J. Dairy Sci. 873561–3573 American Dairy Science Association, 2004. Guidelines for Monito

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。

ISSN AbbreviatedJournal TitleFull Title Category Subcategory Country1550-7416AAPS J AAPS Journal医学药学UNITED STATES 1530-9932AAPS PHARMSCITECH AAPS PHARMSCITECH医学药学UNITED STATES 0942-8925ABDOM IMAGING ABDOMINAL IMAGING医学核医学UNITED STATES 1069-6563ACAD EMERG MED ACADEMIC EMERGENCY MEDIC医学急救医学UNITED STATES 1040-2446ACAD MED ACADEMIC MEDICINE医学卫生保健UNITED STATES 1076-6332ACAD RADIOL ACADEMIC RADIOLOGY医学核医学UNITED STATES 1091-5397ACSMS HEALTH FIT J ACSMS HEALTH & FITNESS J医学运动科学UNITED STATES 0001-5172ACTA ANAESTH SCAND ACTA ANAESTHESIOLOGICA S医学麻醉学DENMARK1726-569X ACTA BIOETH Acta Bioethica医学卫生政策Chile0001-5385ACTA CARDIOL ACTA CARDIOLOGICA医学心血管系统BELGIUM1011-6842ACTA CARDIOL SIN Acta Cardiologica Sinica医学心血管系统TAIWAN0001-5458ACTA CHIR BELG ACTA CHIRURGICA BELGICA医学外科BELGIUM0102-8650ACTA CIR BRAS Acta Cirurgica Brasileir医学外科BRAZIL0353-9466ACTA CLIN CROAT Acta Clinica Croatica医学医学：内科CROATIA1330-027X ACTA DERMATOVENER CR A cta Dermatovenerologica医学皮肤病学CROATIA0001-5555ACTA DERM-VENEREOL ACTA 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Menta医学精神病学AUSTRIA0066-782X ARQ BRAS CARDIOL Arquivos Brasileiros de 医学心血管系统BRAZIL0004-2730ARQ BRAS ENDOCRINOLArquivos Brasileiros de 医学内分泌学与代谢BRAZIL0004-282X ARQ NEURO-PSIQUIAT ARQUIVOS DE NEURO-PSIQUI医学精神病学BRAZIL1079-5642ARTERIOSCL THROM VAS A RTERIOSCLEROSIS THROMBO医学外周血管病UNITED STATES 0004-3591ARTHRITIS RHEUM-US ARTHRITIS & RHEUMATISM-A医学风湿病学UNITED STATES 0749-8063ARTHROSCOPY ARTHROSCOPY医学外科UNITED STATES 1058-2916ASAIO J ASAIO JOURNAL医学工程：生物医学UNITED STATES 0964-7058ASIA PAC J CLIN NUTR A SIA PACIFIC JOURNAL OF 医学营养学AUSTRALIA1008-682X ASIAN J ANDROL ASIAN JOURNAL OF ANDROLO医学泌尿学与肾脏学PEOPLES R CHINA 1015-9584ASIAN J SURG Asian Journal of Surgery医学外科PEOPLES R CHINA 0125-877X ASIAN PAC J ALLERGYASIAN PACIFIC JOURNAL OF医学过敏THAILAND1513-7368ASIAN PAC J CANCER P A sian Pacific Journal of医学肿瘤学JAPAN1743-7555ASIA-PAC J CLIN ONCO A sia-Pacific Journal of 医学肿瘤学AUSTRALIAMALAYSIA1010-5395ASIA-PAC J PUBLIC HE A sia-Pacific Journal of 医学公共卫生、环境卫1540-658X ASSAY DRUG DEV TECHN A SSAY AND DRUG 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HEALAUSTRALIAN AND NEW ZEALA医学公共卫生、环境卫0045-0766AUST OCCUP THER J Australian Occupational 医学康复医学AUSTRALIA 1440-6381AUSTRALAS J AGEING AUSTRALASIAN JOURNAL ON 医学老年医学AUSTRALIA 0004-8380AUSTRALAS J DERMATOL A USTRALASIAN JOURNAL OF 医学皮肤病学AUSTRALIA 1039-8562AUSTRALAS PSYCHIATRY A ustralasian Psychiatry医学精神病学AUSTRALIA 1939-3792AUTISM RES Autism Research医学行为科学UNITED STATES 1568-9972AUTOIMMUN REV AUTOIMMUNITY REVIEWS医学免疫学UNITED STATES 0891-6934AUTOIMMUNITY AUTOIMMUNITY医学免疫学ENGLAND1566-0702AUTON NEUROSCI-BASIC A UTONOMIC NEUROSCIENCE-B医学神经科学NETHERLANDSUNITED STATES 0095-6562AVIAT SPACE ENVIR MD A VIATION SPACE AND ENVIR医学公共卫生、环境卫0001-4079 B ACAD NAT MED PARIS B ULLETIN DE L ACADEMIE N医学医学：内科FRANCE0007-4551 B CANCER BULLETIN DU CANCER医学肿瘤学FRANCERUSSIA0007-4888 B EXP BIOL MED+BULLETIN OF EXPERIMENTAL医学医学：研究与实验0007-5140 B HIST MED BULLETIN OF THE HISTORY 医学科学史与科学哲学UNITED STATESSWITZERLAND 0042-9686 B WORLD HEALTH ORGAN B ULLETIN OF THE WORLD HE医学公共卫生、环境卫1311-0160BALK J MED GENET Balkan Journal of 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PRACT RES CL OB B EST PRACTICE & RESEARCH医学妇产科学ENGLAND1521-6942BEST PRACT RES CL RH B EST PRACTICE & RESEARCH医学风湿病学ENGLAND1330-0962BIOCHEM MEDICA Biochemia Medica医学医学实验技术CROATIA0006-2952BIOCHEM PHARMACOL BIOCHEMICAL PHARMACOLOGY医学药学UNITED STATES 1173-8804BIODRUGS BIODRUGS医学免疫学NEW ZEALAND 0269-9702BIOETHICS BIOETHICS医学医学：伦理ENGLAND1389-5729BIOGERONTOLOGY BIOGERONTOLOGY医学老年医学NETHERLANDS 1083-8791BIOL BLOOD MARROW TR B IOLOGY OF BLOOD AND MAR医学免疫学UNITED STATES 0918-6158BIOL PHARM BULL BIOLOGICAL & PHARMACEUTI医学药学JAPAN0006-3223BIOL PSYCHIAT BIOLOGICAL PSYCHIATRY医学精神病学UNITED STATES0301-0511BIOL PSYCHOL BIOLOGICAL PSYCHOLOGY医学行为科学NETHERLANDS 1099-8004BIOL RES NURS Biological Research for 医学护理UNITED STATESENGLAND1752-0363BIOMARK MED Biomarkers in Medicine医学医学：研究与实验1354-750X BIOMARKERS BIOMARKERS医学毒理学UNITED STATES 0753-3322BIOMED PHARMACOTHERBIOMEDICINE & PHARMACOTH医学药学FRANCEJAPAN0388-6107BIOMED RES-TOKYO BIOMEDICAL RESEARCH-TOKY医学医学：研究与实验0013-5585BIOMED TECH BIOMEDIZINISCHE TECHNIK医学工程：生物医学GERMANY0120-4157BIOMEDICA Biomedica医学热带医学COLOMBIA1976-9148BIOMOL THER Biomolecules & Therapeut医学药学SOUTH KOREA 0960-894X BIOORG MED CHEM LETT B IOORGANIC & MEDICINAL C医学医药化学ENGLANDENGLAND0968-0896BIOORGAN MED CHEM BIOORGANIC & MEDICINAL C医学生化与分子生物学0142-2782BIOPHARM DRUG DISPOS B IOPHARMACEUTICS & DRUG 医学药学ENGLANDUNITED STATES 1542-166X BIOPHARM INT BIOPHARM INTERNATIONAL-T医学生物工程与应用微0006-355X BIORHEOLOGY BIORHEOLOGY医学工程：生物医学NETHERLANDS 1398-5647BIPOLAR DISORD BIPOLAR DISORDERS医学精神病学DENMARK1542-9733BIRTH DEFECTS RES BBIRTH DEFECTS RESEARCH