even thousands of NIRS instruments (slave units)standardized to a central(master)unit.The main goal of a NIRS network is to ensure the transferability of calibrations between instruments and to achieve the same level of accuracy for each individual instrument in the network regardless of operator or location.This is achieved by the standardization procedure that has to be conducted at least once a year in order to achieve the optimal perfor-mance of the slave instruments.After standardization,the systematic instrument-to-instrument variation is virtually eliminated,while the random variation between the slave instruments and the master unit is small and not influenced by the standardization(Büchmann and Runfors1995).The main advantages of the NIRS networks are ensuring reli-ability and uniformity in grain quality control as well as simplified procedures in calibration monitoring and upgrad-ing.The performance of NIR networks is generally tested in different ring trials commonly organized by the manufac-turers of the NIRS instrument included in the network.On the basis of several ring tests,it has been proved that the results of the reference methods agree well with the results of the NIRS method for several constituents within the scope of the network.Moreover,the NIRS method has proved the superior reproducibility over the reference meth-ods(Büning-Pfaue2003;M?ller2009,2011).
In order to ensure the smooth network operation,the necessary procedures and tasks have been defined and di-vided between different organizations.The network struc-ture usually comprises the reference laboratory,which analyzes check samples and monitors the performance of the calibrations used,the Administrative center,which han-dles database maintenance and communication between instruments and the Calibration Centre,which develops new calibrations and makes recommendations about calibra-tion adjustments.The central network authority—Network board,makes decisions about network organization and calibration changes.The first near infrared transmission network was set up Denmark in1991comprising40NIRS instruments Infratec1221and1229Grain Analysers(FOSS Tecator AB)using calibration model developed on the basis of partial least squares regression technique(PLSR).Shortly after,similar PLSR-based networks was established throughout Europe,North America,and Australia compris-ing more than3,000of instruments.Although their exis-tence significantly improved grain quality control,some drawbacks were evident,primarily related to the PLS nature of the calibration https://www.doczj.com/doc/d89243288.html,ly,different PLS models presented in different networks were developed on the basis of different reference methods at different laboratories which were not a good basis for international crop trade. Moreover,the PLS models were not stable over time,while plots of NIRS predicted vs.reference results tended to be curvilinear.In order to avoid the listed drawbacks,the use of ANN regression technique was proposed in NIRS calibra-tion development by Büchman(1996)and soon after was commercially used in Sweden by the Swedish Agrolab NIT network(Agronet).Since1997,the spread of use of ANN calibrations in the NIRS networks has been evident world-wide offering improved performance over PLS models.This improved performance is related to the lower values of standard error of predictions,better accuracy(especially at extremely high moisture levels),better transferability be-tween the instruments in the network,and better stability over time in comparison to those obtained with the PLS models.Global ANN calibrations with data from Europe, North America,and Australia were introduced in1999. Since then,continuous improvements in hardware,software, and calibration models have been apparent(Majcen and Taylor2004).The best support of the importance of NIRS network could be the research of Casada and O’Brien(2003) who demonstrated the significance of standardization and maintenance procedures employed by FGIS for their NIRS instruments for protein determination.They have shown that variations in the NIRS protein content measurements during storage were subjected more to variation between instruments than to the other variables,suggesting the importance of
consistency between instruments included in the wheat mar-keting system.
NIRS in Wheat Processing
An issue of major concern in wheat processing is prompt availability of information on wheat and flour quality in order to enable the process optimization and production of products of consistent quality.These aspirations have been initiated the adoption of the NIRS method in each segment of wheat processing:storage,milling,and baking.Since wheat quality means different things,depending on the place in wheat processing chain,the applications of the NIRS method in wheat processing is characterized by wide diversity.
NIRS in Wheat Storage
The role of NIRS application in wheat storage is twofold. Firstly,its role at the receival point at silos for determination of relevant wheat quality characteristics that will enable reliable segregation of grain according to quality is invalu-able.Secondly,its role to detect quality changes in stored grain is also of high importance(Gergely and Salgó2003, 2005,2007;Cassells et al.2007).
NIRS Monitoring of Post-harvest Maturation Gergely and Salgó(2003,2005,2007)showed that NIR spectroscopy was effective in monitoring the biochemical processes dur-ing post-harvest wheat maturation.Firstly,they analyzed the post-harvest maturation processes in wheat kernel with spe-cial respect to changes in moisture content and natural hydration/dehydration processes.The changes in three water absorption bands(1890–1,920,1,400–1,420,and1,150–1,165nm)indicated that the different transitions of water molecules could be sensitively followed in different NIR spectra regions(Gergely and Salgó2003).Secondly,the amount and variation in content of different carbohydrates during maturation was analyzed.The characteristic changes in three carbohydrate absorption bands(1,585–1,595, 2,270–2,280,and2,325–2,335nm)were identified and concluded that the starch accumulation and synthesis/de-composition of water-soluble carbohydrates during post-harvest maturation could be followed by monitoring these three regions of NIR spectra.Thirdly,they also confirmed the potential of NIRS as being applied to monitor the amount and changes of different proteins during post-harvest maturation.It was found that the accumulation of proteins and gluten network formation could be followed by monitoring the two regions of NIR spectra(2,055–2,065 and2,175–2,180nm).Cassells et al.(2007)also confirmed the ability of NIRS to indicate the changes occurred during storage,confirming that observed changes in the wheat grain happened solely due to changes in the grain,but not
due to the different performance of the NIR instruments. NIR Monitoring of Stored-grain Hygiene The detection of
stored-grain insect infestation is also a good practice in
storage technology(Poji?and Jani?Hajnal2011).Among
the others available techniques for detection of insects in
stored grains,NIRS have proved its usefulness for such
application(Neethirajan et al.2007).One of the first
attempts to detect the external and internal insect infestation
in wheat by NIRS in reflectance mode was reported by
Ridgway and Chambers(1996)who observed large spectral
differences between sound kernels and kernels internally
infested with grain weevil.The discrimination of the sound
and infested kernels was based on second derivative spectra
exhibiting the differences in chemical composition and
physical structure between the sound and infested kernels.
In that case,the recognition of both external and internal
infestation was enabled due to the detection of insect protein
and/or chitin and moisture(Ridgway and Chambers1996).
Soon after,Ghaedian and Wehling(1997)also discriminated
the sound and weevil-larva-infested kernels by means of
NIRS and PCA within the spectral range1,100–2,500nm.
Ridgway and Chambers(1998)highlighted the utilization of
NIRS at certain wavelengths for detection of insects infested
kernels due to different appearances of sound and infested
kernels.It was found that wavelengths of1,202and
1,300nm was useful for differentiation the insect from the
wheat kernels.Ridgway et al.(1999)chose near infrared
region700–1,100nm to detect kernel infestation by grain
weevil.They developed two-wavelength classification mod-
els,either on the basis of982and1,014nm or972and
1,032nm,which proved to correctly classify over96%of
samples.The origins for the spectral differences between
sound and infested kernels could have been subjected to
decrease in starch content or increase in kernel moisture
content which were related with kernel infestation.Further
work of Baker et al.(1999)confirmed the NIRS potential in
detection of insect infested kernels by developing PLS mod-
el for classification of uninfested vs.infested kernels(R20 0.90;SECV00.15).Maghirang et al.(2003)demonstrated
the potential of NIRS to be used for classification of sound
kernels and kernels that contained live or dead rice weevils
in different growth stages with accuracy63–94%.By
selecting calibration sample set of wheat kernels containing
either live or dead insects,the findings of Maghirang et al.
(2003)suggested how calibration sample sets can be han-
dled and indicated that immediate sample processing in
calibration development is not necessary.One of the latest
reports in the field was given by Dowell et al.(2010)who
measured insect infestation of wheat kernels using single
kernel NIR system(15.7–96.9%correctly classified kernels)
and NIR laser array technology(8.8–95.3%correctly
classified kernels).The obtained results indicated that perfor-mance of a NIR laser array system to predict insect traits is similar to a commercial NIR system.
Another NIRS application related with the monitoring of kernel quality was reported by Wang et al.(2001)who dem-onstrated the utilization of the NIRS technique for assessment of heat-damaged wheat kernels.This application was based on the existing of the spectral differences between undamaged and heat-damaged wheat kernels.Developed PLS and two-wavelength models(985/1,050;1,550/1,575nm)were of good performance with classification accuracy of100%and>97%, respectively.
NIRS in Wheat Milling
NIRS has been extensively adopted in milling industry to test wheat as starting raw material,and flour as final product (Osborne2000).Milling properties of wheat are determined on the basis of direct(semolina and flour extraction yields) and indirect quality indicators(virtuousness and grain hard-ness).The starting point for the development of NIRS application for prediction of milling quality of wheat was based on the fact that milling properties are affected by the physico-mechanical properties of wheat endosperm.Also, the fact that the procedure for assessment of milling prop-erties is time-consuming,expensive,and not standardized additionally contributed to the development of indirect methodology for prediction of milling properties of wheat (Bla?ek et al.2005).
