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Mei H, Peng J, Wang T, Zhou T, Zhao H, Zhang T, Yang Z. Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array. NANO-MICRO LETTERS 2024; 16:269. [PMID: 39141168 PMCID: PMC11324646 DOI: 10.1007/s40820-024-01489-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 07/21/2024] [Indexed: 08/15/2024]
Abstract
As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.
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Affiliation(s)
- Haixia Mei
- Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, People's Republic of China
| | - Jingyi Peng
- Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, People's Republic of China
| | - Tao Wang
- Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, People's Republic of China.
| | - Tingting Zhou
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China
| | - Hongran Zhao
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China
| | - Tong Zhang
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China.
| | - Zhi Yang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
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2
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Li P, Li Z, Hu Y, Niu Z, Wang Z, Zhou H, Sun X. Evaluation of fish meal freshness using a metal-oxide semiconductor electronic nose combined with the long short-term memory feature extraction method. J Food Sci 2024; 89:5016-5030. [PMID: 38980966 DOI: 10.1111/1750-3841.17231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/29/2024] [Accepted: 06/18/2024] [Indexed: 07/11/2024]
Abstract
To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.
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Affiliation(s)
- Pei Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
- Shandong Jiashibo Foods Co., Ltd, Weifang, China
| | - Zhaopeng Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
| | - Yangting Hu
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
| | - Zhiyou Niu
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Zhenhe Wang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
| | - Hua Zhou
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China
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3
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Chiu I, Ye H, Aayush K, Yang T. Intelligent food packaging for smart sensing of food safety. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 111:215-259. [PMID: 39103214 DOI: 10.1016/bs.afnr.2024.06.006] [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: 08/07/2024]
Abstract
In this contemporary era, with over 8 billion people worldwide, ensuring food safety has become more critical than ever. To address this concern, the introduction of intelligent packaging marks a significant breakthrough. Essentially, this innovation tackles the challenge of rapid deterioration in perishable foods, which is vital to the well-being of communities and food safety. Unlike traditional methods that primarily emphasize shelf-life extension, intelligent packaging goes further by incorporating advanced sensing technologies to detect signs of spoilage and contamination in real-time, such as changes in temperature, oxygen levels, carbon dioxide levels, humidity, and the presence of harmful microorganisms. The innovation can rely on various packaging materials like plastics, metals, papers, or biodegradable polymers, combined with sophisticated sensing techniques such as colorimetric sensors, time-temperature indicators, radio-frequency identification tags, electronic noses, or biosensors. Together, these elements form a dynamic and tailored packaging system. This system not only protects food from spoilage but also offers stakeholders immediate and adequate information about food quality. Moreover, the real-world application on seafood, meat, dairy, fruits, and vegetables demonstrates the feasibility of using intelligent packaging to significantly enhance the safety and shelf life of a wide variety of perishable goods. By adopting intelligent packaging for smart sensing solutions, both the food industry and consumers can significantly reduce health risks linked with contamination and reduce unnecessary food waste. This underscores the crucial role of intelligent packaging in modern food safety and distribution systems, showcasing an effective fusion of technology, safety, and sustainability efforts aimed at nourishing a rapidly growing global population.
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Affiliation(s)
- Ivy Chiu
- Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada
| | - Haoxin Ye
- Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada
| | - Krishna Aayush
- Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada
| | - Tianxi Yang
- Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada.
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Madhubhashini MN, Liyanage CP, Alahakoon AU, Liyanage RP. Current applications and future trends of artificial senses in fish freshness determination: A review. J Food Sci 2024; 89:33-50. [PMID: 38051021 DOI: 10.1111/1750-3841.16865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/16/2023] [Accepted: 11/16/2023] [Indexed: 12/07/2023]
Abstract
Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e-nose), and electronic tongue (e-tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e-tongue determines the freshness of fish in a completely destructive way. There are developed e-noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large-scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses.
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Affiliation(s)
- M Nerandi Madhubhashini
- Department of Information and Communication Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Chamara P Liyanage
- Department of Information and Communication Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Amali U Alahakoon
- Department of Biosystems Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Rumesh Prasanga Liyanage
- Department of Biosystems Technology, Faculty of Technology, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
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Smaoui S, Tarapoulouzi M, Agriopoulou S, D'Amore T, Varzakas T. Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics. Foods 2023; 12:4254. [PMID: 38231684 DOI: 10.3390/foods12234254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 01/19/2024] Open
Abstract
Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.
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Affiliation(s)
- Slim Smaoui
- Laboratory of Microbial, Enzymatic Biotechnology, and Biomolecules (LBMEB), Center of Biotechnology of Sfax, University of Sfax-Tunisia, Sfax 3029, Tunisia
| | - Maria Tarapoulouzi
- Department of Chemistry, Faculty of Pure and Applied Science, University of Cyprus, P.O. Box 20537, Nicosia CY-1678, Cyprus
| | - Sofia Agriopoulou
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
| | - Teresa D'Amore
- IRCCS CROB, Centro di Riferimento Oncologico della Basilicata, 85028 Rionero in Vulture, Italy
| | - Theodoros Varzakas
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
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Zeng Q, Gao H, Yin S, Peng Y, Yang F, Fu Y, Deng X, Chen Y, Hou X, Wang Q, Jin Z, Song G, He J, Yin Y, Xu K. Genome-Wide Association Study and Identification of Candidate Genes for Intramuscular Fat Fatty Acid Composition in Ningxiang Pigs. Animals (Basel) 2023; 13:3192. [PMID: 37893916 PMCID: PMC10603709 DOI: 10.3390/ani13203192] [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: 07/27/2023] [Revised: 10/03/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
Ningxiang pigs exhibit a diverse array of fatty acids, making them an intriguing model for exploring the genetic underpinnings of fatty acid metabolism. We conducted a genome-wide association study using a dataset comprising 50,697 single-nucleotide polymorphisms (SNPs) and samples from over 600 Ningxiang pigs. Our investigation yielded novel candidate genes linked to five saturated fatty acids (SFAs), four monounsaturated fatty acids (MUFAs), and five polyunsaturated fatty acids (PUFAs). Significant associations with SFAs, MUFAs, and PUFAs were found for 37, 21, and 16 SNPs, respectively. Notably, some SNPs have significant PVE, such as ALGA0047587, which can explain 89.85% variation in Arachidic acid (C20:0); H3GA0046208 and DRGA0016063 can explain a total of 76.76% variation in Elaidic Acid (C18:1n-9(t)), and the significant SNP ALGA0031262 of Arachidonic acid (C20:4n-6) can explain 31.76% of the variation. Several significant SNPs were positioned proximally to previously reported genes. In total, we identified 11 candidate genes (hnRNPU, CEPT1, ATP1B1, DPT, DKK1, PRKG1, EXT2, MEF2C, IL17RA, ITGA1 and ALOX5), six candidate genes (ALOX5AP, MEDAG, ISL1, RXRB, CRY1, and CDKAL1), and five candidate genes (NDUFA4L2, SLC16A7, OTUB1, EIF4E and ROBO2) associated with SFAs, MUFAs, and PUFAs, respectively. These findings hold great promise for advancing breeding strategies aimed at optimizing meat quality and enhancing lipid metabolism within the intramuscular fat (IMF) of Ningxiang pigs.
