Multiwavelength studies of the Seyfert 1 galaxy NGC7469. I - Far UV observations with FUSE

傅里叶红外光谱仪英语Fourier Transform Infrared SpectroscopyFourier Transform Infrared Spectroscopy (FTIR) is a powerful analytical technique used to identify and characterize a wide range of materials, including organic and inorganic compounds. This spectroscopic method relies on the interaction between infrared (IR) radiation and the molecular bonds within a sample to provide valuable information about its chemical composition and structure.The underlying principle of FTIR spectroscopy is the absorption of specific wavelengths of IR radiation by the molecules in a sample. Each type of molecular bond has a unique vibrational frequency that corresponds to a specific wavelength of IR radiation. When the sample is exposed to IR radiation, the molecules absorb energy at wavelengths that match their vibrational frequencies, causing the bonds to stretch, bend, or twist. By analyzing the pattern of absorbed wavelengths, scientists can identify the functional groups and molecular structures present in the sample.The FTIR instrument consists of several key components that work together to generate and analyze the infrared spectrum of a sample.The heart of the system is the interferometer, which uses a moving mirror to create an interference pattern of the incoming IR radiation. This interference pattern is then directed towards the sample, where the interactions between the IR radiation and the sample's molecules occur. The resulting transmitted or reflected IR radiation is then detected and analyzed by a computer, which generates the FTIR spectrum.One of the main advantages of FTIR spectroscopy is its high sensitivity and speed of analysis. Unlike traditional dispersive IR spectroscopy, FTIR uses the Fourier transform algorithm to rapidly acquire the entire infrared spectrum of a sample, making the analysis much faster and more efficient. Additionally, FTIR instruments are generally more compact and cost-effective compared to their dispersive counterparts, making them more accessible for various applications.FTIR spectroscopy has a wide range of applications in various fields, including chemistry, materials science, biology, and environmental science. In the chemical industry, FTIR is used to identify and characterize a wide range of organic and inorganic compounds, such as polymers, pharmaceuticals, and petrochemicals. In materials science, FTIR is employed to study the composition and structure of materials, including ceramics, glasses, and thin films.In the field of biology and medicine, FTIR spectroscopy has found applications in the analysis of biological samples, such as tissues, cells, and body fluids. This technique can provide valuable information about the biochemical composition and changes in the samples, which can be useful for disease diagnosis, drug development, and tissue engineering. Additionally, FTIR has been used in environmental studies to detect and quantify various pollutants, such as greenhouse gases, in air and water samples.One of the key advantages of FTIR spectroscopy is its versatility in sample preparation and analysis. FTIR can be used to analyze samples in various forms, including solids, liquids, and gases, without the need for extensive sample preparation. This makes FTIR a highly valuable tool for researchers and analysts working in a wide range of fields.In conclusion, Fourier Transform Infrared Spectroscopy is a powerful analytical technique that has revolutionized the way we study and characterize materials. Its high sensitivity, speed, and versatility have made it an indispensable tool in various scientific and industrial applications, contributing to advancements in fields ranging from chemistry and materials science to biology and environmental monitoring.。

