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Boshra MH, El-Housseiny GS, Farag MMS, Aboshanab KM. Innovative approaches for mycotoxin detection in various food categories. AMB Express 2024; 14:7. [PMID: 38216801 PMCID: PMC10786816 DOI: 10.1186/s13568-024-01662-y] [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: 11/21/2023] [Accepted: 12/28/2023] [Indexed: 01/14/2024] Open
Abstract
Mycotoxins (MTs), produced by filamentous fungi, represent a severe hazard to the health of humans and food safety, affecting the quality of various agricultural products. They can contaminate a wide range of foods, during any processing phase before or after harvest. Animals and humans who consume MTs-contaminated food or feed may experience acute or chronic poisoning, which may result in serious pathological consequences. Accordingly, developing rapid, easy, and accurate methods of MTs detection in food becomes highly urgent and critical as a quality control and to guarantee food safety and lower health hazards. In this review, we highlighted and discussed innovative approaches like biosensors, fluorescent polarization, capillary electrophoresis, infrared spectroscopy, and electronic noses for MT identification pointing out current challenges and future directions. The limitations, current challenges, and future directions of conventional detection methods versus innovative methods have also been highlighted and discussed.
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Affiliation(s)
- Marina H Boshra
- Department of Mycotoxins, Central Public Health Laboratories (CPHL), Ministry of Health, Cairo, Egypt
| | - Ghadir S El-Housseiny
- Department of Microbiology and Immunology, Faculty of Pharmacy, Organization of African Unity St., Ain Shams University, Abbassia, PO: 11566, Cairo, Egypt
| | - Mohammed M S Farag
- Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Cairo, 11884, Egypt
- Armed Forces College of Medicine (AFCM), Cairo, Egypt
| | - Khaled M Aboshanab
- Department of Microbiology and Immunology, Faculty of Pharmacy, Organization of African Unity St., Ain Shams University, Abbassia, PO: 11566, Cairo, Egypt.
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2
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Gan Z, Zhou Q, Zheng C, Wang J. Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis. Biosens Bioelectron 2023; 237:115540. [PMID: 37523812 DOI: 10.1016/j.bios.2023.115540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/09/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023]
Abstract
Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.
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Affiliation(s)
- Ziyu Gan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Qin'an Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Chengyu Zheng
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Jun Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
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3
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Anastasaki E, Papachristos DP, Antonatos S, Milonas PG. Profiles of Volatiles Emitted from Orange Fruits Infested by Ceratitis capitata (Diptera: Tephritidae). JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:12403-12416. [PMID: 37560776 DOI: 10.1021/acs.jafc.3c01339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
The profiles of volatile organic compounds (VOCs) emitted from orange fruits infested by Ceratitis capitata were examined. VOCs were collected from the headspace of oranges immediately, 24 h, 5 days, 8 days, and 13 days after oviposition and when larvae exit the fruit by applying either static or dynamic sampling techniques. A total of 32 and 47 compounds were detected in infested orange fruits when using static and dynamic techniques, respectively. Differences in the volatile profile of oranges were observed. Classification models were employed, showing that infested fruits emitted a chemical profile distinct from that of non-infested ones. Limonene was associated with cell disruption. (E)-β-Ocimene, (E)-4,8-dimethyl-1,3,7-nonatriene, hexyl butanoate, butyl hexanoate, and hexyl hexanoate were the VOCs that were correlated with the infestation of sweet oranges by C. capitata.
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4
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Analysis of the changes of volatile flavor compounds in a traditional Chinese shrimp paste during fermentation based on electronic nose, SPME-GC-MS and HS-GC-IMS. FOOD SCIENCE AND HUMAN WELLNESS 2023. [DOI: 10.1016/j.fshw.2022.07.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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5
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Monitoring Botrytis cinerea Infection in Kiwifruit Using Electronic Nose and Machine Learning Techniques. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02967-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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6
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Camardo Leggieri M, Mazzoni M, Bertuzzi T, Moschini M, Prandini A, Battilani P. Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees. Toxins (Basel) 2022; 14:toxins14090617. [PMID: 36136555 PMCID: PMC9506558 DOI: 10.3390/toxins14090617] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/26/2022] [Accepted: 09/01/2022] [Indexed: 11/16/2022] Open
Abstract
Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014−2015 and 2017−2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose “AIR PEN 3” (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach “Classification and regression trees” (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.