P医学毒理学UNITED STATES 0730-7659BIRTH-ISS PERINAT CBIRTH-ISSUES IN PERINATA医学妇产科学UNITED STATES 1470-0328BJOG-INT J OBSTET GY B JOG-AN INTERNATIONAL JO医学妇产科学ENGLAND1464-4096BJU INT BJU INTERNATIONAL医学泌尿学与肾脏学ENGLAND0006-4971BLOOD BLOOD医学血液学UNITED STATES 1079-9796BLOOD CELL MOL DIS BLOOD CELLS MOLECULES AN医学血液学UNITED STATES 0957-5235BLOOD COAGUL FIBRINBLOOD COAGULATION & FIBR医学血液学UNITED STATES 1359-5237BLOOD PRESS MONIT BLOOD PRESSURE MONITORIN医学外周血管病UNITED STATES 0803-7051BLOOD PRESSURE BLOOD PRESSURE医学外周血管病ENGLAND0253-5068BLOOD PURIFICAT BLOOD PURIFICATION医学泌尿学与肾脏学SWITZERLAND 0268-960X BLOOD REV BLOOD REVIEWS医学血液学ENGLAND1471-2407BMC CANCER BMC CANCER医学肿瘤学ENGLAND1471-2296BMC FAM PRACT BMC Family Practice医学医学：内科ENGLAND1471-230X BMC GASTROENTEROL BMC GASTROENTEROLOGY医学胃肠肝病学ENGLAND1472-6963BMC HEALTH SERV RESBMC HEALTH SERVICES RESE医学卫生保健ENGLAND1471-2172BMC IMMUNOL BMC IMMUNOLOGY医学免疫学ENGLAND1471-2334BMC INFECT DIS BMC INFECTIOUS DISEASES医学传染病学ENGLAND1741-7015BMC MED BMC Medicine医学医学：内科ENGLAND1471-2350BMC MED GENET BMC Medical Genetics医学遗传学ENGLAND1755-8794BMC MED GENOMICS BMC Medical Genomics医学遗传学ENGLAND1472-6947BMC MED INFORM DECIS B MC Medical Informatics 医学医学：信息ENGLAND1471-2288BMC MED RES METHODOL B MC Medical Research Met医学卫生保健ENGLAND1471-2474BMC MUSCULOSKEL DISBMC MUSCULOSKELETAL DISO医学风湿病学ENGLAND1471-2377BMC NEUROL BMC Neurology医学临床神经学ENGLAND1471-2202BMC NEUROSCI BMC NEUROSCIENCE医学神经科学ENGLAND1471-244X BMC PSYCHIATRY BMC Psychiatry医学精神病学ENGLANDENGLAND1471-2458BMC PUBLIC HEALTH BMC PUBLIC HEALTH医学公共卫生、环境卫8756-3282BONE BONE医学内分泌学与代谢UNITED STATES 0268-3369BONE MARROW TRANSPLBONE MARROW TRANSPLANTAT医学免疫学ENGLANDBOSNIA & HERCEG 1512-8601BOSNIAN J BASIC MEDBosnian Journal of Basic医学医学：研究与实验1538-4721BRACHYTHERAPY Brachytherapy医学核医学UNITED STATES 0006-8950BRAIN BRAIN医学临床神经学ENGLAND0006-8977BRAIN BEHAV EVOLUT BRAIN BEHAVIOR AND EVOLU医学行为科学SWITZERLAND0889-1591BRAIN BEHAV IMMUN BRAIN BEHAVIOR AND IMMUN医学免疫学UNITED STATES 0278-2626BRAIN COGNITION BRAIN AND COGNITION医学神经科学UNITED STATES 0387-7604BRAIN DEV-JPN BRAIN & DEVELOPMENT医学临床神经学NETHERLANDS 1931-7557BRAIN IMAGING BEHAVBrain Imaging and Behavi医学神经成像UNITED STATES 0269-9052BRAIN INJURY BRAIN INJURY医学康复医学ENGLAND0093-934X BRAIN LANG BRAIN AND LANGUAGE医学神经科学UNITED STATES 1015-6305BRAIN PATHOL BRAIN PATHOLOGY医学病理学UNITED STATES 0006-8993BRAIN RES BRAIN RESEARCH医学神经科学NETHERLANDS 0361-9230BRAIN RES BULL BRAIN RESEARCH BULLETIN医学神经科学UNITED STATES 0165-0173BRAIN RES REV BRAIN RESEARCH REVIEWS医学神经科学NETHERLANDS 1935-861X BRAIN STIMUL Brain Stimulation医学临床神经学UNITED STATES 1863-2653BRAIN STRUCT FUNCT Brain Structure & Functi医学解剖学与形态学GERMANY0896-0267BRAIN TOPOGR BRAIN TOPOGRAPHY医学临床神经学UNITED STATES 1433-7398BRAIN TUMOR PATHOL Brain Tumor Pathology医学病理学JAPAN0006-9248BRATISL MED J Bratislava Medical Journ医学医学：内科SLOVAKIA 1413-8670BRAZ J INFECT DIS Brazilian Journal of Inf医学传染病学BRAZIL0100-879X BRAZ J MED BIOL RESBRAZILIAN JOURNAL OF MED医学生物学BRAZIL0960-9776BREAST BREAST医学妇产科学ENGLAND0167-6806BREAST CANCER RES TR B REAST CANCER RESEARCH A医学肿瘤学UNITED STATES 1661-3791BREAST CARE Breast Care医学妇产科学GERMANY1075-122X BREAST J Breast Journal医学妇产科学UNITED STATES 0007-0610BRIT DENT J BRITISH DENTAL JOURNAL医学牙科与口腔外科ENGLAND0007-0912BRIT J ANAESTH BRITISH JOURNAL OF ANAES医学麻醉学ENGLAND0007-0920BRIT J CANCER BRITISH JOURNAL OF CANCE医学肿瘤学ENGLAND0306-5251BRIT J CLIN PHARMACO B RITISH JOURNAL OF CLINI医学药学ENGLAND0007-0963BRIT J DERMATOL BRITISH JOURNAL OF DERMA医学皮肤病学ENGLAND0960-1643BRIT J GEN PRACT BRITISH JOURNAL OF GENER医学医学：内科ENGLAND0007-1048BRIT J HAEMATOL BRITISH JOURNAL OF HAEMA医学血液学ENGLAND1750-8460BRIT J HOSP MED BRITISH JOURNAL OF HOSPI医学医学：内科ENGLAND0007-1102BRIT J MATH STAT PSY B RITISH JOURNAL OF MATHE医学数学跨学科应用ENGLAND0268-8697BRIT J NEUROSURG BRITISH JOURNAL OF NEURO医学临床神经学ENGLAND0007-1145BRIT J NUTR BRITISH JOURNAL OF NUTRI医学营养学ENGLAND0007-1161BRIT J OPHTHALMOL BRITISH JOURNAL OF OPHTH医学眼科学ENGLAND0266-4356BRIT J ORAL MAX SURG B RITISH JOURNAL OF ORAL 医学外科ENGLAND0007-1188BRIT J PHARMACOL BRITISH JOURNAL OF PHARM医学药学ENGLAND0007-1250BRIT J PSYCHIAT BRITISH JOURNAL OF PSYCH医学精神病学ENGLAND0007-1285BRIT J RADIOL BRITISH JOURNAL OF RADIO医学核医学ENGLAND0306-3674BRIT J SPORT MED BRITISH JOURNAL OF SPORT医学运动科学ENGLAND0007-1323BRIT J SURG BRITISH JOURNAL OF SURGE医学外科ENGLAND0007-1420BRIT MED BULL BRITISH MEDICAL BULLETIN医学医学：内科ENGLANDGERMANY1436-9990BUNDESGESUNDHEITSBLA B undesgesundheitsblatt-G医学公共卫生、环境卫0305-4179BURNS BURNS医学皮肤病学ENGLAND0007-9235CA-CANCER J CLIN CA-A CANCER JOURNAL FOR 医学肿瘤学UNITED STATESBRAZIL0102-311X CAD SAUDE PUBLICA Cadernos de Saude Public医学公共卫生、环境卫0171-967X CALCIFIED TISSUE INT C ALCIFIED TISSUE INTERNA医学内分泌学与代谢UNITED STATES 0963-1801CAMB Q HEALTHC ETHIC C AMBRIDGE QUARTERLY OF H医学卫生保健UNITED STATES 0846-5371CAN ASSOC RADIOL J CANADIAN ASSOCIATION OF 医学核医学CANADA0008-350X CAN FAM PHYSICIAN CANADIAN FAMILY PHYSICIA医学医学：内科CANADA。

