鱼粉、精料补充料及其中肉骨粉含量的近红外漫反射光谱分析
|
摘要
饲料化学品质的检测是保证饲料产品质量与安全的重要技术手段,为了研究探讨鱼粉和精料补充料的化学成分及其中肉骨粉含量的近红外反射光谱(NIRS)快速检测方法,收集了145个鱼粉、151个精料补充料样本和34个肉骨粉样本,在鱼粉和精料补充料中加入不同比例(鱼粉5～60%;精料补充料0.5～7%)的肉骨粉,在10000cm~(-1)～4000cm~(-1)谱区范围内,对试验样本进行了近红外反射光谱扫描。采用偏最小二乘(PLS)法,研究建立了鱼粉化学成分和氨基酸、精料补充料化学成分和总能、鱼粉和精料补充料中肉骨粉含量的近红外反射光谱定量分析模型,研究结果如下:
1.鱼粉中水分、粗蛋白、粗脂肪、粗灰分、总磷的化学分析值与NIRS定标模型预测值之间的决定系数R~2达到0.90以上,相对标准差RSD均小于10%,相对分析误差RPD大于3;食盐的决定系数R~2为0.8782,相对标准差RSD为9.86%,相对分析误差RPD为2.98;钙的决定系数R~2为0.7575,相对标准差RSD为17.81%,相对分析误差RPD为2.14。结果表明,利用近红外光谱分析技术能够准确地检测鱼粉中水分、粗蛋白、粗脂肪、粗灰分、总磷的含量;食盐只能进行粗略估测;钙难于进行实际检测。
2.鱼粉中天冬氨酸、蛋氨酸、赖氨酸、苏氨酸、谷氨酸、甘氨酸、丙氨酸、缬氨酸、异亮氨酸、亮氨酸、苯丙氨酸、精氨酸、脯氨酸和总氨基酸的化学分析值与NIRS预测值的决定系数R~2都达到0.87以上,相对标准差RSD均小于10%,相对分析误差RPD均大于3;酪氨酸的相关系数R~2为0.8678,相对标准差RSD为8.65,相对分析误差RPD为2.77;组氨酸、丝氨酸和半胱氨酸的决定系数R~2分别为0.9005、0.7436和0.3541,相对标准差RSD分别为14.19%、17.85%和33.85%;相对分析误差RPD分别为2.96、1.98和1.04。结果表明,利用近红外光谱分析技术能够较准确地检测鱼粉中天冬氨酸、蛋氨酸、赖氨酸、苏氨酸和总氨基酸等14种氨基酸的含量;酪氨酸只能进行粗略估测;组氨酸、丝氨酸和半胱氨酸难于进行实际检测。
3.精料补充料中水分、粗蛋白、粗脂肪、粗灰分、粗纤维、总磷、食盐和总能的化学分析值与NIRS定标模型预测值之间的决定系数R~2均到0.90以上,相对标准差RSD均小于10%,相对分析误差RPD均大于3。结果表明,近红外光谱分析技术可以定量检测精料补充料中水分、粗蛋白、粗脂肪、粗灰分、粗纤维、总磷、食盐和总能的含量,并可获得理想的检测精度。
4.鱼粉中肉骨粉的NIRS定量分析模型,定标集真值与NIRS定标模型预测值之间的决定系数R~2和标准差分别为0.9509和3.222;相对标准差RSD和相对分析误差RPD分别为9.25和4.79。验证集真值与NIRS预测值的决定系数r~2以及标准差RMSEP分别为0.9668和2.68;相对标准差RSD和相对分析误差RPD分别为9.325和5.484。结果表明,用近红外光谱分析技术定量检测鱼粉中肉骨粉含量是可行的,并获得理想的预测精度。
5.精料补充料中肉骨粉含量的NIRS定量分析模型,定标集真值与NIRS定标模型预测值之间的决定系数R~2和标准差分别为0.9447和0.392;相对标准差RSD和相对分析误差RPD分别为9.63和4.73。验证集真值与NIRS预测值决定系数r~2以及标准差RMSEP分别为0.9618和0.345;相对标准差RSD和相对分析误差RPD分别为9.66和5.04。结果表明,用近红外光谱分析技术检测精料补充料中肉骨粉含量是可行的,并获得理想的预测精度。
The measurement of feed quality is a crucial technical method to ensure the quality and safety of feed products. In order to investigate the method of near infrared reflectance spectroscopy (NIRS) technology for rapidly analysis of the chemical composition and meat and bone(MBM) content in fishmeal and concentrate supplement, 145 fishmeal samples, 151 concentrate supplement samples and 34 meat and bone meal(MBM) samples were collected, and deliberately adulterated with meat and bone meal (MBM) in the different proportion (fishmeal at 5-60%, concentrate supplement at 0.5-7%). the samples were scanned at the NIRS region 1000 cm~(-1)~4000cm(-1). The calibration models to predict the chemical conposition and amino acid in fishmeal、 the chemical conposition and gross energy、 the MBM content in fishmeal and concentrate supplement were developed using partial least squares (PLS) technique. The results were as follows:1 .The coefficient of determination in calibration (R~2) of moisture, crude protein, crude fat, crude ash, total phosphor in fishmeal were all over 0.90, the RSD all less than 10%, and the RPD all over 3; the R~2 of NaCl was 0.8782, the RSD and RPD of salt were 9.86% and 2.98 respectively; the R~2 of Calcium was 0.7575, the RSD and RPD of calcium were 17.81% and 2.14 respectively. The results shows that NIRS analysis technique could be adopted to correctly measure the content of moisture, crude protein, crude fat, crude ash and total phosphor in fishmeal, and to predict salt content roughly. However, it could hardly be employed on measurement of Calcium content in practice.2.The coefficient of determination in calibration (R~2 ) of chemical analysis value and NIRS prediction value of aspartic acid, methionine, lysine, threonine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, phenylalanine, arginine, proline and total amino acid were all over 0.87, the RSD all less than 10%, RPD all over 3; the R~2 of Tyrosine was 0.8678, the RSD was 8.65, the RPD was 2.77; the R~2 for histidine, serine and cysteine were 0.9005, 0.7436 and 0.3541 respectively, the RSD were 14.19%, 17.859% and 33.85% respectively, the RPD were 2.96, 1.98 and 1.04 respectively. The results shows that NIRS analysis technique could be used to measure the contents of 14 kinds of amino acids, such as aspartic acid, methionine, lysine, threonine and total amino acid in fish meal accurately and to predict Tyrosine content roughly. However, it can hardly be employed to measure histidine, serine and cysteine content in practice.3. The coefficient of determination in calibration (R~2) of moisture, crude protein, crude fat, crude ash, total phosphor, salt, crude fiber and gross energy in concentrate supplement were all over 0.90, the RSD all less than 10%, the RPD all over 3. The results shows that NIRS analysis technique could be used to quantitatively predict the contents of moisture, crude protein, crude fat, crude ash, crude fiber, total phosphor, salt, and gross energy in concentrate supplement, and a good measuring precision could be expected.4.The NIRS quantitative analysis model was developed for measuring meat and bone meal(MBM) in fish meal. The coefficient of determination (R~2) and standard errors (RMSEC) in calibration sets were