J.Dairy Sci.90:1122–1132©American Dairy Science Association,2007.Evaluating Mid-infrared Spectroscopy as a New Technique for Predicting Sensory Texture Attributes of Processed CheeseC.C.Fagan,*1C.Everard,*C.P.O’Donnell,*G.Downey,†E.M.Sheehan,‡C.M.Delahunty,§andD.J.O’Callaghan ʈ*Biosystems Engineering,UCD School of Agriculture,Food Science and Veterinary Medicine,University College Dublin,Earlsfort Terrace,Dublin 2,Ireland†Teagasc,Ashtown Food Research Centre,Dublin 15,Ireland‡Department of Nutritional Sciences,University College Cork,Cork,Ireland§Department of Food Science,University of Otago,PO Box 56,Dunedin 9015,New Zealand ʈTeagasc,Moorepark Food Research Centre,Fermoy,Co.Cork,IrelandABSTRACTThe objective of this study was to investigate the po-tential application of mid-infrared spectroscopy for de-termination of selected sensory attributes in a range of experimentally manufactured processed cheese samples.This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for pre-dicting sensory texture attributes.Processed cheeses (n =32)of varying compositions were manufactured on a pilot scale.After 2and 4wk of storage at 4°C,mid-infrared spectra (640to 4,000cm −1)were recorded and samples were scored on a scale of 0to 100for 9attributes using descriptive sensory analysis.Models were devel-oped by partial least squares regression using raw and pretreated spectra.The mouth-coating and mass-form-ing models were improved by using a reduced spectral range (930to 1,767cm −1).The remaining attributes were most successfully modeled using a combined range (930to 1,767cm −1and 2,839to 4,000cm −1).The root mean square errors of cross-validation for the models were 7.4(firmness;range 65.3),4.6(rubbery;range 41.7),7.1(creamy;range 60.9),5.1(chewy;range 43.3),5.2(mouth-coating;range 37.4),5.3(fragmentable;range 51.0),7.4(melting;range 69.3),and 3.1(mass-forming;range 23.6).These models had a good practical utility.Model accuracy ranged from approximate quantitative predic-tions to excellent predictions (range error ratio =9.6).In general,the models compared favorably with previously reported instrumental texture models and near-infrared models,although the creamy,chewy,and melting models were slightly weaker than the previously reported near-infrared models.We concluded that mid-infrared spec-troscopy could be successfully used for the nondestruc-tive and objective assessment of processed cheese sen-sory quality.Received April 12,2006.Accepted October 30,2006.1Corresponding author:colette.fagan@ucd.ie1122Key words:descriptive sensory analysis,processed cheese,mid-infrared spectroscopy,chemometricsINTRODUCTIONOver 18million tonnes of cheese were produced world-wide in 2004,and processed cheese is an important seg-ment of this market (Wohlfarth and Richarts,2005).The United States,the largest producer of processed cheese (where 20%of all cheese consumed is processed cheese),produced 1,092,000tonnes in 2003(Wohlfarth and Ric-harts,2005).In the same year,the 25countries of the European Union produced 655,000tonnes of processed cheese (Wohlfarth and Richarts,2005).Consumer preference for a food product is principally determined by its sensory characteristics.Accurate mon-itoring and control of sensory properties will facilitate the production of high-quality products.A number of factors determine the final quality and sensory proper-ties of processed cheese (Caric´and Kala ´b,1993).These include the processing conditions used during manufac-ture,the composition of the ingredients,and the propor-tions of those ingredients added to the blend.Sensory profiling allows various quality attributes to be identified and their intensity determined (Brown et al.,2003).Sensory attributes are traditionally assessed by descriptive sensory evaluation using trained panel-ists.However,this is a time-consuming and expensive process that may lack objectivity (Blazquez et al.,2006).Although instrumental techniques such as texture pro-file analysis (TPA )and the 3-point bend test are avail-able for determining the texture attributes of food prod-ucts,these laboratory-based techniques are time-con-suming and require the use of skilled personnel in their execution (Blazquez et al.,2006).Therefore,considerable interest exists in the development of instrumental tech-niques to enable more objective,faster,and less expen-sive assessments of cheese quality to be made,including sensory aspects (Downey et al.,2005).Such a technique would assist producers to maximize yields,increasePREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1123throughput and efficiency,reduce labor costs,and opti-mize product quality,consistency,and customer satisfac-tion.Critical points in the manufacturing process could be monitored to ensure that the final product would meet required specifications.Recently,Kealy (2006)examined cream cheese using TPA,one of the main instrumental techniques for tex-ture measurement,and compared the results with those of a trained taste panel.Although a reasonably strong correlation was found between the taste panel results and TPA-derived hardness and adhesiveness parame-ters,the correlation for cohesiveness was not straightfor-ward.Everard (2005)also investigated the prediction of sensory attributes of processed cheese from instrumen-tal texture attributes derived from TPA,a compression test,and a 3-point bend test.He could predict the texture attributes of firmness,rubbery,creamy,chewy,frag-mentable,and mass-forming with a good level of accu-racy (Everard,2005).Spectroscopic analysis in combination with predictive mathematical models,developed using multivariate data analysis techniques such as partial least squares (PLS )regression,have potential use in controlling and monitoring the quality of raw materials through to the final product in food processing.In particular,infrared spectroscopy has been applied as an objective and nonde-structive technique to provide a rapid and real-time anal-ysis of both composition and quality (Downey,1998;Lefier et al.,2000;Ozen and Mauer,2002;Blazquez et al.,2004).Blazquez et al.(2006)modeled the sensory attributes of processed cheese using near-infrared re-flectance spectroscopy and PLS regression.They found that it was possible to model a number of attributes including firmness,melting,rubbery,and creamy.Two other studies have investigated the prediction of sensory attributes in natural cheese.Downey et al.(2005)and Sørensen and Jepsen (1998)successfully demonstrated that near-infrared spectroscopy in conjunction with PLS regression can be used to predict several sensory attri-butes of Cheddar and Danbo cheeses,respectively.Mid-infrared spectroscopy has been most widely used for de-termination of the fat and protein contents of cheese (Chen and Irudayaraj,1998).Irudayaraj et al.(1999)also investigated the use of mid-infrared spectroscopy to follow texture development in Cheddar cheese during ripening.They demonstrated that springiness could be successfully correlated with a number of bands in the mid-infrared spectra.Research has shown that mid-in-frared spectroscopy is a useful technique for characteriz-ing the changes in proteins during cheese ripening (Ma-zerolles et al.,2001).Pillonel et al.(2003)also found that mid-infrared spectroscopy may be successfully applied to the discrimination of Emmental cheese based on geo-graphic origin.Journal of Dairy Science Vol.90No.3,2007Table 1.Quantity of ingredients (g/kg)used in the production of experimental processed cheese samples Sample Emulsifyingnumber(s)Cheddar Butter Water salt1,10838.70.0161.39.72838.70.0151.619.43,11838.70.0141.929.04,12838.751.6112.99.75,13838.751.6100.019.46,14838.751.690.329.07,15838.7100.061.39.78838.7100.051.619.49,16838.7100.041.929.017,25848.451.6103.29.718,26838.751.6100.019.419,27829.051.6100.029.020,28751.645.2203.29.721,29745.245.2203.219.422,30738.745.2200.025.823,31651.638.7303.216.124,32645.238.7303.222.6No data are currently available on the application of mid-infrared spectroscopy to determine the sensory attributes in processed cheese,or regarding evaluation of mid-infrared spectroscopy in comparison with other technologies in such an application.Therefore,the objec-tives of this study were to investigate the use of mid-infrared spectroscopy in predicting sensory texture attri-butes using a range of experimentally manufactured pro-cessed cheese samples and to compare the models devel-oped with those recently modeled using near-infrared spectra and instrumental texture attributes.These newly presented data allow for the critical evaluation of mid-infrared spectroscopy as a rapid,nondestructive technique for predicting the sensory texture attributes of processed cheese.MATERIALS AND METHODSProcessed Cheese SamplesThirty-two processed cheese batches were manufac-tured in a pilot plant at Moorepark Food Research Cen-tre,Cork,Ireland.The ingredients and formulations are listed in Table 1.The formulations,which were selected to provide samples with compositional ranges that ex-tended beyond those used commercially by processed cheese manufacturers,provided samples with a wide range of sensory characteristics.The ingredients were mixed for 1min in a jacketed cooker (Stephan UMM/SK5Universal cooker;Stephan u So ¨hne GmbH &Co.,Hameln,Germany).The blend was cooked at 80°C for 2min by indirect steam heating.During cooking,the blend was stirred constantly using a knife at 300rpm and a baffle mixer at 80rpm.The cooked blend was stored in food-grade plastic containers (225g capacity),FAGAN ET AL.1124Table2.Vocabulary of sensory attributes,their definitions,and mastication phases used to carry out the sensory analysis of processed cheese samplesSensoryattribute Definition Mastication phaseFirmness The extent of the initial resistance offered by the cheese,Phase1:Judged on thefirst chew using the front teeth ranging from“soft”to“firm”Rubbery The extent to which the cheese returns/springs to its Phase2:Assessed during thefirst2to3chews initial form after biting,ranging from“a little”to“a lot”Creamy The texture associated with cream that has been whipped,ranging from“a little”to“a lot”Chewy The effort needed to break down the structure of the cheese,Phase3:Judged in the middle phase of mastication ranging from“a little”to“a lot”Mouth-coating The extent to which the cheese clings to the insideof the mouth(roof,teeth,tongue,gums),ranging from“a little”to“a lot”Fragmentable Breaks down to smaller versions of itself,ranging from Phase4:Probably judged toward the end of the chewing “a little”to“a lot”Melting The extent to which the cheese melts in the mouth;smooth,velvet fullness in the mouth,ranging from“a little”to“a lot”Mass-forming The extent to which the cheese form a bolus or massin the mouth after chewing,ranging from“a little”to“a lot”Greasy/oily The extent to which a greasy/oily residue is deposited Phase5:Judged at the end of the chewing sequence in the mouth after the cheese is broken down,rangingfrom“a little”to“a lot”which were lidded,allowed to cool,and placed in storage at4°C for4wk.The independent compositional variables for samples1to16were fat and emulsifying salt,and the variables for samples17to32were moisture and emulsifying salt.Descriptive sensory analysis and mid-infrared spectroscopy were carried out at2and4wk postmanufacture.Sensory AnalysisA panel of10assessors(9females and1male),aged 35to55yr old,were selected and recruited in1998and 2000according to international standards(International Organization for Standardization,1993).The panel de-veloped a vocabulary of9texture terms:firmness,rub-bery,creamy,chewy,mouth-coating,fragmentable, melting,mass-forming,and greasy/oily(Table2),which they used to assess each sample.Samples were prepared for analysis in duplicate by removing them from storage and preparing two5-g cubes.These samples were left to equilibrate to room temperature(21°C).Each equili-brated sample was presented to assessors in a glass tumbler covered with a clock glass and labeled with a randomly selected3-digit code.Assessors were provided with deionized water and unsalted crackers to cleanse their palate between tastings.The assessors scored the samples for each attribute by marking on unstructured 100-mm line scales labeled at both ends with extremes of each attribute.The intensity of each of the descriptive terms was recorded using the Compusense v.4.0sensory data acquisition software(Guelph,Ontario,Canada).At each time point,the descriptive sensory analysis took Journal of Dairy Science Vol.90No.3,2007place over2d.The order of tasting was balanced to account for the order of presentation and carryover ef-fects(MacFie et al.,1989).All assessments were con-ducted in individual booths at the sensory laboratory at University College,Cork,which complies with interna-tional standards for the design of test rooms(Interna-tional Organization for Standardization,1988).Mid-Infrared SpectroscopyMid-infrared spectra were collected over the range of 4,000to640cm−1,with a resolution of8cm−1,using an ATI Mattson Infinity Series Fourier transform spectro-photometer(ATI Mattson,Madison,WI)controlled by WinFirst software(ATI Mattson).The sample accessory used for sample presentation was an attenuated total reflectance ZnSe crystal(Graseby Specac Ltd.,Kent, UK),with an incidence angle of45°and6internal reflec-tions.Sixty-four interferograms were coadded before Fourier transformation.Prior to mid-infrared analysis, samples were removed from storage and left to equili-brate to room temperature.This was confirmed prior to analysis using a digital thermocouple(Sensor-Tech Ltd., Co.Louth,Ireland).Processed cheese samples were wiped across the attenuated total reflectance crystal to ensure even and immediate contact.Triplicate spectra were captured for each sample and replicate spectra were averaged prior to data analysis.Multivariate Data AnalysisMultivariate data analysis was carried out using The Unscrambler software(v.8.0;Camo A/S,Oslo,Norway).PREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1125Figure 1.Typical mid-infrared spectrum of processed cheese.Principal component analysis of the spectra was used to examine the spectral data set for any possible outliers.Models for the prediction of sensory attributes were de-veloped using PLS regression and confirmed by cross-validation.Prior to PLS regression,spectra were pre-treated using multiplicative scatter correction (MSC ),first derivative (Savitzky-Golay,2data points each side),second derivative (Savitzky-Golay,4data points each side),and each derivative plus MSC (Geladi et al.,1985).The potential of the models to predict the sensory attri-butes was evaluated using the root mean square error of