Geographical traceability of Boletaceae mushrooms using data fusion of FT-IR, UV, and ICP-AES combined with SVM. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Yao, Sen; Li, Jieqing; Li, Tao; Duan, Zhili; Wang, Yuanzhong
• Is Part Of: International journal of food properties. Volume 22:Issue 1(2019)
• Page Start: 414
• Page End: 426
• Publication Date: 2019-01-01
• Subjects: Geographical traceability -- data fusion -- Boletaceae mushrooms -- principal components analysis (PCA) -- support vector machine (SVM); Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664.0705
• DOI: 10.1080/10942912.2019.1588299
• Languages: English
• ISSNs: 1094-2912

Abstract

ABSTRACT: Geographical traceability is important to consumer protection and quality control of edible mushrooms. In this work, Fourier transform infrared (FT-IR) spectroscopy, ultraviolet (UV) spectroscopy, and inductively coupled plasma-atomic emission spectrometry were used for traceability of 312 mushroom samples from eight different geographical origins in combination with multivariate statistical analysis. Initially, FT-IR, UV spectra, and 14 elements of 312 samples obtained from 8 geographical origins were analyzed, respectively. Meanwhile, the principal components of three techniques were extracted by principal components analysis for data fusion. Finally, classification models were established in the basis of UV, FT-IR, elements, and fusion datasets combined with support vector machine (SVM). Compared with individual technology, data fusion of multi-technique can obviously promote the classification performance in SVM models for geographical origins traceability. Especially, the accuracy of prediction in SVM model by data fusion of three instruments was 99.04%, which was higher than single technique and data fusion of two spectroscopies techniques. This result indicated that data fusion strategy combined with SVM can provide high synergic effect for geographical origins traceability of Boletaceae mushrooms. The more information is fused, the better performance of the model is. This method may be applied for quality control and evaluation of analogous food.