Purity analysis of multi-grain rice seeds with non-destructive visible and near-infrared spectroscopy

Zhang, Jing; Li, Mingliang; Pan, Tao; Yao, Ling; Chen, Jiemei

doi:10.1016/j.compag.2019.104882

Cited by 29 publications

(12 citation statements)

References 29 publications

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“…The standard normal variate (SNV) is a mathematical transformation method of preprocessing the spectrum to dispel or attenuate the influence of sample differences in the sampling procedure, as well as changes in dispersion and optical distance during sampling 17,18 . First, before data processing, SNV is used to clear up the effects of solid particle size, surface scattering, and light range variations on VNIRS data.…”

Section: Methodsmentioning

confidence: 99%

“…Variable screening of VNIRS data The standard normal variate (SNV) is a mathematical transformation method of preprocessing the spectrum to dispel or attenuate the influence of sample differences in the sampling procedure, as well as changes in dispersion and optical distance during sampling. 17,18 First, before data processing, SNV is used to clear up the effects of solid particle size, surface scattering, and light range variations on VNIRS data. Partial least squares (PLS) is a multivariate regression modeling method for multiple independent variables, which can better solve many problems that could not be solved by ordinary multiple regression in the past.…”

Section: Wileyonlinelibrarycom/jsfamentioning

confidence: 99%

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Application of colorimetric sensor array coupled with machine‐learning approaches for the discrimination of grains based on freshness

et al. 2023

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BackgroundVolatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible–near‐infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine‐learning‐based models – for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm – were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K‐nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies.ResultsCompared with the pattern recognition results of image processing, visible–near‐infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples.ConclusionThe method developed could be used for non‐destructive detection of grain freshness. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

Section: Wileyonlinelibrarycom/jsfamentioning

confidence: 99%

Application of colorimetric sensor array coupled with machine‐learning approaches for the discrimination of grains based on freshness

et al. 2023

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“…Spectroscopy provides a fast and nondestructive method for food detection, and both the quality detection and classification identification of rice have been studied using spectral analyses. Zhang et al 7 developed a method for purity analysis of multi-grain rice seeds using visible and near infrared spectroscopy. Maneenuam 8 used Fourier transform infrared spectroscopy to successfully determine the content of 2-acetyl-1-pyrroline (2AP) in red Mali rice.…”

Section: Introductionmentioning

confidence: 99%

Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin

et al. 2022

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“…There are differences in the element accumulation and organic component content of different rice seeds, which became the basis for analyzing the authenticity of hybrid rice seeds using LIBS and NIRS. Zhang et al 22 used visible and NIR light and successfully distinguished the authenticity of the “Y Liangyou 900” rice variety. Michael et al 23 used LIBS to discriminate brown‐rice from different origins.…”

Section: Introductionmentioning

confidence: 99%

An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS

Wang

Zhang

et al. 2022

Micro & Optical Tech Letters

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This study proposes a method to rapidly determine the authenticity of hybrid rice seeds using the fusion of laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIRS). The male parent, F1 generation, and female parent of two hybrid rice vaieties ("Zhongheyou 1" and "Liangyou 688") were identified by the proposed method. The LIBS and NIR spectra of rice seeds with different physical state (single rice kernels [SRK] and single brown-rice kernels [SBRK]) were measured, and the primary fusion and intermediate fusion were used for the construction of the LIBS and NIRS data fusion models. The comparation results under different conditions show that the intermediate SRK and SBRK fusion models achieve the best test accuracy (96.88% and 98.52% for "Zhongheyou 1", and 98.96% and 95.26% for "Liangyou 688", respectively). The results indicate that the proposed method is effective for rapid and accurate identification of parents and progenies of hybrid rice.

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Purity analysis of multi-grain rice seeds with non-destructive visible and near-infrared spectroscopy

Cited by 29 publications

References 29 publications

Application of colorimetric sensor array coupled with machine‐learning approaches for the discrimination of grains based on freshness

Application of colorimetric sensor array coupled with machine‐learning approaches for the discrimination of grains based on freshness

Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin

An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS

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