cross-validation (RMSECV ),correlation coefficient (r )and the number of PLS loadings (#L ).The range error ratio (RER )was used to determine the practical utility of the models (Williams,1987).It was calculated by di-viding the range in the reference data of a given attribute by the prediction error for that attribute.The ratio of prediction error to deviation (RPD )was calculated by dividing the standard deviation of the reference data by RMSECV.RESULTS AND DISCUSSIONMid-Infrared SpectraA number of studies have assigned the main cheese constituents (fat,protein,moisture)to specific bands in the mid-infrared spectra (Chen and Irudayaraj,1998;Chen et al.,1998;Irudayaraj and Yang,2000;Mazerolles et al.,2001).The positions of these bands are indicated in a typical mid-infrared spectrum of processed cheese from this study (Figure 1).The results of principal component analysis of the spectra were investigated to determine whether any in-fluential outliers were present in the data set.An influ-ential outlier is a sample that has both a high residual and high leverage.A high residual means that the model,Journal of Dairy Science Vol.90No.3,2007Table 3.Statistical summary of sensory attributes (n =64)Sensory attribute Mean Range SD Firmness 37.6 5.6–70.921.9Rubbery 23.3 2.9–44.614.4Creamy 35.710.7–70.722.6Chewy24.8 2.8–46.114.6Mouth-coating 33.117.4–54.810.0Fragmentable 25.6 1.4–52.420.0Melting40.113.2–82.523.6Mass-forming 10.5 1.8–25.4 5.5Greasy/oily36.628.5–43.83.9which nevertheless fits the other samples quite well,poorly describes the sample.Leverage measures the dis-tance from the projected sample to the center or mean point.If a sample has a high leverage,it is exerting a stronger influence on the model than the remaining samples.According to these criteria,no outlier was found.Previous research has recommended that prior to analysis,a portion of the mid-infrared spectra (1,800to 2,700cm −1)might be omitted because of its low signal-to-noise ratio (Pillonel et al.,2003).This approach was used in this study,with the region 1,775to 2,830cm −1having a low signal-to-noise ratio,and was therefore omitted from analysis.In a preliminary investigation of the spectra,the region 640to 923cm −1was found to be of limited use in predicting sensory attributes and was also omitted.Therefore,only spectral data in the ranges of 930to 1,767cm −1and 2,839to 4,000cm −1were used for the multivariate data analysis.Predication of Sensory Texture Attributes by Mid-infrared SpectroscopyA summary of the values scored by the taste panel for each of the 9sensory attributes is shown in Table 3.The table highlights the high degree of variability in the data,which should support the development of robust models.Models were developed using 1)the combined spectral ranges of 930to 1,767cm −1and 2,839to 4,000cm −1,and 2)930to 1,767cm −1.The spectra were used in a number of forms:raw,MSC,first derivative,second derivative,and MSC plus each derivative step,giving 12models for each sensory attribute.A second derivative step offered no improvement in model accuracy for any attribute;hence,those prediction results are not shown.The RMSECV,r,and #L values obtained from the models developed are given in Table 4for the combined spectral range or the 930to 1,767cm −1range.These parameters allow for assessment of model strength.The preferred predictive model for an attribute (highlighted in bold in Table 4)was that which produced the lowest RMSECVFAGAN ET AL.1126Table4.Summary of partial least squares prediction results for sensory attributes using mid-infrared spectra1Scatter correctedRaw data Scatter corrected First derivative+first derivative Sensoryattribute Spectral range,cm−1r RMSECV#L r RMSECV#L r RMSECV#L r RMSECV#L Firmness930–1,767and2,839–4,0000.8810.570.947.4110.928.8100.9010.07 Rubbery930–1,767and2,839–4,0000.92 5.540.95 4.590.95 4.650.95 4.65 Creamy930–1,767and2,839–4,0000.947.840.947.860.957.150.947.55 Chewy930–1,767and2,839–4,0000.92 5.770.93 5.460.89 6.550.94 5.17 Mouth-coating930–1,7670.84 5.4100.85 5.3110.84 5.4100.85 5.211 Fragmentable930–1,767and2,839–4,0000.95 6.190.96 5.390.94 6.540.96 5.87 Melting930–1,767and2,839–4,0000.947.940.957.560.957.750.957.47 Mass-forming930–1,7670.83 3.1100.83 3.1100.83 3.190.63 4.34 Greasy/oily930–1,767and2,839–4,0000.56 3.230.54 3.320.59 3.240.56 3.23 1Preferred model in bold.RMSECV=root mean square error of cross-validation;#L=number of partial least squares loadings.and highest r values.It was also desirable for the pre-ferred model to incorporate the lowest#L possible. The results showed that only2of the models(mouth-coating and mass-forming)were improved when the re-duced spectral range(930to1,767cm−1)was used.None of the preferred models was developed using raw spectral data(i.e.,accuracy was improved by the application of a pretreatment).Thefirmness and fragmentable attri-butes were most successfully modeled using MSC spec-tra.The application of afirst derivative step resulted in the preferred models of rubbery,creamy,mass-forming, and greasy/oily.The most accurate models for the chewy, melting,and mouth-coating attributes were achieved when the spectra were subjected to scatter correction and afirst derivative.In conjunction with the RMSECV,r,and#L,the prac-tical utility of the models can also be assessed using the RER.Models with RER of less than3have little practical utility;RER values of between3and10indicate limited to good practical utility;and values above10show that the model has a high utility value(Williams,1987).The preferred models for predicting thefirmness,rubbery, creamy,chewy,mouth-coating,fragmentable,melting, and mass-forming attributes(shown in bold in Table4) had RMSECV values of between3.1and7.4and resulted in corresponding RER values of between7.2and9.6, indicating that the models had good practical utility. Therefore,these attributes had the potential to be pre-dicted by mid-infrared spectroscopy and multivariate data analysis.The greasy/oily attribute was not success-fully modeled(RER=4.8),possibly because of the small range displayed by the samples analyzed,and will there-fore not be discussed further.A graphical display of the preferred regression model for each attribute(high-lighted in bold in Table4)is shown in Figure2A to2H. Figure2shows that there is minimal scatter in the plots, as indicated by the high r values(0.83to0.96),and that the regression lines also have slopes close to1(0.77to 0.96)and low intercepts(1.0to5.9),demonstrating a Journal of Dairy Science Vol.90No.3,2007goodfit(Figure2).The accuracy of each model can be evaluated using the coefficients of determination(R2) between the predicted and measured values,as stated by Williams(2003).The models for mass-forming and mouth-coating both provided approximate quantitative predictions because their R2lay in the range of0.66to 0.81.Good predictions were achieved for the attributes firmness,rubbery,creamy,and chewy,with R2values of between0.82and0.90.The fragmentable model was considered to be excellent,having an R2greater than 0.91.The#L must also be taken into account.This ranged from5to11for the selected models.The models forfirmness,fragmentable,mouth-coating,and mass-forming incorporated a relatively high number of load-ings(9to11),which may have implications for their robustness,because the lower#L,the more robust the model.Thefirst3loadings of the models,which accounted for greater than90%of the variation in the spectral data,are plotted in Figure3A to3H.Although a number of preferred models were developed using spectra pre-treated with afirst derivative step,interpretation of the loadings associated with these models was difficult.This was because the observed peaks and valleys did not follow the raw spectral pattern.However,second deriva-tive steps were very helpful in spectral interpretation because in this form,band intensity and peak location were maintained with those in the raw spectral pattern. Therefore,although the second derivative step did not improve any of the prediction models,the loadings pre-sented for rubbery,creamy,and mass-forming were ob-tained using second derivative spectra and those for chewy,mouth-coating,and melting were obtained using MSC second derivative spectra.The loading plots pre-sented forfirmness and fragmentable were obtained us-ing MSC spectra.Figure3A to3H shows the relation-ships among the loadings used in the prediction model and the different wavenumbers.If a wavenumber had a large positive or negative loading,this meant that thePREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1127Figure2.Linear regression plots of actual vs.predicted sensory attributes of(A)firmness,(B)rubbery,(C)creamy,(D)chewy,(E)mouth-coating,(F)fragmentable,(G)melting,and(H)mass-forming.RER=range error ratio.Journal of Dairy Science Vol.90No.3,20071128FAGAN ET AL.Figure3.Loading plots for partial least squares models of the sensory attributes of(A)firmness,(B)rubbery,(C)creamy,(D)chewy, (E)mouth-coating,(F)fragmentable(G)melting,and(H)mass-forming.Journal of Dairy Science Vol.90No.3,2007PREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1129wavenumber was important for the attribute concerned.Therefore,they assisted in summarizing the relationship between the spectra and the predicted attribute and provided an aid to interpreting the molecular basis for predicting an attribute.The important loadings were distributed across the full spectral range used in predicting each attribute (Fig-ure 3).There was considerable structure present in all of the loading plots.In comparing the plots produced using similar spectral treatments,that is,MSC (Figure 3A and 3F),second derivative (Figure 3B and 3C),and MSC plus second derivative (Figure 3D and 3G),it was apparent that differences existed in the relative impor-tance of various regions of the spectra in predicting the different sensory attributes.For example,the region around 3,200to 3,900cm −1was shown to be of greater importance in predicting the attributes of firmness (Fig-ure 3A),chewy (Figure 3D),and melting (Figure 3G)than for the other attributes.This region of the spectra corresponded with a broad moisture absorption peak.The loadings incorporated into the firmness model ex-plained the variation across almost the full spectral range used (Figure 3A).Peaks and valleys occurred at around 1,095,1,160,and 1,269to 1,396cm −1,associated with the vibration of C–H,C–O bonds of carbohydrates;1,739,2,846,and 2,931cm −1,associated with lipids;and around 1,554,1,604,and 1,646cm −1,which are known to correspond with amides I and II.The amide I and amide II regions of the spectra were also important in predicting chewy,with peaks observed in the loading plot in the region of 1,504,1,547,1,639,and 1,655cm −1(Figure 3D).The loadings for the chewy model were also found to explain variation in the moisture absorp-tion region (3,359to 3,907cm −1),the peaks associated with lipids (1,739and 2,927cm −1),and the region around 1,080cm −1.The most important regions of the spectra for predicting mouth-coating were the regions associated with the vibration of C–H,C–O bonds of carbohydrates (987,1,072to 1,095,1,176,and 1,334to 1,427cm −1)and lipids (1,732,1,739,and 1,751cm −1;Figure 3E).Peaks were also observed in the amide I and II region (1,542,1,562,and 1,655cm −1).A number of major peaks were clearly identified in the fragmentable loading plot (Fig-ure 3F).These were 1,110,1,169,1,242,and 1,462cm −1,and 1,743,2,854,and 2,924cm −1,which corres-ponded with the vibration of the C–H,C–O bonds of carbohydrates and lipids,respectively.Emulsifying salts chelate calcium,which plays an important role in the 2-dimensional structure of processed cheese.They also aid in the dispersion of proteins,which contributes to the emulsification of fat.In this study,the emulsifying salt used was disodium phosphate.The effect of increasing the phosphate concentration was 2-fold,namely,an in-creasing ability to chelate calcium and an incremental Journal of Dairy Science Vol.90No.3,2007increase in the pH of cheese.The interaction between these 2effects (emulsifying salt and pH)will result in increased firmness of the cheese.However,this result is also dependent on the moisture content because mois-ture acts as a plasticizer in processed cheese and de-creases the concentration of the dispersed phase,hence decreasing the firmness of the processed cheese.Greater firmness is also attributed to a higher concentration of protein,and increases in the fat and water contents weaken the protein structure,thereby decreasing the firmness of the processed cheese.This explains the im-portance of the fingerprint (991to 1,400cm −1),lipid,amide,and moisture-absorption regions in predicting the firmness,chewy,fragmentable,and mass-forming attributes.The regions of the spectra that were most important in predicting the attributes of rubbery (Figure 3B)and creamy (Figure 3C)were all associated with lipids (1,739,1,743,2,846,2,858,2,916,2,919cm −1).Minor peaks were also observed in the 1,079to 1,173,1,542,and 3,556to 3,907cm −1spectral regions,which are associated with the vibration of the C–H,C–O bonds;amide II;and moisture absorption,respectively.These peaks were of particular importance in predicting the rubbery and creamy attributes,because fat in cheese has the effect of preventing the protein network of the cheese matrix from forming a tough,dense structure (Lawlor et al.,2001).The loadings for the melting model (Figure 3G)ex-plained variation in a number of different regions,in-cluding the amide I and II regions (1,547,1,654cm −1),lipid regions (1,751,2,927cm −1),and moisture-absorp-tion region (3367to 3907cm −1).All factors that influence either the content or distribution of fat,or the strength of the protein network are known to influence cheese meltability (Lefevere et al.,2000).This accounts for the significance of the moisture,amide,and lipid regions of the spectra in predicting melting.The regions of the spectra that were the most im-portant in predicting mass-forming were found to be 1,092and 1,130cm −1(C–H,C–O bond vibrations);1,535,1,547,1,646,and 1,647cm −1(amides I and II);and 1,736,1,743,and 1,751cm −1(lipids;Figure 3H).This indicates the role that the fat content and protein structure has in determining the mass-forming potential of pro-cessed cheese.These results highlight the importance of different regions across the entire spectral range used in pre-dicting the sensory textural attributes of processed cheese.The importance of different spectral regions in predicting sensory attributes is related to the effects of the formulation and composition on processed cheese texture.Changes in the formulation and composition of。

