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Jia W, Ferragina A, Hamill R, Koidis A. Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI). Talanta 2024; 276:126199. [PMID: 38714010 DOI: 10.1016/j.talanta.2024.126199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/12/2024] [Accepted: 04/30/2024] [Indexed: 05/09/2024]
Abstract
Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.
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Affiliation(s)
- Wenyang Jia
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| | - Alessandro Ferragina
- Teagasc Food Research Centre, Food Quality and Sensory Science Department, Dublin, Ireland
| | - Ruth Hamill
- Teagasc Food Research Centre, Food Quality and Sensory Science Department, Dublin, Ireland
| | - Anastasios Koidis
- Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK.
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2
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Xun Z, Wang X, Xue H, Zhang Q, Yang W, Zhang H, Li M, Jia S, Qu J, Wang X. Deep machine learning identified fish flesh using multispectral imaging. Curr Res Food Sci 2024; 9:100784. [PMID: 39005497 PMCID: PMC11246001 DOI: 10.1016/j.crfs.2024.100784] [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: 04/18/2024] [Revised: 06/03/2024] [Accepted: 06/13/2024] [Indexed: 07/16/2024] Open
Abstract
Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial COI gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. CNN and QDA achieved more than 99% accuracy on the test set. By extracting the CNN features for optimization, a very high degree of separation was obtained for all species. Furthermore, based on the Gini index in RF, 11 bands were selected as key classification features for CNN, and an accuracy of 98% was achieved. Our study developed a successful pipeline for employing machine learning models (especially CNN) on MSI identification of fish flesh, and provided a convenient and non-destructive method to determine the marketing of fish flesh in the future.
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Affiliation(s)
- Zhuoran Xun
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Xuemeng Wang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Xue
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Qingzheng Zhang
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Wanqi Yang
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Hua Zhang
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Mingzhu Li
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Shangang Jia
- College of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jiangyong Qu
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Xumin Wang
- College of Life Sciences, Yantai University, Yantai, 264005, China
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3
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Fengou LC, Lytou AE, Tsekos G, Tsakanikas P, Nychas GJE. Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud. Food Chem 2024; 440:138184. [PMID: 38100963 DOI: 10.1016/j.foodchem.2023.138184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.
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Affiliation(s)
- Lemonia-Christina Fengou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - Anastasia E Lytou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - George Tsekos
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - Panagiotis Tsakanikas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| | - George-John E Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
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4
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Hassoun A, Anusha Siddiqui S, Smaoui S, Ucak İ, Arshad RN, Bhat ZF, Bhat HF, Carpena M, Prieto MA, Aït-Kaddour A, Pereira JA, Zacometti C, Tata A, Ibrahim SA, Ozogul F, Camara JS. Emerging Technological Advances in Improving the Safety of Muscle Foods: Framing in the Context of the Food Revolution 4.0. FOOD REVIEWS INTERNATIONAL 2022. [DOI: 10.1080/87559129.2022.2149776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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, Boulogne-sur-Mer, France
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
| | - Shahida Anusha Siddiqui
- Department of Biotechnology and Sustainability, Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Straubing, Germany
- German Institute of Food Technologies (DIL e.V.), Quakenbrück, Germany
| | - Slim Smaoui
- Laboratory of Microbial, Enzymatic Biotechnology and Biomolecules (LBMEB), Center of Biotechnology of Sfax, University of Sfax-Tunisia, Sfax, Tunisia
| | - İ̇lknur Ucak
- Faculty of Agricultural Sciences and Technologies, Nigde Omer Halisdemir University, Nigde, Turkey