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Affiliation(s)
- Qinghua Zeng
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Hu Gao
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Shishu Yin
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Yinglin Peng
- Hunan Institute of Animal & Veterinary Science, Changsha 410131, China
| | - Fang Yang
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Yawei Fu
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Xiaoxiao Deng
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Yue Chen
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Xiaohong Hou
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Qian Wang
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Zhao Jin
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Gang Song
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
| | - Jun He
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Yulong Yin
- Animal Nutrition Genome and Germplasm Innovation Research Center, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
| | - Kang Xu
- Laboratory of Animal Nutrition Physiology and Metabolism, The Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
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7
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Anwar H, Anwar T, Murtaza S. Review on food quality assessment using machine learning and electronic nose system. BIOSENSORS AND BIOELECTRONICS: X 2023; 14:100365. [DOI: 10.1016/j.biosx.2023.100365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Putri LA, Rahman I, Puspita M, Hidayat SN, Dharmawan AB, Rianjanu A, Wibirama S, Roto R, Triyana K, Wasisto HS. Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication. NPJ Sci Food 2023; 7:31. [PMID: 37328497 DOI: 10.1038/s41538-023-00205-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 05/26/2023] [Indexed: 06/18/2023] Open
Abstract
Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.
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Affiliation(s)
- Linda Ardita Putri
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
| | - Iman Rahman
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
| | - Mayumi Puspita
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
- Indonesian Oil Palm Research Institute, Jalan Taman Kencana No 1, Bogor, 16128, Indonesia
| | | | - Agus Budi Dharmawan
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Faculty of Information Technology, Universitas Tarumanagara, Jl. Letjen S. Parman No. 1, Jakarta, 11440, Indonesia
| | - Aditya Rianjanu
- Department of Materials Engineering, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Jati Agung, Lampung, 35365, Indonesia
| | - Sunu Wibirama
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Yogyakarta, 55281, Indonesia
| | - Roto Roto
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
| | - Kuwat Triyana
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia.
- Institute of Halal Industry and System (IHIS), Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281, Indonesia.
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Wu K, Debliquy M, Zhang C. Metal-oxide-semiconductor resistive gas sensors for fish freshness detection. Compr Rev Food Sci Food Saf 2023; 22:913-945. [PMID: 36537904 DOI: 10.1111/1541-4337.13095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/09/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Fish are prone to spoilage and deterioration during processing, storage, or transportation. Therefore, there is a need for rapid and efficient techniques to detect and evaluate fish freshness during different periods or conditions. Gas sensors are increasingly important in the qualitative and quantitative evaluation of high-protein foods, including fish. Among them, metal-oxide-semiconductor resistive (MOSR) sensors with advantages such as low cost, small size, easy integration, and high sensitivity have been extensively studied in the past few years, which gradually show promising practical application prospects. Herein, we take the detection, classification, and assessment of fish freshness as the actual demand, and summarize the physical and chemical changes of fish during the spoilage process, the volatile marker gases released, and their production mechanisms. Then, we introduce the advantages, performance parameters, and working principles of gas sensors, and summarize the MOSR gas sensors aimed at detecting different kinds of volatile marker gases of fish spoiling in the last 5 years. After that, this paper reviews the research and application progress of MOSR gas sensor arrays and electronic nose technology for various odor indicators and fish freshness detection. Finally, this review points out the multifaceted challenges (sampling system, sensing module, and pattern recognition technology) faced by the rapid detection technology of fish freshness based on metal oxide gas sensors, and the potential solutions and development directions are proposed from the view of multidisciplinary intersection.