傅里叶变换红外光谱仪英文Fourier Transform Infrared SpectrometerIntroduction:The Fourier Transform Infrared (FTIR) spectrometer is an essential tool in the field of spectroscopy. It utilizes the mathematical technique known as Fourier transform to analyze infrared light, enabling scientists to study the molecular composition and structure of various substances. In this article, we will explore the principles behind the Fourier Transform Infrared Spectrometer and its applications in scientific research.Principles of Fourier Transform Infrared Spectroscopy:Fourier Transform Infrared Spectroscopy is based on the interaction between infrared light and matter. When a substance is exposed to infrared radiation, the energy absorbed by the molecules causes them to vibrate. These vibrations are specific to each molecule and are dependent on the molecular bonds present within the substance.The spectrometer operates by passing an infrared beam through the sample and measuring the amount of light absorbed at different wavelengths. This absorption spectrum is then transformed using Fourier analysis, producing a highly detailed and accurate representation of the substance's molecular structure.Advantages of Fourier Transform Infrared Spectroscopy:1. High Speed and Sensitivity: Fourier Transform Infrared Spectroscopy offers rapid analysis times due to its ability to gather a full range ofwavelengths simultaneously. This allows for efficient data collection, making it ideal for high-throughput applications. Additionally, the technique is highly sensitive, capable of detecting even small quantities of sample material.2. Broad Analytical Range: FTIR spectroscopy covers a wide range of wavelengths, from near-infrared (NIR) to mid-infrared (MIR). This versatility enables the analysis of various substances, including organic and inorganic compounds, polymers, pharmaceuticals, and biological samples.3. Non-destructive Analysis: One of the key advantages of FTIR spectroscopy is that it is a non-destructive technique. Samples do not require any special preparation and can be analyzed directly, allowing for subsequent analysis or retesting if required.Applications of Fourier Transform Infrared Spectrometers:1. Pharmaceutical Analysis: FTIR spectroscopy plays a vital role in drug discovery and development. It is used to identify and characterize the molecular composition of active pharmaceutical ingredients (APIs), excipients, and impurities. By comparing spectra, scientists can ensure the quality and purity of pharmaceutical products.2. Environmental Analysis: Fourier Transform Infrared Spectrometers are employed in environmental monitoring to analyze air, water, and soil samples. It aids in detecting pollutants, identifying unknown substances, and assessing the impact of human activities on the environment.3. Forensic Science: FTIR spectroscopy has proven to be a valuable tool in forensic science. It assists in the analysis of various evidence, such asfibers, paints, and drugs. FTIR spectra can provide crucial information in criminal investigations, helping to identify unknown substances and link them to potential sources.4. Food and Beverage Industry: The FTIR spectrometer allows for the analysis of food quality, safety, and authenticity. It can identify contaminants, detect adulteration, and verify product labeling claims. Both raw materials and finished products can be analyzed using this technique, ensuring compliance with industry regulations.Conclusion:The Fourier Transform Infrared Spectrometer has revolutionized the field of spectroscopy by providing accurate and detailed information about a substance's molecular structure. Its speed, sensitivity, and versatility make it a crucial analytical tool in various scientific disciplines. With ongoing advancements in technology, FTIR spectroscopy continues to contribute to new discoveries and advancements in research.。