0.9509 and 3.222 respectively; the RSD and the RPD were 9.25 and 4.79 respectively. The coefficient of determination (r~) and standard errors (RMSEP) in validation sets were 0.9668 and 2.68 respectively; the RSD and the RPD were 9.325 and 5.484 respectively. The results shows that it is feasible to employ NIRS analysis technique to quantitatively measure the MBM content in fish meal with a good measuring precision.5.The NIRS quantitative analysis model was developed for measuring meat and bone meal content in concentrate supplement. The coefficient of determination R~2 and standard errors (RMSEC) in calibration sets were 0.9447 and 0.392 respectively; the RSD and RPD 9.63 and 4.73 respectively. The coefficient of determination r~2 and standard errors (RMSEP) in validation sets were 0.9618 and 0.345 respect
1.鱼粉中水分、粗蛋白、粗脂肪、粗灰分、总磷的化学分析值与NIRS定标模型预测值之间的决定系数R~2达到0.90以上,相对标准差RSD均小于10%,相对分析误差RPD大于3;食盐的决定系数R~2为0.8782,相对标准差RSD为9.86%,相对分析误差RPD为2.98;钙的决定系数R~2为0.7575,相对标准差RSD为17.81%,相对分析误差RPD为2.14。结果表明,利用近红外光谱分析技术能够准确地检测鱼粉中水分、粗蛋白、粗脂肪、粗灰分、总磷的含量;食盐只能进行粗略估测;钙难于进行实际检测。
2.鱼粉中天冬氨酸、蛋氨酸、赖氨酸、苏氨酸、谷氨酸、甘氨酸、丙氨酸、缬氨酸、异亮氨酸、亮氨酸、苯丙氨酸、精氨酸、脯氨酸和总氨基酸的化学分析值与NIRS预测值的决定系数R~2都达到0.87以上,相对标准差RSD均小于10%,相对分析误差RPD均大于3;酪氨酸的相关系数R~2为0.8678,相对标准差RSD为8.65,相对分析误差RPD为2.77;组氨酸、丝氨酸和半胱氨酸的决定系数R~2分别为0.9005、0.7436和0.3541,相对标准差RSD分别为14.19%、17.85%和33.85%;相对分析误差RPD分别为2.96、1.98和1.04。结果表明,利用近红外光谱分析技术能够较准确地检测鱼粉中天冬氨酸、蛋氨酸、赖氨酸、苏氨酸和总氨基酸等14种氨基酸的含量;酪氨酸只能进行粗略估测;组氨酸、丝氨酸和半胱氨酸难于进行实际检测。
3.精料补充料中水分、粗蛋白、粗脂肪、粗灰分、粗纤维、总磷、食盐和总能的化学分析值与NIRS定标模型预测值之间的决定系数R~2均到0.90以上,相对标准差RSD均小于10%,相对分析误差RPD均大于3。结果表明,近红外光谱分析技术可以定量检测精料补充料中水分、粗蛋白、粗脂肪、粗灰分、粗纤维、总磷、食盐和总能的含量,并可获得理想的检测精度。
4.鱼粉中肉骨粉的NIRS定量分析模型,定标集真值与NIRS定标模型预测值之间的决定系数R~2和标准差分别为0.9509和3.222;相对标准差RSD和相对分析误差RPD分别为9.25和4.79。验证集真值与NIRS预测值的决定系数r~2以及标准差RMSEP分别为0.9668和2.68;相对标准差RSD和相对分析误差RPD分别为9.325和5.484。结果表明,用近红外光谱分析技术定量检测鱼粉中肉骨粉含量是可行的,并获得理想的预测精度。
5.精料补充料中肉骨粉含量的NIRS定量分析模型,定标集真值与NIRS定标模型预测值之间的决定系数R~2和标准差分别为0.9447和0.392;相对标准差RSD和相对分析误差RPD分别为9.63和4.73。验证集真值与NIRS预测值决定系数r~2以及标准差RMSEP分别为0.9618和0.345;相对标准差RSD和相对分析误差RPD分别为9.66和5.04。结果表明,用近红外光谱分析技术检测精料补充料中肉骨粉含量是可行的,并获得理想的预测精度。
The measurement of feed quality is a crucial technical method to ensure the quality and safety of feed products. In order to investigate the method of near infrared reflectance spectroscopy (NIRS) technology for rapidly analysis of the chemical composition and meat and bone(MBM) content in fishmeal and concentrate supplement, 145 fishmeal samples, 151 concentrate supplement samples and 34 meat and bone meal(MBM) samples were collected, and deliberately adulterated with meat and bone meal (MBM) in the different proportion (fishmeal at 5-60%, concentrate supplement at 0.5-7%). the samples were scanned at the NIRS region 1000 cm~(-1)~4000cm(-1). The calibration models to predict the chemical conposition and amino acid in fishmeal、 the chemical conposition and gross energy、 the MBM content in fishmeal and concentrate supplement were developed using partial least squares (PLS) technique. The results were as follows:1 .The coefficient of determination in calibration (R~2) of moisture, crude protein, crude fat, crude ash, total phosphor in fishmeal were all over 0.90, the RSD all less than 10%, and the RPD all over 3; the R~2 of NaCl was 0.8782, the RSD and RPD of salt were 9.86% and 2.98 respectively; the R~2 of Calcium was 0.7575, the RSD and RPD of calcium were 17.81% and 2.14 respectively. The results shows that NIRS analysis technique could be adopted to correctly measure the content of moisture, crude protein, crude fat, crude ash and total phosphor in fishmeal, and to predict salt content roughly. However, it could hardly be employed on measurement of Calcium content in practice.2.The coefficient of determination in calibration (R~2 ) of chemical analysis value and NIRS prediction value of aspartic acid, methionine, lysine, threonine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, phenylalanine, arginine, proline and total amino acid were all over 0.87, the RSD all less than 10%, RPD all over 3; the R~2 of Tyrosine was 0.8678, the RSD was 8.65, the RPD was 2.77; the R~2 for histidine, serine and cysteine were 0.9005, 0.7436 and 0.3541 respectively, the RSD were 14.19%, 17.859% and 33.85% respectively, the RPD were 2.96, 1.98 and 1.04 respectively. The results shows that NIRS analysis technique could be used to measure the contents of 14 kinds of amino acids, such as aspartic acid, methionine, lysine, threonine and total amino acid in fish meal accurately and to predict Tyrosine content roughly. However, it can hardly be employed to measure histidine, serine and cysteine content in practice.3. The coefficient of determination in calibration (R~2) of moisture, crude protein, crude fat, crude ash, total phosphor, salt, crude fiber and gross energy in concentrate supplement were all over 0.90, the RSD all less than 10%, the RPD all over 3. The results shows that NIRS analysis technique could be used to quantitatively predict the contents of moisture, crude protein, crude fat, crude ash, crude fiber, total phosphor, salt, and gross energy in concentrate supplement, and a good measuring precision could be expected.4.The NIRS quantitative analysis model was developed for measuring meat and bone meal(MBM) in fish meal. The coefficient of determination (R~2) and standard errors (RMSEC) in calibration sets were