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Affiliation(s)
- Marco Camardo Leggieri
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
| | - Marco Mazzoni
- Department of Livestock Population Genomics, University of Hohenheim, Garbenstraβe 17, 70599 Stuttgart, Germany
| | - Terenzio Bertuzzi
- Department of Animal Science, Food, and Nutrition, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
| | - Maurizio Moschini
- Department of Animal Science, Food, and Nutrition, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
| | - Aldo Prandini
- Department of Animal Science, Food, and Nutrition, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
| | - Paola Battilani
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy
- Correspondence: ; Tel.: +39-0523-599254
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Labanska M, van Amsterdam S, Jenkins S, Clarkson JP, Covington JA. Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22145453. [PMID: 35891126 PMCID: PMC9315870 DOI: 10.3390/s22145453] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 06/01/2023]
Abstract
The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.
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Affiliation(s)
- Malgorzata Labanska
- The Plant Breeding and Acclimatization Institute-National Research Institute, Radzikow, 05-870 Blonie, Poland
| | - Sarah van Amsterdam
- Warwick Crop Centre, School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK; (S.v.A.); (S.J.); (J.P.C.)
| | - Sascha Jenkins
- Warwick Crop Centre, School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK; (S.v.A.); (S.J.); (J.P.C.)
| | - John P. Clarkson
- Warwick Crop Centre, School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK; (S.v.A.); (S.J.); (J.P.C.)
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8
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Li X, Wang B, Yi C, Gong W. Gas sensing technology for meat quality assessment: A review. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xinxing Li
- Beijing Laboratory of Food Quality and Safety China Agricultural University Beijing China
- Nanchang Institute of Technology Nanchang China
| | - Biao Wang
- Beijing Laboratory of Food Quality and Safety China Agricultural University Beijing China
| | - Chen Yi
- Changchun Urban Planning & Research Center Changchun China
| | - Weiwei Gong
- China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute Beijing China
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Zhang Z, Liu H, Chen D, Zhang J, Li H, Shen M, Pu Y, Zhang Z, Zhao J, Hu J. SMOTE-based method for balanced spectral nondestructive testing of moldy apple core. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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10
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Zou X, Wang C, Luo M, Ren Q, Liu Y, Zhang S, Bai Y, Meng J, Zhang W, Su SW. Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine. SENSORS 2022; 22:s22082997. [PMID: 35458982 PMCID: PMC9025600 DOI: 10.3390/s22082997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 12/04/2022]
Abstract
Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.
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Affiliation(s)
- Xiuguo Zou
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
- Correspondence: (X.Z.); (S.W.S.); Tel.: +86-25-5860-6585 (X.Z.)
| | - Chenyang Wang
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Manman Luo
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Qiaomu Ren
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Yingying Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Shikai Zhang
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Yungang Bai
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China;
| | - Jiawei Meng
- Department of Mechanical Engineering, University College London, London WC1E 7JE, UK;
| | - Wentian Zhang
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Steven W. Su
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Correspondence: (X.Z.); (S.W.S.); Tel.: +86-25-5860-6585 (X.Z.)
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11
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MacDougall S, Bayansal F, Ahmadi A. Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease. BIOSENSORS 2022; 12:bios12040239. [PMID: 35448299 PMCID: PMC9025064 DOI: 10.3390/bios12040239] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/30/2022] [Accepted: 04/08/2022] [Indexed: 05/03/2023]
Abstract
Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently.
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Affiliation(s)
- Samantha MacDougall
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada;
| | - Fatih Bayansal
- Department of Metallurgy and Materials Engineering, Iskenderun Technical University, Hatay TR-31200, Turkey;
| | - Ali Ahmadi
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada;
- Department of Biomedical Science, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
- Correspondence: ; Tel.: +1-902-566-0521
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12
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Chen H, Huo D, Zhang J. Gas Recognition in E-Nose System: A Review. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:169-184. [PMID: 35412988 DOI: 10.1109/tbcas.2022.3166530] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.