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Affiliation(s)
- Kaidi Wu
- College of Mechanical Engineering, Yangzhou University, Yangzhou, China
- Service de Science des Matériaux, Faculté Polytechnique, Université de Mons, Mons, Belgium
| | - Marc Debliquy
- Service de Science des Matériaux, Faculté Polytechnique, Université de Mons, Mons, Belgium
| | - Chao Zhang
- College of Mechanical Engineering, Yangzhou University, Yangzhou, China
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10
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Luo Z, Ni K, Zhou Y, Chang G, Yu J, Zhang C, Yin W, Chen D, Li S, Kuang S, Zhang P, Li K, Bai J, Wang X. Inactivation of two SARS-CoV-2 virus surrogates by electron beam irradiation on large yellow croaker slices and their packaging surfaces. Food Control 2023; 144:109340. [PMID: 36091572 PMCID: PMC9445444 DOI: 10.1016/j.foodcont.2022.109340] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/16/2022] [Accepted: 08/25/2022] [Indexed: 11/15/2022]
Abstract
The detection of infectious SARS-CoV-2 in food and food packaging associated with the cold chain has raised concerns about the possible transmission pathway of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in foods transported through cold-chain logistics and the need for novel decontamination strategies. In this study, the effect of electron beam (E-beam) irradiation on the inactivation of two SARS-CoV-2surrogate, viruses porcine epidemic diarrhea virus (PEDV) and porcine transmissible gastroenteritis virus (TGEV), in culture medium and food substrate, and on food substrate were investigated. The causes of virus inactivation were also investigated by transmission electron microscopy (TEM) and Quantitative Real-time PCR (QRT-PCR). Samples packed inside and outside, including virus-inoculated large yellow croaker and virus suspensions, were irradiated with E-beam irradiation (2, 4, 6, 8, 10 kGy) under refrigerated (0 °C)and frozen (-18 °C) conditions. The titers of both viruses in suspension and fish decreased significantly (P < 0.05) with increasing doses of E-beam irradiation. The maximum D10 value of both viruses in suspension and fish was 1.24 kGy. E-beam irradiation at doses below 10 kGy was found to destroy the spike proteins of both SARS-CoV-2 surrogate viruses by transmission electron microscopy (TEM) and negative staining of thin-sectioned specimens, rendering them uninfectious. E-beam irradiation at doses greater than 10 kGy was also found to degrade viral genomic RNA by qRT-PCR. There were no significant differences in color, pH, TVB-N, TBARS, and sensory properties of irradiated fish samples at doses below 10 kGy. These findings suggested that E-beam irradiation has the potential to be developed as an efficient non-thermal treatment to reduce SARS-CoV-2 contamination in foods transported through cold chain foods to reduce the risk of SARS-CoV-2 infection in humans through the cold chain.
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Affiliation(s)
- Zonghong Luo
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Ke Ni
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Yuancheng Zhou
- Livestock and Poultry Biological Products Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610066, China
| | - Guanhong Chang
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Jiangtao Yu
- Yangling Hesheng Irradiation Technologies Co., Ltd., Yangling, Shaanxi, 712100, China
| | - Chunling Zhang
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Wenqi Yin
- Livestock and Poultry Biological Products Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610066, China
| | - Dishi Chen
- Sichuan Animal Disease Prevention and Control Center, Chengdu, 610041, China
| | - Shuwei Li
- Livestock and Poultry Biological Products Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610066, China
| | - Shengyao Kuang
- Livestock and Poultry Biological Products Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610066, China
| | - Peng Zhang
- Yangling Hesheng Irradiation Technologies Co., Ltd., Yangling, Shaanxi, 712100, China
| | - Kui Li
- Yangling Hesheng Irradiation Technologies Co., Ltd., Yangling, Shaanxi, 712100, China
| | - Junqing Bai
- Yangling Hesheng Irradiation Technologies Co., Ltd., Yangling, Shaanxi, 712100, China
| | - Xin Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
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Hassoun A, Cropotova J, Trollman H, Jagtap S, Garcia-Garcia G, Parra-López C, Nirmal N, Özogul F, Bhat Z, Aït-Kaddour A, Bono G. Use of industry 4.0 technologies to reduce and valorize seafood waste and by-products: A narrative review on current knowledge. Curr Res Food Sci 2023; 6:100505. [PMID: 37151380 PMCID: PMC10160358 DOI: 10.1016/j.crfs.2023.100505] [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: 02/22/2023] [Revised: 04/07/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Fish and other seafood products represent a valuable source of many nutrients and micronutrients for the human diet and contribute significantly to global food security. However, considerable amounts of seafood waste and by-products are generated along the seafood value and supply chain, from the sea to the consumer table, causing severe environmental damage and significant economic loss. Therefore, innovative solutions and alternative approaches are urgently needed to ensure a better management of seafood discards and mitigate their economic and environmental burdens. The use of emerging technologies, including the fourth industrial revolution (Industry 4.0) innovations (such as Artificial Intelligence, Big Data, smart sensors, and the Internet of Things, and other advanced technologies) to reduce and valorize seafood waste and by-products could be a promising strategy to enhance blue economy and food sustainability around the globe. This narrative review focuses on the issues and risks associated with the underutilization of waste and by-products resulting from fisheries and other seafood industries. Particularly, recent technological advances and digital tools being harnessed for the prevention and valorization of these natural invaluable resources are highlighted.
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Affiliation(s)
- Abdo Hassoun
- Univ. Littoral Côte D’Opale, UMRt 1158 BioEcoAgro, USC ANSES, INRAe, Univ. Artois, Univ. Lille, Univ. Picardie Jules Verne, Univ. Liège, Junia, F-62200, Boulogne-sur-Mer, France
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Corresponding author. Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France.
| | - Janna Cropotova
- Department of Biological Sciences, Ålesund, Norwegian University of Science and Technology, Larsgårdsvegen 4, 6025, Ålesund, Norway
- Corresponding author.