Indirect Milling Properties The applicability of the NIRS technique for determination of wheat hardness is related to its potential to distinguish between the strength of adhesion between starch granules and protein matrix being different for hard and soft wheat.Consequently,hard wheat is char-acterized by higher absorbance values in comparison to soft wheat(Maghirang and Dowell2003;Greffeuille et al. 2006).Also,soft and hard wheat exhibit different milling behavior so that the ground material of hard wheat is char-acterized by larger particle size in comparison to that of soft wheat which causes the spectral baseline shift,used for measuring hardness by NIRS,being larger for hard wheat (Kawano2002).The numerous studies dealing with the measurements of kernel hardness by NIRS technique are available in the literature,proving that hardness can be measured in both meal and whole grain,either in reflectance (800–2,500nm)or in transmittance mode(800–1,050nm) (Osborne2006,2007).One of the first attempts to determine wheat hardness by NIRS is attributed to Williams(1979), who suggested separate NIRS model development for hard and soft wheat in order to achieve the best accuracy.Also, the better accuracy of the model developed for hard wheat was demonstrated,due to the stronger interference of starch with protein for soft wheat.Performance of the NIRS tech-
nique in the transmittance mode as being applied for pre-
diction of grain hardness was demonstrated by Williams
(1991)(R200.90–0.92;SEP03.37–3.47).Soon after,mois-ture and protein content and growing season were identified
as factors affecting the measurement of the NIR hardness
index(Brown et al.1993;Windham et al.1993).Conse-
quently,an algorithm for correction of the NIR hardness
index due to the differences in moisture content was pro-
posed,providing measurements of NIRS hardness indepen-
dent of moisture content(Windham et al.1993).Maghirang
and Dowell(2003)indicated better performance of PLS
calibration model developed for visible and NIR spectral
range(R200.91;SECV07.57)over the model developed for NIR spectra alone(R200.88;SECV08.67),where the wavelengths associated with protein,starch,and color(650 to700nm,1,100,1,200,1,380,1,450and1,670nm)con-tributed the most to the hardness prediction of whole wheat kernel.Simultaneously,the attempts for single-kernel cali-bration model development for hardness index were made, which resulted in models of unsatisfactory accuracy(R20 0.40–0.49;SECV020.94–22.68)(Maghirang and Dowell 2003).Fact that no international standard reference method for wheat hardness was not recognized,the NIRS method for measurement of Hardness Index was adopted by the AACC(Osborne2007;AACC2000).Determination of wheat hardness according to AACC method39-70A is based on the measuring reflectance at1,680and2,230nm of the ground wheat.At the wavelength of1,680nm,the absorbance(log1/R)is not affected by major constituents present in the sample,but only by particle size.In this regard,the wavelength of1,680nm has been called the reference wavelength for baseline correction in quantitative NIRS analysis(Kawano2002).Compared with other meth-ods for kernel hardness determination which are predomi-nantly two-dimensional,NIRS hardness measurement is considered as a three-dimensional measurement that recog-nizes size and shape,which therefore make a more compre-hensive principle for measuring wheat hardness index (Williams2008).Manley et al.(2002)for the first time demonstrated the utilization of the Fourier transform NIRS technique for determination of kernel hardness in whole wheat flour(R200.42;SEP02.13).Armstrong et al.(2006) confirmed that the FT-NIRS technique could be used for measuring hardness in whole grain with acceptable calibra-tion performance(R200.83;SECV011.46).
Direct Milling Properties The possibility of the NIRS tech-nique to be used for monitoring of milling process in terms of gap control of first-break rolls was initially demonstrated by Pasikatan et al.(2001).This application was enabled due to spectral variations caused by differences in particle size. PLS models developed against cumulative mass of size
fraction as the reference value was characterized by success-ful predictive ability for prediction of size fraction of first-break ground wheat with particles≤400μm.Promising standard errors of performance and coefficients of determi-nation initiated extended research on the utilization the NIRS technique as a granulation sensor.Therefore,the first attempt to develop granulation models for hard red winter (HRW)resulted in models for four different size ranges >1,041μm(R200.93;SECV03.25;SEP03.25),>375μm (R200.88;SECV01.26;SEP01.28),>240μm(R200.84; SECV00.59;SEP00.42),and>136μm(R200.73;
SECV00.36;SEP00.68),being of improved performance compared to initial models.Second attempt to develop gran-ulation models for soft red winter wheat(SRW)resulted in models of lower predictive ability for finest size fraction (>1,041μm(R200.95;SECV02.48;SEP03.53);>375μm (R200.95;SECV01.30;SEP01.83);240μm(R200.90; SECV00.86;SEP01.38)and>136μm(R200.58;SECV0 0.42;SEP01.30)in comparison to those developed for HRW.The better performance of granulation models for HRW wheat for finest size fraction in comparison to those for SRW wheat was achieved due to narrower particle size distribution and better sieving properties(Pasikatan et al. 2002;Pasikatan et al.2003).
The first contribution to the investigation of applicability of the NIRS method for prediction of direct milling proper-ties of wheat was made by Bla?ek et al.(2005).NIRS coupled with MPLS/PLS and ANN chemometric technique was used for development of models for direct milling properties prediction(semolina extraction rate,flour extrac-tion rate,semolina extractability and Mohse yield).Howev-er,the statistical indicators of models'predictive ability were dependent on the chemometrics technique applied,poor(R2 <0.60)and inconclusive,except for the semolina extract-ability modeled by ANN(R200.98,SECV00.81).
Flour Quality The degree of mechanically damaged starch incurred during flour milling is an important indicator of flour quality.Osborne and Douglas(1981)firstly demon-strated the possibility of use of NIRS to predict the degree of starch damage in flour.The calibration obtained using log (1/R)data at1,442,1,580,2,060,and2,258nm,associated with overtones and combinations of vibration frequencies of free and hydrogen-bonded O–H bonds in starch,resulted in acceptable model predicting ability(R200.90;SEP04.2). Later work of Finney et al.(1988)confirmed the applicabil-ity of the NIRS technique with discrete wavelengths to be used for estimation of damaged starch(R200.93).Mor-gan and Williams(1995)broaden the research on the NIRS starch damage to the whole NIR spectral region (1,100–2,500nm)indicating the superiority of NIRS over traditional wet-chemistry methods(R200.92;SEP03.00). Miralbés(2004)demonstrated the potential of NIRS in the transmittance mode throughout the spectral region
850–1,050nm to be used for prediction of starch damage
in monitoring the milling process(R200.92;SECV01.25; SEP01.63).
Determination of ash content by NIRS is particularly
useful for process control in the wheat milling industry to
monitor the consistency of milling and the compliance with
flour specifications and legal regulations.On the other hand,
ash content determines the purity of the flour and is com-
monly used for the control of the milling process(Miralbés
2004).Although inorganic substances do not absorb energy
in the NIR spectral region,some authors demonstrated that
the NIRS method can be used for reliable prediction of the
ash content in different food matrixes(Clark et al.1987;
Deaville and Flinn2000;Armstrong et al.2006;Dowell et
al.2006b;Mentink et al.2006;Osborne2007;Poji?et al.
2010).Since ash content cannot be directly measured by
NIRS,it is assumed that it is predicted by correlation with
the total amount of organic compounds and water present,
because of the large number of wavelengths used in the
process of calibration development that give significant
information(Garnsworthy et al.2000;Osborne2007;
Frankhuizen2008;AACC2000).Another assumption
which explains why the NIRS determination of flour ash is
possible is high correlation between flour ash and fiber
contents.Hence,the NIRS models for ash prediction could
be associated with bran cellulose present in the flour,rather
than with the mineral content themselves(Williams,et al.
1981).The successful NIRS application for determination of
flour ash content was reported by Miralbés(2004)(R20 0.98;SECV00.021;SEP00.024)and Armstrong et al. (2006)(R200.97;SECV00.03).Eventually,the NIRS method for flour ash determination has been included among the approved methods of the American Association of Cereal Chemists(AACC2000).Nonetheless,the devel-oped calibration models was characterized by better preci-sion in comparison to the standard method for ash determination due to the numerous sources of uncertainties associated with the reference method for ash measurement (Miralbés2003;Poji?2010).
The end-use quality of flour is dependent on the amount
and the quality of the gluten proteins and on the interactions
between the biochemical constituents present in flour.The
development of the calibration models for the parameters of
the end-use quality of wheat/flour is based on the correlation
between each of these quality parameters and protein con-
tent which is easily modelled by NIR(Delwiche et al.1998).
However,this statement could be ambiguous due to NIR
sensitivity to both protein content and quality(Wesley et al.
2001).The reported applications of NIRS for determination
of end-use quality of wheat/flour are summarized in Table3.
First attempt in the research of potential of NIR spectroscopy
to determine the end-use properties of wheat was made by
Rubenthaler and Pomeranz(1987)which resulted in prediction models for water absorption,mixing time and loaf volume, where the model for prediction of water absorption was char-acterized by the highest correlation with the log1/R spectral data(Rubenthaler and Pomeranz1987).The following work presented by Williams et al.(1988)was more comprehensive in terms of diversity of NIRS modeled rheological parameters. It showed the potential of NIRS to predict Farinograph stabil-ity,Extensograph energy,and Alveograph deformation energy with acceptable accuracy(Table3).Further research reported by Pawlinsky and Williams(1998),Hru?kováet al.(2001), Hru?kováand?mejda(2003),Miralbés(2003,2004),(Dowell et al.2006a,b),Vázquez et al.(2007),Jirsa et al.(2008)were carried out by using analyzers of higher generation—scanning monochromators(Table3).The reported results were incon-clusive due to the fact that developed models for prediction of different rheological properties were of variable efficiency.The efficiency of prediction of functional properties of wheat were affected by the form of the samples used(whole grain,ground grain,or flour)as well as by the by the composition of the sample sets which were insufficiently variable with respect to producing year due to the fact that seasonal conditions influ-ence the degree to which protein content is related to functional properties of wheat(Pawlinsky and Williams1998).Moreover, the calibration accuracy for prediction of rheological properties is limited by the inherent variability of the reference rheolog-ical methods.The latest reports on the possibility of NIRS method to be used for prediction of certain rheological param-eters were given by Mutlu et al.(2011)and Arazuri et al. (2012)(Table3).Although different in the form of samples used for scanning(flour and whole grain),chemometrics tech-nique used for modelling(ANN and PLS)and spectral range (400–2,500and570–1,850nm),these works confirmed the potential of NIRS to be used for prediction of functional properties.
NIRS in at-and on-line Process Monitoring Rationalization and optimization of production,and obtaining products of high and consistent quality require the application of con-temporary methods for production monitoring,where at-line or on-line NIRS instrumentation are irreplaceable.At-line NIRS analyzers are commonly placed within the production area,and require available personnel to operate the device. In contrast,on-line analyses are embedded within the pro-duction process where measurements,sample handling,and sample preparation are automatically controlled not requir-ing available operator(Pram Nielsen et al.2001;Ghosh and Rodgers2008).NIR on-line samplers were firstly developed to predict the protein content of flour,but nowadays the diversity of on-line NIRS application has considerably ad-vanced due to the development of new on-line probe acces-sories and introduction of new chemometric procedures (Osborne2007;Siesler2008).
