title: Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice.
abstract: Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that NIR spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Li-juan
authors.initials: LJ
authors.lastname: Xie
authors.forename: Yi-bin
authors.initials: YB
authors.lastname: Ying
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001628:Beverages / Q000032:analysis*; D005504:Food Analysis / Q000379:methods*; D005506:Food Contamination / Q000032:analysis*; D005638:Fruit / Q000737:chemistry*; D016018:Least-Squares Analysis; D018551:Solanum lycopersicum / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*