0.9509 and 3.222 respectively; the RSD and the RPD were 9.25 and 4.79 respectively. The coefficient of determination (r~) and standard errors (RMSEP) in validation sets were 0.9668 and 2.68 respectively; the RSD and the RPD were 9.325 and 5.484 respectively. The results shows that it is feasible to employ NIRS analysis technique to quantitatively measure the MBM content in fish meal with a good measuring precision.5.The NIRS quantitative analysis model was developed for measuring meat and bone meal content in concentrate supplement. The coefficient of determination R~2 and standard errors (RMSEC) in calibration sets were 0.9447 and 0.392 respectively; the RSD and RPD 9.63 and 4.73 respectively. The coefficient of determination r~2 and standard errors (RMSEP) in validation sets were 0.9618 and 0.345 respect
引文
Albanell E, Plaixats J, Mas M T. A note on prediction of maize stover quality by near-infraredreflectance spectroscopy (NIRS) technique. Journal of Animal and Feed Sciences. 1997, 6(4): 559～565
Arminda M, Bruno Soares, Use of near infrared reflectance spectroscopy(NIRS) for the prediction of chemical composition and nutrional attributesof green crop cereals. Animal Feed Science and Technology. 1998,(75):15～25
Atanassova S, Ilchev A. Application of near-infrared reflectance spectroscopy for predicting chemical composition of compound pig and poultry feeds. Bulgarian Journal of Agricultural Science. 1997, 3(2), 171～180
Athens. Use of near-infrared reflectance spectroscopy to analyze vitamin content. Applied Poultry Science, 2001.10 (4):412～418
Bochereau L, Beurgine P. A method for prediction by comdining data analysis and neural network:application to predictior of apple quality using near infrared spectroscopy. Journal of Agricultural Engineering Research, 1992,51:207～206
Chicago. Determination of amino acids in wheat and barley by near-infrared reflectance spectroscopy. Journal Food Science. 1984. 49 (1): 17～20
Cozzolino-D. Prediction of chemical composition of ruminant feeds by near infrared reflectance spectroscopy (NIRS) in Uruguay. Revista-Argentina-de-Produccion-Animal. 2002, 22(2): 81～86
Cozzolino-D, Fassio-A, Gimenez-A. The use of near-infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants. Journal of the Science of Food and Agriculture. 2001, 81: 142～146
Cozzolino-D, Labandera-M. Determination of dry matter and crude protein contents of undried forages by near-infrared reflectance. Journal of the Science of Food and Agriculture. 2002, 82:380-384
D. A. Jackson, etal. Determination of total and digestible amino acid by near infrared reflectance spectroscopy. Proc. Aust. Poult. Sci. Sym. 8:46～52
D. Cozzlino, A. Moron. Exploring the of near infrared reflectance spectroscopy(NIRS) to predict trace minerals in legums. Animal Feed Science and Technology. 2004,(111): 161～173
Dahl P L, Christensen B M, & Engelsen S B. Can spectroscopy in combination with chemometdcs replace minks in digestibility test? J. Sci. Food Agric. 2000,80:365～374
Dolores C, Ana Garrido-Varo, J E Guerrero et al. Near-infrared reflectance spectroscopy (NIRS) for the mandatory labeling of compound feedingstuff: chemical composition and open-declaration[J]. Animal Feed Science and Technology, 2004. (116):333～349
D. Cozzolino, A. Chree, & J R Scaife. The assessment of the chemocal composition of fishmeal by near infrared reflectance spectroscopy. Aquaculture Nutrition, 2002,8:149～155
D. Cozzolino, A Moron. Exploring the use near infrared reflwctance specroscopy(NIRS)to predict trace minerals in legumes. Animal Feed Science and Technology, 2004. (111): 161～173
Fernandez M., Martinez A., Modrono S., De la Roza B. Near infrared reflectance spectroscopy as a tool to predict qualitative and quantitative meat and bone meal presence in compound feed. In: Davies, A. M. C., Cho, R. K. (Eds.), Proceedings of the 10th International Conference on Near Infrared Spectroscopy. NIR Publications, Chichester. 2002.307～312
Femandez V., Garrido A. Report of the workshop on identification of animal ingredients in compound feed focusing on the microscopic method for identification organized by the CEMA group, 25-26 May 1998. 6524
Fontaine-J, Horr-J, Schirmer-B. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. Journal of Agricultural and Food Chemistry. 2001, 49:57～66
Givens-DI, Baker-CW, Moss-AR, Adamson-AH. A comparison of near-infrared reflectance spectroscopy with three in vitro techniques to predict the digestibility in vivo of untreated and ammonia-treated cereal straws. Animal-Feed-Science-and-Technology. 1991, 35:83～94
Garrido A, Guerrero J E. Application of near infrared reflectance spectroscopy(NIRS) for evaluation of animal feedstuffs. 44th Animal Meeting of the EAAP. Aarhus, Denmark.