In wheat processing,whole grain on-line NIR analysis enable analysis of grain as it is conveyed in a transport system by illuminating the sample in a flow by NIR radia-tion,which interacts with the sample,resulting in measuring the reflected or transmitted radiation.Hence,the continuous monitoring the composition of wheat blends and monitoring of tempering process is enabled within3–15s at the rate of 20t/h.In the milling plants,on-line NIR analysis could be applied for monitoring the quality of incoming wheat and for on-line prediction of flour moisture,protein,and ash contents of flour mill streams and final flour.Moreover,on-line NIRS analysis could be employed in the process of flour standard-ization to monitor flour blending,flour fortification,and/or flour improvement before delivery(Gradenecker2003; Osborne2007;Williams2007).
When developing models for on-line applications,special attention should be paid for calibration sample selection. They must cover the whole range of constituents of interest and also must include a comprehensive variability of other components present in the sample matrix,including physical variables(e.g.,particle size).On-line purposes commonly require the simpler calibration model developed by means MLR on the basis of a few selected wavelengths,PLS,and/ or PCA on the basis of spectral regions.Generally,a more complicated calibration models may provide higher accuracy for the current sample set,but their long-term reliability or tolerance to variability is questionable.Also,in NIRS on-line applications,the possible strategy is to develop the initial calibration model useful in finding not included variability (Lee2007).
NIRS in Wheat Safety Monitoring
In the last decade,the increased attention in cereal market-ing has being paid to cereal safety in terms of the presence of toxic substances such as residues of pesticides,fumi-gants,herbicides,heavy metals,and mycotoxins.However, residues of pesticides,fumigants,herbicides,and heavy metals cannot be estimated by NIRS with sufficient accura-cy and precision for industrial use.Nonetheless,calibrations for the prediction of deoxynivalenol(DON)have been reported besides the claims that DON does not possess spectral bands in the NIR spectral region(Dowell et al. 1999).However,developed DON models were not capable of detecting mycotoxins contamination at accepted levels for human food(<2.0ppm)due to too high SEP values.In contrast,higher acceptance limits for feed in terms of the mycotoxins content(≤5ppm),enabled more reliable applica-tion of the NIRS technique within the feed industry(Williams 2007).
The presence of mycotoxins is always associated with fungal infestation either with field Fusarium species or storage fungi,such as Aspergillus and Penicillium species.
Table3An overview of NIRS applications for prediction of selected rheological properties of wheat and flour
Application Acquisition
mode Spectral
range,nm
Type of sample Chemometrics Calibration
performance
Reference
Pre-processing Modeling R2SECV SEP
Farinograph absorption Reflectance1,100–2,400Flour Log1/R MLR0.81n/a n/a Rubenthaler and
Pomeranz(1987) Reflectance1,100–2,500Flour Log1/R,1st,
2nd der.
n/a n/a n/a0.65Williams et al.(1988)
Reflectance400–2,500Intact grain Log1/R PLS0.85–0.91n/a 1.42–1.74Pawlinsky and
Williams(1998) Reflectance400–2,500Flour SNV,1st der.PLS0.52 2.5 3.4Hru?kováet al.(2001) Transmittance850–1,050Flour SNV+D,1st der.MPLS0.980.350.46Miralbés(2004) Transmittance850–1,050Intact grain/
flour
1st der.PLS0.65/0.69 1.35/1.26n/a Dowell et al.(2006b)
Reflectance835–2,502Intact grain/
flour
1st der.PLS0.65/0.63 1.35/1.40n/a Dowell et al.(2006b)
Reflectance950–1,650Intact grain/
flour
1st der.PLS0.66/0.67 1.32/1.32n/a Dowell et al.(2006b) Reflectance400–2,500Intact grain,
Flour
1st der.PLS0.76/0.63 1.12/1.37n/a Dowell et al.(2006b) Reflectance400–2,500Flour n/a ANN0.83n/a n/a Mutlu et al.(2011)
Farinograph stability Reflectance1,100–2,500Flour Log1/R,1st,
2nd der.
n/a n/a n/a 3.3Williams et al.(1988)
Reflectance400–2,500Intact grain Log1/R PLS0.46–0.80n/a 1.9–3.87Pawlinsky and
Williams(1998) Reflectance400–2,500Flour SNV,1st der.PLS0.31 3.4 4.7Hru?kováet al.(2001) Transmittance850–1,050Intact grain/
flour
1st der.PLS0.30/0.06 3.50/3.99n/a Dowell et al.(2006b)
Reflectance835–2,502Intact grain/
flour
1st der.PLS0.16/0.13 3.76/4.15n/a Dowell et al.(2006b)
Reflectance950–1,650Intact grain/
flour
1st der.PLS0.15/0.15 3.92/3.90n/a Dowell et al.(2006b)
Reflectance400–2,500Intact grain/
flour
1st der.PLS0.06/0.06 4.03/3.97n/a Dowell et al.(2006b)
Extensograph
extensibility
Reflectance Flour400–2,500SNV,1st der.PLS0.262127.1Hru?kováet al.(2001) Extensigraph area Reflectance Flour1,100–2,500Log1/R,1st,
2nd der.
n/a n/a n/a29Williams et al.(1988) Reflectance Flour400–2,500SNV,1st der.PLS0.4928.345.6Hru?kováet al.(2001)
Extensigraph resistance Reflectance Intact grain400–2,500Log1/R PLS0.41–0.76n/a25.4–123Pawlinsky and
Williams(1998) Reflectance Flour400–2,500SNV,1st der.PLS0.4679.1102.8Hru?kováet al.(2001)
Alveograph deformation energy(W)Reflectance Flour1,100–2,500Log1/R,1st,
2nd der.
n/a n/a n/a17Williams et al.(1988)
Reflectance Flour400–2,500SNV,1st der.PLS0.1031.6133Hru?kováand
?mejda(2003) Transmittance Intact grain850–1,050SNV+D,1st der.MPLS0.81–0.93 4.7–18.73 6.10–24Miralbés(2003) Transmittance Flour850–1,050SNV+D MPLS0.9216.521.5Miralbés(2004) Transmittance Intact grain/
flour
850–1,0501st der.PLS0.69/0.7043.63/45.63n/a Dowell et al.(2006b)
Reflectance Intact grain/
flour
835–2,5021st der.PLS0.66/0.7048.60/45.69n/a Dowell et al.(2006b) Reflectance Intact grain/
flour
950–1,6501st der.PLS0.69/0.7046.02/45.54n/a Dowell et al.(2006b)
Reflectance Intact grain/
flour
400–2,5001st der.PLS0.70/0.7545.72/41.68n/a Dowell et al.(2006b)
Reflectance Intact grain,
ground
meal,flour
400–2,500SNV+D,1st,
2nd,3rd der.
MPLS0.40–0.51n/a58.2–64.7Vázquez et al.(2007)
Reflectance Flour400–2,500SNV,1st der.PLS0.58–0.6529.7–34.421.0–24.5Jirsa et al.(2008) Reflectance Flour400–2,500SNV,1st der.MPLS0.67–0.8327.9–33.832.0–44.5Jirsa et al.(2008) Reflectance Intact grain570–1,850log1/R,1st,
2nd der.
PLS0.79–0.8611.53–15.5513.07–16.1Arazuri et al.(2012)
Reflectance400–2,500Flour SNV,1st der.PLS0.2420.2321.72Hru?kováand
?mejda(2003)
Since the present concentrations of the mycotoxins, expressed in parts per million,cannot be directly detected, the use of the NIRS technique for approximate predictions of mycotoxins has been based on the identification of fungi-damaged kernels.In this regard,the detection of mycotoxins by NIRS is enabled due to chemical and optical properties occurred as a result of fungal contamination,so the capabil-ity of the NIRS to predict mycotoxin content is attributed to the identification of small changes in the texture of infected kernel and the way of reflectance and/or transmittance of the radiant energy by the infected kernels(Pettersson and?berg 2003;Williams2004).Consequently,the reported wave-lengths from visible spectral region associated with detection of fungal and mycotoxin contamination are due to color changes of infected cereal grains(Fernández-Iba?ez et al. 2009).The overview of wavelengths associated with detection of mycotoxin in cereals,predominantly in wheat,is given in Table4.First attempt to enable NIRS measurements for the estimation of DON and ergosterol in single kernels of highly infected wheat was made by Dowell et al.(1999).It was
demonstrated that spectral interval of500–1,700nm could
be utilized for single-kernel spectral analysis,where wave-
lengths associated with O–H(750,950,1,400nm),C–H
(1,200,1,400,and1,650nm),and N–H(1,050and
1,500nm)overtones contributed the more to the classification
model.Pettersson and?berg(2003)demonstrated the possi-
bility to develop a PLS regression model for prediction of
DON in wheat kernels by NIRS in transmittance mode(670–
1,100nm)just above the maximal limits for wheat flour
proposed by EU(R200.984;SECV0381μg/kg)(Pettersson and?berg2003).Williams(2004)demonstrated the utiliza-
tion of NIRS technique both in transmittance(700–1,098nm)
and reflectance(400–2,500nm)mode as being applied for
prediction of Fusarium-damaged kernels and DON contam-
ination.By observing the NIR spectra of whole wheat sam-
ples with high DON contamination and free of DON,a strong
band around778nm was found.However,the obtained
results for both Fusarium-damaged kernels(R200.62–0.69;
Table3(continued)
Application Acquisition
mode Spectral
range,nm
Type of sample Chemometrics Calibration
performance
Reference
Pre-processing Modeling R2SECV SEP
Alveograph resistance to extension(P)Transmittance850–1,050Intact grain SNV+D,1st der.MPLS0.79–0.85 3.7–5.43 4.8–6.7Miralbés(2003) Transmittance850–1,050Flour SNV+D MPLS0.86 4.67 6.07Miralbés(2004) Transmittance850–1,050Intact grain,
Flour
1st der.PLS0.11–0.2013.81–14.54n/a Dowell et al.(2006b)
Reflectance835–2,502Intact grain/
flour
1st der.PLS0.05/0.0415.20/15.06n/a Dowell et al.(2006b) Reflectance950–1,650Intact grain/
flour
1st der.PLS0.23/0.0713.74/14.66n/a Dowell et al.(2006b)
Reflectance400–2,500Intact grain/
flour
1st der.PLS0.26/0.2413.34/13.72n/a Dowell et al.(2006b)
Reflectance400–2,500Intact grain,
ground
meal,flour
SNV+D,1st,
2nd,3rd der.