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Mohammad-Razdari A, Rousseau D, Bakhshipour A, Taylor S, Poveda J, Kiani H. Recent advances in E-monitoring of plant diseases. Biosens Bioelectron 2022; 201:113953. [PMID: 34998118 DOI: 10.1016/j.bios.2021.113953] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 02/09/2023]
Abstract
Infectious plant diseases are caused by pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, phytoplasma, and nematodes. Plant diseases have a significant effect on the plant quality and yield and they can destroy the entire plant if they are not controlled in time. To minimize disease-related losses, it is essential to identify and control pathogens in the early stages. Plant disease control is thus a fundamental challenge both for global food security and sustainable agriculture. Conventional methods for plant diseases control have given place to electronic control (E-monitoring) due to their lack of portability, being time consuming, need for a specialized user, etc. E-monitoring using electronic nose (e-nose), biosensors, wearable sensors, and 'electronic eyes' has attracted increasing attention in recent years. Detection, identification, and quantification of pathogens based on electronic sensors (E-sensors) are both convenient and practical and may be used in combination with conventional methods. This paper discusses recent advances made in E-sensors as component parts in combination with wearable sensors, in electronic sensing systems to control and detect viruses, bacteria, pathogens and fungi. In addition, future challenges using sensors to manage plant diseases are investigated.
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Affiliation(s)
- Ayat Mohammad-Razdari
- Department of Mechanical Engineering of Biosystems, Shahrekord University, 8818634141, Shahrekord, Iran.
| | - David Rousseau
- Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAe IRHS, Université d'Angers, France
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Stephen Taylor
- Mass Spectrometry and Instrumentation Group, Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK.
| | - Jorge Poveda
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra (UPNA), Campus Arrosadía, Pamplona, Spain
| | - Hassan Kiani
- Department of Biosystems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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14
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Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. SENSORS 2021; 21:s21227620. [PMID: 34833693 PMCID: PMC8619411 DOI: 10.3390/s21227620] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 02/07/2023]
Abstract
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
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15
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Calle JLP, Ferreiro-González M, Ruiz-Rodríguez A, Barbero GF, Álvarez JÁ, Palma M, Ayuso J. A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars. Foods 2021; 10:foods10061411. [PMID: 34207095 PMCID: PMC8233915 DOI: 10.3390/foods10061411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022] Open
Abstract
Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.
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Affiliation(s)
- José Luis P. Calle
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - Marta Ferreiro-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
- Correspondence: ; Tel.: +34-956-01-6359
| | - Ana Ruiz-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - Gerardo F. Barbero
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - José Á. Álvarez
- Department of Physical Chemistry, Faculty of Sciences, Institute of Biomolecules (INBIO), University of Cadiz, 11510 Puerto Real, Spain; (J.Á.Á.); (J.A.)
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - Jesús Ayuso
- Department of Physical Chemistry, Faculty of Sciences, Institute of Biomolecules (INBIO), University of Cadiz, 11510 Puerto Real, Spain; (J.Á.Á.); (J.A.)
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Ma H, Yang J, Chen X, Jiang X, Su Y, Qiao S, Zhong G. Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy. J Microbiol 2021; 59:563-572. [PMID: 33779956 DOI: 10.1007/s12275-021-1013-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/10/2021] [Accepted: 02/10/2021] [Indexed: 12/14/2022]
Abstract
Fungi of the genus Aspergillus are ubiquitously distributed in nature, and some cause invasive aspergillosis (IA) infections in immunosuppressed individuals and contamination in agricultural products. Because microscopic observation and molecular detection of Aspergillus species represent the most operator-dependent and time-intensive activities, automated and cost-effective approaches are needed. To address this challenge, a deep convolutional neural network (CNN) was used to investigate the ability to classify various Aspergillus species. Using a dissecting microscopy (DM)/stereomicroscopy platform, colonies on plates were scanned with a 35× objective, generating images of sufficient resolution for classification. A total of 8,995 original colony images from seven Aspergillus species cultured in enrichment medium were gathered and autocut to generate 17,142 image crops as training and test datasets containing the typical representative morphology of conidiophores or colonies of each strain. Encouragingly, the Xception model exhibited a classification accuracy of 99.8% on the training image set. After training, our CNN model achieved a classification accuracy of 99.7% on the test image set. Based on the Xception performance during training and testing, this classification algorithm was further applied to recognize and validate a new set of raw images of these strains, showing a detection accuracy of 98.2%. Thus, our study demonstrated a novel concept for an artificial-intelligence-based and cost-effective detection methodology for Aspergillus organisms, which also has the potential to improve the public's understanding of the fungal kingdom.