journal of dairy science模板标题：Journal of Dairy Science模板引言概述：Journal of Dairy Science是国际知名的乳制品科学领域的学术期刊，它为乳制品科学家和研究人员提供了一个重要的交流平台。

本文将详细介绍Journal of Dairy Science的模板，包括其结构和内容要求，以及如何使用模板撰写一篇高质量的论文。

正文内容：1. 标题和摘要1.1 标题的撰写：标题应简明扼要地概括论文的主要内容，避免使用过于晦涩的词汇，同时要具有吸引读者的特点。

1.2 摘要的撰写：摘要是论文的简短概述，应包含研究的目的、方法、结果和结论。

摘要要求简明扼要，具有代表性，能够吸引读者进一步阅读全文。

2. 引言2.1 研究背景：引言部分应明确介绍研究领域的背景和研究问题的重要性，引出研究的目的和意义。

2.2 文献综述：引言还应对相关领域的前沿研究进行综述，指出已有研究的不足之处，并阐明本研究的创新点。

2.3 研究目标和假设：引言的最后部分应明确阐述本研究的目标和假设，为读者提供进一步阅读的动力。

3. 材料与方法3.1 实验设计：详细描述实验的设计和操作步骤，包括实验对象、实验组和对照组的设置，以及实验的重复次数等。

3.2 数据采集和分析：说明数据采集的方法和仪器设备的使用，以及数据分析的统计方法和软件工具。

3.3 实验验证和可重复性：提供实验验证的方法和实验结果的可重复性分析，以确保研究结果的可靠性和可信度。

4. 结果与讨论4.1 结果呈现：详细呈现实验结果，包括图表和统计数据，以清晰地展示研究的主要发现。

4.2 结果解读：对实验结果进行解读和分析，与研究目标和假设进行对比，指出实验结果的意义和启示。

4.3 结果讨论：对实验结果进行深入讨论，包括与已有研究的对比和差异分析，提出可能的解释和进一步研究的方向。

5. 结论5.1 主要发现总结：总结研究的主要发现，强调研究的创新点和重要性。

american journal of nursing science影响因子【原创版】目录1.介绍美国护理科学杂志（American Journal of Nursing Science）2.美国护理科学杂志的影响因子3.该杂志的投稿要求和周期4.如何提高投稿成功率正文美国护理科学杂志（American Journal of Nursing Science）是一本知名的护理学领域的学术期刊，旨在发布高质量的护理研究成果，为护理实践提供科学依据。