Garrido A, Marin M D, Guerrero J. E., et al. Near infrared reflectance spectroscopy as an essential tool in food safety programmes: predicting ingredients in commercial compound feed. Proceedings of the 10th International Conference on Near Infrared Spectroscopy. NIR Publications, Chichester, West Sussex, 2002.145～150
G Xiccato, Angela Trocino, A Carazzolo. Nutritive evaluation and ingredient prediction of compound feeds for rabbits by near-infrared reflectance spctroscopy(NIRS). Animal Feed Science and Technology. 1999, 77:201～212
Hartwig P A, P H Charles. Near Reflectance Measurement of moisyure, protein and oil content of ground crambe seed. JAOCS, 1990(67):7
Jackson D A, et al. Determination of total and digestible amino acids by near infrared reflectance spectroscopy. Proc Aust Poult Sci Sym. 1996,(65):46～52
Jung ChanSik, Kim BytmgJoo, Kwon YilChan. Analysis of protein and moisture contents in pea using near-infrared reflectance spectroscopy. Korean Journal of Crop Science. 1998, 43(2): 101～104
J. G. Wu, & C. H. Shi. Prediction of grain weight, brown rice weight and amylose content in single rice grains using near-infrared reflectance spectroscopy. Field Crops Research. 2004,87:13～21
J L de Boever, B g Cottyn. The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Science and Technology. 1995, 51:243～253
Jocelyne Aufrere, Dominique Graviou. Near infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants. Animal Feed Science and Technology. 1996, 62:77-90
Law D P. Determination moisyure content in wheat by NIR diffuse reflectance spectrophotometry. Cereal chem. 1977,54(4):874～881
Lavrencic-A, StefAnon-B, Oresnik-A. Estimation of mineral content in forages by near-infrared reflectance spectroscopy. Zbomik Biotehniske Fakultete Univerze jubljani. Kmetijstvo, Zootehnika. 2002, 80:41～49
Lesson S, Potential for real-time ingredient quality control procedure. J Appl Poultry Res. 1997,(6):501～506
Long J R. Spectroscopy calibration and quantitation using artifcial neural neetwork. Analytical Chemistry. 1990, 62(5): 1791～1797
Moron-A, Cozzolino-D. Determination of macro elements in alfalfa and white clover by near-infrared reflectance spectroscopy. Journal-of-Agricultural-Science.2002, 139(4): 413～423
Malley D F. D L Findlay. & B Zippel. Feasibility of using near-infrared reflectance spectroscopy for the analysis of C, N, P, and doatoms in lake sediments. Journal of Paleolimnology. 1999,21:295～306
Murray. I., Aucott L. S., Pike I. H. Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fishmeal with meat and bone meal[J]. Journal of Near Infrared Spectroscopy. 2001.9(4):297～311
Piraux F., Dardenne P. Feed authentication by near-infiared microscopy. In: Davies, A. M. C.,
Giangiacomo, R. (Eds.), Proceedings of the Ninth International Conference on Near Infrared Spectroscopy. NIR Publications, Chichester, West Sussex, 2000,535～541
Rabault J L, Downey G. Rapid analysis of oats for moisture and protein by near-infrared reflectance spectroscopy. Journa of Food Science and Technology. 1990,14(2): 85～93
Atanassova S. Ilchev A. Application of near-infrared reflectance spectroscopy for predicting chemical composition of compound pig and poultry feeds. Bulgarian Journal of Agricultural Science. 1997, 3(2): 171～180
Reeves J B, T H Blosser. Near infrared reflectance spectroscopy for analyzing undried silage. J. Dairy Sci. 1989,(72):79～88
Sato-T, Morishita-T, Hara-T. Near-infrared reflectance spectroscopic analysis of moisture, fat, protein, and physiological activity in buckwheat flour for breeding selection. Plant Production Science. 2001, 4:270～277
Starr C A G Morgen & D B Smith. On evaluatuin of near-infiared reflentance analysis in some plant breeding programes. J. Agric. Sci. 1997:107～118
Shenk, J. S. Workman J, & Westerhaus M O. Application of NIR spectroscopy to agricultural products. Handbook of NIR Analysis. Chapter 15:383～431
Theo van Kempen, Jean-Christophe Bodin. Near infrared reflectance spectroscopy(NIRS) appears to be superior to nitrogen-base regression as a prappid tool in predicting the poultry digestible amino acid content of commonly used feedstuff. Animal Feed Science and Technology. 1998(76): 139～147
Villamarin-B, Fernandez-E;,Mendez-J. Analysis of grass silage from Northwestern Spain by near-infrared reflectance spectroscopy. Joumal-of-AOAC-Intemational. 2002, 85(3): 541～545
Wu J G, Shi-ChunHai. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy. Field-Crops-Research. 2002, 75(1): 1～7
Williams P C & K H Norris. Near-infared Technology in the Agriculture and Food Industries. American Association of Cereal Chemists. 1987:330
Williams P C, & Soebering D C. Comparusion of commercial infrared transmitance instruments for analysis of whole graise and seeds. Journal of Near Ifrared Spectroscopy, 1993, 1:25～32
Xiccato G, Trocino A, Boever J.Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology. 2003, 104:153～168
Yibin Ying, Yande Liu. A nondestrctive method for soluble solids content and availiable acidity in apples using FT-NIR Spectroscopy. 2004 CIGR International Conference. Beijing Sponsored by CIGR, CSAM and CSAE, Beijing, Chia, 2004
Yibin Ying, Huanyu Jiang. Prediction of water, Chlorophyll Content of Tomato by Near lnfraed Spectroscopy Analysis. 2004 CIGR International Conference. Beijing Sponsored by CIGR, CSAM and CSAE, Beijing, Chia.2004
陈颖,吴亚均,徐宝梁等.进出口动物源性产品中牛羊成分的检测方法.食品工业科技.2004,(8):144～146
陈邵苹.近红外漫反射光谱测定饲料中粗纤维.饲料工业.1996.017(006).32-33
陈斌,方如明,赵杰文.方便面含油率的近红外漫反射光谱快速测定.农业机械学报,2000,31(5):56～58
陈如明,陈国拄.疯牛病的研究进展.畜牧与兽医.2004,36(2):41～44
关秀琴 崔淑文.应用近红外光谱法测定饲料中的植酸磷含量.仪器仪表与分析监测.1991.(2):35～37
丁丽敏.近红外光谱技术预测饲料中氨基酸和真可消化氨基酸含量的研究.中国农业大学博士学位论文,1997年
丁丽敏 计成等.近红外(NIRS)和粗蛋白预测氨基酸含量的精度比较研究.饲料工业.2002.023(4):15～18
丁丽敏 计成.近红外光谱技术快速测定棉籽粕,菜籽粕真可利用氨基酸含量的研究.动物营养学报.2000.12(1).21～25
丁丽敏 计成.近红外光谱技术快速测定豆粕,玉米真可消化氨基酸含量的研究.动物营养学报.1999.11(3):12～1
丁丽敏 刘向阳.近红外光谱分析技术快速测定鱼粉氨基酸含量的研究.饲料研究.1999.5:24～26.
丁丽敏.近红外光谱技术(NIRS)可实现对氨基酸和可利用氨基酸的快速检测.饲料工业.1998.19(1):41～4
杜荣等等.近红外光谱分析技术在粗饲料质量检测上的应用—饲料用大麦部分.见:中国农业科学院畜牧研究所编.近红外光谱分析技术.北京:中国农业科技出版社.1993:196～203
范世福.近红外光谱分析技术的复兴和发展.仪器分析.1991,89(3):1～8
方彦,王汉宁.近红外光谱法在玉米粗蛋白含量测定研究中的应用.甘肃农业大学学报.2004,39(1)32～35
冯平等.近红外光谱分析技术在麦麸的饲料营养价值评定上的应用.中国畜牧杂质.1986,(2):10～14
冯新泸,史永刚编著.近红外光谱及其在石油产品分析中的应用.北京:中国石化出版社,2002
洪涛.疯牛病的流行与控制.中华预防医学会首届学术年会,济南,2002,8
高军.2003年进口鱼粉市场简析与2004年展望.饲料广角.2004,(2):14～17
高荣强,范世福,严衍禄等.近红外光谱的数据预处理研究.光谱学与光谱分析,2004,24(12):1563～1565
甘利 雅弘,大谷 文博,松本 光人.近赤外分析法化成肥料中含肉骨粉定量.2004年度日本草地学会会誌:442～443
籍保平.近红外光谱技术在农产品加工中的应用.粮油加工与食品机械.2000,(6):31～33
姜名宇.以畜牧业为主导,推进农业现代化的思考.宏观经济研究.2005,(1):54～56
刘晓敏,杨林,陈璇.NIR1501型近红外分析仪在鱼粉质检中的应用.粮食与饲料工业.1996,(9),23～27
刘晓敏,杨林等.NIR1501型近红外分析仪检测饲料的研究.中国饲料.1996.000(016).35～36.