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Affiliation(s)
- Haozhong Ma
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jinshan Yang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xiaolu Chen
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xinyu Jiang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yimin Su
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Shanlei Qiao
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Guowei Zhong
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
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17
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An electronic nose supported by an artificial neural network for the rapid detection of aflatoxin B1 and fumonisins in maize. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107722] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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18
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Camardo Leggieri M, Mazzoni M, Battilani P. Machine Learning for Predicting Mycotoxin Occurrence in Maize. Front Microbiol 2021; 12:661132. [PMID: 33897675 PMCID: PMC8062859 DOI: 10.3389/fmicb.2021.661132] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 03/16/2021] [Indexed: 11/17/2022] Open
Abstract
Meteorological conditions are the main driving variables for mycotoxin-producing fungi and the resulting contamination in maize grain, but the cropping system used can mitigate this weather impact considerably. Several researchers have investigated cropping operations' role in mycotoxin contamination, but these findings were inconclusive, precluding their use in predictive modeling. In this study a machine learning (ML) approach was considered, which included weather-based mechanistic model predictions for AFLA-maize and FER-maize [predicting aflatoxin B1 (AFB1) and fumonisins (FBs), respectively], and cropping system factors as the input variables. The occurrence of AFB1 and FBs in maize fields was recorded, and their corresponding cropping system data collected, over the years 2005-2018 in northern Italy. Two deep neural network (DNN) models were trained to predict, at harvest, which maize fields were contaminated beyond the legal limit with AFB1 and FBs. Both models reached an accuracy >75% demonstrating the ML approach added value with respect to classical statistical approaches (i.e., simple or multiple linear regression models). The improved predictive performance compared with that obtained for AFLA-maize and FER-maize was clearly demonstrated. This coupled to the large data set used, comprising a 13-year time series, and the good results for the statistical scores applied, together confirmed the robustness of the models developed here.
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Affiliation(s)
| | | | - Paola Battilani
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
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19
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John AT, Murugappan K, Nisbet DR, Tricoli A. An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring. SENSORS (BASEL, SWITZERLAND) 2021; 21:2271. [PMID: 33804960 PMCID: PMC8036444 DOI: 10.3390/s21072271] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 01/05/2023]
Abstract
An electronic nose (Enose) relies on the use of an array of partially selective chemical gas sensors for identification of various chemical compounds, including volatile organic compounds in gas mixtures. They have been proposed as a portable low-cost technology to analyse complex odours in the food industry and for environmental monitoring. Recent advances in nanofabrication, sensor and microcircuitry design, neural networks, and system integration have considerably improved the efficacy of Enose devices. Here, we highlight different types of semiconducting metal oxides as well as their sensing mechanism and integration into Enose systems, including different pattern recognition techniques employed for data analysis. We offer a critical perspective of state-of-the-art commercial and custom-made Enoses, identifying current challenges for the broader uptake and use of Enose systems in a variety of applications.
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Affiliation(s)
- Alishba T. John
- Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra 2601, Australia;
| | - Krishnan Murugappan
- Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra 2601, Australia;
| | - David R. Nisbet
- Laboratory of Advanced Biomaterials, Research School of Chemistry and the John Curtin School of Medical Research, The Australian National University, Canberra 2601, Australia;
| | - Antonio Tricoli
- Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra 2601, Australia;
- Nanotechnology Research Laboratory, Faculty of Engineering, The University of Sydney, Camperdown 2006, Australia
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20
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Increasing Profitability and Monitoring Environmental Performance: A Case Study in the Agri-Food Industry through an Edge-IoT Platform. SUSTAINABILITY 2020. [DOI: 10.3390/su13010283] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Globalization has led to a new paradigm where the traditional industries, such as agriculture, employ vanguard technologies to broaden its possibilities into what is known as smart farming and the agri-food industry 4.0. This industry needs to adapt to the current market through an efficient use of resources while being environmentally friendly. The most commonly used approaches for analyzing efficiency and sustainability on farms are production efficiency based analyses, such as Data Envelopment Analysis and Stochastic Frontier Analysis, since they allow to see how efficient the outputs are generated regardless of the units of measurement of the inputs. This work presents a real scenario for making farms more profitable and sustainable through the analysis of the Data Envelopment Analysis and the application of the Internet of Things and Edge Computing. What makes this model interesting is that it allows monitoring the ambient conditions with real-time data from the different sensors that have been installed on the farm, minimizing costs and gaining robustness in the transmission of the data to the cloud with Edge Computing, and then to have a complete overview in terms of monthly resource efficiency through the Data Envelopment Analysis. The results show that including the costs of edge and non-edge data transfer have an impact on the efficiency. This small-scale study set the basis for a future test with many farms simultaneously.
