학술논문
Seabream Quality Monitoring Throughout the Supply Chain Using a Portable Multispectral Imaging Device.
Document Type
Academic Journal
Author
Lytou A; 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.; Fengou LC; 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.; Koukourikos A; SCiO P.C. Technology Park Lefkippos, P. Grigoriou & Neapoleos Str, Agia Paraskevi GR-15310, Greece.; Karampiperis P; SCiO P.C. Technology Park Lefkippos, P. Grigoriou & Neapoleos Str, Agia Paraskevi GR-15310, Greece.; Zervas P; SCiO P.C. Technology Park Lefkippos, P. Grigoriou & Neapoleos Str, Agia Paraskevi GR-15310, Greece.; Carstensen AS; Videometer A/S, Hørkær 12B 3, DK-2730 Herlev, Denmark.; Genio AD; Videometer A/S, Hørkær 12B 3, DK-2730 Herlev, Denmark.; Carstensen JM; Videometer A/S, Hørkær 12B 3, DK-2730 Herlev, Denmark.; Schultz N; Videometer A/S, Hørkær 12B 3, DK-2730 Herlev, Denmark.; Chorianopoulos N; 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.; Nychas GJ; 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. Electronic address: gjn@aua.gr.
Source
Publisher: Elsevier Country of Publication: United States NLM ID: 7703944 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1944-9097 (Electronic) Linking ISSN: 0362028X NLM ISO Abbreviation: J Food Prot Subsets: MEDLINE
Subject
Language
English
Abstract
Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision-making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402-0.547) and the skin side (RMSE = 0.500-0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061-1.414) compared to the models validated with data partitioning (RMSE = 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. It should be noted that SCiO builds solutions using the model training and validation technologies applied for the production of the NN architectures described in the manuscript. Moreover, it shall be stated that Videometer develops and markets the Videometer technology.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. It should be noted that SCiO builds solutions using the model training and validation technologies applied for the production of the NN architectures described in the manuscript. Moreover, it shall be stated that Videometer develops and markets the Videometer technology.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)