刘继红.氨基酸含量异常与鱼粉质量的关系.饲料博览.2001,(7):29～30
刘继红,余大杰等.氨基酸与肉粉类产品质量评价继掺假分析.饲料博览.2004,(1):42～43
刘建学,吴守一等.大米直链淀粉含量的近红外光谱分析.农业工程学报.2000,16(3):94～96
刘建学,吴守一,方如明.基于近红外光谱的神经网络预测大米直链淀粉含量.农业机械学报.2002,32(02):55～57
李宁,闵顺耕,覃方丽等.近红外光谱法非破坏性测定黄豆籽粒中蛋白质、脂肪含量.光谱学与光谱分析,2004,14(1):45～49
李宏 莫放.应用绵羊粪便的近红外光谱方程评定其日粮的可消化有机物.中国畜牧杂志.2004.40(6):22-24
李瑾.蛋白质与氨基酸营养研究进展.中国饲料.2004,(9):34～35
李晓川,王联珠.鱼粉质量安全的技术保证—国家标准GB/T19164-2003《鱼粉》解析.农业质量标准.2004,(3):23～24
梁逸曾,俞汝勤.化学计量学.北京:高等教育出版社,2003年
芦永军,曲艳玲,朴仁官等.近红外光谱分析技术定标和预测中的相似样品剔除算法.2004,24(2):158～161
陆国权等.近红外光谱分析技术在粗饲料质量检测上的应用—饲料用甘薯茎叶部分.见:中国农业科学院畜牧研究所编.近红外光谱分析技术.北京:中国农业科技出版社.1993:120～124
陆婉珍,袁洪福,徐广通等.现代近红外光谱分析技术.北京:中国石化出版社,2000:167
闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化.光谱学与光谱分析.2004,24(10):1205～1209
闵顺耕,覃方丽,李宁等.傅里叶变换近红外光谱法测定大麦中蛋白质、淀粉和赖氨酸含量.分析化学.2003,31(7):843～845
潘良文,沈禹飞等.肉骨粉中羊源性成分检测方法研究.检验检疫科学.2004,14(2):15～18
全国饲料工作办公室.全国饲料工业统计资料(2003年).2004年5月.138～139
全国饲料工作办公室,中国饲料工业协会编.饲料法规文件与标准.北京:中国农业科学技术出版社,2003:315
舒庆尧,吴殿星,夏英武等.稻米表观直链淀粉含量近红外光谱测定技术校正设置的优化.浙江 大学学报,1999,25(4):343～346
宋洪远,赵长保.我国的饲料安全问题:现状、成因及对策.中国农村经济,2003.11:28～35
苏彩珠,伊平河,肖昕等.近红外透射技术检测肉骨粉中蛋白质地方法及初步应用.粮食与饲料工业.2003,(9):48～49
苏彩珠 梁震 黄宇鹏等.应用近红外透射技术快速检测肉骨粉中脂肪含量.检验检疫科学.2004.14(3):39～41
沈恒胜 陈君琛 种藏文.近红外漫反射光谱法(NIRS)分析稻草纤维及硅化物组成.中国农业科学.2003,36(9):1086～1090
唐绍孟等编著.饲料配制和使用技术.北京:中国农业出版社,2003年.62～63
王邕,黄艳等.从必须氨基酸含量组成看鱼粉的品质.广西大学学报.2004,29(增刊):112～115
王丹红,吴文昕,冯劲华.进口鱼粉品质的NIRS定量分析与验证.检验检疫科学.2004年,14(1):18～21
王文杰,穆淑琴,刘兰兰等.近红外光谱法检测预混料中的维生素A.天津畜牧兽医.1998,15(2):12～14
魏瑞兰,张子仪.近红外光谱分析技术在花生饼粕可利用氨基酸判定中的应用.中国畜牧杂质.1987,(6):3～7
吴坚主编.应用概率统计.北京:高等教育出版社,2003年
吴瑾光.近代傅里叶变换近红外光谱技术及应用.北京:科学技术文献出版社.1994.251～281
吴秀琴.应用51A型测定小麦种子中的赖氨酸含量.农业测试分析.1985,2(1):15
熊智新,方如明,陈斌等.方便面油分近红外漫反射光谱特征波长的确定.江苏理工大学学报.1999,20(3):28～31
许彩萍,任鹏,陈雪秀等.近红外光谱分析技术在粗饲料质量检测上的应用.见:中国农业科学院畜牧研究所编.近红外光谱分析技术.北京:中国农业科技出版社.1993:112～119
徐广通,袁洪福,陆婉珍.近红外光谱定量校正模型适应性研究.光谱学与光谱分析.2001,21(4):459～463
徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展.光谱学与光谱分析.2000,20(2):134～142
徐洁,钱爱萍,陈体强等.利用特征氨基酸组分分析与显微观察鉴别掺假鱼粉.福建农业学报.2004,19(2):121～125
严衍禄主编.近红外光谱分析基础与应用.北京:中国轻工出版社,2005年,pp355
严衍禄,赵龙莲,李军会等.现代近红外光谱分析的信息处理技术.光谱学与光谱分析,2000,(6):777～780
杨曙明,张瑜.近红外光谱分析技术及其在饲料质量分析中的应用.分析测试仪器通讯,1997,7(4):215～220
杨曙明,张瑜.应用近红外光谱分析技术快速分析饲料质量.分析测试学报,1999,18(1):83～87
杨南林,程翼宇,吴永江.中药材三七中皂苷成分的近红外光谱快速无损分析新方法.化学学报,2003,61(3):293～398
伊腾桂.谈谈鱼粉的掺杂及鉴别技术.江西饲料.2004,(1):22～24
赵丽丽,赵龙莲,李军会等.傅里叶变换近红外光谱仪扫描条件对数学模型预测精度的影响.光谱学与光谱分析.2004,24(1):41～44
张璐,郝永忱等.近红外光谱法测定豆粕中常规化学成分的研究.检验检疫科学.2003.013(001).25-27
张录达,严衍禄等.傅里叶变换近红外光谱定量分析数学模型的建立与评价.北京农业大学学报,1990(增):18～25
张丽英主编.饲料分析及饲料质量年技术.北京:中国农业大学出版社,2002年
赵环环 胡跃高 等.近红外光谱分析技术在黑麦草粉粗蛋白测定中的应用.动物营养学 116.报.2001.13(4).40～43
中国饲料工业协会,全国饲料工业标准化委员会.饲料工业标准汇编(2002年).北京:中国标准出版社,2002:70～95
祝诗平,王一鸣,张小超等.近红外光谱建模异常样品剔除准则与方法.农业机械学报,2004,35(4):115～119
祝诗平.近红外光谱品质检测方法研究.中国农业大学博士学位论文.2003年
诸小立,袁洪福,王艳斌等.近红外稳健分析校正模型的建立(Ⅰ)—样品温度的影响.光谱学与光谱分析,2004,24(6):666～671
诸小立,袁洪福等.在线近红外光谱过程分析技术及其应用.现代科学仪器.2004,(2):3～21
朱尔一,杨凡原.化学计量学技术及应用.北京:科学出版社,2001,92～96
Arminda M, Bruno Soares, Use of near infrared reflectance spectroscopy(NIRS) for the prediction of chemical composition and nutrional attributesof green crop cereals. Animal Feed Science and Technology. 1998,(75):15～25
Atanassova S, Ilchev A. Application of near-infrared reflectance spectroscopy for predicting chemical composition of compound pig and poultry feeds. Bulgarian Journal of Agricultural Science. 1997, 3(2), 171～180
Athens. Use of near-infrared reflectance spectroscopy to analyze vitamin content. Applied Poultry Science, 2001.10 (4):412～418
Bochereau L, Beurgine P. A method for prediction by comdining data analysis and neural network:application to predictior of apple quality using near infrared spectroscopy. Journal of Agricultural Engineering Research, 1992,51:207～206
Chicago. Determination of amino acids in wheat and barley by near-infrared reflectance spectroscopy. Journal Food Science. 1984. 49 (1): 17～20
Cozzolino-D. Prediction of chemical composition of ruminant feeds by near infrared reflectance spectroscopy (NIRS) in Uruguay. Revista-Argentina-de-Produccion-Animal. 2002, 22(2): 81～86
Cozzolino-D, Fassio-A, Gimenez-A. The use of near-infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants. Journal of the Science of Food and Agriculture. 2001, 81: 142～146
Cozzolino-D, Labandera-M. Determination of dry matter and crude protein contents of undried forages by near-infrared reflectance. Journal of the Science of Food and Agriculture. 2002, 82:380-384