该杂志创刊于 1971 年，由 Wiley-Blackwell 出版公司负责出版，发行地区包括美国以及全球范围内。

美国护理科学杂志的影响因子是衡量其学术影响力的重要指标。

根据我国科研评价体系，该杂志的影响因子为 2.567（2021 年），在护理学领域具有较高的地位。

然而，影响因子并非衡量期刊质量的唯一标准，还应综合考虑期刊的学术声誉、稿件处理速度、发文量等因素。

若想向美国护理科学杂志投稿，首先需要了解该杂志的投稿要求和周期。

根据官方网站的信息，该杂志接收原创研究文章、综述文章、案例报告等类型的稿件。

投稿前，请务必仔细阅读官方网站的投稿指南，确保稿件符合期刊的要求。

在投稿过程中，为了提高成功率，建议作者注意以下几点：1.选择适当的投稿类型：根据您的研究成果和期刊要求，选择最合适的投稿类型。

2.确保稿件质量：仔细检查稿件的学术内容、数据、统计方法等，确保无误。

3.关注投稿格式：按照期刊要求的格式投稿，如字体、字号、行距、页边距等。

4.遵循投稿要求：在投稿信中注明稿件类型、作者信息、基金项目等，按照期刊要求提交相关材料。

5.耐心等待审稿结果：投稿后，耐心等待审稿人的意见，根据意见进行修改，并及时向编辑部反馈。

总之，向美国护理科学杂志投稿需要充分了解期刊要求和投稿流程，并努力提高稿件质量。

american journal of nursing science影响因子（最新版）目录1.美国护理科学杂志（American Journal of Nursing Science）概述2.American Journal of Nursing Science 的影响因子3.该期刊在我国护理学领域的地位和影响力4.对我国护理学者的启示和建议正文一、美国护理科学杂志（American Journal of Nursing Science）概述《美国护理科学杂志》（American Journal of Nursing Science，简称 AJNS）是一本享有盛誉的国际护理学专业期刊，致力于发表高质量的护理学研究文章。

该期刊创办于 1905 年，拥有超过 110 年的悠久历史，是护理学领域最早的学术期刊之一。

AJNS 由 Wiley 出版公司负责发行，主要面向全球范围内的护理学者、研究人员、临床护士等。

二、American Journal of Nursing Science 的影响因子根据我国国家图书馆的统计数据，American Journal of Nursing Science 的最新影响因子（2021 年）为 2.576。

这一数据表明，该期刊在护理学领域的研究成果具有较高的学术价值和影响力。

此外，AJNS 在近年来的影响因子呈稳定上升趋势，表明该期刊在全球护理学领域的地位逐渐上升。

三、该期刊在我国护理学领域的地位和影响力在我国，American Journal of Nursing Science 被视为护理学领域的顶级国际期刊之一。

许多高质量的护理学研究成果都发表在该期刊上，为我国护理学者提供了宝贵的学术资源。

同时，该期刊在我国也具有较高的学术声誉，发表在该期刊上的文章常常被国内护理学者广泛引用。

四、对我国护理学者的启示和建议1.提高自身学术水平：我国护理学者应不断提高自身的学术水平，努力撰写出高质量的研究论文，以期在国际顶级期刊如 AJNS 上发表。

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american journal of gastroenterology杂志引文格式
（实用版）
目录
1.美国胃肠病学杂志（American Journal of Gastroenterology）简介
2.杂志的引文格式要求
3.引用示例
正文
美国胃肠病学杂志（American Journal of Gastroenterology）是一本知名的学术期刊，专注于发表关于胃肠病学和肝脏病学的研究文章。

该杂志创刊于 1919 年，由美国胃肠病学协会（American College of Gastroenterology）主办，是该领域研究的重要参考资料。

在引用美国胃肠病学杂志的文章时，需要遵循一定的引文格式。

本文主要介绍该杂志的引文格式要求。

一般来说，美国胃肠病学杂志的引文格式遵循以下原则：
- 作者姓名：文章作者的姓名应按照字母顺序排列，最多列出 7 位作者，超过 7 位作者时，需要使用“et al.”表示。

- 标题：文章标题应简短明了，准确反映文章主题。

- 期刊名称：需要使用全名，不能使用缩写。

- 出版年份：文章发表的年份，用小括号括起，紧跟在期刊名称后。

- 卷号和页码：文章所在的卷号和页码范围，用逗号分隔。

举个例子，如果您要引用一篇美国胃肠病学杂志的文章：
作者：张三，李四，王五等（共 7 位作者）
标题：关于胃肠病学的研究
期刊名称：美国胃肠病学杂志
出版年份：2021
卷号和页码：第 100 卷 (1-10 页)
那么引用该文章的格式应为：
张三，李四，王五等。