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21
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Aouadi B, Zaukuu JLZ, Vitális F, Bodor Z, Fehér O, Gillay Z, Bazar G, Kovacs Z. Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5479. [PMID: 32987908 PMCID: PMC7583984 DOI: 10.3390/s20195479] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/15/2020] [Accepted: 09/21/2020] [Indexed: 01/28/2023]
Abstract
Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.
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Affiliation(s)
- Balkis Aouadi
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - John-Lewis Zinia Zaukuu
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Flora Vitális
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Zsanett Bodor
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - Orsolya Fehér
- Institute of Agribusiness, Faculty of Economics and Social Sciences, Szent István University, H-2100 Gödöllő, Hungary;
| | - Zoltan Gillay
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
| | - George Bazar
- Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, H-7400 Kaposvár, Hungary;
- ADEXGO Kft., H-8230 Balatonfüred, Hungary
| | - Zoltan Kovacs
- Department of Measurement and Process Control, Faculty of Food Science, Szent István University, H-1118 Budapest, Hungary; (B.A.); (J.-L.Z.Z.); (F.V.); (Z.B.); (Z.G.)
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22
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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.
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23
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Jiang W, Gao D. Five Typical Stenches Detection Using an Electronic Nose. SENSORS 2020; 20:s20092514. [PMID: 32365549 PMCID: PMC7248900 DOI: 10.3390/s20092514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/03/2020] [Accepted: 04/07/2020] [Indexed: 02/06/2023]
Abstract
This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.
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24
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Agriopoulou S, Stamatelopoulou E, Varzakas T. Advances in Analysis and Detection of Major Mycotoxins in Foods. Foods 2020; 9:E518. [PMID: 32326063 PMCID: PMC7230321 DOI: 10.3390/foods9040518] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 04/16/2020] [Indexed: 12/19/2022] Open
Abstract
Mycotoxins are the most widely studied biological toxins, which contaminate foods at very low concentrations. This review describes the emerging extraction techniques and the current and alternatives analytical techniques and methods that have been used to successfully detect and identify important mycotoxins. Some of them have proven to be particularly effective in not only the detection of mycotoxins, but also in detecting mycotoxin-producing fungi. Chromatographic techniques such as high-performance liquid chromatography coupled with various detectors like fluorescence, diode array, UV, liquid chromatography coupled with mass spectrometry, and liquid chromatography-tandem mass spectrometry, have been powerful tools for analyzing and detecting major mycotoxins. Recent progress of the development of rapid immunoaffinity-based detection techniques such as immunoassays and biosensors, as well as emerging technologies like proteomic and genomic methods, molecular techniques, electronic nose, aggregation-induced emission dye, quantitative NMR and hyperspectral imaging for the detection of mycotoxins in foods, have also been presented.
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Affiliation(s)
| | | | - Theodoros Varzakas
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece; (S.A.); (E.S.)
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25
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More AS, Ranadheera CS, Fang Z, Warner R, Ajlouni S. Biomarkers associated with quality and safety of fresh-cut produce. FOOD BIOSCI 2020. [DOI: 10.1016/j.fbio.2019.100524] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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26
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A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine. SENSORS 2019; 19:s19122656. [PMID: 31212827 PMCID: PMC6631614 DOI: 10.3390/s19122656] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/06/2019] [Accepted: 06/10/2019] [Indexed: 11/17/2022]
Abstract
Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time.
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