关于绍兴的科学家作文英语Shaoxing, a city steeped in history and culture, has been home to numerous scientists who have made significant contributions to the field of science and technology. This essay aims to highlight the achievements of some of these remarkable individuals and the impact they have had on the world.The city's rich intellectual heritage is evident in the lives of its scientists. One such luminary is Mr. Wu, a renowned physicist who has dedicated his life to the study of quantum mechanics. His research has shed light on the fundamental principles governing the universe, earning him international acclaim and several prestigious awards.Another notable figure is Dr. Li, a biotechnologist who has made groundbreaking advancements in genetic engineering. Her work has revolutionized the way we understand and manipulate genetic material, leading to new treatments for various diseases and a deeper understanding of the human genome.The contributions of these scientists extend beyond the realm of academia. Ms. Chen, an environmental scientist, has been at the forefront of sustainable development initiatives in Shaoxing. Her efforts have not only improved the quality of life for the city's residents but also set a standard foreco-friendly practices that other cities aspire to follow.Moreover, the spirit of innovation is alive and well among the younger generation of scientists in Shaoxing. Young researchers like Mr. Zhang are already making waves in their respective fields.张先生, a prodigious chemist, has developed novel materials with applications in energy storage and electronics, showcasing the potential of Shaoxing'sscientific community to innovate and adapt to the challenges of the future.In conclusion, the scientists of Shaoxing are a testament to the city's commitment to scientific excellence and progress. Their work has not only enriched the scientific community but also improved the lives of people around the world. As we celebrate their achievements, we are reminded of the importance of fostering a culture of curiosity, inquiry, and innovation that has been the hallmark of Shaoxing'sscientific legacy.This essay is a tribute to the scientists of Shaoxing, whose tireless pursuit of knowledge continues to inspire and shape the world we live in.。