| | - Rai Naveed Arshad
- Institute of High Voltage & High Current, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Zuhaib F. Bhat
- Division of Livestock Products Technology, SKUASTof Jammu, Jammu, Kashmir, India
| | - Hina F. Bhat
- Division of Animal Biotechnology, SKUASTof Kashmir, Kashmir, India
| | - María Carpena
- Nutrition and Bromatology Group, Analytical and Food Chemistry Department. Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
| | - Miguel A. Prieto
- Nutrition and Bromatology Group, Analytical and Food Chemistry Department. Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolonia, Bragança, Portugal
| | | | - Jorge A.M. Pereira
- CQM—Centro de Química da Madeira, Universidade da Madeira, Funchal, Portugal
| | - Carmela Zacometti
- Istituto Zooprofilattico Sperimentale Delle Venezie, Laboratorio di Chimica Sperimentale, Vicenza, Italy
| | - Alessandra Tata
- Istituto Zooprofilattico Sperimentale Delle Venezie, Laboratorio di Chimica Sperimentale, Vicenza, Italy
| | - Salam A. Ibrahim
- Food and Nutritional Sciences Program, North Carolina A&T State University, Greensboro, North Carolina, USA
| | - Fatih Ozogul
- Department of Seafood Processing Technology, Faculty of Fisheries, Cukurova University, Adana, Turkey
| | - José S. Camara
- CQM—Centro de Química da Madeira, Universidade da Madeira, Funchal, Portugal
- Departamento de Química, Faculdade de Ciências Exatas e Engenharia, Campus da Penteada, Universidade da Madeira, Funchal, Portugal
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Zhang Y, Kim Y, Puolanne E, Ertbjerg P. Role of freezing-induced myofibrillar protein denaturation in the generation of thaw loss: A review. Meat Sci 2022; 190:108841. [DOI: 10.1016/j.meatsci.2022.108841] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/13/2022] [Accepted: 05/01/2022] [Indexed: 01/08/2023]
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6
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Tirado-Kulieva VA, Hernández-Martínez E, Suomela JP. Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04023-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractThe constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.
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7
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Chu Y, Tan M, Bian C, Xie J. Effect of ultrasonic thawing on the physicochemical properties, freshness, and protein-related properties of frozen large yellow croaker (Pseudosciaena crocea). J Food Sci 2021; 87:52-67. [PMID: 34897680 DOI: 10.1111/1750-3841.15983] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 09/28/2021] [Accepted: 10/20/2021] [Indexed: 11/30/2022]
Abstract
Ultrasonic treatment (UT) was used to thaw large yellow croaker in this study, and the effect of various ultrasonic power levels on the quality of large yellow croaker was evaluated after thawing. The effects of ultrasonic on water holding capacity (WHC), moisture distribution, thiobarbituric acid-reactive substance (TBARs), total volatile base nitrogen (TVB-N), ATP degradation (related to K value), surface color change, free amino acid (FAA) content, total sulfhydryl group (SH) content, Fourier transform infrared absorption spectra (FT-IR), fluorescence emission spectra, and microscopic observations of large yellow croaker myofibrillar proteins were investigated. The thawing times of the control sample, 200UT, 240UT, 280UT, and 320UT samples were 1750, 1190, 810, 580, and 570 s, respectively, which indicated that ultrasonic radiation could improve thawing efficiency. Additionally, ultrasonic thawing maintained better freshness and color and inhibited lipid oxidation. Compared with fresh samples, the TVB-N of large yellow croaker thawed by ultrasonication increased by 12.68%, and the K value increased by 0.9%. The 240UT sample had tightly arranged myofibrils and fewer changes in the structures of myogenic fibrillar proteins than the fresh samples, and the SH content of 240UT was decreased by 8.17%. Use of excessive ultrasonic power (320 W) damaged the protein microstructure and the microstructure of large yellow croaker. In conclusion, sample 240UT maintained the quality of large yellow croaker better with minimal damage, which is recommended for rapid thawing. PRACTICAL APPLICATION: Ultrasonic waves improve the thawing efficiency of large yellow croaker and maintain the freshness and color of the fish. According to results, sample 240UT exhibited slight changes in the structure of the myofibril protein, but excessive ultrasonic power destroyed the microstructure and protein structure. Appropriate ultrasonic treatment to the thawing of fish has good prospects.
