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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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Huo J, Zhang M, Wang D, S Mujumdar A, Bhandari B, Zhang L. New preservation and detection technologies for edible mushrooms: A review. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3230-3248. [PMID: 36700618 DOI: 10.1002/jsfa.12472] [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: 05/19/2022] [Revised: 09/11/2022] [Accepted: 01/26/2023] [Indexed: 06/17/2023]
Abstract
Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Jingyi Huo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, China
| | - Dayuan Wang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald College, McGill University, Quebec, Canada
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia
| | - Lujun Zhang
- R&D Center, Shandong Qihe Biotechnology Co., Ltd, Zibo, China
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Xia R, Hou Z, Xu H, Li Y, Sun Y, Wang Y, Zhu J, Wang Z, Pan S, Xin G. Emerging technologies for preservation and quality evaluation of postharvest edible mushrooms: A review. Crit Rev Food Sci Nutr 2023:1-19. [PMID: 37083462 DOI: 10.1080/10408398.2023.2200482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Edible mushrooms are the highly demanded foods of which production and consumption have been steadily increasing globally. Owing to the quality loss and short shelf-life in harvested mushrooms, it is necessary for the implementation of effective preservation and intelligent evaluation technologies to alleviate this issue. The aim of this review was to analyze the development and innovation thematic lines, topics, and trends by bibliometric analysis and review of the literature methods. The challenges faced in researching these topics were proposed and the mechanisms of quality loss in mushrooms during storage were updated. This review summarized the effects of chemical processing (antioxidants, ozone, and coatings), physical treatments (non-thermal plasma, packaging and latent thermal storage) and other emerging application on the quality of fresh mushrooms while discussing the efficiency in extending the shelf-life. It also discussed the emerging evaluation techniques based on the various chemometric methods and computer vision system in monitoring the freshness and predicting the shelf-life of mushrooms which have been developed. Preservation technology optimization and dynamic quality evaluation are vital for achieving mushroom quality control. This review can provide a comprehensive research reference for reducing mushroom quality loss and extending shelf-life, along with optimizing efficiency of storage and transportation operations.
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Affiliation(s)
- Rongrong Xia
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Zhenshan Hou
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Heran Xu
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Yunting Li
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Yong Sun
- Beijing Academy of Food Sciences, Beijing, China
| | - Yafei Wang
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Jiayi Zhu
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Zijian Wang
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Song Pan
- College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Guang Xin
- College of Food Science, Shenyang Agricultural University, Shenyang, China
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Jin S, Liu X, Wang J, Pan L, Zhang Y, Zhou G, Tang C. Hyperspectral imaging combined with fluorescence for the prediction of microbial growth in chicken breasts under different packaging conditions. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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Li Z, Fu J, Chen Z, Fu Q, Luo X. Hyperspectral detection of fresh corn peeling damage using germinating sparse classification method. FRONTIERS IN PLANT SCIENCE 2022; 13:1039110. [PMID: 36523611 PMCID: PMC9745089 DOI: 10.3389/fpls.2022.1039110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Peeling damage reduces the quality of fresh corn ear and affects the purchasing decisions of consumers. Hyperspectral imaging technique has great potential to be used for detection of peeling-damaged fresh corn. However, conventional non-machine-learning methods are limited by unsatisfactory detection accuracy, and machine-learning methods rely heavily on training samples. To address this problem, the germinating sparse classification (GSC) method is proposed to detect the peeling-damaged fresh corn. The germinating strategy is developed to refine training samples, and to dynamically adjust the number of atoms to improve the performance of dictionary, furthermore, the threshold sparse recovery algorithm is proposed to realize pixel level classification. The results demonstrated that the GSC method had the best classification effect with the overall classification accuracy of the training set was 98.33%, and that of the test set was 95.00%. The GSC method also had the highest average pixel prediction accuracy of 84.51% for the entire HSI regions and 91.94% for the damaged regions. This work represents a new method for mechanical damage detection of fresh corn using hyperspectral image (HSI).
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Affiliation(s)
- Zhenye Li
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
- Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China
| | - Jun Fu
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
- Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China
| | - Zhi Chen
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
- Chinese Academy of Agricultural Mechanization Sciences, Beijing, China
| | - Qiankun Fu
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
- Key Laboratory of Efficient Sowing and Harvesting Equipment, Ministry of Agriculture and Rural Affairs, Jilin University, Changchun, China
| | - Xiwen Luo
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
- College of Engineering, South China Agricultural University, Guangzhou, China
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Liu H, Liu H, Li J, Wang Y. Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation. Crit Rev Anal Chem 2022:1-24. [PMID: 36154534 DOI: 10.1080/10408347.2022.2124839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.
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Affiliation(s)
- Hong Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Honggao Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Zhaotong University, Zhaotong, China
| | - Jieqing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Wen X, Geng F, Xu Y, Li X, Liu D, Liu Z, Luo Z, Wang J. Quantitative transcriptomic and metabolomic analyses reveal the changes in Tricholoma matsutake fruiting bodies during cold storage. Food Chem 2022; 381:132292. [DOI: 10.1016/j.foodchem.2022.132292] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 01/19/2022] [Accepted: 01/27/2022] [Indexed: 01/05/2023]
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