关于胃肠病学的研究。

美国胃肠病学杂志。

AMERICAN JOURNAL OF INDUSTRIAL MEDICINE38:355±360(2000) Relationship Between Delta-Aminolevulinic Acid Dehydratase Genotypes andHeme Precursors in Lead WorkersTadashi Sakai,PhD,1,2ÃYoko Morita,MD,DMSc,2Takaharu Araki,MD,DMSc,1,2 Mitsumasa Kano,MD,DMSc,3and Tsutomu Yoshida MD,DMSc4Background The objective of this study was to investigate the relationships betweengenotypes of delta-aminolevulinic acid(ALA)dehydratase(ALAD)and disturbances inthe heme biosynthetic pathway by lead exposure.Methods The subjects were192male lead workers and125control subjects.Blood leadconcentrations(Pb-B),plasma ALA concentrations(ALA-P),and ALAD genotypes weredetermined for all subjects.In lead workers,ALAD activity,ALA in urine(ALA-U),anderythrocyte zinc protoporphyrin(ZP)were also determined.Results The frequency of ALAD2(minor type of ALAD allele)was calculated to be0.087in all subjects.No signi®cant relationship was found between ALAD2frequency andPb-B levels in lead workers.ALAD1homozygotes showed signi®cantly higher levels ofZP and ALA-P in comparison with those of ALAD2carriers at Pb-B levels more than20m g/dL and40m g/dL,respectively.Conclusion ALAD1homozygotes might be more susceptible than ALAD2carriers todisturbances in heme metabolism caused by lead exposure.Am.J.Ind.Med.38:355±360,2000.ß2000Wiley-Liss,Inc.KEY WORDS:delta-aminolevulinic acid dehydratase;polymorphism;lead;delta-aminolevulinic acid;zinc protoporphyrin;plasma;urine;blood;susceptibilityINTRODUCTIONDelta-aminolevulinic acid(ALA)dehydratase(ALAD) is the second enzyme in the heme biosynthetic pathway which catalyses the condensation of two molecules of ALA to form one molecule of porphobilinogen[Granick and Mauserall,1958].Erythrocyte ALAD activity is rapidly inhibited by lead exposure[Lichtman and Feldman,1963] and as a result,ALA in plasma and urine(ALA-P and ALA-U)increases[Haeger-Aronsen,1971;Morita et al.,1994]. ALA is structurally similar to the neurotransmitter g-aminobutyric acid(GABA)and seems to compete with it for the receptor[Brennan and Cautrill,1979].Accumulated ALA may be responsible for the neurotoxic effects of lead in humans[Silbergeld and Lamon,1980].The®nal step of heme synthesis,introducing Fe2 into protoporphyrin IX (PP),is also affected by lead.Inhibition of Fe3 reduc-tion by lead exposure causes a decrease in transport of Fe2 into mitochondria[Taketani et al.,1985],resulting in an accumulation of PP in erythrocytes.PP is enzymatically or nonenzymatically chelated with Zn2 to form zinc proto-porpyrin(ZP).ALAD activity,ALA-P,ALA-U,and ZP are widely used as effect indices of lead exposure[Sakai and Morita,1996].In blood,more than98%of the lead is found in blood cells[DeSilva,1981;SchuÈtz et al.,1996].The amounts of plasma lead are relatively small,but it is very important1Center of Occupational Medicine,Tokyo Rosai Hospital,Tokyo,Japan2Industrial Poisoning Center,Tokyo Rosai Hospital,Tokyo,Japan3Center of Health Examination,Asahi Glass Corporation,Funabashi,Japan4Department of Public Health,Fujita Health University School of Medicine,Toyoake,Japan *Correspondence to:Tadashi Sakai,Centerof Occupational Medicine,Tokyo Rosai Hospital, 13-21,Omoriminami-4,Ota-ku,Tokyo,143-0013,Japan.E-mail:opc@msa.biglobe.ne.jp Accepted8March2000ß2000Wiley-Liss,Inc.because of its biological activity in lead metabolism.Lead in plasma circulates in the body,affects the body lead burden and causes the toxicity of lead in some soft tissues,such as bone marrow.The larger fraction of lead in blood cells is mainly bound to ALAD protein[Sakai et al.,1982;Bergdahl et al.,1997].Thus,ALAD might also play an important role in the dynamic interchange of the body lead pool.Human ALAD gene is located in chromosome9q34 [Potluri et al.,1987].The gene frequencies of ALAD alleles 1and2were reportedly about0.9and0.1,respectively, in several Caucasian populations[Battistuzzi et al.,1981; Benkmann et al.,1983;Astrin et al.,1987].In1991,a simple method using polymerase chain reaction(PCR)was developed for the analysis of the ALAD genotype[Wetmur et al.,1991a].In comparison with the ALAD1sequence, the only difference in the ALAD2cDNA was a G-to-C transversion of nucleotide177in the coding region,which creates an MspI restriction site and causes the replacement of a lysine by a asparagine[Wetmur et al.,1991a].The difference in the amino acid might result in a distinct charge isozyme,and the ALAD2protein may bind lead more tightly than a dose of the ALAD1protein[Wetmur,1994].Recently,several investigations have been conducted on the relationships between ALAD polymorphism and susceptibility to lead toxicity[Wetmur et al.,1991b; Schwartz et al.,1995,1997;Sakai et al.,1996b;Sithisas-rankul et al.1997;Alexander et al.,1998].In these studies, the effect indices of lead exposure,such as ALAD,ALA-P, ALA-U and ZP,were examined to determine whether the ALAD genotype in¯uences the values in the indices. However,none of the previous reports determined all the four indices in a suf®cient number of lead workers with a wide range of Pb-B levels.Furthermore,some disagree-ments were shown in the levels of Pb-B where differences in the effect indices existed between two different genetic groups.In the present study,we simultaneously examined four indices in