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Chen M, Song J, He H, Yu Y, Wang R, Huang Y, Li Z. Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics. Foods 2024; 13:3241. [PMID: 39456303 DOI: 10.3390/foods13203241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2024] [Revised: 10/07/2024] [Accepted: 10/10/2024] [Indexed: 10/28/2024] Open
Abstract
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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Affiliation(s)
- Mengting Chen
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Jiahui Song
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Haiyan He
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Ruoni Wang
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yue Huang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
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Jin C, Zhou X, He M, Li C, Cai Z, Zhou L, Qi H, Zhang C. A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:8332-8342. [PMID: 39031773 DOI: 10.1002/jsfa.13668] [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: 03/21/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 07/22/2024]
Abstract
BACKGROUND Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. RESULTS This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. CONCLUSION The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Chen Jin
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Xinyue Zhou
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Mengyu He
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Cheng Li
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Zeyi Cai
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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Kang Z, Fan R, Zhan C, Wu Y, Lin Y, Li K, Qing R, Xu L. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning. Molecules 2024; 29:682. [PMID: 38338424 PMCID: PMC10856461 DOI: 10.3390/molecules29030682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
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Affiliation(s)
- Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rongsheng Fan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Chunyi Zhan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Youli Wu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Yi Lin
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Kunyu Li
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rui Qing
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:8655. [PMID: 36433249 PMCID: PMC9697730 DOI: 10.3390/s22228655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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Lu X, Ge H, Jiang Y, Zhang Y. A Dual-Band High-Sensitivity THz Metamaterial Sensor Based on Split Metal Stacking Ring. BIOSENSORS 2022; 12:bios12070471. [PMID: 35884274 PMCID: PMC9313385 DOI: 10.3390/bios12070471] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/24/2022] [Accepted: 06/26/2022] [Indexed: 11/22/2022]
Abstract
Terahertz (THz)-detection technology has been proven to be an effective and rapid non-destructive detection approach in biomedicine, quality control, and safety inspection, among other applications. However, the sensitivity of such a detection method is limited due to the insufficient power of the terahertz source and the low content, or ambiguous characteristics, of the analytes to be measured. Metamaterial (MM) is an artificial structure in which periodic sub-wavelength units are arranged in a regular manner, resulting in extraordinary characteristics beyond those possessed by natural materials. It is an effective method to improve the ability of terahertz spectroscopy detection by utilizing the metamaterial as a sensor. In this paper, a dual-band, high-sensitivity THz MM sensor based on the split metal stacking ring resonator (SMSRR) is proposed. The appliance exhibited two resonances at 0.97 and 2.88 THz in the range of 0.1 to 3 THz, realizing multi-point matching between the resonance frequency and the characteristic frequency of the analytes, which was able to improve the reliability and detection sensitivity of the system. The proposed sensor has good sensing performance at both resonant frequencies and can achieve highest sensitivities of 304 GHz/RIU and 912 GHz/RIU with an appropriate thickness of the analyte. Meanwhile, the advantage of multi-point matching of the proposed sensor has been validated by distinguishing four edible oils based on their different refractive indices and demonstrating that the characteristics obtained in different resonant frequency bands are consistent. This work serves as a foundation for future research on band extension and multi-point feature matching in terahertz detection, potentially paving the way for the development of high-sensitivity THz MM sensors.
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Affiliation(s)
- Xuejing Lu
- PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China;
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.G.); (Y.J.)
| | - Hongyi Ge
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.G.); (Y.J.)
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Yuying Jiang
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.G.); (Y.J.)
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Yuan Zhang
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
- Correspondence:
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