| | - Hana Trollman
- School of Business, University of Leicester, Leicester, LE2 1RQ, UK
| | - Sandeep Jagtap
- Sustainable Manufacturing Systems Centre, School of Aerospace, Transport & Manufacturing, Cranfield University, Cranfield, MK43 0AL, UK
| | - Guillermo Garcia-Garcia
- Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), P.O. Box 2027, 18080, Granada, Spain
| | - Carlos Parra-López
- Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), P.O. Box 2027, 18080, Granada, Spain
| | - Nilesh Nirmal
- Institute of Nutrition, Mahidol University, 999 Phutthamonthon 4 Road, Salaya, Phutthamonthon, Nakhon Pathom, 73170, Thailand
| | - Fatih Özogul
- Department of Seafood Processing Technology, Faculty of Fisheries, Cukurova University, 01330, Balcali, Adana, Turkey
| | - Zuhaib Bhat
- Division of Livestock Products Technology, SKUAST-Jammu, Jammu, 181102, J&K, India
| | | | - Gioacchino Bono
- Institute for Biological Resources and Marine Biotechnologies, National Research Council (IRBIM-CNR), Mazara Del Vallo, Italy
- Dipartimento di Scienze e Technologie Biologiche, Chimiche e Farmaceutiche (STEBICEF), Università Di Palermo, Viale Delle Scienze, 90128, Palermo, Italy
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12
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Kuswandi B, Hasanah F, Pratoko DK, Kristiningrum N. Colorimetric Paper-Based Dual Indicator Label for Real-Time Monitoring of Fish Freshness. Food Technol Biotechnol 2022; 60:499-508. [PMID: 36816881 PMCID: PMC9901331 DOI: 10.17113/ftb.60.04.22.7588] [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: 12/03/2021] [Accepted: 07/18/2022] [Indexed: 11/12/2022] Open
Abstract
Research background Fish freshness and quality monitoring are of high importance for consumers, retailers and fishing industry. Therefore, developing novel approaches that are simple, fast, non-destructive and inexpensive to monitor fish freshness in real time is of great value. One alternative is using Intelligent or smart packaging to monitor the freshness or conditions of packaged fish. Experimental approach On-package dual indicator label based on paper-based pH sensors was developed for real-time monitoring of the milkfish (Chanos chanos) freshness. The paper-based pH sensor was prepared using bromocresol purple (BCP) and bromothymol blue (BTB) that were immobilized onto a filter paper by dip coating. Herein, the fish degradation could be monitored visually by the dual indicator label, where the BCP changes from yellow to pink, then finally to purple, while the BTP changes from orange to green-yellow, and finally to green-blue to indicate fresh, medium fresh or spoiled product, respectively. Results and conclusion The label responds to the pH change caused by the fish degradation and the colour of dual indicator changes to show the fish freshness at room temperature and chiller conditions. This pH change was followed by changes in the other parameters related to fish freshness, such as total volatile basic nitrogen (TVBN), total viable count (TVC), texture and odour. The threshold of fish spoilage at room temperature was observed at 8 h and under chiller conditions at 7 days when the deterioration time point was indicated by the colour changes. Thus, it can be concluded that the dual indicator label can be applied as a simple and low-cost on-package active label for fish freshness monitoring. Novelty and scientific contribution Increasing consumer concerns about quality and safe food worldwide has boosted the search for a novel approach to food monitoring. In this work, a simple and practical on-package dual indicator label for real-time monitoring of fish freshness was developed. The colorimetric pH sensor was obtained simply by dip-coating of filter paper, yet it enables easy and accurate detection of fish spoilage with the naked eye. Similarly, the dual indicator label changes colour for other freshness parameters, such as TVBN, TVC, texture and odour.
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13
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Formation and Analysis of Volatile and Odor Compounds in Meat-A Review. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27196703. [PMID: 36235239 PMCID: PMC9572956 DOI: 10.3390/molecules27196703] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/03/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
The volatile composition and odor of meat and meat products is based on the precursors present in the raw meat. These are influenced by various pre-slaughter factors (species, breed, sex, age, feed, muscle type). Furthermore, post-mortem conditions (chiller aging, cooking conditions, curing, fermentation, etc.) determine the development of meat volatile organic compounds (VOCs). In this review, the main reactions leading to the development of meat VOCs such as the Maillard reaction; Strecker degradation; lipid oxidation; and thiamine, carbohydrate, and nucleotide degradation are described. The important pre-slaughter factors and post-mortem conditions influencing meat VOCs are discussed. Finally, the pros, cons, and future perspectives of the most commonly used sample preparation techniques (solid-phase microextraction, stir bar sorptive extraction, dynamic headspace extraction) and analytical methods (gas chromatography mass spectrometry and olfactometry, as well as electronic noses) for the analysis of meat VOCs are discussed, and the continued importance of sensorial analysis is pinpointed.
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14
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Andre RS, Mercante LA, Facure MHM, Sanfelice RC, Fugikawa-Santos L, Swager TM, Correa DS. Recent Progress in Amine Gas Sensors for Food Quality Monitoring: Novel Architectures for Sensing Materials and Systems. ACS Sens 2022; 7:2104-2131. [PMID: 35914109 DOI: 10.1021/acssensors.2c00639] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The increasing demand for food production has necessitated the development of sensitive and reliable methods of analysis, which allow for the optimization of storage and distribution while ensuring food safety. Methods to quantify and monitor volatile and biogenic amines are key to minimizing the waste of high-protein foods and to enable the safe consumption of fresh products. Novel materials and device designs have allowed the development of portable and reliable sensors that make use of different transduction methods for amine detection and food quality monitoring. Herein, we review the past decade's advances in volatile amine sensors for food quality monitoring. First, the role of volatile and biogenic amines as a food-quality index is presented. Moreover, a comprehensive overview of the distinct amine gas sensors is provided according to the transduction method, operation strategies, and distinct materials (e.g., metal oxide semiconductors, conjugated polymers, carbon nanotubes, graphene and its derivatives, transition metal dichalcogenides, metal organic frameworks, MXenes, quantum dots, and dyes, among others) employed in each case. These include chemoresistive, fluorometric, colorimetric, and microgravimetric sensors. Emphasis is also given to sensor arrays that record the food quality fingerprints and wireless devices that operate as radiofrequency identification (RFID) tags. Finally, challenges and future opportunities on the development of new amine sensors are presented aiming to encourage further research and technological development of reliable, integrated, and remotely accessible devices for food-quality monitoring.
