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Yang G, Li B, Chen K, Du M, Zalán Z, Hegyi F, Kan J. Isolation and evaluation of probiotics from traditional Chinese foods for aflatoxin B 1 detoxification: Geotrichum candidum XG1 (yeast) and mechanistic insights. Food Chem 2024; 452:139541. [PMID: 38718457 DOI: 10.1016/j.foodchem.2024.139541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/11/2024] [Accepted: 05/01/2024] [Indexed: 06/01/2024]
Abstract
Identifying aflatoxin-detoxifying probiotics remains a significant challenge in mitigating the risks associated with aflatoxin contamination in crops. Biological detoxification is a popular technique that reduces mycotoxin hazards and garners consumer acceptance. Through multiple rounds of screening and validation tests, Geotrichum candidum XG1 demonstrated the ability to degrade aflatoxin B1 (AFB1) by 99-100%, exceeding the capabilities of mere adsorption mechanisms. Notably, the degradation efficiency was demonstrably influenced by the presence of copper and iron ions in the liquid medium, suggesting a potential role for proteases in the degradation process. Subsequent validation experiments with red pepper revealed an 83% reduction in AFB1 levels following fermentation with G. candidum XG1. Furthermore, mass spectrometry analysis confirmed the disruption of the AFB1 furan ring structure, leading to a subsequent reduction in its toxicity. Collectively, these findings establish G. candidum XG1 as a promising candidate for effective aflatoxin degradation, with potential applications within the food industry.
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Affiliation(s)
- Gang Yang
- College of Food Science, Southwest University, 2 Tiansheng Road, Beibei, Chongqing 400715, PR China; Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Chongqing 400715, PR China
| | - Bin Li
- College of Food Science, Southwest University, 2 Tiansheng Road, Beibei, Chongqing 400715, PR China; Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Chongqing 400715, PR China
| | - Kewei Chen
- College of Food Science, Southwest University, 2 Tiansheng Road, Beibei, Chongqing 400715, PR China; Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Chongqing 400715, PR China; Laboratory of Quality & Safety Risk Assessment for Agro-products on Storage and Preservation (Chongqing), Ministry of Agriculture, Chongqing 400715, PR China
| | - Muying Du
- College of Food Science, Southwest University, 2 Tiansheng Road, Beibei, Chongqing 400715, PR China; Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Chongqing 400715, PR China; Laboratory of Quality & Safety Risk Assessment for Agro-products on Storage and Preservation (Chongqing), Ministry of Agriculture, Chongqing 400715, PR China
| | - Zsolt Zalán
- Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Food Science and Technology Institute, Hungarian University of Agriculture and Life Sciences, Buda Campus, Herman Ottó str. 15, Budapest 1022, Hungary.
| | - Ferenc Hegyi
- Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Food Science and Technology Institute, Hungarian University of Agriculture and Life Sciences, Buda Campus, Herman Ottó str. 15, Budapest 1022, Hungary.
| | - Jianquan Kan
- College of Food Science, Southwest University, 2 Tiansheng Road, Beibei, Chongqing 400715, PR China; Chinese-Hungarian Cooperative Research Centre for Food Science, Chongqing 400715, PR China; Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Chongqing 400715, PR China; Laboratory of Quality & Safety Risk Assessment for Agro-products on Storage and Preservation (Chongqing), Ministry of Agriculture, Chongqing 400715, PR China.
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Hamad R, Chakraborty SK. A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging. Sci Rep 2024; 14:15643. [PMID: 38977722 PMCID: PMC11231289 DOI: 10.1038/s41598-024-63073-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/24/2024] [Indexed: 07/10/2024] Open
Abstract
The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.
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Affiliation(s)
- Rajendra Hamad
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Beraisa Road, Nabibagh, Bhopal, 462038, India
| | - Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Beraisa Road, Nabibagh, Bhopal, 462038, India.
