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Jiang M, Li N, Li M, Wang Z, Tian Y, Peng K, Sheng H, Li H, Li Q. E-Nose: Time-Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction. SENSORS (BASEL, SWITZERLAND) 2024; 24:4126. [PMID: 39000905 PMCID: PMC11243837 DOI: 10.3390/s24134126] [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: 05/29/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024]
Abstract
In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model's robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.
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Affiliation(s)
- Minglv Jiang
- Key Laboratory of Physical Electronics and Devices for Ministry of Education and Shaanxi Provincial Key Laboratory of Photonics & Information Technology, Xi’an Jiaotong University, Xi’an 710049, China;
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Na Li
- Northwest Survey & Planning Institute of National Forestry and Grassland Administration, Xi’an 710048, China; (N.L.); (Z.W.)
- Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Xi’an 710048, China
| | - Mingyong Li
- CSSC AlphaPec Instrument (Hubei) Co., Ltd., Yichang 443005, China;
| | - Zhou Wang
- Northwest Survey & Planning Institute of National Forestry and Grassland Administration, Xi’an 710048, China; (N.L.); (Z.W.)
- Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Xi’an 710048, China
| | - Yuan Tian
- China National Engineering Laboratory for Coal Mining Machinery, CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030032, China;
| | - Kaiyan Peng
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Haoran Sheng
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Haoyu Li
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Qiang Li
- Key Laboratory of Physical Electronics and Devices for Ministry of Education and Shaanxi Provincial Key Laboratory of Photonics & Information Technology, Xi’an Jiaotong University, Xi’an 710049, China;
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
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2
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Taiwo OR, Onyeaka H, Oladipo EK, Oloke JK, Chukwugozie DC. Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models. Int J Microbiol 2024; 2024:6612162. [PMID: 38799770 PMCID: PMC11126350 DOI: 10.1155/2024/6612162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.
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Affiliation(s)
| | - Helen Onyeaka
- School of Chemical Engineering, University of Birmingham, Edgbaston B15 2TT, Birmingham, UK
| | - Elijah K. Oladipo
- Genomics Unit, Helix Biogen Institute, Ogbomosho, Oyo, Nigeria
- Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun, Nigeria
| | - Julius Kola Oloke
- Department of Natural Science, Microbiology Unit, Precious Cornerstone University, Ibadan, Oyo, Nigeria
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Fan M, Rakotondrabe TF, Chen G, Guo M. Advances in microbial analysis: based on volatile organic compounds of microorganisms in food. Food Chem 2023; 418:135950. [PMID: 36989642 DOI: 10.1016/j.foodchem.2023.135950] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/30/2022] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
In recent years, microbial volatile organic compounds (mVOCs) produced by microbial metabolism have attracted more and more attention because they can be used to detect food early contamination and flaws. So far, many analytical methods have been reported for the determination of mVOCs in food, but few integrated review articles discussing these methods are published. Consequently, mVOCs as indicators of food microbiological contamination and their generation mechanism including carbohydrate, amino acid, and fatty acid metabolism are introduced. Meanwhile, a detailed summary of the mVOCs sampling methods such as headspace, purge trap, solid phase microextraction, and needle trap is presented, and a systematic and critical review of the analytical methods (ion mobility spectrometry, electronic nose, biosensor, and so on) of mVOCs and their application in the detection of food microbial contamination is highlighted. Finally, the future concepts that can help improve the detection of food mVOCs are prospected.
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Affiliation(s)
- Minxia Fan
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China.
| | - Tojofaniry Fabien Rakotondrabe
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Guilin Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Mingquan Guo
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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Paramithiotis S. Molecular Targets for Foodborne Pathogenic Bacteria Detection. Pathogens 2023; 12:pathogens12010104. [PMID: 36678453 PMCID: PMC9865778 DOI: 10.3390/pathogens12010104] [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: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
The detection of foodborne pathogenic bacteria currently relies on their ability to grow on chemically defined liquid and solid media, which is the essence of the classical microbiological approach. Such procedures are time-consuming and the quality of the result is affected by the selectivity of the media employed. Several alternative strategies based on the detection of molecular markers have been proposed. These markers may be cell constituents, may reside on the cell envelope or may be specific metabolites. Each marker provides specific advantages and, at the same time, suffers from specific limitations. The food matrix and chemical composition, as well as the accompanying microbiota, may also severely compromise detection. The aim of the present review article is to present and critically discuss all available information regarding the molecular targets that have been employed as markers for the detection of foodborne pathogens. Their strengths and limitations, as well as the proposed alleviation strategies, are presented, with particular emphasis on their applicability in real food systems and the challenges that are yet to be effectively addressed.
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Affiliation(s)
- Spiros Paramithiotis
- Laboratory of Food Process Engineering, Department of Food Science and Human Nutrition, Agricultural University of Athens, 75 Iera Odos St., 11855 Athens, Greece
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Munekata PES, Finardi S, de Souza CK, Meinert C, Pateiro M, Hoffmann TG, Domínguez R, Bertoli SL, Kumar M, Lorenzo JM. Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:672. [PMID: 36679464 PMCID: PMC9860605 DOI: 10.3390/s23020672] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/21/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.
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Affiliation(s)
- Paulo E. S. Munekata
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sarah Finardi
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Carolina Krebs de Souza
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Caroline Meinert
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Mirian Pateiro
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Tuany Gabriela Hoffmann
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
- Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
| | - Rubén Domínguez
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sávio Leandro Bertoli
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR–Central Institute for Research on Cotton Technology, Mumbai 400019, India
| | - José M. Lorenzo
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
- Facultade de Ciencias, Universidade de Vigo, Área de Tecnoloxía dos Alimentos, 32004 Ourense, Spain
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Multi-objective scheduling in the vegetable processing and packaging facility using metaheuristic based framework. FOOD AND BIOPRODUCTS PROCESSING 2023. [DOI: 10.1016/j.fbp.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Pampoukis G, Lytou AE, Argyri AA, Panagou EZ, Nychas GJE. Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective. SENSORS (BASEL, SWITZERLAND) 2022; 22:2800. [PMID: 35408414 PMCID: PMC9003504 DOI: 10.3390/s22072800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 06/14/2023]
Abstract
Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.
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Affiliation(s)
- George Pampoukis
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
- Food Microbiology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
| | - Anastasia E. Lytou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
| | - Anthoula A. Argyri
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Sofokli Venizelou 1, 14123 Lycovrisi, Greece;
| | - Efstathios Z. Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
| | - George-John E. Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
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