MPLS0.30–0.50n/a18.3–23.7Vázquez et al.(2007)
Reflectance400–2,500Flour SNV,1st der.PLS0.82–0.8716.2–19.721.0Jirsa et al.(2008) Reflectance400–2,500Flour SNV,1st der.MPLS0.85–0.9314.0–18.922.6–28.4Jirsa et al.(2008) Reflectance400–2,500Flour n/a ANN0.95n/a n/a Mutlu et al.(2011) Reflectance570–1,850Intact grain log1/R,1st,
2nd der.
PLS0.49–0.77 3.98–6.18 4.87–6.74Arazuri et al.(2012)
Alveograph extensibility(L)Reflectance400–2,500Flour SNV,1st der.PLS0.1012.0612.92Hru?kováand
?mejda(2003) Transmittance850–1,050Intact grain/
flour
1st der.PLS0.69/0.7117.44/16.83n/a Dowell et al.(2006b)
Reflectance835–2,502Intact grain/
flour
1st der.PLS0.65/0.7218.47/16.55n/a Dowell et al.(2006b)
Reflectance950–1,650Intact grain/
flour
1st der.PLS0.70/0.7117.33/16.50n/a Dowell et al.(2006b) Reflectance400–2,500Intact grain/
flour
1st der.PLS0.69/0.7117.45/16.91n/a Dowell et al.(2006b) Reflectance400–2,500Flour n/a ANN0.36n/a n/a Mutlu et al.(2011) Reflectance570–1,850Intact grain log1/R,1st,
2nd der.
PLS0.81–0.92 5.89–17.948.05–12.81Arazuri et al.(2012)
PLS partial least square;MPLS modified partial least square;MSC multiplicative scatter correction;SNV standard normal variate;D Detrend;1st der.first derivative;2nd der.second derivative;R2coefficient of determination(R2values≥0.70shown in bold font);SECV standard error of cross validation;SEP standard error of prediction;n/a data not available
SEP01.11–1.27%)and DON(R200.64–0.72;SEP01.14–1.36ppm)were not sufficiently accurate for official food quality control,in contrast to their application in feed industry (Williams2004).Delwiche(2003)by means of NIRS diode array spectrometer(940to1,700nm)demonstrated that two-wavelength model(1,182and1,242nm)could be accurately applied for classification of Fusarium-and other mold-damaged kernels,provided it is applied for precisely aligned kernels using a combination of kernel mass.These findings highlighted that utilization of the NIRS technique for classi-fication of Fusarium-damaged kernels does not require the spectral data from a broad wavelength region(1,000–1,700nm).Further research of Delwiche and Hareland (2004)additionally demonstrated the potential of NIRS to be used for classification of Fusarium-damaged kernels re-vealing that wavelength around1,200nm was very effective for discrimination of sound and Fusarium-damaged kernels. Also,it was indicated that most effective classification was achieved when single kernel NIR reflectance is combined with kernel mass.The promising results of previous studied initiated the design of NIRS-based optical sorting devices for reduction of Fusarium-damaged kernels and deoxynivalenol (Delwiche2005;Delwiche and Gaines2005).First attempt revealed NIR wavelength regions important for design of monochromatic and bichromatic high-speed sorters for reduc-tion of Fusarium-damaged kernels.In this regard,the best two-wavelength models was of500and550nm for the visible region alone(94%accuracy),1,152and1,248nm for the NIR region alone(97%),and750and1,476nm for the hybrid region(86%)(Delwiche and Gaines2005).Second attempt revealed the efficiency of two-wavelength model(675and 1,480nm)for the reduction of DON concentration of Fusa-rium-infected soft red winter wheat.It was found that one-pass sorting decrease the DON concentration to51%of the original concentration,while successive passes additionally decrease
the original concentration of DON(Delwiche2005).Due to
the existence of certain relation between mycotoxin and ergos-
terol(fungal-specific lipid)concentrations,reports dealing
with determination of ergosterol by NIRS are also important
(Lindell2011).Although performed for barley samples,study
of B?rjesson et al.(2007)proved that prediction of ergosterol
could be performed either in reflectance or transmittance
mode.Despite using different wavelength regions850–1,050
and400–2,500nm,the quite similar results were obtained.
The utilization of the FT-NIR technique for the determina-
tion of DON in durum and common wheat at levels far below
the DON maximum permitted EU limits for unprocessed wheat
was reported by De Girolamo et al.(2009)(R200.71–0.83; RMSECV0470–555μg/kg;RMSEP0306–379μg/kg).The
discrimination between high and low DON contaminated sam-
ples was carried out by qualitative models developed on the
basis of discriminant analysis for and by semi-quantitative
models developed on the basis of PLS regression models.
The work of Peiris et al.(2009)contributed to the prog-
ress in application of the NIRS technique for prediction of
DON and Fusarium-damaged kernels.The potential of
NIRS for prediction of Fusarium-damaged kernels on the
basis of DON levels was demonstrated.For this purpose,the
examination of NIR spectra of DON and NIR spectra of
single kernels with and without of DON was performed.
DON absorption bands were found at1,408,1,904and
1,919nm.Moreover,the differences in spectral responses
between Fusarium-damaged and sound kernels were notice-
able at1,204,1,365,and1,700nm and could be associated
with the differences in main structural kernel compounds
(protein,starch,lipids).Shifts in absorption peaks between
Fusarium-damaged and sound kernels noticeable in spectral
intervals1,425–1,440and1,915–1,930nm might have been
Table4Summary of wavelengths associated with detection of mycotoxin and fungal contamination in cereals
Application Spectral range Wavelength,nm Type of cereal Reference
FDK/DON400–1,700750,950,1,050,1,200,1,400,1,500,1,650Wheat Dowell et al.(1999)
FDK940–1,7001,182,1,242Wheat Delwiche(2003)
DON700–1,098778Wheat Williams(2004)
FDK1,000–1,7001,200Wheat Delwiche and Hareland(2004) DON Interference filters675,1,480Wheat Delwiche(2005)
FDK940–1,700500,550,750,1,152,1,248,1,476Wheat Delwiche and Gaines(2005) FDK350–2,5001,204,1,365,1,700Wheat Peiris et al.(2009)
DON350–2,5001,408,1,904,1,919Wheat Peiris et al.(2009)
FDK950–1,6501,160–1,220and1,395–1,440Wheat Peiris et al.(2010)
FB1600–1,050650,710,935,990Corn Dowell et al.(2002)
FB1550–1,700590,995,1,200,1,410Corn Dowell et al.(2002)
Total fungal infection400–2,5001,430,1,470,1,820,2,140,2,180Corn Berardo et al.(2005)
FB1400–2,5001,190,1,954–2,378Corn Berardo et al.(2005)
AFB1400–2,500480–600,870–1,200Corn,barley Fernández-Iba?ez et al.(2009) FDK Fusarium damaged kernels;DON deoxynivalenol;FB1fumonisin B1;AFB1aflatoxin B1
attributed to DON.Further research of Peiris et al.(2010)
demonstrated the potential of automated single kernel NIRS
method for identification of Fusarium-damaged kernels and
prediction of DON.The achieved classification accuracy of
sound and Fusarium-damaged kernels was98.9–99.9%,
while DON calibration performance was characterized by
R200.87and SEP060.8ppm.The spectral differences be-tween NIR spectra of Fusarium-damaged and sound kernels
were indicated,with distinct differences in spectral regions
1,160–1,220nm and1,395–1,440nm,associated with car-
bohydrates,lipids,proteins,and DON levels.These findings
were in accordance with the previous research of Peiris et al.
(2009)which showed an NIR absorption peak of DON at ≈1,410nm.Further studies should be undertaken aiming at lower DON detection limits and lower SEP values.
Although beyond the scope of this review,it should be
noted that recent research conducted by means of NIR
hyperspectral imaging indicated that the capability of NIRS
for approximate predictions of mycotoxins could be also
attributed to the presence of chitin and ergosterol in Fusa-
rium-damaged kernels(Delwiche et al.2011).
Despite the fact that wheat is one of the most consumed
cereal worldwide and the fact that mycotoxins are carcinogenic,
mutagenic,hepatotoxic,and immunosuppressive toxins,
reports on determination of other types of mycotoxins(e.g.,
aflatoxin,fumonisin)in wheat and wheat-based products by
either NIRS of FT-NIRS are limited in the relevant academic
literature(Fernández-Iba?ez et al.2009).However,the certain
number of reports is available related to detecting aflatoxin in
corn and barley(Pearson et al.2001;Dowell et al.2002;
Berardo et al.2005;Fernández-Iba?ez et al.2009).Therefore,
one of the future perspectives in NIRS application for determi-
nation of wheat safety should encompass this segment of
development(Table4).
NIRS in Wheat Functionality Monitoring
Extensive studies on dietary and health issues of cereals have
initiated the research on feasibility of the NIRS technique as
being applied in prediction of its functional properties from the
nutrition standpoint.The applicability of the NIRS technique
for determination of fiber content in cereals and cereal products
has already been demonstrated.However,a small number of
studies have addressed the applicability of NIRS to predict
total dietary fiber and its constituents,soluble and insoluble
fiber,in unprocessed cereals in relation to the studies dealing
with determination of fiber contents in cereals products(Kays
et al.1999).One of the most recent successful applications of
the NIRS technique has been related to the development of
calibration models to predict the total arabinoxylan and water-
extractable arabinoxylan content in cereals including wheat.
