title: Hot topic: application of support vector machine method in prediction of alfalfa protein fractions by near infrared reflectance spectroscopy.
abstract: The object of this study was to explore the potential for support vector machine (SVM) to improve the precision of predicting protein fractions by near infrared reflectance spectroscopy (NIRS). Generally, most protein fractions determined in Cornell Net Carbohydrate and Protein System (CNCPS), especially the neutral detergent insoluble protein (NDFCP) and acid detergent insoluble protein (ADFCP), could not be accurately predicted by the commonly used partial least squares (PLS) method. A recently developed chemometric method, SVM, was applied in NIRS prediction of alfalfa protein fractions in this study. Two hundred thirty alfalfa samples were scanned on a near infrared reflectance spectrophotometer, and analyzed for crude protein (CP), true protein precipitated in tungstic acid (TCP), borate-phosphate buffer-insoluble protein (BICP), NDFCP, and ADFCP. These 5 laboratory proteins and the CNCPS protein fractions A, B1, B2, B3, and C were predicted by NIRS using the PLS and SVM methods. According to PLS-NIRS regression, CP, TCP, BICP, A, and B2 obtained the determination coefficient of prediction (R(p)(2)) of 0.96, 0.91, 0.94, 0.94, and 0.93, and the ratios of standard deviation of prediction samples: standard error of prediction samples (RPD) values were 5.07, 3.31, 3.98, 3.96, and 3.91. Neutral detergent insoluble protein, ADFCP (fraction C), B1, and B3 were predicted with R(p)(2) of 0.75, 0.83, 0.30, and 0.62, and RPD values of 1.98, 2.42, 1.20, and 1.62; Calibrated by the SVM-NIRS method, R(p)(2) values of CP, TCP, BICP, NDFCP, ADFCP(C), A, and B2 achieved 0.99, 0.97, 0.97, 0.90, 0.93, 0.97, and 0.97, respectively. The RPD values of those fractions were 8.68, 8.26, 6.11, 3.08, 3.69, 5.97, and 5.81, respectively. The R(p)(2) and RPD values of fractions B1 and B3 were 2.67 and 0.87 (B1) and 2.51 and 0.75 (B3) directly predicted by SVM-NIRS model. In this study, the chemical analysis results of B1 and B3 were also correlated with calculated results from TCP-BICP and NDFCP-ADFCP, which were predicted by SVM-NIRS models. The B1 protein fraction achieved R(p)(2) and RPD values of 0.87 and 3.61, whereas values for B2 were 0.75 and 2.00. Data suggested that use of SVM methods in NIRS technology could improve the accuracy of predicting protein fractions. This study showed the potential of increasing the NIRS prediction accuracy to a level of practical use for all protein fractions, except B3.
authors.affiliation: Department of Grassland Science, College of Animal Science and Technology, China Agricultural University, Beijing 10094, China.
authors.forename: Z
authors.initials: Z
authors.lastname: Nie
authors.forename: J
authors.initials: J
authors.lastname: Han
authors.forename: T
authors.initials: T
authors.lastname: Liu
authors.forename: X
authors.initials: X
authors.lastname: Liu
mesh_terms: D000821:Animal Feed / Q000032:analysis*; D000824:Animal Nutritional Physiological Phenomena; D000818:Animals; D002417:Cattle; D004044:Dietary Proteins / Q000032:analysis*; D000455:Medicago sativa / Q000737:chemistry*; D009753:Nutritive Value; D010940:Plant Proteins / Q000032:analysis*; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012995:Solubility; D019265:Spectroscopy, Near-Infrared / Q000379:methods / Q000662:veterinary*
chemical_list: D004044:Dietary Proteins; D010940:Plant Proteins