美国伦斯勒理工学院破解家族性老年痴呆症由美国伦斯勒理工学院的研究员王春雨带领的一项最新研究，破解了家族性老年痴呆症（Familial Alzheimer’s Disease，FAD）发展中的一个谜团，即这种影响一小部分老年痴呆症人群的疾病的遗传突变。

在2014年1月6日《自然通讯》（Nature Communications）上发表的这项研究中，Wang及其团队追踪研究了已知能引起FAD的两个遗传突变——V44M和V44A，并指出这些突变是如何引起与疾病相关的生化变化的。

FAD的标志是——β-类淀粉样蛋白42肽（一个氨基酸短链）的积累，在大脑中以异乎寻常的高浓度存在。

研究人员在健康大脑中发现，β-类淀粉样蛋白42肽（Aβ42）和一个类似的肽——β-类淀粉蛋白40（Aβ40），两者的比例是1：9。

而在受FAD影响的大脑中，这个比例更高。

这两种肽几乎是完全相同的：Aβ40在长度上，是一连串的40个氨基酸；Aβ42在长度上是42个氨基酸。

然而，Aβ42对神经元更加具有毒性，在记忆障碍中起着至关重要的作用。

Wang是伦斯勒理工学院科学学院的生物学副教授、生物化学和生物物理研究生项目主任、伦斯勒生物技术和多学科研究中心成员，他说：“引起FAD的这些突变，能够导致Aβ42的比例超过Aβ40。

这是一个生物化学过程，并且已经被很多人观察到。

但是我们要问的问题是：这些突变如何引起这个比例的增加？”有数百个基因突变已知与FAD相关，但是它们都与一个大的蛋白——淀粉样前体蛋白（APP）的加工过程有关，这个蛋白从部分嵌入脑细胞细胞膜中开始其一生，后来它被剪切成几个片段，其中一个片段成为Aβ42或Aβ40。

在一个多步骤的过程中，酶使几个剪切片段成为APP，而这些剪切片段的位置，决定着APP的结果片段到底成为Aβ42还是Aβ40。

如果一种酶——γ-分泌酶（γ-secretase），从APP内的一个氨基酸（称为苏氨酸48，Threonine 48或T48）开始剪切，那么，剩余的剪切片段会产生Aβ42，而如果第一次剪切是从亮氨酸49上开始，则这个过程将产生Aβ40。

55第三章不規則波理論風浪使海面形成一種極為不規則(irregular) 的波形。

從風洞水槽或現地觀測中均可發現水面上的風浪，如照片3.1顯示，大波上面疊有小波，縱橫各方向的波重重疊疊，隨著時間和空間變化，同樣的波形不可能再次發生。

故風浪之波形本身構造複雜，是屬於時間及空間上的一種隨機性(random) 變動量。

風浪既是一種隨機現象，則須以統計的方法來描述其特性。

統計方法中波別分析法(individual wave analysis) 和波譜分析法(spectral analysis) 是目前被採用做為敘述海洋風浪之不規則性最普遍的方法。

利用這兩種方法從不規則波中定義出波高和週期，使其能適用於規則波的波浪理論，以達到各種工程設計應用的目的。

一般將這種統計稱為波浪的短期統計，此外另有波浪的長期統計，又分為波候統計和極值統計。

波候統計是對於長年監測到的資料做歸納、整理、和一般統計分析。

而極值統計是討論重現期的問題，將在第四章中再行敘述。

本章只就短期統計對水面波形之不規則性的問題來討論。

照片3.1實際海面風浪的照片3.1 不規則波的表示方法3.1.1 波別解析法不規則波理論56 單純來看，若視風浪的水面變位為一維的波形變化，如圖3.1所示。

對此不規則波形信號來定義個別波之波高與週期有三種方式。

第一種是零位上切 (zero up cross) 法，所謂上切零點是水位上昇曲線與平均水位線之交點，如圖3.1中小圓圈所示各點。

計算二相鄰上切零點間，水位變動之最高峰與最低谷點間之垂直高差即為波高，二相鄰上切零點的時間長度即為週期。

第二種是以水位下降曲線與平均水位線之交點，如圖3.1中小三角形所示各點，定義出個別波的方法，稱為零位下切 (zero down cross) 法。

另外第三種是無視平均水位的存在，兩相鄰波峰波谷的高差即為波高，兩相鄰波峰之間的時間即為週期，依此定義個別波的方法稱為峰至峰 (crest to crest) 法。