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Affiliation(s)
- Rafaela S Andre
- Nanotechnology National Laboratory for Agriculture (LNNA), Embrapa Instrumentação, 13560-970, Sao Carlos, São Paulo, Brazil
| | - Luiza A Mercante
- Institute of Chemistry, Federal University of Bahia (UFBA), 40170-280, Salvador, Bahia, Brazil
| | - Murilo H M Facure
- Nanotechnology National Laboratory for Agriculture (LNNA), Embrapa Instrumentação, 13560-970, Sao Carlos, São Paulo, Brazil.,PPGQ, Department of Chemistry, Center for Exact Sciences and Technology, Federal University of Sao Carlos (UFSCar), 13565-905, Sao Carlos, São Paulo, Brazil
| | - Rafaela C Sanfelice
- Science and Technology Institute, Federal University of Alfenas, 37715-400, Poços de Caldas, Minas Gerais, Brazil
| | - Lucas Fugikawa-Santos
- São Paulo State University - UNESP, Institute of Geosciences and Exact Sciences, 13506-700, Rio Claro, São Paulo, Brazil
| | - Timothy M Swager
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Daniel S Correa
- Nanotechnology National Laboratory for Agriculture (LNNA), Embrapa Instrumentação, 13560-970, Sao Carlos, São Paulo, Brazil.,PPGQ, Department of Chemistry, Center for Exact Sciences and Technology, Federal University of Sao Carlos (UFSCar), 13565-905, Sao Carlos, São Paulo, Brazil
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15
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Govari M, Tryfinopoulou P, Panagou EZ, Nychas GJE. Application of Fourier Transform Infrared (FT-IR) Spectroscopy, Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets. Foods 2022; 11:foods11152356. [PMID: 35954122 PMCID: PMC9367857 DOI: 10.3390/foods11152356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/25/2022] Open
Abstract
The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO2, 19% O2, 48% N2) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R2) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSEC) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R2 values of 0.79 and 0.77, and RMSEC values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R2 (0.34 and 0.17) and the highest RMSEC (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets.
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16
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Grassi S, Benedetti S, Magnani L, Pianezzola A, Buratti S. Seafood freshness: e-nose data for classification purposes. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108994] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Abstract
Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.
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18
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Discrimination of spoiled beef and salmon stored under different atmospheres by an optoelectronic nose. Comparison with GC-MS measurements. FUTURE FOODS 2022. [DOI: 10.1016/j.fufo.2021.100106] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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19
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Effects of chilling rate on the freshness and microbial community composition of lamb carcasses. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112559] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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20
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Pereira PF, de Sousa Picciani PH, Calado V, Tonon RV. Electrical gas sensors for meat freshness assessment and quality monitoring: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.08.036] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using Sensor Fusion. BIOSENSORS-BASEL 2021; 11:bios11100366. [PMID: 34677322 PMCID: PMC8534206 DOI: 10.3390/bios11100366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/27/2021] [Accepted: 09/29/2021] [Indexed: 11/17/2022]
Abstract
The continuous development of more accurate and selective bio- and chemo-sensors has led to a growing use of sensor arrays in different fields, such as health monitoring, cell culture analysis, bio-signals processing, or food quality tracking. The analysis and information extraction from the amount of data provided by these sensor arrays is possible based on Machine Learning techniques applied to sensor fusion. However, most of these computing solutions are implemented on costly and bulky computers, limiting its use in in-situ scenarios outside complex laboratory facilities. This work presents the application of machine learning techniques in food quality assessment using a single Field Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power consumption as well as low-size allow the application of the proposed solution even in space constrained places, as in food manufacturing chains. As an example, the proposed system is tested on an e-nose developed for beef classification and microbial population prediction.
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22
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Karunathilaka SR, Ellsworth Z, Yakes BJ. Detection of decomposition in mahi-mahi, croaker, red snapper, and weakfish using an electronic-nose sensor and chemometric modeling. J Food Sci 2021; 86:4148-4158. [PMID: 34402528 DOI: 10.1111/1750-3841.15878] [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: 01/21/2021] [Revised: 06/15/2021] [Accepted: 07/13/2021] [Indexed: 12/01/2022]
Abstract
This study evaluated an electronic-nose (e-nose) sensor in combination with support vector machine (SVM) modeling for predicting the decomposition state of four types of fish fillets: mahi-mahi, croaker, red snapper, and weakfish. The National Seafood Sensory Expert scored fillets were thawed, 10-g portions were weighed into glass jars which were then sealed, and the jars were held at approximately 30°C to allow volatile components to be trapped and available for analysis. The measurement of the sample vial headspace was performed with an e-nose device consisting of nanocomposite, metal oxide semiconductor (MOS), electrochemical, and photoionization sensors. Classification models were then trained based on the sensory grade of each fillet, and the e-nose companion chemometric software identified that eight MOS were the most informative for determining a sensory pass from sensory fail sample. For SVM, the cross-validation (CV) correct classification rates for mahi-mahi, croaker, red snapper, and weakfish were 100%, 100%, 97%, and 97%, respectively. When the SVM prediction performances of the eight MOS were evaluated using a calibration-independent test set of samples, correct classification rates of 93-100% were observed. Based on these results, the e-nose measurements coupled with SVM models were found to be potentially promising for predicting the spoilage of these four fish species. PRACTICAL APPLICATION: This report describes the application of an electronic-nose sensor as a potential rapid and low-cost screening method for fish spoilage. It could provide regulators and stakeholders with a practical tool to rapidly and accurately assess fish decomposition.