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Affiliation(s)
- Yuanming Chu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.,National Experimental Teaching Demonstration Center for Food Science and Engineering, Shanghai Ocean University, Shanghai, China
| | - Mingtang Tan
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.,National Experimental Teaching Demonstration Center for Food Science and Engineering, Shanghai Ocean University, Shanghai, China
| | - Chuhan Bian
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.,National Experimental Teaching Demonstration Center for Food Science and Engineering, Shanghai Ocean University, Shanghai, China
| | - Jing Xie
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.,National Experimental Teaching Demonstration Center for Food Science and Engineering, Shanghai Ocean University, Shanghai, China.,Shanghai Professional Technology Service Platform on Cold Chain Equipment Performance and Energy Saving Evaluation, Shanghai Ocean University, Shanghai, China.,Shanghai Engineering Research Center of Aquatic Product Processing & Preservation, Shanghai Ocean University, Shanghai, China
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8
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Massaro A, Stella R, Negro A, Bragolusi M, Miano B, Arcangeli G, Biancotto G, Piro R, Tata A. New strategies for the differentiation of fresh and frozen/thawed fish: A rapid and accurate non-targeted method by ambient mass spectrometry and data fusion (part A). Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108364] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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9
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Wang K, Li Z, Li J, Lin H. Raman spectroscopic techniques for nondestructive analysis of agri-foods: A state-of-the-art review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.10.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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10
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Abstract
Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.
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Affiliation(s)
- George-John Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece;
| | - Emma Sims
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, United Kingdom
| | - Panagiotis Tsakanikas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece;
| | - Fady Mohareb
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, United Kingdom
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11
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Fengou LC, Tsakanikas P, Nychas GJE. Rapid detection of minced pork and chicken adulteration in fresh, stored and cooked ground meat. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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12
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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14
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Detection of Meat Adulteration Using Spectroscopy-Based Sensors. Foods 2021; 10:foods10040861. [PMID: 33920872 PMCID: PMC8071343 DOI: 10.3390/foods10040861] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 03/31/2021] [Accepted: 04/13/2021] [Indexed: 11/16/2022] Open
Abstract
Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.
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15
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Measurement of water fractions in freeze-dried shiitake mushroom by means of multispectral imaging (MSI) and low-field nuclear magnetic resonance (LF-NMR). J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2020.103694] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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16
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Dourou D, Grounta A, Argyri AA, Froutis G, Tsakanikas P, Nychas GJE, Doulgeraki AI, Chorianopoulos NG, Tassou CC. Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning. Front Microbiol 2021; 11:623788. [PMID: 33633698 PMCID: PMC7901899 DOI: 10.3389/fmicb.2020.623788] [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: 10/30/2020] [Accepted: 12/22/2020] [Indexed: 11/13/2022] Open
Abstract
Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 103 colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R2), and the bias (Bf) and accuracy (Af) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited Af and Bf between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R2 and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver.
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Affiliation(s)
- Dimitra Dourou
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece
| | - Athena Grounta
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece
| | - Anthoula A Argyri
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece
| | - George Froutis
- Laboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, Greece
| | - Panagiotis Tsakanikas
- Laboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, Greece
| | - George-John E Nychas
- Laboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, Greece
| | - Agapi I Doulgeraki
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece
| | - Nikos G Chorianopoulos
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece
| | - Chrysoula C Tassou
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece
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17
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Spyrelli ED, Ozcan O, Mohareb F, Panagou EZ, Nychas GJE. Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis. Curr Res Food Sci 2021; 4:121-131. [PMID: 33748779 PMCID: PMC7961306 DOI: 10.1016/j.crfs.2021.02.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 02/19/2021] [Accepted: 02/19/2021] [Indexed: 01/07/2023] Open
Abstract
The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n = 215) were conducted at 0, 5, 10, and 15 °C for up to 480 h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets. Poultry meat’s vulnerability to spoilage demands rapid quality assessment LWT-Food Sci. Technol.methods. FT-IR and MSI are non-invasive methods applied in a variety of meat products. SorfML is a web platform providing diverse machine learning algorithms. FT-IR analysis via lars predicted efficiently microbial loads of TVC.