学术英语写作杨新亮课文翻译In recent years there has been considerable interest in explorin g the nature of expert performance across domains ( e.g.,Ericsson, Hoffman,Charness,Feltovich.2006;Ericsson Williams, 2007).For example,scientists with an interest in sports have analyzed the perceptualcognitive skills underpinning anticipation in this domain and identified how these processes are acquired through prolonged engagement in practice (for reviews, see Hodges, Huys, Starkes, 2O 07: Williams Ford. 2008 : Williams Ward, 2007 ).The scientific study of skill acquisition has along history in experimental psychology,dating back to the early st udies of Bryan and Harter (1899).In more recent times, Poulton (19 57) was the firstto systematically discriminate between different types of anticipati on judgements using experimetal methods common to this discipl ine. The scientific study of anticipation as a field of inquiry in its ow n right in sport psychologyhas a much shorter history, emergingprimarily in 1970s ( far a historical overview, see Williams. Davids. Williams, 1999).The majority of sport psychologists work in multi_d isciplinary departments where research in traditional discipline area s,such as physiology. psychology. and biomechanics, often develops somewhat independently of academic endeavour within the main disciplines themselves. The empirical findings that have been reported on anticipation in the field of sport psychology could therefore contribute to the generation of new knowledge on this topic in the parent discipline area, and part icularly in applied cognitive psychology.近年来，在探索专家性能的跨域的性质得到了相当大的兴趣（例如，爱立信，霍夫曼，feltovich查尼斯， 2006；爱立信威廉姆斯，2007）。

法布里珀罗基模共振英文The Fabryperot ResonanceOptics, the study of light and its properties, has been a subject of fascination for scientists and researchers for centuries. One of the fundamental phenomena in optics is the Fabry-Perot resonance, named after the French physicists Charles Fabry and Alfred Perot, who first described it in the late 19th century. This resonance effect has numerous applications in various fields, ranging from telecommunications to quantum physics, and its understanding is crucial in the development of advanced optical technologies.The Fabry-Perot resonance occurs when light is reflected multiple times between two parallel, partially reflective surfaces, known as mirrors. This creates a standing wave pattern within the cavity formed by the mirrors, where the light waves interfere constructively and destructively to produce a series of sharp peaks and valleys in the transmitted and reflected light intensity. The specific wavelengths at which the constructive interference occurs are known as the resonant wavelengths of the Fabry-Perot cavity.The resonant wavelengths of a Fabry-Perot cavity are determined bythe distance between the mirrors, the refractive index of the material within the cavity, and the wavelength of the incident light. When the optical path length, which is the product of the refractive index and the physical distance between the mirrors, is an integer multiple of the wavelength of the incident light, the light waves interfere constructively, resulting in a high-intensity transmission through the cavity. Conversely, when the optical path length is not an integer multiple of the wavelength, the light waves interfere destructively, leading to a low-intensity transmission.The sharpness of the resonant peaks in a Fabry-Perot cavity is determined by the reflectivity of the mirrors. Highly reflective mirrors result in a higher finesse, which is a measure of the ratio of the spacing between the resonant peaks to their width. This high finesse allows for the creation of narrow-linewidth, high-resolution optical filters and laser cavities, which are essential components in various optical systems.One of the key applications of the Fabry-Perot resonance is in the field of optical telecommunications. Fiber-optic communication systems often utilize Fabry-Perot filters to select specific wavelength channels for data transmission, enabling the efficient use of the available bandwidth in fiber-optic networks. These filters can be tuned by adjusting the mirror separation or the refractive index of the cavity, allowing for dynamic wavelength selection andreconfiguration of the communication system.Another important application of the Fabry-Perot resonance is in the field of laser technology. Fabry-Perot cavities are commonly used as the optical resonator in various types of lasers, providing the necessary feedback to sustain the lasing process. The high finesse of the Fabry-Perot cavity allows for the generation of highly monochromatic and coherent light, which is crucial for applications such as spectroscopy, interferometry, and precision metrology.In the realm of quantum physics, the Fabry-Perot resonance plays a crucial role in the study of cavity quantum electrodynamics (cQED). In cQED, atoms or other quantum systems are placed inside a Fabry-Perot cavity, where the strong interaction between the atoms and the confined electromagnetic field can lead to the observation of fascinating quantum phenomena, such as the Purcell effect, vacuum Rabi oscillations, and the generation of nonclassical states of light.Furthermore, the Fabry-Perot resonance has found applications in the field of optical sensing, where it is used to detect small changes in physical parameters, such as displacement, pressure, or temperature. The high sensitivity and stability of Fabry-Perot interferometers make them valuable tools in various sensing and measurement applications, ranging from seismic monitoring to the detection of gravitational waves.The Fabry-Perot resonance is a fundamental concept in optics that has enabled the development of numerous advanced optical technologies. Its versatility and importance in various fields of science and engineering have made it a subject of continuous research and innovation. As the field of optics continues to advance, the Fabry-Perot resonance will undoubtedly play an increasingly crucial role in shaping the future of optical systems and applications.。

细胞分子生物学文章第十卷（2005），711-719 pl2005.7.15寄出200510.6收到脂质体：一项先进制造技术的概述新西兰，北帕默斯顿，专用邮袋11222，梅西大学Riddet中心，M.REZA MOZAFARI摘要：近几十年来，脂质体作为生物膜的理想模型，也是药物、诊断、疫苗、营养物和其他生物活性剂的有效载体，引起了广泛关注。

在不同背景下研究者们对脂质体学领域的文献报道广泛地不断地增加，这表明这一领域引人入胜。

自从大约40年前脂质体被介绍到科学界，许多技术和方法在或大或小的脂质体制造规模上得到发展。

这篇文章将在大体上提供脂质体制备方法优缺点的概览，特别强调在我们实验室开发的加热法，作为一种脂质囊泡快速生产的模式技术。

关键词：载体系统，加热法，脂质囊泡，脂质体学，制造技术引言脂质体科学技术是一个正在飞速发展的科学，举几个例子，它用于诸如药物递送，化妆品，生物膜的结构和功能，探索生命起源等领域。

这是由于脂质体有一些有利的特性，例如，它不仅能包含水溶性药物也能包含脂溶性药物，在体内识别特定靶向位点，在流动性、大小、电荷、层数的方面具有多样性。

脂质体作为生物膜模型的应用限于在实验室中研究，它们在生物活性剂的包载和递送的成功应用不仅取决于脂质体载体可以达到预期目的的优越性的示范，还取决于技术和经济可行性的规划。