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Affiliation(s)
- Sanjeewa R Karunathilaka
- Joint Institute for Food Safety and Applied Nutrition, University of Maryland, College Park, Maryland, USA
| | - Zachary Ellsworth
- Joint Institute for Food Safety and Applied Nutrition, University of Maryland, College Park, Maryland, USA
| | - Betsy Jean Yakes
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland, USA
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23
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Matindoust S, Farzi G, Nejad MB, Shahrokhabadi MH. Polymer-based gas sensors to detect meat spoilage: A review. REACT FUNCT POLYM 2021. [DOI: 10.1016/j.reactfunctpolym.2021.104962] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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24
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Cardoso PG, Gonçalves O, Carvalho MF, Ozório R, Vaz-Pires P. Seasonal Evaluation of Freshness Profile of Commercially Important Fish Species. Foods 2021; 10:foods10071567. [PMID: 34359437 PMCID: PMC8307230 DOI: 10.3390/foods10071567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/25/2021] [Accepted: 07/02/2021] [Indexed: 12/30/2022] Open
Abstract
Freshness is considered one of the most important parameters to judge the quality of most fish products. In the current study, the seasonality effect on the freshness profile of different economic fish species was evaluated for the first time, using three different approaches (sensory: Quality Index Method (QIM) and European (EC) Scheme; physical: Torrymeter (TRM) values; and microbiological analyses: Total Viable Counts (TVC) and degradative bacteria). Over a year, individuals of farmed fish Sparus aurata and Dicentrarchus labrax, as well as the wild fish Trachurus trachurus, Scomber colias, and Sardina pilchardus, were sampled seasonally for the evaluation of their freshness profile over 10 days on ice. In general, data showed an increase in QIM values, a decline in TRM, and an increase of spoilage bacteria throughout the storage time, revealing a clear temporal degradation of the quality of the fish. Additionally, some signs of seasonality effect could only be observed for some species. For example, the seabass D. labrax showed lower numbers of degradative bacteria in winter than in the other seasons, suggesting a high potential to be marketed in a fresher condition, especially during that season. On the other hand, S. colias showed higher freshness scores (i.e., higher TRM values in spring and autumn and lower numbers of bacteria in summer) from spring to autumn. However, from the five studied species, S. colias presented the lowest freshness values, indicating a higher fragility of this species. This information is extremely relevant for consumers and retailers that want to invest in higher quality products, as they would thus be able to choose certain species in detriment of others. Additionally, obtained data showed that farmed species reached day 10 of storage time with lower values of QIM and microbial counts (cfu), as well as higher values of TRM, in relation to wild species. These results reinforce the idea that farmed fish can, under proper conditions, present high quality/freshness profile.
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Affiliation(s)
- Patrícia G. Cardoso
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros de Leixões, Av. General Norton de Matos, S/N, 4450-208 Matosinhos, Portugal; (O.G.); (M.F.C.); (R.O.); (P.V.-P.)
- Correspondence:
| | - Odete Gonçalves
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros de Leixões, Av. General Norton de Matos, S/N, 4450-208 Matosinhos, Portugal; (O.G.); (M.F.C.); (R.O.); (P.V.-P.)
| | - Maria F. Carvalho
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros de Leixões, Av. General Norton de Matos, S/N, 4450-208 Matosinhos, Portugal; (O.G.); (M.F.C.); (R.O.); (P.V.-P.)
- ICBAS—Abel Salazar Institute for the Biomedical Sciences, University of Porto, R. Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
| | - Rodrigo Ozório
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros de Leixões, Av. General Norton de Matos, S/N, 4450-208 Matosinhos, Portugal; (O.G.); (M.F.C.); (R.O.); (P.V.-P.)
- ICBAS—Abel Salazar Institute for the Biomedical Sciences, University of Porto, R. Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
| | - Paulo Vaz-Pires
- CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros de Leixões, Av. General Norton de Matos, S/N, 4450-208 Matosinhos, Portugal; (O.G.); (M.F.C.); (R.O.); (P.V.-P.)
- ICBAS—Abel Salazar Institute for the Biomedical Sciences, University of Porto, R. Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
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25
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Li Y, Cui L, Du F, Han X, Li J. Impacts of ε‐polylysine hydrochloride with thymol on biogenic amines formation and biochemical changes of squid (
Illex
argentinus
). J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15505] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Yingchang Li
- College of Food Science and Technology Bohai University Jinzhou China
- Food Safety Key Lab of Liaoning Province Bohai University Jinzhou China
- National & Local Joint Engineering Research Center of Storage Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products Jinzhou China
| | - Lei Cui
- College of Food Science and Technology Bohai University Jinzhou China
- Food Safety Key Lab of Liaoning Province Bohai University Jinzhou China
- National & Local Joint Engineering Research Center of Storage Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products Jinzhou China
| | - Fengxia Du
- College of Food Science and Technology Bohai University Jinzhou China
- Food Safety Key Lab of Liaoning Province Bohai University Jinzhou China
- National & Local Joint Engineering Research Center of Storage Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products Jinzhou China
| | - Xiao Han
- College of Food Science and Technology Bohai University Jinzhou China
- Food Safety Key Lab of Liaoning Province Bohai University Jinzhou China
- National & Local Joint Engineering Research Center of Storage Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products Jinzhou China
| | - Jianrong Li
- College of Food Science and Technology Bohai University Jinzhou China
- Food Safety Key Lab of Liaoning Province Bohai University Jinzhou China
- National & Local Joint Engineering Research Center of Storage Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products Jinzhou China
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26
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Makarichian A, Amiri Chayjan R, Ahmadi E, Mohtasebi SS. Assessment the influence of different drying methods and pre-storage periods on garlic (Allium Sativum L.) aroma using electronic nose. FOOD AND BIOPRODUCTS PROCESSING 2021. [DOI: 10.1016/j.fbp.2021.02.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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27
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Galvan D, Aquino A, Effting L, Mantovani ACG, Bona E, Conte-Junior CA. E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review. Crit Rev Food Sci Nutr 2021; 62:6605-6645. [PMID: 33779434 DOI: 10.1080/10408398.2021.1903384] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
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Affiliation(s)
- Diego Galvan
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Adriano Aquino
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
| | - Luciane Effting
- Chemistry Department, State University of Londrina (UEL), Londrina, PR, Brazil
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Federal University of Technology Paraná (UTFPR), Campo Mourão, PR, Brazil
| | - Carlos Adam Conte-Junior
- Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, RJ, Brazil.,Nanotechnology Network, Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ), Rio de Janeiro, RJ, Brazil
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Kim S, Brady J, Al-Badani F, Yu S, Hart J, Jung S, Tran TT, Myung NV. Nanoengineering Approaches Toward Artificial Nose. Front Chem 2021; 9:629329. [PMID: 33681147 PMCID: PMC7935515 DOI: 10.3389/fchem.2021.629329] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 01/05/2021] [Indexed: 12/16/2022] Open
Abstract
Significant scientific efforts have been made to mimic and potentially supersede the mammalian nose using artificial noses based on arrays of individual cross-sensitive gas sensors over the past couple decades. To this end, thousands of research articles have been published regarding the design of gas sensor arrays to function as artificial noses. Nanoengineered materials possessing high surface area for enhanced reaction kinetics and uniquely tunable optical, electronic, and optoelectronic properties have been extensively used as gas sensing materials in single gas sensors and sensor arrays. Therefore, nanoengineered materials address some of the shortcomings in sensitivity and selectivity inherent in microscale and macroscale materials for chemical sensors. In this article, the fundamental gas sensing mechanisms are briefly reviewed for each material class and sensing modality (electrical, optical, optoelectronic), followed by a survey and review of the various strategies for engineering or functionalizing these nanomaterials to improve their gas sensing selectivity, sensitivity and other measures of gas sensing performance. Specifically, one major focus of this review is on nanoscale materials and nanoengineering approaches for semiconducting metal oxides, transition metal dichalcogenides, carbonaceous nanomaterials, conducting polymers, and others as used in single gas sensors or sensor arrays for electrical sensing modality. Additionally, this review discusses the various nano-enabled techniques and materials of optical gas detection modality, including photonic crystals, surface plasmonic sensing, and nanoscale waveguides. Strategies for improving or tuning the sensitivity and selectivity of materials toward different gases are given priority due to the importance of having cross-sensitivity and selectivity toward various analytes in designing an effective artificial nose. Furthermore, optoelectrical sensing, which has to date not served as a common sensing modality, is also reviewed to highlight potential research directions. We close with some perspective on the future development of artificial noses which utilize optical and electrical sensing modalities, with additional focus on the less researched optoelectronic sensing modality.