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Affiliation(s)
- Evgenia D Spyrelli
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece
| | - Onur Ozcan
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK
| | - Fady Mohareb
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK
| | - Efstathios Z Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece
| | - George-John E Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece
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Younas S, Mao Y, Liu C, Liu W, Jin T, Zheng L. Efficacy study on the non-destructive determination of water fractions in infrared-dried Lentinus edodes using multispectral imaging. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2020.110226] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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19
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Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling. Molecules 2020; 25:molecules25081845. [PMID: 32316308 PMCID: PMC7221759 DOI: 10.3390/molecules25081845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 11/17/2022] Open
Abstract
Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908-1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%-93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%-100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy's potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.
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Spyrelli ED, Doulgeraki AI, Argyri AA, Tassou CC, Panagou EZ, Nychas GJE. Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products. Microorganisms 2020; 8:E552. [PMID: 32290382 PMCID: PMC7232414 DOI: 10.3390/microorganisms8040552] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 03/31/2020] [Accepted: 04/09/2020] [Indexed: 12/03/2022] Open
Abstract
The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.
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Affiliation(s)
- Evgenia D. Spyrelli
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855 Athens, Greece; (E.D.S.); (E.Z.P.)
| | - Agapi I. Doulgeraki
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization “Demeter”, Sof. Venizelou 1, Lycovrissi, 14123 Attica, Greece; (A.I.D.); (A.A.A.); (C.C.T.)
| | - Anthoula A. Argyri
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization “Demeter”, Sof. Venizelou 1, Lycovrissi, 14123 Attica, Greece; (A.I.D.); (A.A.A.); (C.C.T.)
| | - Chrysoula C. Tassou
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization “Demeter”, Sof. Venizelou 1, Lycovrissi, 14123 Attica, Greece; (A.I.D.); (A.A.A.); (C.C.T.)
| | - Efstathios Z. Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855 Athens, Greece; (E.D.S.); (E.Z.P.)
| | - George-John E. Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855 Athens, Greece; (E.D.S.); (E.Z.P.)
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Younas S, Liu C, Qu H, Mao Y, Liu W, Wei L, Yan L, Zheng L. Multispectral imaging for predicting the water status in mushroom during hot-air dehydration. J Food Sci 2020; 85:903-909. [PMID: 32147837 DOI: 10.1111/1750-3841.15081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 12/23/2019] [Accepted: 01/24/2020] [Indexed: 12/28/2022]
Abstract
In-depth understanding of the shifting of water status during dehydration is crucial for obtaining better quality of dried food. In this work, we report a nondestructive method to measure the water status in hot-air dried mushroom via multispectral imaging (MSI) technology combined with chemometric methods. The low-field nuclear magnetic resonance (LF-NMR) measurements were performed as reference. During drying process, the moisture content changed dramatically with notable migration and conversion of different water phases. Partial least squares (PLS), back propagation neural network (BPNN), and least squares-support vector machine (LS-SVM) models were applied to develop quantitative models. Among all, BPNN model showed considerably better performance of prediction with coefficient of determination R2 c = 0.9829, R2 p = 0.9639. The results demonstrated that MSI technology combined with chemometric methods is an impressive approach for determination of the water status in hot-air dried mushrooms, which would facilitate infield of food processing by providing applicable and appropriate platform. PRACTICAL APPLICATION: Experimental investigation of different water status during food processing. Assessment of the potential of multispectral imaging to predict water status. Usage of novel measurement method for food processors.