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Lu Y, Jia B, Yoon SC, Ni X, Zhuang H, Guo B, Gold SE, Fountain JC, Glenn AE, Lawrence KC, Zhang F, Wang W, Lu J, Wei C, Jiang H, Luo J. Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology. Int J Food Microbiol 2024; 416:110661. [PMID: 38457888 DOI: 10.1016/j.ijfoodmicro.2024.110661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/07/2024] [Accepted: 03/03/2024] [Indexed: 03/10/2024]
Abstract
Aspergillus flavus and its toxic metabolites-aflatoxins infect and contaminate maize kernels, posing a threat to grain safety and human health. Due to the complexity of microbial growth and metabolic processes, dynamic mechanisms among fungal growth, nutrient depletion of maize kernels and aflatoxin production is still unclear. In this study, visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) combined with the scanning electron microscope (SEM) was used to elucidate the critical organismal interaction at kernel (macro-) and microscopic levels. As kernel damage is the main entrance for fungal invasion, maize kernels with gradually aggravated damages from intact to pierced to halved kernels with A. flavus were cultured for 0-120 h. The spectral fingerprints of the A. flavus-maize kernel complex over time were analyzed with principal components analysis (PCA) of hyperspectral images, where the pseudo-color score maps and the loading plots of the first three PCs were used to investigate the dynamic process of fungal infection and to capture the subtle changes in the complex with different hardness of the maize matrix. The dynamic growth process of A. flavus and the interactions of fungus-maize complexes were explained on a microscopic level using SEM. Specifically, fungus morphology, e.g., hyphae, conidia, and conidiophore (stipe) was accurately captured on the microscopic level, and the interaction process between A. flavus and nutrient loss from the maize kernel tissues (i.e., embryo, and endosperm) was described. Furthermore, the growth stage discrimination models based on PLSDA with the results of CCRC = 100 %, CCRV = 97 %, CCRIV = 93 %, and the prediction models of AFB1 based on PLSR with satisfactory performance (R2C = 0.96, R2V = 0.95, R2IV = 0.93 and RPD = 3.58) were both achieved. In conclusion, the results from both macro-level (Vis/NIR-HSI) and micro-level (SEM) assessments revealed the dynamic organismal interactions in A. flavus-maize kernel complex, and the detailed data could be used for modeling, and quantitative prediction of aflatoxin, which would establish a theoretical foundation for the early detection of fungal or toxin contaminated grains to ensure food security.
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Affiliation(s)
- Yao Lu
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai'an 271018, China
| | - Beibei Jia
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Seung-Chul Yoon
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
| | - Xinzhi Ni
- Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
| | - Hong Zhuang
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
| | - Baozhu Guo
- Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
| | - Scott E Gold
- Toxicology & Mycotoxin Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
| | - Jake C Fountain
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS 39762, USA
| | - Anthony E Glenn
- Toxicology & Mycotoxin Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
| | - Kurt C Lawrence
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
| | - Feng Zhang
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
| | - Wei Wang
- Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Jian Lu
- Google, LLC, Mountain View, CA 94043, USA
| | - Chaojie Wei
- Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
| | - Hongzhe Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Jiajun Luo
- Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
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Zhiyuan H, Lin C, Yihan W, Meng D, Yanzi L, Zhenggang X. Reexamination of Aspergillus cristatus phylogeny in dark tea: Characteristics of the mitochondrial genome. Open Life Sci 2024; 19:20220838. [PMID: 38585639 PMCID: PMC10997147 DOI: 10.1515/biol-2022-0838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/18/2024] [Accepted: 02/12/2024] [Indexed: 04/09/2024] Open
Abstract
To enhance our understanding of Aspergillus cristatus, an important functional microorganism, the characteristics of its mitochondrial genome were analyzed and compared with related species. The mitochondrial genome of A. cristatus was determined to be 77,649 bp in length, with 15 protein-coding regions. Notably, its length surpassed that of the other species, primarily attributable to the intron length. Gene order exhibited significant variations, with greater conservation observed in the genus Penicillium compared to Aspergillus. Phylogenetic tree analyses indicated that the genera Aspergillus and Penicillium are closely related but monophyletic. Furthermore, the phylogenetic tree constructed based on protein-coding genes effectively distinguished all strains with high branching confidence. This approach provides a robust reflection of the evolutionary relationship between A. cristatus and its related species, offering potential for the development of molecular markers suitable for Aspergillus and Penicillium.
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Affiliation(s)
- Hu Zhiyuan
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, School of Materials and Chemical Engineering, Hunan City University, Yiyang413000, Hunan, China
| | - Chen Lin
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, School of Materials and Chemical Engineering, Hunan City University, Yiyang413000, Hunan, China
| | - Wang Yihan
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, School of Materials and Chemical Engineering, Hunan City University, Yiyang413000, Hunan, China
| | - Dong Meng
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, School of Materials and Chemical Engineering, Hunan City University, Yiyang413000, Hunan, China
| | - Li Yanzi
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, School of Materials and Chemical Engineering, Hunan City University, Yiyang413000, Hunan, China
| | - Xu Zhenggang
- Hunan Provincial Key Lab of Dark Tea and Jin-hua, School of Materials and Chemical Engineering, Hunan City University, Yiyang413000, Hunan, China
- College of Forestry, Northwest A & F University, Yangling712100, Shaanxi, China
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Bai Z, Du D, Zhu R, Xing F, Yang C, Yan J, Zhang Y, Kang L. Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI. Front Nutr 2024; 11:1325934. [PMID: 38406188 PMCID: PMC10884184 DOI: 10.3389/fnut.2024.1325934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/22/2024] [Indexed: 02/27/2024] Open
Abstract
Introduction Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers. Objectives The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored. Materials and methods The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (108, 107, 106, 105, 104, 103 and 102 CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored. Results and discussion The best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments. Conclusion The results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.