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Affiliation(s)
- Sanggon Kim
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
| | - Jacob Brady
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
| | - Faraj Al-Badani
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
| | - Sooyoun Yu
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Joseph Hart
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Sungyong Jung
- Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, United States
| | - Thien-Toan Tran
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Nosang V. Myung
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
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Franceschelli L, Berardinelli A, Dabbou S, Ragni L, Tartagni M. Sensing Technology for Fish Freshness and Safety: A Review. SENSORS 2021; 21:s21041373. [PMID: 33669188 PMCID: PMC7919655 DOI: 10.3390/s21041373] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/09/2021] [Accepted: 02/12/2021] [Indexed: 02/06/2023]
Abstract
Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided.
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Affiliation(s)
- Leonardo Franceschelli
- Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy;
- Correspondence:
| | - Annachiara Berardinelli
- Department of Industrial Engineering, University of Trento, Via Sommarive, 9, Povo, 38123 Trento, Italy;
- Centre Agriculture Food Environment, University of Trento, Via E. Mach, 1, S. Michele All’Adige, 38010 Trento, Italy;
| | - Sihem Dabbou
- Centre Agriculture Food Environment, University of Trento, Via E. Mach, 1, S. Michele All’Adige, 38010 Trento, Italy;
| | - Luigi Ragni
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Piazza Goidanich 60, 47521 Cesena, Italy;
- Interdepartmental Center for Industrial Agri-Food Research, University of Bologna, Via Q. Bucci 336, 47521 Cesena, Italy
| | - Marco Tartagni
- Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell’Università, 50, 47521 Cesena, Italy;
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Castrica M, Chiesa LM, Nobile M, De Battisti F, Siletti E, Pessina D, Panseri S, Balzaretti CM. Rapid safety and quality control during fish shelf-life by using a portable device. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:315-326. [PMID: 32627837 DOI: 10.1002/jsfa.10646] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/23/2020] [Accepted: 07/06/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Fish consumption is increasing nowadays both because of its positive role for health due to the abundant presence of unsaturated fatty acids and for its use in many new food preparations (e.g. raw fillet used for uncooked sushi and sashimi dishes). The growing food industry and increased demand for the long-term storage and preservation of food have created the need to develop methods that can easily track and preserve food freshness and safety throughout shelf-life (production, storage, shipment, and consumption). While E-nose technologies have already been used and tested for these purposes, scarce information is available in the literature on the feasibility of using other food devices to detect changes in perishable food like fish during shelf-life in order to predict and correctly manage all food storage phases. The aim of the present study was to investigate the potential of Food Sniffer® portable devices to define the quality and safety of salmon fillet and burger (Salmo salar) packaged in modified atmosphere at two refrigerated conditions (4 and 8 °C). RESULTS An increase in biogenic amines and volatile compounds especially ketones and alcohols were observed, with large amounts at final storage times of 8 °C temperature. CONCLUSION The Food Sniffer® application was able to anticipate unacceptability conditions of salmon samples also correlated with chemical and microbiological parameters. This could represent a valid support for food industry and retail to manage perishable food commodities preventing possible food risk as well. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Marta Castrica
- Department of Health, Animal Science and Food Safety, Università degli Studi di Milano, Milan, Italy
| | - Luca Maria Chiesa
- Department of Health, Animal Science and Food Safety, Università degli Studi di Milano, Milan, Italy
| | - Maria Nobile
- Department of Health, Animal Science and Food Safety, Università degli Studi di Milano, Milan, Italy
| | | | - Elena Siletti
- Department of Economics, Management, and Quantitative Methods, Milan, Italy
| | - Davide Pessina
- Quality Department, Italian retail Il Gigante SpA, Milan, Italy
| | - Sara Panseri
- Department of Health, Animal Science and Food Safety, Università degli Studi di Milano, Milan, Italy
| | - Claudia M Balzaretti
- Department of Health, Animal Science and Food Safety, Università degli Studi di Milano, Milan, Italy
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Guo L, Wang T, Wu Z, Wang J, Wang M, Cui Z, Ji S, Cai J, Xu C, Chen X. Portable Food-Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2004805. [PMID: 33006183 DOI: 10.1002/adma.202004805] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/24/2020] [Indexed: 05/14/2023]
Abstract
Artificial scent screening systems (known as electronic noses, E-noses) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-noses have been commercialized because they suffer from either sensing or pattern-recognition issues. Here, cross-reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes-comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate-form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non-destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.
