title: Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine.
abstract: More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ=0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (R(p)) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar.
authors.affiliation: School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013 Zhenjiang, Jiangsu, China.
authors.forename: Shi
authors.initials: S
authors.lastname: Ji-yong
authors.forename: Zou
authors.initials: Z
authors.lastname: Xiao-bo
authors.forename: Huang
authors.initials: H
authors.lastname: Xiao-wei
authors.forename: Zhao
authors.initials: Z
authors.lastname: Jie-wen
authors.forename: Li
authors.initials: L
authors.lastname: Yanxiao
authors.forename: Hao
authors.initials: H
authors.lastname: Limin
authors.forename: Zhang
authors.initials: Z
authors.lastname: Jianchun
mesh_terms: D019342:Acetic Acid / Q000737:chemistry*; D000143:Acids / Q000032:analysis*; D002681:China; D016018:Least-Squares Analysis; D025341:Principal Component Analysis; D011786:Quality Control; D019265:Spectroscopy, Near-Infrared / Q000295:instrumentation / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D000143:Acids; D019342:Acetic Acid