title: Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools.
abstract: Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486 g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.
authors.affiliation: School of Food & Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, PR China. q.s.chen@hotmail.com
authors.forename: Quansheng
authors.initials: Q
authors.lastname: Chen
authors.forename: Jiao
authors.initials: J
authors.lastname: Ding
authors.forename: Jianrong
authors.initials: J
authors.lastname: Cai
authors.forename: Jiewen
authors.initials: J
authors.lastname: Zhao
mesh_terms: D019342:Acetic Acid / Q000737:chemistry*; D000143:Acids / Q000032:analysis*; D000465:Algorithms; D016571:Neural Networks, Computer; D012044:Regression Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D000143:Acids; D019342:Acetic Acid