The initially developed calibration models reported by Shewry
et al.(2012)were transferable between different types of NIR instrumentation.The authors indicated the need to increase the robustness of the calibrations by broadening the calibration sets to encompass higher variability(Shewry et al.2012). Conclusion
The NIRS technique provides a rapid,non-destructive,en-vironmental friendly,and accurate analysis of the sample composition,its functional properties,and safety.Due to the listed advantages,it represents a suitable tool for the routine use in a wide range of laboratory and industry applications. Having demonstrated the potential of the NIRS technique for prediction of certain constituents and/or functional prop-erties of wheat and wheat-based products that do not exhibit absorption in the NIR spectral range,it can be stated that the NIRS technique is characterized by high R&D potential.In this regard,the future perspectives of the NIRS technique in wheat technology should equally comprise each aspect of NIRS technique:instrumentation,chemometrics,and appli-cations.The NIRS instrumentation evolves in terms of de-velopment of hyphenated instruments,NIR imaging and portable NIR spectrometers.More versatile instruments in terms of design of sampling accessories and different acqui-sition modes should be provided with the minimal changes in the instrument operational configuration.The complexity of high dimensional and nonlinear measured data has im-posed the need for new chemometric techniques,while the assessment of phytochemicals and other bioactive and toxic hazardous compounds in wheat represents one of the chal-lenges for future NIRS applications.To facilitate the spectral data management obtained by versatility of available NIRS instruments,it is of upmost importance to allow unrestricted access,data import,export and conversion to different com-mercially available chemometrics software packages. Acknowledgments This work has been supported by the Ministry of Education and Science,Republic of Serbia(Project No.III46001). References
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Comparatives and Superlatives 比较级和最高级一、形容词比较级和最高级的规则 其它不规则的变化：
二、形容词比较级基本用法 1、定义：两个人或物之间的比较。表示“较……”或“更……一些”。标志词：than (比）eg: Cats are more lovely than other animals. 2、与than搭配的词语形式 （1）名词/代词He is older than me. (2) 动名词/从句Skiing is more exciting than skating. (3) 状语/动词/形容词 3、形容词/副词比较级前的修饰语 （1）much/a lot/ a bit/ a little/ slightly She is feeling a lot/much better today. (2) any/ no/ even/ some/ still Do you feel any better today? She is no older than mike. (3) 数词 My sister is ten years younger than me. 4、比较级的特殊搭配 1)“比较级+and+比较级”表示“越来越……” eg: He becomes fatter and fatter. 他越来越胖了。 2)“The +比较级…，the +比较级”表示“越……，越……” eg：The busier he is ,the happier he feels他越忙就越高兴。 The more ,the better. 3)表示两者中”较……“时，用the + 形容词比较级+ of 短语来表达。 eg: He is the taller of the two. 4)the more… the more… he harder you worker, the greater you will make. 5)more B than A = less A than B He is more lazy than slow at his work
形容词比较级和最高级的用法 形容词原级的用法 1.说明人或事物自身的特征、性质或状态时，用形容词原级。 Eg.The flowers in the garden are beautiful. 2.有表示绝对概念的副词very,so,too,enough,quite等修饰时，用形容词原级。 3.表示A与B在某方面程度相同或不同时用形容词原级。 肯定句中的结构：A +as+形容词原级+as+B 否定句中的结构：A+as/so+形容词原级+as+B 表示“A是B的……倍”时，用“A+倍数+as+形容词原级+as+B”结构（一倍once，两倍twice，三倍及以上：数字+times） half as +形容词原级+as表示“……是……的一半” 形容词比较级的用： 1．比较级：常用于“比较级+than”结构。如：Cats aremore lovely than other animals 2．形容词的比较级可用much，a little，a lot，even。a bit，still，far，rather，any等修饰，使原来的比较级在语意上更加明确。如：Chickens are much smaller than cows. 3．表示两者之间进行选择“哪一个更……”时，用句型“which/who is +形容词比较级，A or B？”表示。 4．表示“几倍于……”时，用“倍数+比较级+than”表示。如:I’m three years younger than you 比较结构中还须注意以下问题： 1．比较级与最高级的结构还可以转换，意思不变。如：The Changjiang River is longer than any other river in China=The Changjiang River is the longest river in China. 2．当某一事物与其他事物做比较时，被比较事物中不能包括本身。如：He is taller than any other student in his class. 3．如果比较对象相同，可用that/those代替第二个比较对象。如：The weather in Shanghai is better than that in Wuhan. 4．两个比较级用and 连在一起可表达全面增加或减少，意为“越来越……”。
招标投标企业报告 广东省机场管理集团有限公司揭阳潮汕机场公 司
本报告于 2019年11月30日 生成 您所看到的报告内容为截至该时间点该公司的数据快照 目录 1. 基本信息：工商信息 2. 招投标情况：招标数量、招标情况、招标行业分布、投标企业排名、中标企业 排名 3. 股东及出资信息 4. 风险信息：经营异常、股权出资、动产抵押、税务信息、行政处罚 5. 企业信息：工程人员、企业资质 * 敬启者：本报告内容是中国比地招标网接收您的委托，查询公开信息所得结果。中国比地招标网不对该查询结果的全面、准确、真实性负责。本报告应仅为您的决策提供参考。
一、基本信息 1. 工商信息 企业名称：广东省机场管理集团有限公司揭阳潮汕机场公司统一社会信用代码：91445200582918988R 工商注册号：445200000037474组织机构代码：582918988 法定代表人：郭大杰成立日期：2011-08-16 企业类型：/经营状态：在业 注册资本：/ 注册地址：广东省揭阳空港经济区登岗镇 营业期限：2011-08-16 至 / 营业范围：航空器起降服务；旅客过港服务；安全检查服务；应急求援服务；航空地面服务；客货销售代理；停车场、仓储、物流配送；货邮处理、园林绿化、保洁服务；制作、发布代理务类广告；房地产开发、航空运输技术协作中介、航空信息咨询及航空运输业务有关的其他服务。 联系电话：*********** 二、招投标分析 2.1 招标数量 企业招标数： 个 （数据统计时间：2017年至报告生成时间）180
2.2 企业招标情况（近一年） 2019年05月14 企业近十二个月中，招标最多的月份为，该月份共有个招标项目。 仅展示最近10条招标项目 序号地区日期标题 1广东2019-11-26揭阳X射线 2广东2019-11-26揭阳X射线 3广东2019-11-25揭阳潮汕机场航站区临时应急停车场和晴雨连廊建设工程项目4广东2019-11-25揭阳潮汕机场航站区临时应急停车场和晴雨连廊建设工程5广东2019-11-20揭阳潮汕国际机场助航灯光保障、场务及鸟害防治项目 6广东2019-11-19揭阳潮汕国际机场助航灯光保障、场务及鸟害防治项目 7广东2019-11-19揭阳潮汕国际机场助航灯光保障、场务及鸟害防治项目 8广东2019-11-19广东省揭阳潮汕国际机场停车场管理服务项目 9广东2019-11-18揭阳潮汕国际机场停车场管理服务项目 10广东2019-11-14揭阳潮汕机场飞行区道面嵌缝料更新项目
