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Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine

Ji-Yong, Shi ; Xiao-Bo, Zou ; Xiao-Wei, Huang ; Jie-Wen, Zhao ; Yanxiao, Li ; Limin, Hao ; Jianchun, Zhang

Food Chemistry, 1 May 2013, Vol.138(1), pp.192-199 [Tạp chí có phản biện]

ISSN: 0308-8146 ; DOI: 10.1016/j.foodchem.2012.10.060

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  • Nhan đề:
    Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine
  • Tác giả: Ji-Yong, Shi ; Xiao-Bo, Zou ; Xiao-Wei, Huang ; Jie-Wen, Zhao ; Yanxiao, Li ; Limin, Hao ; Jianchun, Zhang
  • Chủ đề: Near Infrared Spectroscopy ; Least-Squares Support Vector Machine ; Vinegar ; Total Acid Content ; Principle Component Analysis ; Back Propagation Artificial Neural Network ; Partial Least-Square
  • Là 1 phần của: Food Chemistry, 1 May 2013, Vol.138(1), pp.192-199
  • Mô tả: Highlights► NIR coupled with LS-SVM was used to detection quality of 95 Chinese vinegars. ► White vinegar and fruit vinegar were separated from traditional vinegar categories by PCA. ► LS-SVM was firstly applied to identify mature vinegar, aromatic vinegar and ric vinegar. ► LS-SVM also be used to predication total acid content in all vinegar samples. More than 3.2millionlitres 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 (Rp) 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.
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 0308-8146 ; DOI: 10.1016/j.foodchem.2012.10.060

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