The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

Song, Jiahui; Yu, Yue; Wang, Ruoni; Chen, Menting; Li, Zhanming; He, Xueming; Ren, Zhongyang; Dong, Hao

doi:10.1016/j.microc.2024.110032

Microchemical Journal

2024

DOI: 10.1016/j.microc.2024.110032

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The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

Jiahui Song,

Yue Yu,

Ruoni Wang

et al.

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Cited by 3 publications

References 35 publications

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Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

Yu,

Chai,

et al. 2025

Food Chemistry

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Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

Yu,

Chai,

et al. 2025

Food Chemistry

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A method of maize seed variety identification based on near-infrared spectroscopy combined with improved DenseNet model

Zhou,

Guan,

et al. 2024

Microchemical Journal

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Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics

Chen,

Song,

et al. 2024

Foods

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Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models’ versatility and practical applicability.

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The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

Cited by 3 publications

References 35 publications

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

A method of maize seed variety identification based on near-infrared spectroscopy combined with improved DenseNet model

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics

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