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Affiliation(s)
- Lingling Guo
- International Joint Research Laboratory for Biointerface and Biodetection, State Key Lab of Food Science and Technology, and School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu Province, 214122, P. R. China
| | - Ting Wang
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhonghua Wu
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Jianwu Wang
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ming Wang
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zequn Cui
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Shaobo Ji
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Jianfei Cai
- Department of Data Science & AI, Monash University, Clayton, Victoria, 3168, Australia
| | - Chuanlai Xu
- International Joint Research Laboratory for Biointerface and Biodetection, State Key Lab of Food Science and Technology, and School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu Province, 214122, P. R. China
| | - Xiaodong Chen
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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Feng H, Zhang M, Liu P, Liu Y, Zhang X. Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage. Foods 2020; 9:foods9111579. [PMID: 33143312 PMCID: PMC7692724 DOI: 10.3390/foods9111579] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 10/27/2020] [Indexed: 11/16/2022] Open
Abstract
Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.
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Affiliation(s)
- Huanhuan Feng
- College of Engineering, China Agricultural University, Beijing 100083, China; (H.F.); (M.Z.); (P.L.)
- Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing 100083, China
| | - Mengjie Zhang
- College of Engineering, China Agricultural University, Beijing 100083, China; (H.F.); (M.Z.); (P.L.)
- Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing 100083, China
| | - Pengfei Liu
- College of Engineering, China Agricultural University, Beijing 100083, China; (H.F.); (M.Z.); (P.L.)
- Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing 100083, China
| | - Yiliu Liu
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
| | - Xiaoshuan Zhang
- College of Engineering, China Agricultural University, Beijing 100083, China; (H.F.); (M.Z.); (P.L.)
- Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing 100083, China
- Correspondence: ; Tel.: +86-(0)-10-6273-6717
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Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device. Foods 2020; 9:foods9091294. [PMID: 32938016 PMCID: PMC7555696 DOI: 10.3390/foods9091294] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/09/2020] [Accepted: 09/11/2020] [Indexed: 12/29/2022] Open
Abstract
This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950-1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square-discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.
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Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm. SENSORS 2020; 20:s20154065. [PMID: 32707788 PMCID: PMC7436048 DOI: 10.3390/s20154065] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/19/2020] [Accepted: 07/19/2020] [Indexed: 01/02/2023]
Abstract
Electronic nose is a kind of widely-used artificial olfactory system for the detection and classification of volatile organic compounds. The high dimensionality of data collected by electronic noses can hinder the process of pattern recognition. Thus, the feature selection is an essential stage in building a robust and accurate model for gas recognition. This paper proposed an improved grey wolf optimizer (GWO) based algorithm for feature selection and applied it on electronic nose data for the first time. Two mechanisms are employed for the proposed algorithm. The first mechanism contains two novel binary transform approaches, which are used for searching feature subset from electronic nose data that maximizing the classification accuracy while minimizing the number of features. The second mechanism is based on the adaptive restart approach, which attempts to further enhance the search capability and stability of the algorithm. The proposed algorithm is compared with five efficient feature selection algorithms on three electronic nose data sets. Three classifiers and multiple assessment indicators are used to evaluate the performance of algorithm. The experimental results show that the proposed algorithm can effectively select the feature subsets that are conducive to gas recognition, which can improve the performance of the electronic nose.
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35
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Curto B, Moreno V, García-Esteban JA, Blanco FJ, González I, Vivar A, Revilla I. Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network. SENSORS 2020; 20:s20123566. [PMID: 32599728 PMCID: PMC7349398 DOI: 10.3390/s20123566] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/18/2020] [Accepted: 06/22/2020] [Indexed: 11/17/2022]
Abstract
The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.
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Affiliation(s)
- Belén Curto
- Department Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain; (B.C.); (J.A.G.-E.); (F.J.B.)
| | - Vidal Moreno
- Department Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain; (B.C.); (J.A.G.-E.); (F.J.B.)
- Correspondence: ; Tel.: +34-628-480-616
| | - Juan Alberto García-Esteban
- Department Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain; (B.C.); (J.A.G.-E.); (F.J.B.)
| | - Francisco Javier Blanco
- Department Computer Science and Automation, University of Salamanca, 37008 Salamanca, Spain; (B.C.); (J.A.G.-E.); (F.J.B.)
| | - Inmaculada González
- Department of Analytical Chemistry, Nutrition and Bromatology, University of Salamanca, 37008 Salamanca, Spain;
| | - Ana Vivar
- Department Construction and Agronomy, University of Salamanca, 37008 Salamanca, Spain; (A.V.); (I.R.)
| | - Isabel Revilla
- Department Construction and Agronomy, University of Salamanca, 37008 Salamanca, Spain; (A.V.); (I.R.)
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Mohd Ali M, Hashim N, Abd Aziz S, Lasekan O. Principles and recent advances in electronic nose for quality inspection of agricultural and food products. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.02.028] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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37
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38
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Fish meal freshness detection by GBDT based on a portable electronic nose system and HS-SPME–GC–MS. Eur Food Res Technol 2020. [DOI: 10.1007/s00217-020-03462-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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39
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Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets. SENSORS 2020; 20:s20041065. [PMID: 32075334 PMCID: PMC7070273 DOI: 10.3390/s20041065] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/05/2020] [Accepted: 02/11/2020] [Indexed: 02/07/2023]
Abstract
Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).
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40
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Lu B, Fu L, Nie B, Peng Z, Liu H. A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose. SENSORS 2019; 19:s19235333. [PMID: 31817006 PMCID: PMC6928832 DOI: 10.3390/s19235333] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 11/27/2019] [Accepted: 11/29/2019] [Indexed: 12/11/2022]
Abstract
The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios.
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Affiliation(s)
- Binchun Lu
- Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China; (B.L.); (L.F.)
| | - Lidan Fu
- Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China; (B.L.); (L.F.)
| | - Bo Nie
- Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China;
| | - Zhiyun Peng
- State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China;
| | - Hongying Liu
- Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China;
- Correspondence:
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