压缩比~~~~~~汽油标号~~~~~~垂直涡流稀薄燃烧（MVV） 高压缩比发动机用低号油的原因在我们日常为爱车选择加多少标号的燃油时总会有一种误解，认为高压缩比的发动机一定要加高标号的燃油，低压缩比就没必要加高标号燃油了，更有人会认为进口车或档次比较高的车就要加标号高的油，用车的价格来衡量加多少标号的燃油等等。 压缩比确实能作为判断发动机采用燃油标号的依据之一，按照过去的说法，压缩比在8以下的发动机可以加90号汽油，压缩比在9以下可以采用93号汽油，压缩比在9以上则应该采用97号汽油。而实际上，凭我们现在的经验会发现，这个数据与厂家给出的数据并不贴服，例如现在绝大部分的发动机压缩比都在9以上，但大多数厂家都是标称可以加93号汽油的，甚至许多压缩比达到10的发动机，也可以采用93号汽油。更为极端的例子，像东风标致的2.0发动机，压缩比高达11，仍然说可以采用93号汽油。而三菱的EVO，它的压缩比只有8.8，但厂家仍然要求必须使用97号以上的燃油。 到底是以压缩比的判断为准，还是以厂家推荐的数据为准呢？厂家推荐数据为何会与常规的压缩比判断相悖呢？实际上燃油标号的选择，除了压缩比以外，还有很多的影响因素，我们必须综合考虑才能确定最佳的燃油选择，而厂家显然对自己的发动机是最有发言权的，所有我们在这一点上应该严格按照厂家的要求来做。除了压缩比，还有那些因素会对燃油标号的选择产生影响呢？ 我们现在市场上销售的汽油主要有90、93、97和98等标号，这些数字代表汽油的辛烷值，也就是汽油的抗爆性，即实际汽油抗爆性与标准汽油的抗爆性的比值。燃油标号越高的燃油，它的抗爆性就越好，反之，燃油标号低的燃油它的抗爆性就相对来说要差一些。那么汽车压缩比和燃油标号之间究竟有什么关系呢，通常情况下高标号的燃油它的抗爆性好，适合使用高压缩比的发动机，低标号的燃油适合低压缩比的发动机。
形容词、副词的比较级和最高级用法 大多数形容词和副词有三个等级：原级、比较级和最高级。 1、原级即原型。 2、比较级，表示“较……”或“更……一些”。 3、最高级表“最……”。 一、形容词、副词比较级和最高级的构成。 比较级和最高级有规则变化和不规则变化两种。（一）规则变化 ★★1.单音节以及少数双音节的词后面直接加-er , -est tall taller tallest ★★2.以不发音的e结尾只加-r，-st nice nicer nicest ★★3.以一个元音加一个辅音字母结尾的单音的辅音字母，再加-er，-est thin thinner thinnest 特别提醒： (1)以形容词+ly构成的副词要在前面加 more，most new, few, slow, clean等词含有字母组合,末尾的辅音字母不 用双写。 ★★5.大部分双音节词和多音节的词(即音标中含有三个或三个以上元音音素的词)，要在前面加more，most 1 / 5
特别提醒：由ing分词和ed分词演变过来的形容词(talent—talented)只能加more或most来表示它们的比较级和最高级。 特别提醒：★比较级前最常见的修饰词是：much，a little, even 等。★very, quite, too修饰原级。 （二）不规则变化 ★little-less-least用来修饰不可数名称，若修饰可数名称复数则用few-fewer- fewest; ★elder仅用于同辈之间的排行。“年长的”。如: elder brother(哥哥/ 兄长)；elder sister（姐姐）；elder boy（长子）；elder sister（长女）。 在有than的句子中，只能用older。 【归纳大荟萃】 形容词副词的比较级和最高级用法口诀 一者比较用原级，两者相比比较级，三者或以上最高级； 比较级，还是最高级？往往由他们来决定：有than就用比较级； i n或of最高级；若是没有这三词，那就看句意来定。 比较级和最高级变化口诀 原级变成比较级,er结尾要牢记；规则变化要牢记，特殊规则有三条：1．若是以e来结尾，只加r就可以；2.两辅（辅音字母）夹一元（元音字母）结尾的，双写末尾的辅音，最后再加er； 3. 辅音字母若加y，要先把y变成i，最后再加er。（还有一条要 注意：形容词+ l y结尾的，根本不合这一条。）若不符合这三条，直接就加er。其余双音、多音节,词前加more就可以；不规则词没几个,它们需要特殊记。总共就有这五组：好坏多少加上远。 变最高级,也容易, 原级后加est, 规则类同比较级, 提醒一点便可以；其余双音、多音节,词前加most就可以。还有一点要留意, 最高级前要用the；若是副词最高级, 用不用the皆可以。 二、句子构成： 1.当两者比较时，用句型： 2 / 5
93#和97#油的区别 目前市场上汽油有90、93、95、97等标号，这些数字代表汽油的辛烷值，也就是代表汽油的抗爆性，与汽油的清洁无关。所谓“高标号汽油更清洁”的纯属误导。按照发动机的压缩比或汽车使用说明书的要求加油，更科学、更经济，并能充分发挥发动机的效率。 汽车发动机在设计阶段，会根据压缩比设定所用燃油的标号。压缩比是发动机的一个非常重要的结构参数，它表示活塞在下止点压缩开始时的气体体积与活塞在上止点压缩终了时的气体体积之比。从动力性和经济性方面来说，压缩比应该越大越好。压缩比高，动力性好、热效率高，车辆加速性、最高车速等会相应提高。但是受汽缸材料性能以及汽油燃烧爆震的制约，汽油机的压缩比又不能太大。简单地说，高压缩比车使用高标号的燃油。燃油标号越高，油的燃烧速度就越慢，燃烧爆震就越低，发动机需要较高的压缩比；反之，低标号燃油的燃烧速度较快，燃烧爆震大，发动机压缩比较低 燃油的标号还涉及到发动机点火正时的问题。低标号汽油燃烧速度快，点火角度要滞后；高标号燃油燃烧速度慢，点火角度要提前。例如一台发动机按照说明书要求应加93号汽油，现在加了90号汽油，可能会造成发动机启动困难；加速时，发动机内有清脆的金属碰撞声音；长途行车后，关闭点火开关时发动机抖动。 选择汽油标号的主要依据是发动机的压缩比。盲目使用高标号汽油，不仅会在行驶中产生加速无力的现象，而且其高抗爆性的优势无法发挥出来，还会造成金钱的浪费。 油号的基本概念 93汽油与97汽油 一、基本概念： 1、压缩比： 汽车选择汽油标号的首要标准就是发动机的压缩比，也是当代汽车的核心节能指标。引擎的运行是由汽缸的“吸气——压缩——燃烧——排气——吸气”这样周而复始的运动所组成，活塞在行程的最远点和最近点时的汽缸体积之比就是压缩比。降低油耗的成本最低效果最好的方法就是提高发动机的压缩比。提高压缩比只是改变活塞行程，混合油气压缩得越厉害，它燃烧的反作用也越大，燃烧越充分。但压缩比不是轻易能动的，因为得有另一个指标配合，即汽油的抗爆性指标，亦称辛烷值，即汽油标号。
机场工程项目飞机维修区设计方案
揭阳潮汕机场南航基地工程 飞机维修区 设计任务书
编制单位:汕头航空有限公司 编制时间:二〇〇八年六月二十三日 第一章项目概况及周边条件 一、项目名称:南航汕头公司揭阳潮汕机场基地工程 二、地理位置: (一)机场区位: 潮汕机场(登岗北)场址位于汕头、潮州和揭阳三市之间,揭阳市揭东县炮台镇以东登岗镇以北,枫江以南,虎岗山以北山脚。机场近期跑道中心点位于东经116度30分09.41秒北纬23度33分07.60秒。以机场位置点为坐标中心,到三市(汕头、揭阳、潮州)中心距离及方位分别为:汕头市26公里,方位角为131.2 °即东偏南41.2度;揭阳市18公里,方位角为271°即西偏北1 度;潮州市17.5公里,方位角为40.5 °即北偏东40.5度。 从当前的城市总体规划来看,机场的东侧是汕揭梅高速路,南侧则是登岗镇,在机场西侧、国道以西将建设一条城市东环路,机场附近建设登洪高速路选线未定,会依据机场选址调整,在这个区域内将包含机场近期、中期、远期及远景用地,不能转为其它用途。
图表 1机场区位 图表 2机场工作区区位 (二)南航汕头公司基地区位: 整个机场总体分为3大部分:飞行区、航站核心区和工作区。而南航汕头公司基地主要由三个区组成: 1、机坪区:位于机场飞行区以南,该区建设用地面积约160亩(不清晰),最远端距航站楼中轴线距离约1.5公里。 2、生产生活区:位于机场工作区以南,该区建设用地面积约11.7公顷(约176亩),最近端距航站楼中轴线距离约1.2公里。 3、一线生产办公区:机场工作区以北,地块建设用地面积约0.4公顷(约6.3亩),地块中心距航站楼中轴线距离约600米。 图表 3南航汕头公司区位 三、建设规模 9月26日国家发展和改革委员会以<关于新建广东潮汕民用机场项目可行性研究报告的批复>(发改交运[ ]101号)对潮汕民用机场项目进行批复,其中对南航基地工程主要建设内容批复如下:建设5机位停机坪4.6万平方米,维修基地1.68万平方米及其它配套生产生活设施等。(注:当前南航总部批复近期建5机位停机坪及总建筑规模为37400平方米基地)。 四、机场总规及工作区控制性详规 (一)机场总体规划
汽油辛烷值......争论97,93,90汽油好坏 汽车用油主要成分是C5H12~C12H26之烃类混合物，当汽油蒸气在汽缸内燃烧时(活塞将汽油与空气混合压缩后，火星塞再点火燃烧)，常因燃烧急速而发生引擎不正常燃爆现象，称为爆震(震爆) 。在燃烧过程中如果火焰传播速度或火焰波之波形发生突变，如引起燃烧室其它地方自动着火(非火星塞点火漫延)，燃烧室内之压力突然增高此压力碰击四周机件而产生类如金属的敲击声，有如爆炸，故称为爆震(震爆)。汽油一旦辛烷值过低，将使引擎内产生连续震爆现象，造成机件伤害连续的震爆容易烧坏气门，活塞等机件。 爆震之原因： (1) 汽油辛烷值太低。(2)压缩比过高。(3)点火时间太早。(4)燃烧室局部过热。(5)混合汽温度或压力太高。(6)混合汽太稀。(7)预热。(8)汽缸内部积碳。(9)其他如冷却系或故障等。 减少爆震方法： (1) 提高汽油辛烷值。(2)减低压缩比。(3)校正点火正时。(4)降低进汽温度.(5) 减少燃烧室尾部混合汽量。(6)增加进汽涡流。(7)缩短火焰路程。 (8)保持冷却系作用良好. 辛烷值 爆震时大大减低引擎动力，实验显示，烃类的化学结构在震爆上有极大的影响。燃烧的抗震程度以辛烷值表示，辛烷值越高表示抗震能力愈高。其中燃烧正庚烷CH3(CH2)5CH3的震爆情形最严重，定义其辛烷值为０。异辛烷(2,2,4-三甲基戊烷) 的辛烷值定义为100。辛烷值可为负，也可以超过100。 当某种汽油之震爆性与90%异辛烷和10%正庚烷之混合物之震爆性相当时，其辛烷值定为90。如环戊烷之辛烷值为85，表示燃烧环戊烷时与燃烧85%异辛烷和15%正庚烷之混合物之震爆性相当。 此为无铅汽油标示来源，目前有辛烷值为92，95，98等级之无铅汽油，此类汽油含有高支链成分及更多芳香族成分之烃类，如苯，芳香烃，硫合物等。 例如95无铅汽油的抗震爆强度相当于标准油中含有百分之九十五的异辛烷及百分之五的正庚烷的抗震爆强度。
揭阳市人民政府办公室印发揭阳潮汕机场区域及周边营运秩 序综合整治工作方案的通知 【法规类别】航空运输 【发文字号】揭府办[2011]110号 【发布部门】揭阳市政府 【发布日期】2011.12.15 【实施日期】2011.12.15 【时效性】现行有效 【效力级别】XP10 揭阳市人民政府办公室印发揭阳潮汕机场区域及周边营运秩序综合整治工作方案的通知 （揭府办〔2011〕110号） 各县（市、区）人民政府（管委会），市府直属有关单位： 《揭阳潮汕机场区域及周边营运秩序综合整治工作方案》已经市人民政府同意，现印发给你们，请认真贯彻执行。执行中遇到问题，请径与市交通运输局联系。 二〇一一年十二月十五日揭阳潮汕机场区域及周边营运秩序综合整治工作方案