suf®ciently large numbers of lead workers, and discussed the relationships between ALAD genotypes and the levels of the effect indices.MATERIALS AND METHODSVenous blood treated with heparin or EDTA-2K was obtained from192male lead workers.The lead workers were employed in a secondary smelter,two glass factories, in electrical appliance manufacture(soldering),or worked as painters.Venous blood treated with EDTA-2K was also obtained from125subjects(62males and63females)who had no occupational exposure to lead.These samples were collected during physical examinations.At the time of collection,spot urine was also collected from the lead workers.The study procedure was explained to all subjects and their informed consent was obtained.ALAD genotype was determined by the PCR-based method of Wetmur et al.[1991a].Pb-B was determined by ¯ameless atomic absorption spectrometry(Hitachi Z-8000, Tokyo,Japan).Erythrocyte ALAD activities were deter-mined by the Commission of European Communities(CEC) standard method,and the activity was expressed as units (u:m mol minÀ1L RBCÀ1)[Berlin and Schaller,1974].ALA in plasma and urine(ALA-P and ALA-U)was determined by the method of high-performance liquid chromatography (HPLC)previously reported[Morita et al.,1994;Sakai and Morita,1996a].ALA-U values were corrected for creatinine concentration.Creatinine concentration was determined by the method of Jaffe with the``Creatinine Determination Kit''by Wako Pure Chemicals(Osaka,Japan).ZP was determined by the HPLC method previously reported[Sakai et al.,1988].RESULTSTable I shows the distribution of ALAD genotypes and frequency of the ALAD2allele in the present study.ALAD2 frequency was calculated to be0.086in lead workers.In control subjects,ALAD2frequency was0.065in males and 0.111in females,though the difference between genders was not signi®cant(12test,12 1.33,P 0.247).In all control subjects,the ALAD2frequency was0.088,andTABLE I.Distribution ofALAD Genotypes and Frequency of the ALAD2Allele Among192Male Lead Workers and125Unexposed WorkersALAD genotypeWorkers1^11^22^2Total ALAD2frequencyLead(Male)1612921920.088Unexposed(All)1042011250.088(Male)5480620.065(Female)50121630.111Total2654933170.087356Sakai et al.almost equal to that in lead workers.Thus,no difference in ALAD2frequency was found between lead workers and control subjects.Table II shows the comparison of age,Pb-B,and ALA-P among ALAD genotypes in the control group.No signi®cant difference of age,Pb-B,or ALA-P was found between ALAD1homozygotes and ALAD2carriers for either gender.Table III shows the distribution of age,Pb-B,and indices of the heme biosynthetic pathway between two ALAD genotypes in lead workers.Means and ranges of age and Pb-B levels are nearly the same between the two genetic groups.In the heme parameters,levels of ALA-P and ZP in the ALAD1homozygotes are slightly higher than those in ALAD2carriers,although the difference is not signi®cant.Table IV shows the comparison of ALAD,ALA-P,ALA-U,and ZP levels between ALAD1homozygotes and ALAD2carriers in lead workers with different Pb-B levels.The workers were divided into ®ve groups according to Pb-B level.The mean value of each parameter was statistically compared by Student's t test or by Welch's test where their variance was signi®cantly different.The frequency of ALAD genotypes was not signi®cantly different among the ®ve groups.At Pb-B levels higher than 20m g/dL,ZP levels in ALAD1homozygotes are signi®cantly higher than that in ALAD2carriers.At Pb-B levels higher than 40m g/dL,ALAD1homozygotes have higher levels of ALA-P than those of ALAD2carriers.Figure 1(A)demonstrates the comparison of ALA-P levels between ALAD1homozygotes and ALAD2carriers in lead workers with increasing levels of Pb-B.The mean and standard deviation of ALA-P are plotted for the ®ve groups with various Pb-B levels.ALAD1homozygotes show signi®cantly higher levels of ALA compared with those of ALAD2carriers at Pb-B levels between 40andTABLE parison of Effect Indices of Lead Exposure Between Two ALAD Genotypes in Lead Workers (n 192)with Different Pb-B LevelsPb-B group (l g/dL)`2020^3940^5960^7980`Number of genotype 9139253318#6511Pb-B (m g/dL)7.7(5.0)28.2(5.9)49.1(6.6)67.4(7.1)86.3(8.0)8.3(4.8)27.2(6.3)49.1(6.4)61.290.0ALAD(u)57.8(14.4)30.0(9.6)14.6(5.2) 6.3(1.9) 5.5(1.9)53.8(12.1)28.7(5.8)14.9(3.0)9.5 5.5ALAP (m g/L)9.8(1.9)15.0(3.1)25.7(10.1)117.9(32.0)258.5(121.7)9.9(2.2)13.6(1.3)18.3(3.8)*53.1144.5ALAU (mg/gCre)0.9(0.4)1.1(0.4)1.9(1.0)8.7(4.2)26.4(9.9)0.9(0.3)1.1(0.4)1.5(0.5) 2.320.6ZP88(30)133(104)199(76)635(295)1319(555)(m g/dLRBC)73(23)79(34)*125(21)**291740Mean(SD):1stand 2ndrow:ALAD1homozygotes and ALAD2carriers.#:Containingtwosubjectswith ALAD2homozygotes.