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Matenda RT, Rip D, Fernández Pierna JA, Baeten V, Williams PJ. Differentiation of Listeria monocytogenes serotypes using near infrared hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124579. [PMID: 38850824 DOI: 10.1016/j.saa.2024.124579] [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: 02/29/2024] [Revised: 05/27/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Juan A Fernández Pierna
- Quality and authentication of products Unit, Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur,24, 5030 Gembloux, Belgium
| | - Vincent Baeten
- Quality and authentication of products Unit, Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur,24, 5030 Gembloux, Belgium
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [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: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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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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Bhunia AK, Singh AK, Parker K, Applegate BM. Petri-plate, bacteria, and laser optical scattering sensor. Front Cell Infect Microbiol 2022; 12:1087074. [PMID: 36619754 PMCID: PMC9813400 DOI: 10.3389/fcimb.2022.1087074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.
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Affiliation(s)
- Arun K. Bhunia
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, United States,Purdue University, Purdue University Interdisciplinary Life Science Program (PULSe), West Lafayette, IN, United States,Purdue Institute of Inflammation, Immunology and Infectious Disease, Purdue University, West Lafayette, IN, United States,Department of Comparative Pathobiology, Purdue University, West Lafayette, IN, United States,*Correspondence: Arun K. Bhunia,
| | - Atul K. Singh
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, United States,Clear Labs, San Carlos, CA, United States
| | - Kyle Parker
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Bruce M. Applegate
- Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, United States,Purdue University, Purdue University Interdisciplinary Life Science Program (PULSe), West Lafayette, IN, United States,Purdue Institute of Inflammation, Immunology and Infectious Disease, Purdue University, West Lafayette, IN, United States,Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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Huiyong W, Shuchun J, Zhu J. Simulation model and fault analysis of air circulation system of the aircraft based on grasshopper optimization algorithm: support vector machine. Soft comput 2022. [DOI: 10.1007/s00500-022-07403-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Bu Y, Jiang X, Tian J, Hu X, Fei X, Huang D, Luo H. Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Youhua Bu
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Xinna Jiang
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Jianping Tian
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Xinjun Hu
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Xue Fei
- College of Mechanical Engineering Sichuan University of Science and Engineering Yibin China
| | - Dan Huang
- College of Bioengineering Sichuan University of Science and Engineering Yibin China
- Sichuan Engineering Technology Research Center for Liquor‐Making Grains Yibin China
| | - Huibo Luo
- College of Bioengineering Sichuan University of Science and Engineering Yibin China
- Sichuan Engineering Technology Research Center for Liquor‐Making Grains Yibin China
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