D. A. Jackson, etal. Determination of total and digestible amino acid by near infrared reflectance spectroscopy. Proc. Aust. Poult. Sci. Sym. 8:46～52
D. Cozzlino, A. Moron. Exploring the of near infrared reflectance spectroscopy(NIRS) to predict trace minerals in legums. Animal Feed Science and Technology. 2004,(111): 161～173
Dahl P L, Christensen B M, & Engelsen S B. Can spectroscopy in combination with chemometdcs replace minks in digestibility test? J. Sci. Food Agric. 2000,80:365～374
Dolores C, Ana Garrido-Varo, J E Guerrero et al. Near-infrared reflectance spectroscopy (NIRS) for the mandatory labeling of compound feedingstuff: chemical composition and open-declaration[J]. Animal Feed Science and Technology, 2004. (116):333～349
D. Cozzolino, A. Chree, & J R Scaife. The assessment of the chemocal composition of fishmeal by near infrared reflectance spectroscopy. Aquaculture Nutrition, 2002,8:149～155
D. Cozzolino, A Moron. Exploring the use near infrared reflwctance specroscopy(NIRS)to predict trace minerals in legumes. Animal Feed Science and Technology, 2004. (111): 161～173
Fernandez M., Martinez A., Modrono S., De la Roza B. Near infrared reflectance spectroscopy as a tool to predict qualitative and quantitative meat and bone meal presence in compound feed. In: Davies, A. M. C., Cho, R. K. (Eds.), Proceedings of the 10th International Conference on Near Infrared Spectroscopy. NIR Publications, Chichester. 2002.307～312
Femandez V., Garrido A. Report of the workshop on identification of animal ingredients in compound feed focusing on the microscopic method for identification organized by the CEMA group, 25-26 May 1998. 6524
Fontaine-J, Horr-J, Schirmer-B. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. Journal of Agricultural and Food Chemistry. 2001, 49:57～66
Givens-DI, Baker-CW, Moss-AR, Adamson-AH. A comparison of near-infrared reflectance spectroscopy with three in vitro techniques to predict the digestibility in vivo of untreated and ammonia-treated cereal straws. Animal-Feed-Science-and-Technology. 1991, 35:83～94
Garrido A, Guerrero J E. Application of near infrared reflectance spectroscopy(NIRS) for evaluation of animal feedstuffs. 44th Animal Meeting of the EAAP. Aarhus, Denmark.
Garrido A, Marin M D, Guerrero J. E., et al. Near infrared reflectance spectroscopy as an essential tool in food safety programmes: predicting ingredients in commercial compound feed. Proceedings of the 10th International Conference on Near Infrared Spectroscopy. NIR Publications, Chichester, West Sussex, 2002.145～150
G Xiccato, Angela Trocino, A Carazzolo. Nutritive evaluation and ingredient prediction of compound feeds for rabbits by near-infrared reflectance spctroscopy(NIRS). Animal Feed Science and Technology. 1999, 77:201～212
Hartwig P A, P H Charles. Near Reflectance Measurement of moisyure, protein and oil content of ground crambe seed. JAOCS, 1990(67):7
Jackson D A, et al. Determination of total and digestible amino acids by near infrared reflectance spectroscopy. Proc Aust Poult Sci Sym. 1996,(65):46～52
Jung ChanSik, Kim BytmgJoo, Kwon YilChan. Analysis of protein and moisture contents in pea using near-infrared reflectance spectroscopy. Korean Journal of Crop Science. 1998, 43(2): 101～104
J. G. Wu, & C. H. Shi. Prediction of grain weight, brown rice weight and amylose content in single rice grains using near-infrared reflectance spectroscopy. Field Crops Research. 2004,87:13～21
J L de Boever, B g Cottyn. The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Science and Technology. 1995, 51:243～253
Jocelyne Aufrere, Dominique Graviou. Near infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants. Animal Feed Science and Technology. 1996, 62:77-90
Law D P. Determination moisyure content in wheat by NIR diffuse reflectance spectrophotometry. Cereal chem. 1977,54(4):874～881
Lavrencic-A, StefAnon-B, Oresnik-A. Estimation of mineral content in forages by near-infrared reflectance spectroscopy. Zbomik Biotehniske Fakultete Univerze jubljani. Kmetijstvo, Zootehnika. 2002, 80:41～49
Lesson S, Potential for real-time ingredient quality control procedure. J Appl Poultry Res. 1997,(6):501～506
Long J R. Spectroscopy calibration and quantitation using artifcial neural neetwork. Analytical Chemistry. 1990, 62(5): 1791～1797
Moron-A, Cozzolino-D. Determination of macro elements in alfalfa and white clover by near-infrared reflectance spectroscopy. Journal-of-Agricultural-Science.2002, 139(4): 413～423