*P `0.05,**P `0.01compared with ALAD1homozygotes.TABLE II.Age and Concentration of Pb-B and ALA-P inTwo Genetic Groups of Control SubjectsMaleFemale Number ofgenotypes 5450813Age (years)41.3Æ9.1(20^59)22.3Æ5.1(19^40)42.3Æ7.8(34^56)24.7Æ9.9(18^50)Pb-B (m g/dL) 3.7Æ1.1(2.0^6.9) 2.4Æ0.8(1.4^3.7)3.6Æ1.0(2.5^4.9) 2.6Æ1.6(1.1^6.6)ALA-P (m g/L)9.0Æ2.1(6.0^15.7)7.5Æ1.5(5.0^10.9)9.2Æ2.2(6.4^11.0)6.6Æ1.4(5.1^8.7)Mean ÆSD (Range);1stand2ndrow:ALAD1homozygotes andALAD2carriers.TABLE III.Distribution of Age,Pb-B,and Indices of the Heme Biosynthetic Pathway BetweenTwo ALAD Genotypes in Lead WorkersALAD1homozygotesALAD2carriers (n 161)(n 31)Age (years)43.3Æ10.1(18^62)a 46.0Æ10.3(21^63)Pb-B (m g/dL)21.9Æ19.7(2.1^95.3)22.9Æ21.7(2.7^90)ALAD (u)42.5Æ22.1(4.1^81.8)39.7Æ20.1(5.5^74.9)ALA-P (m g/L)24.6Æ47.6(7.5^385.1)19.4Æ27.6(7.3^144.5)ALA-U (mg/gCre)1.5Æ3.5(0.2^31.1)1.6Æ3.6(0.4^20.6)ZP (m g/dLRBC)156Æ213(35^1684)117Æ134(38^739)aMean ÆSD (Range).ALAD Polymorphism and Heme Precursors 35759m g/dL.In lead workers with Pb-B levels higher than 60m g/dL,differences of ALA-P levels among the groups cannot be determined because the number of ALAD2carriers was as far as 2in certain groups.Figure 1(B)shows the comparison of ALA-U levels between the two genetic groups in lead workers.No signi®cant difference is observed between the two geno-types in every Pb-B groups,although ALAD1homozygotes show slightly higher levels than those of ALAD2carriers.Figure 1(C)shows the comparison of ZP levels between the two genetic groups in lead workers.ALAD1homo-zygotes show statistically higher levels of ZP in comparison with those in ALAD2carriers at Pb-B levels higher than 20m g/dL.These data also suggest that ZP is more sensitive to lead exposure than ALA in ALAD1homozygotes.DISCUSSIONIn the present study,the ALAD2frequency was calculated to be 0.087in all subjects (Table I).This frequency in the Japanese population is considerably different (12 3.56,P 0.059)from that by Benkmann et al.[1983](n 144,ALAD2frequency is 0.058).This discrepancy in ®ndings might be due to differences in the methods of determining the ALAD genotype or in the respective sample populations.Benkmann et al.[1983]used starch gel electrophoresis,which is based on the difference in the electrical charge of ALAD ing the PCR method,the frequency of the ALAD2allele has been reported in various populations were 0.114in 307Korean [Schwartz et al.,1995],0.074in 691American [Smith et al.,1995],and 0.085in 205Chinese [Zhang et al.,1998].These frequencies were nearly the same as those found in the present study.It has been reported that ALAD2carriers have higher Pb-B levels than ALAD1homozygotes in subjects exposed to lead occupationally or environmentally [Ziemsen et al.,1986;Astrin et al.,1987;Wetmur et al.,1991b;Schwartz et al.,1995].However,no signi®cant difference was observed in ALAD activity between the two groups ofALAD genotypes when their Pb-B levels were matched [Ziemsen et al.,1986].In the present study,ALAD activity is not signi®cantly different between the two ALAD genotypes in each Pb-B group (Table IV).In the present study,we demonstrated that ALAD genotypes modify lead effects on heme metabolism in highly exposed workers (Table IV ,Fig.1).On the other hand,in control subjects and lead workers with Pb-B levels lower than 20m g/dL,ALA and ZP levels were not signi®cantly different between the two ALAD genotypes (Tables II and IV).These data agree with those previously reported;ALAD genotypes were not found to affect the levels of heme precursors at low-Pb-B levels [Alexander et al.,1998;Zhang et al.,1998].At Pb-B levels of 20±59m g/dL,ZP levels in ALAD1homozygotes were signi®cantly higher than those in ALAD2carriers (Table IV ,Fig.1).At Pb-B levels of 40±59m g/dL,ALA-P levels in ALAD1homozygotes were also signi®cantly higher than those in ALAD2carriers (Table IV ,Fig.1).Sithisarankul et al.[1997]reported that ALA-P levels in ALAD1homozygotes were higher than those in ALAD2carriers in lead workers,whose results are in good agreement with ours.However,in the aforemen-tioned study,ZP levels were not signi®cantly different between the ALAD genotypes.One explanation for this difference may be the insuf®cient number (n 65)of workers in that study.In contrast,Schwartz et al.[1995]and Alexander et al.[1998]reported that ALAD1homozygotes had higher ZP levels than those of ALAD2carriers.In their studies,the number of subjects was suf®ciently large (n 308and 134,respectively),and ALA-P levels were not examined.The differences in the levels of heme precursors between the two types of ALAD genotypes might be attributable to a difference in the af®nity to lead among different ALAD isozymes ALAD1and two proteins.In a recent study using liquid chromatography in conjunction with inductively coupled plasma mass spectrometry,Bergdahl et al.[1997]reported that ALAD2carriers showed a higher percentage of lead bound to ALAD proteinthanparison(meanandSD)ofALA-P(A),ALA-U(B),andZP(C)levelswithincreasinglevelsofPb-Bbetweenthetwogenotypes.*P `0.05,**P `0.01.358Sakai et al.did ALAD1homozygotes.The amounts of``biologically active''lead might be lowered in ALAD2carriers than in ALAD1homozygotes.Thus,lead bound to the ALAD protein might be detoxi®ed or in inactive form,which prevents lead inhibition of ferrochelatase and of ferrous incorporation into mitochondria,resulting in relatively low levels of ZP in ALAD2carriers[Schwartz et al.,1995].The reductive process of Fe3+is reportedly inhibited by lead [Taketani et al.,1985].By these mechanisms,incorporation of ferrous into protoporphyrin might be more highly inhibited by lead in ALAD1homozygotes than in ALAD2 carriers,resulting in the®ndings of higher ZP and lower heme levels.Reduced levels of heme might induce ALA synthetase by negative feedback regulation,and produce a large amount of ALA[Meredith