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Affiliation(s)
- Zongxiu Bai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Dongdong Du
- Analysis and Test Center, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Rongguang Zhu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi University, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi University, Shihezi, China
| | - Fukang Xing
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Chenyi Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Jiufu Yan
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Yixin Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Lichao Kang
- Analysis and Test Center, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
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Guo Z, Zhang J, Sun J, Dong H, Huang J, Geng L, Li S, Jing X, Guo Y, Sun X. A multivariate algorithm for identifying contaminated peanut using visible and near-infrared hyperspectral imaging. Talanta 2024; 267:125187. [PMID: 37722342 DOI: 10.1016/j.talanta.2023.125187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/29/2023] [Accepted: 09/07/2023] [Indexed: 09/20/2023]
Abstract
In this study, a novel uniform manifold approximation and projection combined-improved simultaneous optimization genetic algorithm-convolutional neural network (UMAP-ISOGA-CNN) algorithm was proposed. The improved simultaneous optimization genetic algorithm (ISOGA) combined with convolutional neural network (CNN) to optimize the architecture, hyperparameters, and optimizer of the CNN model simultaneously. Additionally, a uniform manifold approximation and projection (UMAP) method was used to visualize the feature space of all feature layers of the ISOGA-CNN model. The UMAP-ISOGA-CNN algorithm combined with visible and near-infrared hyperspectral imaging was used to identify peanut kernels contaminated with Aspergillus flavus and to distinguish their storage time, which is essential for the food industry to monitor the freshness of products. Overall, the UMAP-ISOGA-CNN algorithm provides useful insights into the feature space of the ISOGA-CNN model, contributing to a better understanding of the model's internal mechanisms. This study has practical implications for the food industry and future research on deep learning optimization.
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Affiliation(s)
- Zhen Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jing Zhang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jiashuai Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Haowei Dong
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Jingcheng Huang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Lingjun Geng
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Shiling Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Xiangzhu Jing
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China
| | - Yemin Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
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7
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Yin ZB, Liu FY, Geng H, Xi YJ, Zeng DB, Si CJ, Shi MD. A high-precision jujube disease spot detection based on SSD during the sorting process. PLoS One 2024; 19:e0296314. [PMID: 38180957 PMCID: PMC10769016 DOI: 10.1371/journal.pone.0296314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/09/2023] [Indexed: 01/07/2024] Open
Abstract
The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 (mAP@0.5) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production.
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Affiliation(s)
- Zhi-Ben Yin
- College of Information Engineering, Tarim University, Alaer, 843300, China
| | - Fu-Yong Liu
- College of Information Science and Engineering, Xinjiang University of Science & Technology, Korla, 841000, China
| | - Hui Geng
- College of Information Engineering, Tarim University, Alaer, 843300, China
| | - Ya-Jun Xi
- Tarim University Library, Tarim University, Alaer, 843300, China
| | - De-Bin Zeng
- College of Information Engineering, Tarim University, Alaer, 843300, China
| | - Chun-Jing Si
- College of Information Engineering, Tarim University, Alaer, 843300, China
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alaer, 843300, China
| | - Ming-Deng Shi
- College of Information Engineering, Tarim University, Alaer, 843300, China
- Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alaer, 843300, China
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8
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Hu H, Wang T, Wei Y, Xu Z, Cao S, Fu L, Xu H, Mao X, Huang L. Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix. FRONTIERS IN PLANT SCIENCE 2023; 14:1271320. [PMID: 37954990 PMCID: PMC10634472 DOI: 10.3389/fpls.2023.1271320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023]
Abstract
Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.
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Affiliation(s)
- Huiqiang Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Tingting Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yunpeng Wei
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhenyu Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiyu Cao
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Ling Fu
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Huaxing Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Xiaobo Mao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Luqi Huang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, Zhengzhou University, Zhengzhou, Henan, China
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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9
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Li H, Luo X, Haruna SA, Zareef M, Chen Q, Ding Z, Yan Y. Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea. Food Chem 2023; 428:136798. [PMID: 37423106 DOI: 10.1016/j.foodchem.2023.136798] [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: 11/17/2022] [Revised: 06/29/2023] [Accepted: 07/01/2023] [Indexed: 07/11/2023]
Abstract
Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu2O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference (P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea.