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Affiliation(s)
- Shoaib Younas
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Changhong Liu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Hao Qu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Yu Mao
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Wei Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei, 230601, China
| | - Liyang Wei
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Ling Yan
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Lei Zheng
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China.,Research Laboratory of Agricultural Environment and Food Safety, Anhui Modern Agricultural Industry Technology System, Hefei, 230009, China
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Zareef M, Chen Q, Hassan MM, Arslan M, Hashim MM, Ahmad W, Kutsanedzie FYH, Agyekum AA. An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis. FOOD ENGINEERING REVIEWS 2020. [DOI: 10.1007/s12393-020-09210-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Pavli F, Argyri AA, Skandamis P, Nychas GJ, Tassou C, Chorianopoulos N. Antimicrobial Activity of Oregano Essential Oil Incorporated in Sodium Alginate Edible Films: Control of Listeria monocytogenes and Spoilage in Ham Slices Treated with High Pressure Processing. MATERIALS (BASEL, SWITZERLAND) 2019; 12:E3726. [PMID: 31718078 PMCID: PMC6887770 DOI: 10.3390/ma12223726] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 10/29/2019] [Accepted: 11/07/2019] [Indexed: 01/02/2023]
Abstract
The aim of the study was to evaluate the efficacy of oregano essential oil (OEO) incorporated in Na-alginate edible films when applied to sliced ham inoculated with a cocktail of Listeria monocytogenes strains, with or without pretreatment by high pressure processing (HPP). Microbiological, physicochemical and sensory analyses (in Listeria-free slices) were performed, while, the presence/absence and the relative abundance of each Listeria strain, was monitored by pulsed field gel electrophoresis (PFGE). The OEO incorporation in the films, caused approximately 1.5 log reduction in Listeria population at 8 and 12 °C at the end of the storage period, and almost 2.5 log reduction at 4 °C. The HPP treatment caused 1 log reduction to the initial Listeria population, while levels kept on decreasing throughout the storage for all the tested temperatures. The pH of the samples was higher in the cases where HPP was involved, and the samples were evaluated as less spoiled. Furthermore, the presence of OEO in the films resulted in color differences compared to the control samples, whilst the aroma of these samples was improved. In conclusion, the combined application of HPP and OEO edible films on the slices, led to a significant reduction or absence of the pathogen.
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Affiliation(s)
- Foteini Pavli
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization-DEMETER, Lycovrissi, 14123 Attica, Greece; (F.P.); (A.A.A.); (C.T.)
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, 11855 Athens, Greece;
| | - Anthoula A. Argyri
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization-DEMETER, Lycovrissi, 14123 Attica, Greece; (F.P.); (A.A.A.); (C.T.)
| | - Panagiotis Skandamis
- Laboratory of Food Quality Control and Hygiene, Department of Food Science and Human Nutrition, Agricultural University of Athens, 11855 Athens, Greece;
| | - George-John Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, 11855 Athens, Greece;
| | - Chrysoula Tassou
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization-DEMETER, Lycovrissi, 14123 Attica, Greece; (F.P.); (A.A.A.); (C.T.)
| | - Nikos Chorianopoulos
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization-DEMETER, Lycovrissi, 14123 Attica, Greece; (F.P.); (A.A.A.); (C.T.)