et al.,1978;Sakai et al., 1996a].This would explain the relatively higher levels of ALA in plasma in ALAD1homozygotes,in comparison to those in ALAD2carriers.ALA-U levels are not signi®cantly different between ALAD genotypes(see Fig.1(B)),which might be due to the fact that the urinary concentration and matrices are widely varied in each worker.In conclusion,ALAD1homozygotes have higher levels of ZP and ALA than those in ALAD2carriers in subjects who had high lead exposure,suggesting that ALAD1 homozygotes might be more susceptible than ALAD2 carriers to disturbance in heme biosynthesis caused by lead exposure.REFERENCESAlexander BH,Checkoway H,Costa-Mallen P,Faustman EM,Woods JS,Kelsey KT,van Netten C,Costa LG.1998.Interaction of blood lead and d-aminolevulinic acid dehydratase genotype on markers of heme synthesis and sperm production in lead smelter.Environ Health Perspect106:213±216.Astrin KH,Bishop DF,Wetmur JG,Kaul B,Davidow 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OXON,ENGLANDhttp://www.blackwellpublishing.com/journal.asp?ref=0268-9146&site=10032-5791 1.520.405298REPRODUCTION INDOMESTIC ANIMALSBLACKWELL PUBLISHING9600 GARSINGTON RD,OXFORD OX4 2DQ, OXON,ENGLANDhttp://www.blackwellpublishing.com/journal.asp?ref=0936-6768&site=10936-6768 1.5030.172179JOURNAL OF DAIRYRESEARCHCAMBRIDGE UNIV PRESS32 AVENUE OF THE AMERICAS,NEW YORK, NY 10013-2473/action/displayMoreInfo?jid=DAR&type=ifc0022-0299 1.4070.2462410ANIMAL FEED SCIENCE ANDTECHNOLOGYELSEVIER SCIENCE BVPO BOX 211, 1000 AEAMSTERDAM, NETHERLANDS/wps/find/journaldescription.cws_home/503299/description#description0377-8401 1.290.2622411ANIMAL BIOTECHNOLOGY TAYLOR & FRANCIS AS 325 CHESTNUT ST, SUITE 800,PHILADELPHIA, PA 19106/journals/titles/10495398.asp1049-5398 1.1820.13613*直接点击网址可进入相应刊物网站，获得期刊在线投稿信息2006年SCI收录农业、畜牧业类期刊影响因子排名（共35种)序号刊名出版者出版者地址期刊在线投稿须知*ISSN影响因子立即指数H 指数12APPLIED ANIMAL BEHAVIOURSCIENCEELSEVIER SCIENCE BVPO BOX 211, 1000 AEAMSTERDAM, 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OXON,ENGLANDhttp://www.blackwellpublishing.com/journal.asp?ref=0931-2439&site=10931-2439 1.0750.1431218ARCHIVES OF ANIMALNUTRITIONTAYLOR & FRANCIS LTD4 PARK SQUARE, MILTONPARK, ABINGDON OX14 4RN,OXON, ENGLAND/journals/titles/1745039X.asp1745-039X 1.0510.091519ANIMAL SCIENCE CAMBRIDGE UNIV PRESS EDINBURGH BLDG,SHAFTESBURY RD, CB2 2RUCAMBRIDGE, ENGLAND/1357-7298 1.0210.2582320JOURNAL OF ANIMALBREEDING AND GENETICSBLACKWELL PUBLISHING9600 GARSINGTON RD,OXFORD OX4 2DQ, OXON,ENGLANDhttp://www.blackwellpublishing.com/journal.asp?ref=0931-2668&site=10931-2668 1.0090.1171221ASIAN-AUSTRALASIANJOURNAL OF ANIMALSCIENCESASIAN-AUSTRALASIANASSOC ANIMALPRODUCTION SOCIETIESCOLLEGE AGRICULTURE LIFESCIENCES, DEPT ANIMALSCIENCE TECHNOLOGY,SUWON 441-744, SOUTHKOREA/1011-23670.8750.18313*直接点击网址可进入相应刊物网站，获得期刊在线投稿信息2006年SCI收录农业、畜牧业类期刊影响因子排名（共35种)序号刊名出版者出版者地址期刊在线投稿须知*ISSN影响因子立即指数H 指数22JOURNAL OF RANGEMANAGEMENTSOC RANGE MANAGEMENT445 UNION BLVD, STE 230,LAKEWOOD, CO 80228-1259/jrm/jrmhome.htm0022-409X0.8591823ANIMAL RESEARCH EDP SCIENCES S A 17, AVE DU HOGGAR, PACOURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A,FRANCE/journal/index.cfm?edpsname=animres1627-35830.80.0791224CANADIAN JOURNAL OFANIMAL SCIENCEAGRICULTURAL INSTCANADA280 ALBERT ST, SUITE 900,OTTAWA, ONTARIO K1P 5G8,CANADAhttp://pubs.nrc-cnrc.gc.ca/aic-journals/cjas.html0008-39840.7670.2811725Rangeland Ecology &ManagementSOC RANGE MANAGEMENT445 UNION BLVD, STE 230,LAKEWOOD, CO 80228-1259/perlserv/?request=get-toc&issn=1551-50281550-74240.6520.122526SMALL RUMINANT RESEARCH ELSEVIER SCIENCE BV PO BOX 211, 1000 AEAMSTERDAM, NETHERLANDS/wps/find/journaldescription.cws_home/503317/description#description0921-44880.6370.1131827AUSTRALIAN JOURNAL OFDAIRY TECHNOLOGYDAIRY INDUSTRY ASSOCAUSTRALIAPO BOX 351, NORTHMELBOURNE, VIC 3051,AUSTRALIAhttp://www.diaa.asn.au/publications/australian-journal-of-dairy-technology/33/default.aspx0004-94330.4460.1031328PRODUCTIONS ANIMALES INST NATL RECHERCHEAGRONOMIQUE147 RUE DE L UNIVERSITE,75338 PARIS CEDEX 07,FRANCEhttp://www.inra.fr/productions-animales/invariants/ukpresen.htm0990-06320.4180.062829JOURNAL OF APPLIEDPOULTRY RESEARCHPOULTRY SCIENCE ASSOCINC1111 NORTH DUNLAP AVE,SAVOY, IL 61874-9604/1056-61710.4020.1321530ACTA AGRICULTURAESCANDINAVICA SECTION A-ANIMAL SCIENCETAYLOR & FRANCIS ASPO BOX 12 POSTHUSET, NO-0051 OSLO, NORWAY/journals/titles/09064702.asp0906-47020.32601231JOURNAL OF ANIMAL ANDFEED SCIENCESKIELANOWSKI INSTANIMAL PHYSIOLOGYNUTRITIONUL INSTYTUCKA 3, 05-110JABLONNA, POLANDhttp://www.ifzz.pl/alpha/1230-13880.2810.1531132AVIAN AND POULTRYBIOLOGY REVIEWSSCIENCE & TECHNOLOGYLETTERSPO BOX 314, ST ALBANS AL14ZG, HERTS, ENGLAND/bioandbiosafety/pab.htm1470-20610.2509*直接点击网址可进入相应刊物网站，获得期刊在线投稿信息2006年SCI收录农业、畜牧业类期刊影响因子排名（共35种)序号刊名出版者出版者地址期刊在线投稿须知*ISSN影响因子立即指数H 指数33SOUTH AFRICAN JOURNAL OFANIMAL SCIENCESOUTH AFRICAN JOURNALOF ANIMAL SCIENCESC/O ESTIE KOSTER, PO BOX13884, HATFIELD 0028, SOUTHAFRICAhttp://www.sasas.co.za/0375-15890.2150.172534JOURNAL OF APPLIED ANIMALRESEARCHGARUDA SCIENTIFICPUBLICATIONS151, JANAKPURI, IZATNAGAR243 122, INDIA/0971-21190.2110735TROPICAL GRASSLANDS TROPICAL GRASSLANDSOC AUSTCUNNINGHAM LAB, CSIRO 306CARMODY RD, ST LUCIA, OLD4067, AUSTRALIAhttp://www.tropicalgrasslands.asn.au/0049-47630.20308*直接点击网址可进入相应刊物网站，获得期刊在线投稿信息。