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Liu B, Zhang H, Zhu J, Chen Y, Pan Y, Gong X, Yan J, Zhang H. Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model. SENSORS (BASEL, SWITZERLAND) 2024; 24:3457. [PMID: 38894248 PMCID: PMC11174722 DOI: 10.3390/s24113457] [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: 03/21/2024] [Revised: 04/21/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024]
Abstract
Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red ginseng, it is susceptible to grow mold and produce mycotoxins, generating security issues. This study proposes a novel approach using hyperspectral imaging technology and a 1D-convolutional neural network-residual-bidirectional-long short-term memory attention mechanism (1DCNN-ResBiLSTM-Attention) for pixel-level mycotoxin recognition in red ginseng. The "Red Ginseng-Mycotoxin" (R-M) dataset is established, and optimal parameters for 1D-CNN, residual bidirectional long short-term memory (ResBiLSTM), and 1DCNN-ResBiLSTM-Attention models are determined. The models achieved testing accuracies of 98.75%, 99.03%, and 99.17%, respectively. To simulate real detection scenarios with potential interfering impurities during the sampling process, a "Red Ginseng-Mycotoxin-Interfering Impurities" (R-M-I) dataset was created. The testing accuracy of the 1DCNN-ResBiLSTM-Attention model reached 96.39%, and it successfully predicted pixel-wise classification for other unknown samples. This study introduces a novel method for real-time mycotoxin monitoring in traditional Chinese medicine, with important implications for the on-site quality control of herbal materials.
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Affiliation(s)
- Biao Liu
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
| | - Hongxu Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
| | - Jieqiang Zhu
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
| | - Yuan Chen
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
| | - Yixia Pan
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
| | - Xingchu Gong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China;
| | - Jizhong Yan
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
| | - Hui Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China; (B.L.); (H.Z.); (J.Z.); (Y.C.); (Y.P.)
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Wang Z, An T, Wang W, Fan S, Chen L, Tian X. Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122679. [PMID: 37011441 DOI: 10.1016/j.saa.2023.122679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (Rp) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.
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Affiliation(s)
- Zheli Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ting An
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenchao Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Liping Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xi Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review. Processes (Basel) 2023. [DOI: 10.3390/pr11030651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
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Kumar P, Gupta A, Mahato DK, Pandhi S, Pandey AK, Kargwal R, Mishra S, Suhag R, Sharma N, Saurabh V, Paul V, Kumar M, Selvakumar R, Gamlath S, Kamle M, Enshasy HAE, Mokhtar JA, Harakeh S. Aflatoxins in Cereals and Cereal-Based Products: Occurrence, Toxicity, Impact on Human Health, and Their Detoxification and Management Strategies. Toxins (Basel) 2022; 14:toxins14100687. [PMID: 36287956 PMCID: PMC9609140 DOI: 10.3390/toxins14100687] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/08/2022] Open
Abstract
Cereals and cereal-based products are primary sources of nutrition across the world. However, contamination of these foods with aflatoxins (AFs), secondary metabolites produced by several fungal species, has raised serious concerns. AF generation in innate substrates is influenced by several parameters, including the substrate type, fungus species, moisture content, minerals, humidity, temperature, and physical injury to the kernels. Consumption of AF-contaminated cereals and cereal-based products can lead to both acute and chronic health issues related to physical and mental maturity, reproduction, and the nervous system. Therefore, the precise detection methods, detoxification, and management strategies of AFs in cereal and cereal-based products are crucial for food safety as well as consumer health. Hence, this review provides a brief overview of the occurrence, chemical characteristics, biosynthetic processes, health hazards, and detection techniques of AFs, along with a focus on detoxification and management strategies that could be implemented for food safety and security.
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Affiliation(s)
- Pradeep Kumar
- Department of Botany, University of Lucknow, Lucknow 226007, India
- Applied Microbiology Laboratory, Department of Forestry, North Eastern Regional Institute of Science and Technology, Nirjuli 791109, India
- Correspondence: (P.K.); (D.K.M.)
| | - Akansha Gupta
- Department of Dairy Science and Food Technology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
- CASS Food Research Centre, School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC 3125, Australia
| | - Dipendra Kumar Mahato
- CASS Food Research Centre, School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC 3125, Australia
- Correspondence: (P.K.); (D.K.M.)
| | - Shikha Pandhi
- Department of Dairy Science and Food Technology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Arun Kumar Pandey
- MMICT&BM(HM), Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala 133207, India
| | - Raveena Kargwal
- Department of Processing and Food Engineering, College of Agricultural Engineering and Technology, Chaudhary Charan Singh Haryana Agricultural University, Hisar 125004, India
| | - Sadhna Mishra
- Department of Dairy Science and Food Technology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
- Faculty of Agricultural Sciences, GLA University, Mathura 281406, India
| | - Rajat Suhag
- Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, 39100 Bolzano, Italy
| | - Nitya Sharma
- Food and Bioprocess Engineering Laboratory, Centre for Rural Development and Technology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Vivek Saurabh
- Division of Food Science and Postharvest Technology, ICAR—Indian Agricultural Research Institute, New Delhi 110012, India
| | - Veena Paul
- Department of Dairy Science and Food Technology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR—Central Institute for Research on Cotton Technology, Mumbai 400019, India
| | - Raman Selvakumar
- Centre for Protected Cultivation Technology, ICAR-Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India
| | - Shirani Gamlath
- CASS Food Research Centre, School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC 3125, Australia
| | - Madhu Kamle
- Applied Microbiology Laboratory, Department of Forestry, North Eastern Regional Institute of Science and Technology, Nirjuli 791109, India
| | - Hesham Ali El Enshasy
- Institute of Bioproduct Development, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia
- City of Scientific Research and Technology Applications, New Burg Al Arab, Alexandria 21934, Egypt
| | - Jawahir A. Mokhtar
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah 21589, Saudi Arabia
- Vaccines and Immunotherapy Unit, King Fahad Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Steve Harakeh
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Yousef Abdul Latif Jameel Scientific Chair of Prophetic Medicine Application, Faculty of Medicine (FM), King Abdulaziz University, Jeddah 21589, Saudi Arabia
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