为确保揭阳潮汕机场区域及周边良好运营秩序，保障机场安全运营，营造整洁、优美、文明、有序的城市客运环境，提升揭阳城市形象，根据有关规定，结合我市实际，特制定本工作方案。 一、指导思想 以科学发展观为指导，按城市客运管理“高起点、高水平、高效益”工作要求，以改善城市客运环境，提高服务水平为重点，集中力量、明确时限，高标准、高质量地提升揭阳潮汕机场城市客运行业形象，为国内外来宾和广大市民创造整洁、优美、舒适的出行环境，迎接国内外嘉宾。 二、总体目标 开展揭阳潮汕机场区域及周边营运秩序综合整治，实现良好的运营秩序和服务质量，基本消除拉客、揽客、“黑的”非法营运及摆摊设点、沿路叫卖、强索强卖、流浪乞讨等行为以及各种非法中介活动，确保揭阳潮汕机场容貌清新、环境卫生干净、运营秩序井然。 三、整治内容 （一）擅自在机场候机楼、停车场及周边区域散发广告、宣传品，开展募捐，拍摄影视片，举办产品展销、促销，开展文娱、体育等影响机场运营秩序的活动。
由辛烷值来说说用什么标号燃油 汽油最初是煤油提炼的废弃品，后来被利用作为内燃机燃料并促进了汽车工业的发展。汽油是碳氢化合物，主要成分为五碳至十二碳烃类有机物。以前用蒸馏法提炼汽油时，汽油中一部分是链式分子结构，还有一部分是环式分子结构，链式分子结构的成分会早于环式分子结构的成分提前燃烧，而且是无规则。如果汽油在发动机活塞还没有达到上顶点、火花塞未及点火就压燃了，就形成爆震。爆震轻者影响发动机工作效率、增加油耗、发动机抖动增加，重者冲击气缸、损坏发动机。当出现明显爆震的时候会产生敲击声，俗称敲缸。为了抗爆震燃烧，即防止汽油在发动机汽缸中加压时不规则的提前燃烧，就要提高汽油的抗爆性。早期的汽油靠添加适量的四乙基铅来阻滞汽油受压提前燃烧，四乙基铅对人体有毒，污染环境，因此需要用无铅汽油替代含铅汽油。不同烷烃的抗爆情况不同，异辛烷抗爆性最高，正庚烷抗爆性最差，将这两种烃按不同体积比例混合，可配制成辛烷值由0到100的标准燃料。汽油辛烷值是汽油抗爆性的表示单位，在数值上等于在规定条件下与试样抗爆性相同时的标准燃料中所含异辛烷的体积百分数。在石油提炼技术改进后，采用催化裂解法，可以把链式分子合成辛烷，也可以把重分子裂解成较小的汽油分子并催化变成辛烷。因此汽油不再用添加四乙基铅而是靠调节辛烷值的程度来控制抗爆性。 汽车发动机的工作离不开燃料，燃油的性能指标是发动机设计的一个重要依据，汽车应用消费也需要一个固定的燃油标准，辛烷值就是一项重要指标。汽油由原油分馏及重质馏分裂化制得，在原油加工过程中，蒸馏、催化裂化、热裂化、加氢裂化、催化重整、烷基化等单元都产出汽油组分，但辛烷值不同。如辛烷值太低，其易燃性在并不大的压缩下就会燃爆。将石油炼制得到的直馏汽油组分、催化裂化汽油组分、催化重整汽油组分等不同汽油组分经精制后与高辛烷值组分经调和，得到各种标号的汽油产品（90、93、97等），过辛烷值区分不同的抗爆震性能，标号越高、抗爆性也越高。原油中不同程度含杂质、含硫，为避免硫排放而导致酸雨，因此硫含量高的汽油组分还需加以脱硫精制。烃类充分燃烧后形成水和二氧化碳，去除汽油中的胶质、硫和其它杂质越彻底，汽油也越干净，排放也就更环保，发动机燃烧残留积碳也越少，所以现在推广应用清洁汽油对环保和发动机本身都有好处。清洁汽油与原对应标号93、97号油抗爆震效果相当的
比较级和最高级专题讲练 一、形容词比较级和最高级的规则 二、形容词比较级基本用法 1、定义：两个人或物之间的比较。表示“较……”或“更……一些”。标志词：than (比）eg: Cats are more lovely than other animals. 2、与than搭配的词语形式 （1）名词/代词He is older than me. (2) 动名词/从句Skiing is more exciting than skating. (3) 状语/动词/形容词 3、形容词/副词比较级前的修饰语 （1）much/a lot/ a bit/ a little/ slightly She is feeling a lot/much better today. (2) any/ no/ even/ some/ still Do you feel any better today? She is no older than mike.
(3) 数词 My sister is ten years younger than me. 4、比较级的特殊搭配 1)“比较级+and+比较级”表示“越来越……” eg: He becomes fatter and fatter. 他越来越胖了。 2)“The +比较级…，the +比较级”表示“越……，越……” eg：The busier he is ,the happier he feels他越忙就越高兴。 The more ,the better. 3)表示两者中”较……“时，用the + 形容词比较级+ of 短语来表达。 eg: He is the taller of the two. 4)the more… the more… he harder you worker, the greater you will make. 5)more B than A = less A than B He is more lazy than slow at his work = he is less slow than lazy at his work. 6)not so much… as… 与其说不如是 7)no/not any more… than… 两个都不（neither） No/ not any less… than… 8)More than 不仅仅是 Less than 不到 No less than 不少于 9)more or less 差不多 The work is more or less finished. 10)As+形容词/副词原级+as… (与…一样） not as(so)…as（与…不一样）中间用原级。 eg: The story is as interesting as that one. 11)表示倍数的词或其他程度副词做修饰语，放在as的前面 She can read twice as fast as you do. 12) 1、比较级与最高级可以转换，意思不变。如： She is taller than any other girl in Class5. She is the tallest girl in Class5. 2、当某一事物与其它事物作比较时，被比较事物中不能包括本身。如：
揭阳空港经济区概况 一、空港经济区概况 揭阳空港经济区位于汕潮揭地理中心，总面积234平方公里，辖地都、砲台、登岗、渔湖4个镇和京冈、凤美、溪南3个街道，人口约43万。空港经济是推动揭阳加快发展的两大引擎之一，设立空港经济区是揭阳市贯彻落实广东省促进粤东西北地区振兴发展战略、创新驱动战略部署的重要举措，也是推动市区扩容提质、做大做强中心城区的关键抓手。2013年12月4日，广东省政府审议通过揭阳新区总体发展规划，将空港新城确定为揭阳新区核心区、起步区。在全省加快粤东西北地区建设的大潮中，空港新城成为揭阳新区建设的主战场，成为吸引资本、技术和人才加快集聚的价值洼地与投资热土。 空港经济区定位： 粤东国际化前沿平台 汕潮揭同城化先行区 揭阳转型升级集聚区 奋力争当全省新区建设排头兵
区位优势：地理位置得天独厚。空港经济区地处汕头、潮州、揭阳三市“金三角”，是汕潮揭同城化的核心地带，是连接珠三角和海西经济区的纽带。在这黄金地带建设全新的空港新城，发展高端的临空产业，将辐射带动汕潮揭城市群的建设，助推粤东产业转型升级。 交通优势：交通枢纽效应凸显。揭阳潮汕国际机场是粤东和粤闽赣边区最大的干线机场；厦深高铁潮汕总站距潮汕机场才9公里，近在咫尺；榕江就在空港的地都镇奔流入海，这条“黄金水道”可进出万吨级货轮，直通浩瀚南海；汕梅高速和206国道以及在建的梅汕高铁、潮惠高速、汕湛高速、揭惠高速穿境而过。这里已经成为名副其实的粤东交通枢纽。 产业优势：产业发展基础好、定位高、前景佳。区内已形成石材、模具、家电、塑料、服装、金属制品等六大传统产业，正在重点打造的临空型产业集群，将引进航空物流业、总部经济、金融服务、高端装备制造等高端临空产业，着力打造电子商务物流区、动漫玩具创意产业区、汕潮揭特色产业展示区、模具集聚区、石材转型升级产业区和航天育种产业区等六大产业集聚区。 政策优势：政策给力，服务高效。揭阳空港经济区享受到广东省扶持粤东西北新区建设的一系列特殊优惠政策，揭阳市委市政府专门出台了支持空港经济区起步区建设的优惠政策，倾全市之力支持空港的建设。空港经济区还是全市唯一一个借鉴顺德模式实行大部制改革的地区。以大部制的体制优势、三级政务平台的高效服务，打造国际化、法治化的营商环境，为广大投资者提供最好的创富热土。
汽油的标号 所谓90号、93号、97号无铅汽油，是指它们分别含有90%、93%、97%的抗爆震能力强的“异辛烷”，也就是说分别含有10%、7%、3%的抗爆震能力差的正庚烷。于是辛烷值的高低就成了汽油发动机对抗爆震能力高低的指标。应该用97号汽油的发动机，如果用90号汽油，当然容易产生爆震。 发动机压缩比与爆震 目前汽车使用最多的是所谓的四行程发动机，它是利用活塞在气缸里往复运动，以“进气、压缩、爆发、排气”四个行程，吸入汽油与空气的混合物，然后压缩它，再用火花塞点爆它而获得动力，得到动力后，再排出点爆后的废气。 首先我们要了解的是，四行程发动机用的燃料不一定是汽油，压缩天然气、液化石油气，甚至酒精，都可用来作为发动机的燃料。汽油之所以会成为主要燃料，是因为它相对容易取得，较容易储存，相对价廉。 正因为发动机可使用多种燃料，因此，在发动机发展之初，工程师们也做过许多尝试，除了尝试发动机不同的设计会有不一样的性能之外，也尝试使用不同的燃料会得到什么不同的效果。结果发现，当其它条件不变时，只要把发动机的压缩比提得愈高，就会得到更大的马力输出。然而，压缩比却不是可以无限制提高
的，当压缩比提得太高时，发动机就会出现爆震现象。所谓爆震，是经过压缩的油或气混合物，在火花塞还没点火之前，就因为被压缩行程所造成的气体分子运动产生的高热点燃，形成所谓的自燃现象，随后火花塞又再次点燃压缩油或气混合物，造成两团高爆火球在燃烧室里剧烈碰撞，因而产生如敲门一般的“喀、喀、喀”声。经过仔细研究，工程师们发现，原来爆震又和燃料的选择有关，如果选对了燃料，那么即使提高发动机压缩比，也不会发生爆震。 爆震与辛烷值 知道了爆震与燃料的关系后，工程师们开始把炼油厂里所产生的，可以作为发动机的各种油料逐一拿来测试和实验，结果发现，抗爆震效果最差的是“正庚烷”，因此工程师们就把最强的抗震指数100给了异辛烷，而最差的正庚烷则给了它一个0的抗震指数。于是，从此开始，辛烷值的高低就成了汽油发动机对抗爆震能力高低的指标。 那么什么是辛烷值呢？那是工程师们在实验室里，利用一部可调整压缩比的单缸发动机做试验所测得的数据。在实验中，随着压缩比的逐渐提高，测试燃料从没有爆震、燃烧顺畅的状况，逐渐调整到开始出现爆震。当爆震一开始出现的时候，就去比对异辛烷与正庚烷混合物的状况，如果出现爆震的状况时机，正好