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24
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Zhu Z, Zhou Q, Sun DW. Measuring and controlling ice crystallization in frozen foods: A review of recent developments. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.05.012] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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25
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Estimation of Minced Pork Microbiological Spoilage through Fourier Transform Infrared and Visible Spectroscopy and Multispectral Vision Technology. Foods 2019; 8:foods8070238. [PMID: 31266168 PMCID: PMC6678698 DOI: 10.3390/foods8070238] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 06/21/2019] [Accepted: 06/21/2019] [Indexed: 11/16/2022] Open
Abstract
Spectroscopic and imaging methods coupled with multivariate data analysis have been increasingly studied for the assessment of food quality. The objective of this work was the estimation of microbiological quality of minced pork using non-invasive spectroscopy-based sensors. For this purpose, minced pork patties were stored aerobically at different isothermal (4, 8, and 12 °C) and dynamic temperature conditions, and at regular time intervals duplicate samples were subjected to (i) microbiological analyses, (ii) Fourier transform infrared (FTIR) and visible (VIS) spectroscopy measurements, and (iii) multispectral image (MSI) acquisition. Partial-least squares regression models were trained and externally validated using the microbiological/spectral data collected at the isothermal and dynamic temperature storage conditions, respectively. The root mean squared error (RMSE, log CFU/g) for the prediction of the test (external validation) dataset for the FTIR, MSI, and VIS models was 0.915, 1.173, and 1.034, respectively, while the corresponding values of the coefficient of determination (R2) were 0.834, 0.727, and 0.788. Overall, all three tested sensors exhibited a considerable potential for the prediction of the microbiological quality of minced pork.
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26
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ElMasry G, Mandour N, Al-Rejaie S, Belin E, Rousseau D. Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring-An Overview. SENSORS 2019; 19:s19051090. [PMID: 30836613 PMCID: PMC6427362 DOI: 10.3390/s19051090] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 02/17/2019] [Accepted: 02/22/2019] [Indexed: 12/02/2022]
Abstract
As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.
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Affiliation(s)
- Gamal ElMasry
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, Riyadh 11564, Saudi Arabia.
- Faculty of Agriculture, Suez Canal University, Ring Road Km 4.5, Ismailia P.O. Box 41522, Egypt.
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 rue Georges Morel CS 60057, F-49071 Beaucouzé CEDEX, Angers, France.
| | - Nasser Mandour
- Faculty of Agriculture, Suez Canal University, Ring Road Km 4.5, Ismailia P.O. Box 41522, Egypt.
| | - Salim Al-Rejaie
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, Riyadh 11564, Saudi Arabia.
| | - Etienne Belin
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 rue Georges Morel CS 60057, F-49071 Beaucouzé CEDEX, Angers, France.
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France.
| | - David Rousseau
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 rue Georges Morel CS 60057, F-49071 Beaucouzé CEDEX, Angers, France.
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France.
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Trinderup CH, Møller F, Dahl AB, Conradsen K. Investigation of pausing fermentation of salamis with multispectral imaging for optimal sensory evaluations. Meat Sci 2018; 146:9-17. [PMID: 30081378 DOI: 10.1016/j.meatsci.2018.07.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 07/19/2018] [Accepted: 07/23/2018] [Indexed: 11/17/2022]
Abstract
The fermentation process of salamis involves several parameters influencing taste, texture, and color of the salami. One significant parameter is the fermentation time. It is difficult to conduct sensory evaluations to assess the effect of time without introducing variation between observation days. It may be possible to overcome this by stalling or pausing the fermentation by deep-chilling the salamis. This study investigates the difference of non- and deep-chilled salamis with the use of a multispectral imaging system. The statistical investigation, based on image features relating to size, visual texture, and color of the sausages over time, showed that it may be possible to stall the fermentation process. It was shown that a statistical difference in the two kinds of samples is present. For the size feature the difference could be quantified into a number of days. However, for the important color feature only a statistical difference was observed, whereas the visual difference expressed in terms of ΔEab⁎ was barely present.
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Affiliation(s)
- Camilla H Trinderup
- Technical University of Denmark, Richard Petersens Plads, 2800 Kgs. Lyngby, Denmark.
| | - Flemming Møller
- DuPont Nutrition Biosciences ApS, Edwin Rahrs Vej 38, 8220 Brabrand, Denmark.
| | - Anders Bjorholm Dahl
- Technical University of Denmark, Richard Petersens Plads, 2800 Kgs. Lyngby, Denmark.
| | - Knut Conradsen
- Technical University of Denmark, Richard Petersens Plads, 2800 Kgs. Lyngby, Denmark.
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