对于递送应用，脂质体配方应该具有高包封率，窄粒度分布，持久稳定性和理想的释放特性（根据预期的应用）。

这些要求制备方法有产生脂质体的可能性，且脂质体可采用多种成分分子，例如：脂质/磷脂可提高脂质体稳定性。

除了上述特性，对于蛋白质、核酸之类敏感的分子/化合物的递送，脂质体也应该能保护复合制剂，防止其退化。

尽管在脂质体上进行了大量的研究开发工作，但只有少数脂质体产品已被批准为人类使用至今。

这也许有许多原因：一些脂质体配方的毒性，分子和化合物在脂质体中的低包封，脂质体载体的不稳定性，脂质载体的不稳定性，特别是大尺度的脂质体生产成本高。

红外光谱的英文书籍Title: English Books on Infrared SpectroscopyIntroduction:Infrared spectroscopy plays a crucial role in various scientific fields, such as chemistry, materials science, and biology. Understanding the principles and applications of infrared spectroscopy is essential for researchers and scientists in these disciplines. In this article, we will explore some recommended English books on infrared spectroscopy that provide comprehensive knowledge and insights into this fascinating subject.Book 1: "Infrared and Raman Spectroscopy: Principles and Spectral Interpretation" by Peter Larkin- Author's Background: Peter Larkin is a renowned spectroscopist and professor with extensive experience in infrared and Raman spectroscopy.- Book Description: This book offers a comprehensive introduction to the principles and applications of both infrared and Raman spectroscopy. It provides a detailed explanation of the theory behind these techniques, along with practical examples and spectral interpretation. Furthermore, it covers the instrumentation, data analysis, and troubleshooting involved in using infrared spectroscopy.- Why Recommended: Larkin's book is well-regarded for its clear and concise explanations, making it suitable for both beginners and experienced researchers in the field. The inclusion of spectral interpretation examples enhances the reader's understanding of the subject.Book 2: "Infrared Spectroscopy: Fundamentals and Applications" by Barbara Stuart- Author's Background: Barbara Stuart is a distinguished professor and researcher specializing in infrared spectroscopy.- Book Description: Stuart's book provides a comprehensive overview of infrared spectroscopy, covering the fundamentals and applications in various fields. It delves into the theory, instrumentation, and data analysis techniques, allowing readers to gain a solid understanding of the subject. Additionally, the book explores the applications of infrared spectroscopy in areas such as environmental science, pharmaceutical analysis, and forensics.- Why Recommended: Stuart's book is praised for its in-depth coverage of applications, making it particularly useful for researchers seeking practical knowledge. The inclusion of case studies and real-world examples enhances the reader's ability to apply the concepts learned.Book 3: "Infrared Spectroscopy: Theory and Applications" by John Coates- Author's Background: John Coates is a highly experienced spectroscopist and professor in the field of infrared spectroscopy.- Book Description: Coates' book offers a detailed exploration of the theory and practical applications of infrared spectroscopy. It provides a solid foundation in the principles of infrared spectroscopy and covers advanced topics such as quantitative analysis, imaging, and microscopy. Moreover, the book includes numerous illustrations and diagrams to aid understanding.- Why Recommended: Coates' book is widely regarded as a comprehensive reference for researchers in the field. Its detailed coverage of advanced techniques and practical applications makes it a valuable resource for scientists looking to expand their knowledge.Conclusion:English books on infrared spectroscopy are essential resources for researchers and scientists seeking to deepen their understanding of this indispensable analytical technique. The recommended books by Peter Larkin, Barbara Stuart, and John Coates provide comprehensive coverage of the principles, instrumentation, and applications of infrared spectroscopy. Whether for beginners or experienced researchers, these books serve as valuable references in the study and practice of infrared spectroscopy.。

Unit 4 Text A传统中医和现代西医的融通人们对传统医学和补充医学的兴趣正在引起医疗界、政府部门、媒体和公众等美国社会各界的关注。

越来越多的保险公司和管理式医疗机构为传统医学大开方便之门，现在大多数美国医学院也开设了传统医学课程。

艾森伯格的多项全国性研究表明也有更多人在使用补充疗法。

为了便于研究替代疗法的有效性，美国国家补充与替代医学中心于1999年获得了多达五千万美元的预算。

由于认识到除了要对饮食补充剂安全性和有效性进行系统性评估之外，还需要提升植物药材科学数据的质量和数量，今年为此设立了两个研究中心，以研究植物药材的生物学作用。

许多患者传统模式和现代模式同时并用，这就需要将两种医学进行合理平稳地结合。

传统中医的理论和技术涵盖了美国归为补充医学的多数实践，在医疗保健体系中变得日益重要。

若运用得当，传统中医费用合理，技术含量低，安全且有效。

在全球，正在展开针对针灸、草药、按摩和太极的诸多研究，这可阐释传统中医的一些理论和实践。

雄心勃勃的研究设计提供的证据和巨大的患者需求正在推动传统中医和现代医学在临床层面的结合，而学术研究者和学术机构对两种治疗体系结合的潜力也有越来越浓厚的兴趣。

针刺基于1997年美国国立卫生研究院（NIH）专家共识会议审查的证据，NIH 专家共识发展小组保守建议针刺可以作为多种疾患的辅助疗法、替代疗法或综合管理方案的一部分。

该专家组确认针刺可用于治疗手术后出现的和化疗引起的恶心和呕吐，也可治疗术后牙痛。

专家组同时也建议针灸可作为辅助疗法或可接受的替代疗法，用以治疗成瘾、卒中康复、头痛、经痛、网球肘、纤维肌痛、肌筋膜疼痛、骨关节炎、下背痛、腕管综合症和哮喘等。

未来在传统中医架构下进行的针刺临床试验与当前这一代主要主要从生物医学的角度对针刺疗效进行评判的临床试验相比，可能对针刺的疗效提供更恰当更有临床意义的评估。

临床研究中现有的科学严谨性必须保持。

然而，NIH数据分析的方法过于严格，限制了潜在的适应症。

傅里叶红外光谱仪英文Fourier Transform Infrared SpectrometerA Fourier transform infrared spectrometer (FTIR) is a powerful analytical tool that can identify and quantify the chemical composition of a sample. The FTIR works by analyzing the absorption of infrared radiation by a sample. This absorption occurs at specific wavelengths, which are characteristic of the chemical bonds present in the sample. By measuring the intensity of the absorbed radiation at different wavelengths, the FTIR can provide detailed information about the molecular structure of the sample.The FTIR works by first passing infrared radiation through the sample. The radiation that is transmitted through the sample is then detected and measured. The FTIR then uses a Fourier transform algorithm to convert the measured data into a spectrum that displays the intensity of the absorbed radiation as a function of the frequency of the radiation. FTIR spectroscopy has a wide range of applications in fields such as chemistry, biology, and materials science. It can be used to identify unknown compounds, determine the degree of purity of a sample, and monitor chemical reactions in real-time. It is also commonly used in the pharmaceuticalindustry for drug discovery and development.In summary, the FTIR is a powerful analytical instrument that can provide detailed information about the composition and molecular structure of a sample. Its versatility and wide range of applications make it an essential tool for many scientific disciplines.。