比较级最高级的用法 在英语中通常用下列方式表示的词:在形容词或副词前加more(如more natural,more clearly )或加后缀-er(newer,sooner )。典型的是指形容词或副词所表示的质、量或关系的增加。英语句子中，将比较两个主体的方法叫做“比较句型”。其中，像“A比B更……”的表达方式称为比较级；而“A最……”的表达方式则称为最高级。组成句子的方式是将形容词或副词变化成比较级或最高级的形态。 一、形容词、副词的比较级和最高级的构成规则 1．一般单音节词和少数以-er，-ow结尾的双音节词，比较级在后面加-er，最高级在后面加-est； （1）单音节词 如：small→smaller→smallest short→shorter→shortest tall→taller→tallest great→greater→greatest （2）双音节词 如：clever→cleverer→cleverest narrow→narrower→narrowest 2．以不发音e结尾的单音节词，比较在原级后加-r，最高级在原级后加-st； 如：large→larger→largest nice→nicer→nicest able→abler→ablest 3．在重读闭音节（即：辅音＋元音＋辅音）中，先双写末尾的辅音字母，比较级加-er，最高级加-est； 如：big→bigger→biggest hot→hotter→hottest fat→fatter→fattest 4．以“辅音字母＋y”结尾的双音节词，把y改为i，比较级加-er，最高级加-est； 如：easy→easier→easiest heavy→heavier→heaviest busy→busier→busiest happy→happier→happiest 5．其他双音节词和多音节词，比较级在前面加more，最高级在前面加most； 如：beautiful→more beautiful→most beautiful different→more different→most different easily→more easily→most easily 注意：（1）形容词最高级前通常必须用定冠词the，副词最高级前可不用。 例句：The Sahara is the biggest desert in the world. （2）形容词most前面没有the，不表示最高级的含义，只表示"非常"。 It is a most important problem. =It is a very important problem. 6．有少数形容词、副词的比较级和最高级是不规则的，必须熟记。 如：good→better→best well→better→best bad→worse→worst ill→worse→worst old→older/elder→oldest/eldest many/much→more→most little→less→least far →further/farther→ furthest/farthest 二、形容词、副词的比较级和最高级的用法 1．“A + be +形容词比较级+ than + B” 意思为“A比B更……”。 如：This tree is taller than that one. 这棵树比那棵树高。 注意：①在含有连词than的比较级中，前后的比较对象必须是同一范畴，即同类事物之间的比较。 ②在比较级前面使用much，表示程度程度“强得多”。如：A watermelon is much bigger than an apple. ③very, quite一般只能修饰原级，不能修饰比较级。 2．“比较级+ and + 比较级”或“more and more +原级”表示“越来越……” 如：It becomes warmer and warmer when spring comes.春天来了，天气变得越来越暖和了。 It is getting cooler and cooler.天气越来越凉爽。 The wind became more and more heavily.风变得越来越大。 Our school is becoming more and more beautiful. 我们的学校变得越来越美丽。 3．在含有or的选择疑问句中，如果有两者供选择，前面的形容词要用比较级形式。 如：Who is taller,Tim or Tom? 谁更高，Tim还是Tom? 4. “the +比较级……, the+比较级”，表示“越……越……”。 The more money you make, the more you spend. 钱你赚得越多，花得越多。 The sooner，the better.越快越好。 5. 表示倍数的比较级用法： ①. A is …times the size /height/length/width of B. 如：The new building is three times the height of the old one. 这座新楼比那座旧楼高三倍。(新楼是旧楼的四倍高) ②. A is …times as big /high/long/wide/large as B. 如：Asia is four times as large as Europe. 亚洲是欧洲的四倍大。(亚洲比欧洲大三倍) ③. A is …times larger /higher/longer/wider than B. 如：Our school is twice bigger than yours. 我们学校比你们学校大两倍。 6.形容词、副词的最高级形式主要用来表示三者或三者以上人或事物的比较，表示“最……”的意思。 句子中有表示范围的词或短语。如：of the three, in our class等等。 如：He is the tallest in our class.他在我们班里是最高的。 7."否定词语+比较级"，"否定词语+ so… as"结构表示最高级含义。 Nothing is so easy as this. =Nothing is easier than this. =This is the easiest thing. 8. 比较级与最高级的转换： Mike is the most intelligent in his class. Mike is more intelligent than any other student in his class 9.修饰比较级和最高级的词 1）可修饰比较级的词 ①.a bit, a little, rather, much, far, by far, many, a lot, lots, a great deal, any, still, even等。 ②. 还可以用表示倍数的词或度量名词作修饰语。 ③. 以上词(除by far)外，必须置于比较级形容词或副词的前面。 注意：使用最高级要注意将主语包括在比较范围内。 (错) Tom is the tallest of his three brothers. (对) Tom is the tallest of the three brothers.
辛烷值详解 爆震(震爆Knocking) 汽车用油主要成分是C5H12~C12H26之烃类混合物，当汽油蒸汽在气缸内燃烧时(活塞将汽油与空气混合压缩后，火花塞再点火燃烧)，常因燃烧急速而发生引擎不正常燃爆现象，称为爆震(震爆) 。在燃烧过程中如果火焰传播速度或火焰波与波形发生突变，如引起燃烧室其它地方自动着火(非火星塞点火漫延)，燃烧室内之压力突然增高此压力碰击四周机件而产生类如金属的敲击声，有如爆炸，故称为爆震(震爆)。汽油一旦辛烷值过低，将使引擎内产生连续震爆现象，造成机件伤害连续的震爆容易烧坏气门，活塞等机件。 爆震之原因： (1) 汽油辛烷值太低。(2)压缩比过高。(3)点火时间太早。(4)燃烧室局部过热。(5)混合汽温度或压力太高。(6)混合汽太稀。(7)预热。(8)汽缸内部积碳。(9)其他如冷却系或故障等。减少爆震方法： (1) 提高汽油辛烷值。(2)减低压缩比。(3)校正点火正时。(4)降低进汽温度。(5) 减少燃烧室尾部混合汽量。(6)增加进汽涡流。(7)缩短火焰路程。(8)保持冷却系作用良好。 辛烷值 爆震时大大减低引擎动力，实验显示，烃类的化学结构在震爆上有极大的影响。燃烧的抗震程度以辛烷值表示，辛烷值越高表示抗震能力愈高。其中燃烧正庚烷CH3(CH2)5CH3的震爆情形最严重，定义其辛烷值为０。异辛烷(2,2,4-三甲基戊烷) 的辛烷值定义为100。辛烷值可为负，也可以超过100。 当某种汽油之震爆性与90%异辛烷和10%正庚烷之混合物之震爆性相当时，其辛烷值定为90。如环戊烷之辛烷值为85，表示燃烧环戊烷时与燃烧85%异辛烷和15%正庚烷之混合物之震爆性相当。 此为无铅汽油标示来源，目前有辛烷值为92，95，98等级之无铅汽油，此类汽油含有高支链成分及更多芳香族成分之烃类，如苯，芳香烃，硫合物等。 例如95无铅汽油的抗震爆强度相当于标准油中含有百分之九十五的异辛烷及百分之五的正庚烷的抗震爆强度。 汽油亦可藉再加入其它添加物而提升辛烷值。如普通汽油辛烷值不高(约为50)，若再加入四乙基铅(C2H5)4P b时，其辛烷值提高至75左右，此为含铅汽油之来源，为除去铅在引擎内之沉积，再加入二溴乙烷，使产生P b Br2之微粒排放出来，但造成环境之污染。一般无铅汽油不含四乙基铅，改用甲基第三丁基醚，甲醇，乙醇，第三丁醇等添加物。 某一汽油在引擎中所产生之爆震，正好与98%异辛烷及2%正庚烷之混合物的爆震程度相同，即称此汽油之辛烷值为98。此燃油若再渗合其它添加剂，辛烷值可大于98或小于98甚或超过100。 一般所谓的95、92无铅汽油即是指其辛烷值，所以95比92的抗爆性来的好。 辛烷值只是一个相对指标，而不是真的只以正庚烷或异辛烷来混合，所以有些燃油再渗合其它添加剂时的辛烷值可以超过100，可以为负。 若车辆『压缩比』在9.1以下者应以92无铅汽油为燃料；压缩比9.2至9.8使用95无
揭阳潮汕机场南航基地工程 飞机维修区 设计任务书
编制单位：汕头航空有限公司 编制时间：二〇〇八年六月二十三日 第一章项目概况及周边条件 一、项目名称：南航汕头公司揭阳潮汕机场基地工程 二、地理位置: （一）机场区位： 潮汕机场（登岗北）场址位于汕头、潮州和揭阳三市之间，揭阳市揭东县炮台镇以东登岗镇以北，枫江以南，虎岗山以北山脚。机场近期跑道中心点位于东经116度30分09.41秒北纬23度33分07.60秒。以机场位置点为坐标中心，到三市(汕头、揭阳、潮州)中心距离及方位分别为：汕头市26公里，方位角为131.2 °即东偏南41.2度；揭阳市18公里，方位角为271°即西偏北1 度；潮州市17.5公里，方位角为40.5 °即北偏东40.5度。 从目前的城市总体规划来看，机场的东侧是汕揭梅高速路，南侧则是登岗镇，在机场西侧、国道以西将建设一条城市东环路，机场附近建设登洪高速路选线未定，会依据机场选址调整，在这个区域内将包含机场近期、中期、远期及远景用地，不能转为其他用途。 图表 1机场区位 图表 2机场工作区区位 （二）南航汕头公司基地区位： 整个机场总体分为3大部分：飞行区、航站核心区和工作区。而南航汕头公司基地主要由三个区组成： 1、机坪区：位于机场飞行区以南，该区建设用地面积约160亩（不清晰），最远端距航站楼中轴线距离约1.5公里。 2、生产生活区：位于机场工作区以南，该区建设用地面积约11.7公顷（约176亩），最近端距航站楼中轴线距离约1.2公里。 3、一线生产办公区：机场工作区以北，地块建设用地面积约0.4公顷（约6.3亩），地块中心距航站楼中轴线距离约600米。 图表 3南航汕头公司区位 三、建设规模 2007年9月26日国家发展和改革委员会以《关于新建广东潮汕民用机场项目可行性研究报告的批复》（发改交运[2007]101号）对潮汕民用机场项目进行批复，其中对南航基地工程主要建设内容批复如下：建设5机位停机坪4.6万平方米，维修基地1.68万平方米及其他配套生产生活设施等。（注：目前南航总部批复近期建5机位停机坪及总建筑规模为37400平方米基地）。