Malley D F. D L Findlay. & B Zippel. Feasibility of using near-infrared reflectance spectroscopy for the analysis of C, N, P, and doatoms in lake sediments. Journal of Paleolimnology. 1999,21:295～306
Murray. I., Aucott L. S., Pike I. H. Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fishmeal with meat and bone meal[J]. Journal of Near Infrared Spectroscopy. 2001.9(4):297～311
Piraux F., Dardenne P. Feed authentication by near-infiared microscopy. In: Davies, A. M. C.,
Giangiacomo, R. (Eds.), Proceedings of the Ninth International Conference on Near Infrared Spectroscopy. NIR Publications, Chichester, West Sussex, 2000,535～541
Rabault J L, Downey G. Rapid analysis of oats for moisture and protein by near-infrared reflectance spectroscopy. Journa of Food Science and Technology. 1990,14(2): 85～93
Atanassova S. Ilchev A. Application of near-infrared reflectance spectroscopy for predicting chemical composition of compound pig and poultry feeds. Bulgarian Journal of Agricultural Science. 1997, 3(2): 171～180
Reeves J B, T H Blosser. Near infrared reflectance spectroscopy for analyzing undried silage. J. Dairy Sci. 1989,(72):79～88
Sato-T, Morishita-T, Hara-T. Near-infrared reflectance spectroscopic analysis of moisture, fat, protein, and physiological activity in buckwheat flour for breeding selection. Plant Production Science. 2001, 4:270～277
Starr C A G Morgen & D B Smith. On evaluatuin of near-infiared reflentance analysis in some plant breeding programes. J. Agric. Sci. 1997:107～118
Shenk, J. S. Workman J, & Westerhaus M O. Application of NIR spectroscopy to agricultural products. Handbook of NIR Analysis. Chapter 15:383～431
Theo van Kempen, Jean-Christophe Bodin. Near infrared reflectance spectroscopy(NIRS) appears to be superior to nitrogen-base regression as a prappid tool in predicting the poultry digestible amino acid content of commonly used feedstuff. Animal Feed Science and Technology. 1998(76): 139～147
Villamarin-B, Fernandez-E;,Mendez-J. Analysis of grass silage from Northwestern Spain by near-infrared reflectance spectroscopy. Joumal-of-AOAC-Intemational. 2002, 85(3): 541～545
Wu J G, Shi-ChunHai. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy. Field-Crops-Research. 2002, 75(1): 1～7
Williams P C & K H Norris. Near-infared Technology in the Agriculture and Food Industries. American Association of Cereal Chemists. 1987:330
Williams P C, & Soebering D C. Comparusion of commercial infrared transmitance instruments for analysis of whole graise and seeds. Journal of Near Ifrared Spectroscopy, 1993, 1:25～32
Xiccato G, Trocino A, Boever J.Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology. 2003, 104:153～168
Yibin Ying, Yande Liu. A nondestrctive method for soluble solids content and availiable acidity in apples using FT-NIR Spectroscopy. 2004 CIGR International Conference. Beijing Sponsored by CIGR, CSAM and CSAE, Beijing, Chia, 2004
Yibin Ying, Huanyu Jiang. Prediction of water, Chlorophyll Content of Tomato by Near lnfraed Spectroscopy Analysis. 2004 CIGR International Conference. Beijing Sponsored by CIGR, CSAM and CSAE, Beijing, Chia.2004
陈颖,吴亚均,徐宝梁等.进出口动物源性产品中牛羊成分的检测方法.食品工业科技.2004,(8):144～146
陈邵苹.近红外漫反射光谱测定饲料中粗纤维.饲料工业.1996.017(006).32-33
陈斌,方如明,赵杰文.方便面含油率的近红外漫反射光谱快速测定.农业机械学报,2000,31(5):56～58
陈如明,陈国拄.疯牛病的研究进展.畜牧与兽医.2004,36(2):41～44
关秀琴 崔淑文.应用近红外光谱法测定饲料中的植酸磷含量.仪器仪表与分析监测.1991.(2):35～37
丁丽敏.近红外光谱技术预测饲料中氨基酸和真可消化氨基酸含量的研究.中国农业大学博士学位论文,1997年
丁丽敏 计成等.近红外(NIRS)和粗蛋白预测氨基酸含量的精度比较研究.饲料工业.2002.023(4):15～18
丁丽敏 计成.近红外光谱技术快速测定棉籽粕,菜籽粕真可利用氨基酸含量的研究.动物营养学报.2000.12(1).21～25
丁丽敏 计成.近红外光谱技术快速测定豆粕,玉米真可消化氨基酸含量的研究.动物营养学报.1999.11(3):12～1
丁丽敏 刘向阳.近红外光谱分析技术快速测定鱼粉氨基酸含量的研究.饲料研究.1999.5:24～26.
丁丽敏.近红外光谱技术(NIRS)可实现对氨基酸和可利用氨基酸的快速检测.饲料工业.1998.19(1):41～4
杜荣等等.近红外光谱分析技术在粗饲料质量检测上的应用—饲料用大麦部分.见:中国农业科学院畜牧研究所编.近红外光谱分析技术.北京:中国农业科技出版社.1993:196～203
范世福.近红外光谱分析技术的复兴和发展.仪器分析.1991,89(3):1～8
方彦,王汉宁.近红外光谱法在玉米粗蛋白含量测定研究中的应用.甘肃农业大学学报.2004,39(1)32～35
冯平等.近红外光谱分析技术在麦麸的饲料营养价值评定上的应用.中国畜牧杂质.1986,(2):10～14
冯新泸,史永刚编著.近红外光谱及其在石油产品分析中的应用.北京:中国石化出版社,2002
洪涛.疯牛病的流行与控制.中华预防医学会首届学术年会,济南,2002,8
高军.2003年进口鱼粉市场简析与2004年展望.饲料广角.2004,(2):14～17
高荣强,范世福,严衍禄等.近红外光谱的数据预处理研究.光谱学与光谱分析,2004,24(12):1563～1565
甘利 雅弘,大谷 文博,松本 光人.近赤外分析法化成肥料中含肉骨粉定量.2004年度日本草地学会会誌:442～443
籍保平.近红外光谱技术在农产品加工中的应用.粮油加工与食品机械.2000,(6):31～33
姜名宇.以畜牧业为主导,推进农业现代化的思考.宏观经济研究.2005,(1):54～56
刘晓敏,杨林,陈璇.NIR1501型近红外分析仪在鱼粉质检中的应用.粮食与饲料工业.1996,(9),23～27
刘晓敏,杨林等.NIR1501型近红外分析仪检测饲料的研究.中国饲料.1996.000(016).35～36.