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Affiliation(s)
- Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaofeng Luo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
| | - Zhen Ding
- Changzhou Jintan Jiangnan Powder Co., Ltd, Changzhou 213200, PR China
| | - Yiyong Yan
- Shenzhen Bioeasy Biotechnology Co. Ltd, Shenzhen 518101, PR China; Shenzhen Senlanthy Technology Co., Ltd, Shenzhen 518107, PR China
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10
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Chu X, Zhang K, Wei H, Ma Z, Fu H, Miao P, Jiang H, Liu H. A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae. FRONTIERS IN PLANT SCIENCE 2023; 14:1180203. [PMID: 37332705 PMCID: PMC10272841 DOI: 10.3389/fpls.2023.1180203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 05/09/2023] [Indexed: 06/20/2023]
Abstract
Introduction Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. Methods This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. Results The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. Discussion These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.
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Affiliation(s)
- Xuan Chu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Kun Zhang
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongyu Wei
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiyu Ma
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Han Fu
- College of Engineering, South China Agricultural University, Guangzhou, China
| | - Pu Miao
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Hongzhe Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Hongli Liu
- College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China
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11
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Zhong Q, Zhang H, Tang S, Li P, Lin C, Zhang L, Zhong N. Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection. Foods 2023; 12:foods12102089. [PMID: 37238907 DOI: 10.3390/foods12102089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935-1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.
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Affiliation(s)
- Qiongda Zhong
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Hu Zhang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Shuqi Tang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Peng Li
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
| | - Caixia Lin
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Ling Zhang
- College of Biology and Food Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Nan Zhong
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
- Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Heyuan 517000, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China
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12
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Hao Y, Li X, Zhang C, Lei Z. Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling. BIOSENSORS 2023; 13:bios13020203. [PMID: 36831969 PMCID: PMC9954006 DOI: 10.3390/bios13020203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/18/2023] [Accepted: 01/21/2023] [Indexed: 05/31/2023]
Abstract
Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears.
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Affiliation(s)
- Yong Hao
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
- Key Laboratory of Conveyance Equipment of the Ministry of Education, Nanchang 330013, China
| | - Xiyan Li
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Chengxiang Zhang
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Zuxiang Lei
- School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China
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13
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Ye W, Xu W, Yan T, Yan J, Gao P, Zhang C. Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review. Foods 2022; 12:foods12010132. [PMID: 36613348 PMCID: PMC9818947 DOI: 10.3390/foods12010132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/06/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
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Affiliation(s)
- Weixin Ye
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Wei Xu
- College of Agriculture, Shihezi University, Shihezi 832003, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Jingkun Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
- Correspondence: (P.G.); (C.Z.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
- Correspondence: (P.G.); (C.Z.)
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14
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Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics. Foods 2022; 11:foods11213318. [DOI: 10.3390/foods11213318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/09/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022] Open
Abstract
Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC–MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.
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Chen S, Du X, Zhao W, Guo P, Chen H, Jiang Y, Wu H. Olive oil classification with Laser-induced fluorescence (LIF) spectra using 1-dimensional convolutional neural network and dual convolution structure model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121418. [PMID: 35689846 DOI: 10.1016/j.saa.2022.121418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/15/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Laser-induced fluorescence (LIF) spectroscopy is widely used for the analysis and classification of olive oil. This paper proposes the classification of LIF data using a specific 1-dimensional convolutional neural network (1D-CNN) model, which does not require pre-processing steps such as normalisation or denoising and can be flexibly applied to massive data. However, by adding a dual convolution structure (Dual-conv) to the model, the features of the 1-dimensional spectra are more scattered within one convolution-pooling process; thus, the classification effects are improved. The models were validated through an olive oil classification experiment which contained a total of 72,000 sets of LIF spectra data, and the classification accuracy rate reached ∼99.69%. Additionally, a common classification approach, the support vector machine (SVM), was utilised for the comparison of the results. The results show that the neural networks perform better than the SVM. The Dual-conv model structure has a faster convergence speed and higher evaluation parameters than those of the 1D-CNN in the same period of iterations, without increasing the data dimension.
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Affiliation(s)
- Siying Chen
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Xianda Du
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Wenqu Zhao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Pan Guo
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - He Chen
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Yurong Jiang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
| | - Huiyun Wu
- Academy of Military Medical Sciences, Academy of Military Sciences, Beijing 100850, China.
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16
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Hongfei Z, Lianhe Y, Wangkun D, Zhongzhi H. Pixel-level rapid detection of Aflatoxin B1 based on 1D-modified temporal convolutional network and hyperspectral imaging. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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