Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion

In this study, a rapid and non-destructive method was proposed for the quality discrimination of rice at different stages during the early mildew process. The change in volatile organic compounds (VOCs) during the mildew process was analyzed using headspace solid-phase microextraction-gas chromatogr...

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Main Author: LI Haiyu, WEI Ziyu, CHEN Tong, HUANG Guangwei, HU Yongzhi, MENG Hecheng
Format: Article
Language:English
Published: China Food Publishing Company 2025-05-01
Series:Shipin Kexue
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Online Access:https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-9-032.pdf
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author LI Haiyu, WEI Ziyu, CHEN Tong, HUANG Guangwei, HU Yongzhi, MENG Hecheng
author_facet LI Haiyu, WEI Ziyu, CHEN Tong, HUANG Guangwei, HU Yongzhi, MENG Hecheng
author_sort LI Haiyu, WEI Ziyu, CHEN Tong, HUANG Guangwei, HU Yongzhi, MENG Hecheng
collection DOAJ
description In this study, a rapid and non-destructive method was proposed for the quality discrimination of rice at different stages during the early mildew process. The change in volatile organic compounds (VOCs) during the mildew process was analyzed using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Meanwhile, Fourier transform infrared spectroscopy (FTIR) was employed to monitor the structural change of starch. Following feature variable selection, a feature-level fusion method was applied for data fusion of GC-MS and FTIR. Partial least square-discriminant analysis (OPLS-DA) was used to establish a discriminant model for determining the quality of rice during the early mildew process. Cluster analysis performed on physicochemical indicators categorized the mildew process into three stages. Based on total mold counts, rice became moldy on the 22th day. The model developed through feature variable fusion of GC-MS and FTIR data proved to be the most effective in differentiating the quality of rice at different stages of mildew, which was successfully clustered into 7 stages. The model demonstrated a goodness of fit (R2 = 0.95) and a goodness of prediction (Q2 = 0.86). In conclusion, data fusion of GC-MS and FTIR could accurately discriminate the quality of rice during the early mildew process, thereby providing the basis for the rapid and non-destructive inspection of rice quality during the early mildew process.
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publishDate 2025-05-01
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spelling doaj-art-c13a4d8ee8014b1fbb750516a7d38dca2025-08-20T02:41:11ZengChina Food Publishing CompanyShipin Kexue1002-66302025-05-0146931432110.7506/spkx1002-6630-20241111-071Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information FusionLI Haiyu, WEI Ziyu, CHEN Tong, HUANG Guangwei, HU Yongzhi, MENG Hecheng0(1. Guangxi Liuzhou Luosifen Center of Technology Innovation, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China; 2. Liuzhou Liangmianzhen Co. Ltd., Liuzhou 545006, China; 3. School of Food Science and Engineering, South China University of Technology, Guangzhou 510006, China)In this study, a rapid and non-destructive method was proposed for the quality discrimination of rice at different stages during the early mildew process. The change in volatile organic compounds (VOCs) during the mildew process was analyzed using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Meanwhile, Fourier transform infrared spectroscopy (FTIR) was employed to monitor the structural change of starch. Following feature variable selection, a feature-level fusion method was applied for data fusion of GC-MS and FTIR. Partial least square-discriminant analysis (OPLS-DA) was used to establish a discriminant model for determining the quality of rice during the early mildew process. Cluster analysis performed on physicochemical indicators categorized the mildew process into three stages. Based on total mold counts, rice became moldy on the 22th day. The model developed through feature variable fusion of GC-MS and FTIR data proved to be the most effective in differentiating the quality of rice at different stages of mildew, which was successfully clustered into 7 stages. The model demonstrated a goodness of fit (R2 = 0.95) and a goodness of prediction (Q2 = 0.86). In conclusion, data fusion of GC-MS and FTIR could accurately discriminate the quality of rice during the early mildew process, thereby providing the basis for the rapid and non-destructive inspection of rice quality during the early mildew process.https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-9-032.pdfrice aroma; fourier transform infrared spectroscopy; chemometrics; gas chromatography-mass spectrometry; stage quality; information fusion
spellingShingle LI Haiyu, WEI Ziyu, CHEN Tong, HUANG Guangwei, HU Yongzhi, MENG Hecheng
Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion
Shipin Kexue
rice aroma; fourier transform infrared spectroscopy; chemometrics; gas chromatography-mass spectrometry; stage quality; information fusion
title Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion
title_full Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion
title_fullStr Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion
title_full_unstemmed Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion
title_short Rapid Quality Discrimination of Rice during the Mildew Process Based on Multi-Source Information Fusion
title_sort rapid quality discrimination of rice during the mildew process based on multi source information fusion
topic rice aroma; fourier transform infrared spectroscopy; chemometrics; gas chromatography-mass spectrometry; stage quality; information fusion
url https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-9-032.pdf
work_keys_str_mv AT lihaiyuweiziyuchentonghuangguangweihuyongzhimenghecheng rapidqualitydiscriminationofriceduringthemildewprocessbasedonmultisourceinformationfusion