刘继红.氨基酸含量异常与鱼粉质量的关系.饲料博览.2001,(7):29～30
刘继红,余大杰等.氨基酸与肉粉类产品质量评价继掺假分析.饲料博览.2004,(1):42～43
刘建学,吴守一等.大米直链淀粉含量的近红外光谱分析.农业工程学报.2000,16(3):94～96
刘建学,吴守一,方如明.基于近红外光谱的神经网络预测大米直链淀粉含量.农业机械学报.2002,32(02):55～57
李宁,闵顺耕,覃方丽等.近红外光谱法非破坏性测定黄豆籽粒中蛋白质、脂肪含量.光谱学与光谱分析,2004,14(1):45～49
李宏 莫放.应用绵羊粪便的近红外光谱方程评定其日粮的可消化有机物.中国畜牧杂志.2004.40(6):22-24
李瑾.蛋白质与氨基酸营养研究进展.中国饲料.2004,(9):34～35
李晓川,王联珠.鱼粉质量安全的技术保证—国家标准GB/T19164-2003《鱼粉》解析.农业质量标准.2004,(3):23～24
梁逸曾,俞汝勤.化学计量学.北京:高等教育出版社,2003年
芦永军,曲艳玲,朴仁官等.近红外光谱分析技术定标和预测中的相似样品剔除算法.2004,24(2):158～161
陆国权等.近红外光谱分析技术在粗饲料质量检测上的应用—饲料用甘薯茎叶部分.见:中国农业科学院畜牧研究所编.近红外光谱分析技术.北京:中国农业科技出版社.1993:120～124
陆婉珍,袁洪福,徐广通等.现代近红外光谱分析技术.北京:中国石化出版社,2000:167
闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化.光谱学与光谱分析.2004,24(10):1205～1209
闵顺耕,覃方丽,李宁等.傅里叶变换近红外光谱法测定大麦中蛋白质、淀粉和赖氨酸含量.分析化学.2003,31(7):843～845
潘良文,沈禹飞等.肉骨粉中羊源性成分检测方法研究.检验检疫科学.2004,14(2):15～18
全国饲料工作办公室.全国饲料工业统计资料(2003年).2004年5月.138～139
全国饲料工作办公室,中国饲料工业协会编.饲料法规文件与标准.北京:中国农业科学技术出版社,2003:315
舒庆尧,吴殿星,夏英武等.稻米表观直链淀粉含量近红外光谱测定技术校正设置的优化.浙江 大学学报,1999,25(4):343～346
宋洪远,赵长保.我国的饲料安全问题:现状、成因及对策.中国农村经济,2003.11:28～35
苏彩珠,伊平河,肖昕等.近红外透射技术检测肉骨粉中蛋白质地方法及初步应用.粮食与饲料工业.2003,(9):48～49
苏彩珠 梁震 黄宇鹏等.应用近红外透射技术快速检测肉骨粉中脂肪含量.检验检疫科学.2004.14(3):39～41
沈恒胜 陈君琛 种藏文.近红外漫反射光谱法(NIRS)分析稻草纤维及硅化物组成.中国农业科学.2003,36(9):1086～1090
唐绍孟等编著.饲料配制和使用技术.北京:中国农业出版社,2003年.62～63
王邕,黄艳等.从必须氨基酸含量组成看鱼粉的品质.广西大学学报.2004,29(增刊):112～115
王丹红,吴文昕,冯劲华.进口鱼粉品质的NIRS定量分析与验证.检验检疫科学.2004年,14(1):18～21
王文杰,穆淑琴,刘兰兰等.近红外光谱法检测预混料中的维生素A.天津畜牧兽医.1998,15(2):12～14
魏瑞兰,张子仪.近红外光谱分析技术在花生饼粕可利用氨基酸判定中的应用.中国畜牧杂质.1987,(6):3～7
吴坚主编.应用概率统计.北京:高等教育出版社,2003年
吴瑾光.近代傅里叶变换近红外光谱技术及应用.北京:科学技术文献出版社.1994.251～281
吴秀琴.应用51A型测定小麦种子中的赖氨酸含量.农业测试分析.1985,2(1):15
熊智新,方如明,陈斌等.方便面油分近红外漫反射光谱特征波长的确定.江苏理工大学学报.1999,20(3):28～31
许彩萍,任鹏,陈雪秀等.近红外光谱分析技术在粗饲料质量检测上的应用.见:中国农业科学院畜牧研究所编.近红外光谱分析技术.北京:中国农业科技出版社.1993:112～119
徐广通,袁洪福,陆婉珍.近红外光谱定量校正模型适应性研究.光谱学与光谱分析.2001,21(4):459～463
徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展.光谱学与光谱分析.2000,20(2):134～142
徐洁,钱爱萍,陈体强等.利用特征氨基酸组分分析与显微观察鉴别掺假鱼粉.福建农业学报.2004,19(2):121～125
严衍禄主编.近红外光谱分析基础与应用.北京:中国轻工出版社,2005年,pp355
严衍禄,赵龙莲,李军会等.现代近红外光谱分析的信息处理技术.光谱学与光谱分析,2000,(6):777～780
杨曙明,张瑜.近红外光谱分析技术及其在饲料质量分析中的应用.分析测试仪器通讯,1997,7(4):215～220
杨曙明,张瑜.应用近红外光谱分析技术快速分析饲料质量.分析测试学报,1999,18(1):83～87
杨南林,程翼宇,吴永江.中药材三七中皂苷成分的近红外光谱快速无损分析新方法.化学学报,2003,61(3):293～398
伊腾桂.谈谈鱼粉的掺杂及鉴别技术.江西饲料.2004,(1):22～24
赵丽丽,赵龙莲,李军会等.傅里叶变换近红外光谱仪扫描条件对数学模型预测精度的影响.光谱学与光谱分析.2004,24(1):41～44
张璐,郝永忱等.近红外光谱法测定豆粕中常规化学成分的研究.检验检疫科学.2003.013(001).25-27
张录达,严衍禄等.傅里叶变换近红外光谱定量分析数学模型的建立与评价.北京农业大学学报,1990(增):18～25
张丽英主编.饲料分析及饲料质量年技术.北京:中国农业大学出版社,2002年
赵环环 胡跃高 等.近红外光谱分析技术在黑麦草粉粗蛋白测定中的应用.动物营养学 116.报.2001.13(4).40～43
中国饲料工业协会,全国饲料工业标准化委员会.饲料工业标准汇编(2002年).北京:中国标准出版社,2002:70～95
祝诗平,王一鸣,张小超等.近红外光谱建模异常样品剔除准则与方法.农业机械学报,2004,35(4):115～119
祝诗平.近红外光谱品质检测方法研究.中国农业大学博士学位论文.2003年
诸小立,袁洪福,王艳斌等.近红外稳健分析校正模型的建立(Ⅰ)—样品温度的影响.光谱学与光谱分析,2004,24(6):666～671
诸小立,袁洪福等.在线近红外光谱过程分析技术及其应用.现代科学仪器.2004,(2):3～21
朱尔一,杨凡原.化学计量学技术及应用.北京:科学出版社,2001,92～96