spfpХранение и переработка сельхозсырьяStorage and Processing of Farm Products2072-96692658-767XРОСБИОТЕХ10.36107/spfp.2025.4.681spfp-681Research ArticleКОНТРОЛЬ КАЧЕСТВА И БЕЗОПАСНОСТИ ПРОДУКЦИИ АПКCONTROL OVER QUALITY AND SAFETY OF AGRIBUSINESS PRODUCTSПрименение гиперспектральной визуализации и искусственного интеллекта для контроля свежести рыбного сырья при холодильном храненииApplication of Hyperspectral Imaging and Artificial Intelligence for Freshness Assessment of Fish Raw Material during Refrigerated Storagehttps://orcid.org/0000-0002-1330-9782КутузовМихаил НиколаевичKutuzovMikhail Nikolaevich

научный сотрудник кафедры биологии

Researcher of Chair of Biology

kutuzov35@gmail.comhttps://orcid.org/0009-0008-6466-3854БеловаМария АлексеевнаBelovaMaria Alexeevna

младший научный сотрудник кафедры биологии ЧГУ, аспирант РЭУ имени Г.В. Плеханова

Junior Researcher of Chair of Biology, Cherepovets State University; postgraduate student, Plekhanov Russian University of Economics

mabelova@chsu.ruhttps://orcid.org/0000-0002-4282-9728НовиченкоОльга ВикторовнаNovichenkoOlga Victorovna

канд. технич. наук, доцент кафедры биотехнологии, аквакультуры, почвоведения и управления земельными ресурсами, старший научный сотрудник лаборатории «Биотехнология, микробиология и почвоведение» АГУ им. В.Н. Татищева, старший научный сотрудник кафедры биологии ЧГУ

Сand. Techn. Sci., Associate Professor of Chair of Biotechnology, Aquaculture, Soil Science and Land Management, Astrakhan Tatishchev State University; Leading Researcher of Chair of Biology, Cherepovets State University

ollevi@bk.ruhttps://orcid.org/0000-0002-8988-5911НикитинИгорь АлексеевичNikitinIgor Alexeevich

д-р технич. наук, заведующий кафедрой пищевых технологий и биоинженерии РЭУ имени Г.В. Плеханова, главный научный сотрудник кафедры биологии ЧГУ

Doctor of Technical Sciences; Head of the Chief of Food Technologies and Bioengineering, Plekhanov Russian University of Economics, Chief Researcher of Chair of Biology, Cherepovets State University

nikito.igor@gmail.comhttps://orcid.org/0000-0002-1571-5354ЗаруалХишамZaroualHicham

профессор, доцент факультета наук и технологий университета Абдула Малика Аль Саади

Professor, Assistant Professor at Faculty of Sciences and Technologies, Abdelmalek Essaadi University

aa.hzaroual@gmail.comhttps://orcid.org/0000-0002-2869-8709ВилковаДарья ДмитриевнаVilkovaDaria Dmitrievna

PhD инженер биологических систем, ведущий научный сотрудник кафедры биологии, заведущий лабораторией прикладной биотехнологии ЧГУ

PhD of Engineering of Biological Functions, Leading Researcher of Chair of Biology, Head of the Laboratory of Applied Biotechnology, Cherepovets State University

dariavilkova333@gmail.comЧереповецкий государственный университетРоссияCherepovets State UniversityRussian FederationЧереповецкий государственный университет, Российский экономический университет им. Г.В. ПлехановаРоссияCherepovets State University, Plekhanov Russian University of EconomicsRussian FederationАстраханский государственный университет им. В.Н. Татищева, Череповецкий государственный университетРоссияAstrakhan Tatishchev State University, Cherepovets State UniversityRussian FederationРоссийский экономический университет им. Г.В. Плеханова, Череповецкий государственный университетРоссияPlekhanov Russian University of Economics, Cherepovets State UniversityRussian FederationУниверситет Абдула Малика Аль СаадиМароккоAbdelmalek Essaadi UniversityMorocco202507022026334127142Copyright © Кутузов М.Н., Белова М.А., Новиченко О.В., Никитин И.А., Заруал Х., Вилкова Д.Д., 20262026Кутузов М.Н., Белова М.А., Новиченко О.В., Никитин И.А., Заруал Х., Вилкова Д.Д.Kutuzov M.N., Belova M.A., Novichenko O.V., Nikitin I.A., Zaroual H., Vilkova D.D.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.spfp-mgupp.ru/jour/article/view/681Введение

Введение: Мониторинг свежести охлажденной рыбы остается одной из наиболее сложных задач рыбоперерабатывающей отрасли. Существующие стандартные методы контроля являются деструктивными и не способны отразить пространственную неоднородность распределения маркеров порчи. Перспективным направлением является разработка неразрушающих методов анализа, таких как гиперспектральная визуализация (HSI), позволяющая получать пространственную и спектральную информацию для каждого пикселя изображения.

Цель

Цель: Целью настоящего исследования является оценка эффективности использования гиперспектральной визуализации для бинарной классификации образцов охлажденного филе радужной форели по признаку раннего (до 48 ч) и позднего (более 48 ч) срока хранения.

Материалы и методы

Материалы и методы: Исследование проводилось на образцах филе радужной форели, хранившихся при температуре +2 ± 2 °C в течение 16 суток. Для получения гиперспектральных изображений использовалась камера FigSpec FS-23 (диапазон 400–1000 нм). Проводился анализ главных компонент (PCA), а для бинарной классификации образцов была разработана нейросетевая модель на основе фреймворка TensorFlow и высокоуровневого API Keras.

Результаты

Результаты: Выявлена характерная нелинейная динамика коэффициента отражения в процессе хранения. Метод PCA показал, что первая главная компонента объясняет 93,8 % дисперсии данных. Нейросетевая модель продемонстрировала 90 % точности при бинарной классификации образцов.

Выводы

Выводы: Подтверждена эффективность метода гиперспектральной визуализации для неразрушающего контроля свежести рыбного сырья. Разработанный подход позволяет достоверно дифференцировать свежие и несвежие образцы и может быть рекомендован для внедрения в систему входного контроля на рыбоперерабатывающих предприятиях.

Introduction

Introduction: Monitoring the freshness of refrigerated fish remains one of the persistent difficulties in the fish processing industry. Existing reference methods for assessing  refrigerated fish freshness are destructive and inherently unable to reflect the spatial distribution of spoilage markers. Hyperspectral imaging (HSI) has emerged as a powerful non-destructive tool that captures both spatial and spectral data at the pixel scale.

Purpose

Purpose: The aim of this study is to evaluate the effectiveness of hyperspectral imaging for the binary classification of refrigerated rainbow trout (Oncorhynchus mykiss) fillets into early (≤48 h) and late (>48 h) storage stages.

Materials and Methods

Materials and Methods: The study was conducted on rainbow trout fillets stored at +2 ± 2 °C for 16 days. Hyperspectral images were acquired using a FigSpec FS-23 camera (spectral range 400–1000 nm). Principal component analysis (PCA) was performed, and a neural network model for binary classification of samples was developed using the TensorFlow framework and the high-level Keras API.

Results

Results: A characteristic nonlinear dynamics of reflectance was observed during storage. PCA showed that the first principal component accounted for 93.8 % of the data variance. The neural network model achieved 90 % accuracy in binary classification of the samples.

Conclusion

Conclusion: The results demonstrate the potential of hyperspectral imaging as a non-destructive tool for assessing fish freshness. The developed method provides accurate discrimination between fresh and non-fresh samples and can be recommended for adoption in industrial incoming inspection protocols.

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The authors declare that there are no conflicts of interest present.