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Dong X, Dong Y, Liu J, Wang C, Bao C, Wang N, Zhao X, Chen Z. Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 323:124938. [PMID: 39126863 DOI: 10.1016/j.saa.2024.124938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/07/2024] [Accepted: 08/04/2024] [Indexed: 08/12/2024]
Abstract
As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.
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Affiliation(s)
- Xinyi Dong
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Ying Dong
- Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China; Huangpu Customs Technology Center, Sanyuan Road 66, Dongguan 523000, China
| | - Jinming Liu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China; Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China.
| | - Chunqi Wang
- College of Food, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163319, China
| | - Changhao Bao
- College of Economics and Management, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163319, China
| | - Na Wang
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Xiaoyu Zhao
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Zhengguang Chen
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
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Li Q, Zhou W, Zhang X, Li H, Li M, Liang H. Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2024; 15:1334961. [PMID: 38332766 PMCID: PMC10850333 DOI: 10.3389/fpls.2024.1334961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024]
Abstract
Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.
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Affiliation(s)
- Qingxu Li
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
- Institute of Cotton Engineering, Anhui University of Finance & Economics, Bengbu, China
| | - Wanhuai Zhou
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
- Institute of Cotton Engineering, Anhui University of Finance & Economics, Bengbu, China
| | - Xuedong Zhang
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
| | - Hao Li
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
| | - Mingjie Li
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
| | - Houjun Liang
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
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Ma XH, Chen ZG, Liu JM. Wavelength selection method for near-infrared spectroscopy based on Max-Relevance Min-Redundancy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123933. [PMID: 38309007 DOI: 10.1016/j.saa.2024.123933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/13/2024] [Accepted: 01/19/2024] [Indexed: 02/05/2024]
Abstract
Near-infrared spectroscopy (NIRS) is a rapid, nondestructive analytical technique utilized in various fields. However, the NIR data, which consists of hundreds of dimensions, may exhibit considerable duplication in the spectrum information. This redundancy might impair modeling effectiveness. As a result, feature selection on the spectral data becomes critical. The Max-Relevance Min-Redundancy (mRMR) method stands out among the different feature selection techniques for dimensional reduction. The approach depends on mutual information (MI) between random variables as the basis for feature selection and is unaffected by modeling methods. However, it is necessary to clarify the benefits of the maximum correlation minimal redundancy algorithm in the context of near-infrared spectral feature selection, as well as its adaptability to various modeling methods. This research focuses on the NIR spectral dataset of maize germination rate, and the mRMR method is utilized to select spectral features. Based on the preceding foundation, we create models for Support Vector Regression, Gaussian Process Regression, Random Forest, and Neural Networks. The experimental findings demonstrate that, among the feature selection methods employed in this paper, the Max-Relevance Min-Redundancy algorithm outperforms others regarding the corn germination rate dataset.
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Affiliation(s)
- Xiao-Hui Ma
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Zheng-Guang Chen
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
| | - Jin-Ming Liu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
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Yang CB, Cai ZL, Li QZ, Tang F, Wu JJ, Yang J, Zhang YR, Li B, Yang P, Ye X, Yang LM. Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index. JOURNAL OF BIOPHOTONICS 2024; 17:e202300323. [PMID: 37769060 DOI: 10.1002/jbio.202300323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/23/2023] [Accepted: 09/27/2023] [Indexed: 09/30/2023]
Abstract
To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.
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Affiliation(s)
- Cheng-Bo Yang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | | | - Qing-Zhi Li
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Feng Tang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Jing-Jun Wu
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Jia Yang
- Sichuan Science City Hospital, Mianyang, China
| | | | - Bo Li
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Ping Yang
- Sichuan Science City Hospital, Mianyang, China
| | - Xin Ye
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Li-Ming Yang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
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Pan L, Li H, Zhao J. Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123075. [PMID: 37423101 DOI: 10.1016/j.saa.2023.123075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/11/2023]
Abstract
Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation.
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Affiliation(s)
- Liulei Pan
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
| | - Hao Li
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
| | - Juan Zhao
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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