Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis

Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured t...

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Main Authors: Masafumi Yagi, Akira Sakai, Suguru Yasutomi, Kanata Suzuki, Hiroki Kashikura, Keiichi Goto
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Foods
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Online Access:https://www.mdpi.com/2304-8158/13/23/3860
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author Masafumi Yagi
Akira Sakai
Suguru Yasutomi
Kanata Suzuki
Hiroki Kashikura
Keiichi Goto
author_facet Masafumi Yagi
Akira Sakai
Suguru Yasutomi
Kanata Suzuki
Hiroki Kashikura
Keiichi Goto
author_sort Masafumi Yagi
collection DOAJ
description Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured the actual fat content in albacore using chemical analysis and compared the results with those obtained using the tail-cutting method. Significant discrepancies (99% CI, <i>t</i>-test) were observed in fat content among the tail-cutting samples. Using chemical analysis as the ground truth, the accuracy of tail-cutting from two different companies was 70.0% for company A and 51.9% for company B. An ultrasound inspection revealed that a higher fat content reduced the amplitude of ultrasound signals with statistical significance (99% CI, <i>t</i>-test). Finally, machine learning algorithms were used to enforce the ultrasound inspection. The best combination of ultrasound inspection and a machine learning algorithm achieved an 84.2% accuracy for selecting fat-rich albacore, which is better than tail-cutting (73.6%). Our findings suggested that ultrasound inspection could be a valuable and non-destructive method for estimating the fat content of albacore, achieving better accuracy than the traditional tail-cutting method.
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institution OA Journals
issn 2304-8158
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publishDate 2024-11-01
publisher MDPI AG
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spelling doaj-art-c09418daea084b84b809dd171c1f9ddc2025-08-20T01:55:38ZengMDPI AGFoods2304-81582024-11-011323386010.3390/foods13233860Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical AnalysisMasafumi Yagi0Akira Sakai1Suguru Yasutomi2Kanata Suzuki3Hiroki Kashikura4Keiichi Goto5School of Marine Science and Technology, Tokai University, 3-20-1 Orido, Shimizu-ku, Shizuoka-shi 424-8610, Shizuoka, JapanArtificial Intelligence Laboratory, Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi 211-8588, Kanagawa, JapanArtificial Intelligence Laboratory, Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi 211-8588, Kanagawa, JapanArtificial Intelligence Laboratory, Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi 211-8588, Kanagawa, JapanGraduate School of Marine Science and Technology, Tokai University, 3-20-1 Orido, Shimizu-ku, Shizuoka-shi 424-8610, Shizuoka, JapanSchool of Marine Science and Technology, Tokai University, 3-20-1 Orido, Shimizu-ku, Shizuoka-shi 424-8610, Shizuoka, JapanFat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured the actual fat content in albacore using chemical analysis and compared the results with those obtained using the tail-cutting method. Significant discrepancies (99% CI, <i>t</i>-test) were observed in fat content among the tail-cutting samples. Using chemical analysis as the ground truth, the accuracy of tail-cutting from two different companies was 70.0% for company A and 51.9% for company B. An ultrasound inspection revealed that a higher fat content reduced the amplitude of ultrasound signals with statistical significance (99% CI, <i>t</i>-test). Finally, machine learning algorithms were used to enforce the ultrasound inspection. The best combination of ultrasound inspection and a machine learning algorithm achieved an 84.2% accuracy for selecting fat-rich albacore, which is better than tail-cutting (73.6%). Our findings suggested that ultrasound inspection could be a valuable and non-destructive method for estimating the fat content of albacore, achieving better accuracy than the traditional tail-cutting method.https://www.mdpi.com/2304-8158/13/23/3860frozen tunafrozen albacoretail-cuttingfat contentultrasoundmachine learning
spellingShingle Masafumi Yagi
Akira Sakai
Suguru Yasutomi
Kanata Suzuki
Hiroki Kashikura
Keiichi Goto
Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis
Foods
frozen tuna
frozen albacore
tail-cutting
fat content
ultrasound
machine learning
title Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis
title_full Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis
title_fullStr Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis
title_full_unstemmed Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis
title_short Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis
title_sort assessment of tail cutting in frozen albacore i thunnus alalunga i through ultrasound inspection and chemical analysis
topic frozen tuna
frozen albacore
tail-cutting
fat content
ultrasound
machine learning
url https://www.mdpi.com/2304-8158/13/23/3860
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