foodsystFood systemsПищевые системы2618-97712618-7272Федеральный научный центр пищевых систем им. В.М. Горбатова РАН10.21323/2618-9771-2023-6-1-46-52foodsyst-228Research ArticleСтатьиAvocado fruit sorting by hyperspectral imagesСортировка плодов авокадо по гиперспектральным изображениямhttps://orcid.org/0000-0002-7006-2253МетленкинД. А.MetlenkinD. A.

Метленкин Дмитрий Андреевич — инженер, Инжиниринговый центр

117997, Москва, Стремянный переулок, 36

Тел.: +7–963–656–79–92

Dmitrii A.  Metlenkin, Engineer, Engineering Center

Stremyanny lane, 36, Moscow, 117997

Tel.: +7–963–656–79–92

Metlenkin.DA@rea.ruhttps://orcid.org/0000-0001-9583-8188ПлатоваР. А.PlatovaR. A.

Платова Раиса Абдулгафаровна — кандидат технических наук, доцент, кафедра товароведения и товарной экспертизы

117997, Москва, Стремянный переулок, 36

Тел.: +7–963–656–79–92

Raisa A. Platova, Candidate of Technical Sciences, Docent, Department of Commodity Science

Stremyanny lane, 36, Moscow, 117997

Tel.: +7–963–656–79–92

raisa.platova@yandex.ruhttps://orcid.org/0000-0001-6157-572XПлатовЮ. Т.PlatovYu. T.

Платов Юрий Тихонович — доктор технических наук, профессор, кафедра товароведения и товарной экспертизы

117997, Москва, Стремянный переулок, 36

Тел.: +7–963–656–79–92

Yuri T. Platov, Doctor of Technical Sciences, Professor, Department of Commodity Science

Stremyanny lane, 36, Moscow, 117997

Tel.: +7–963–656–79–92

Platov.YT@rea.ruФедосеенкоО. В.FedoseenkoO. V.

Федосеенко Ольга Владимировна — начальник лаборатории

113054, Москва, ул. Валовая, д. 8/18, Россия

Тел.: +7–916–564–50–02

Olga V. Fedoseenko, Head of Laboratory

Valovaya street, 8/18, Moscow, 113054

Tel.: +7–916–564–50–02

ofedoseenko@azbukavkusa.ruСадковаО. В.SadkovaO. V.

Садкова Олеся Вячеславовна — химик

113054, Москва, ул. Валовая, д. 8/18, Россия

Тел.: +7–916–564–50–02

Olesya V. Sadkova, Chemist

Valovaya street, 8/18, Moscow, 113054

Tel.: +7–916–564–50–02

osadkova@azbukavkusa.ruРоссийский экономический университет им. Г. В. ПлехановаРоссияPlekhanov Russian University of EconomicsRussian FederationООО «Городской супермаркет»РоссияCity Supermarket LLCRussian Federation202307042023614652Copyright © Metlenkin D.A., Platova R.A., Platov Y.T., Fedoseenko O.V., Sadkova O.V., 20232023Метленкин Д.А., Платова Р.А., Платов Ю.Т., Федосеенко О.В., Садкова О.В.Metlenkin D.A., Platova R.A., Platov Y.T., Fedoseenko O.V., Sadkova O.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.fsjour.com/jour/article/view/228

The paper shows the use of the methods of hyperspectral imaging (HSI) in a range of 400–1000 nm and multivariate analysis for sorting Hass avocado fruits. The decomposition of the data matrix of HSIs of avocado fruits was carried out using the principle component analysis. The reflection bands in the visible and near-infrared spectral regions interrelated with the process of maturation and the moisture content of avocado fruits were revealed. It has been established that visualization upon avocado inline sorting by moisture is possible when using factor loadings as pseudo-color. Calibration models for determination of moisture and dry matter of avocado fruits were built based on the data of moisture measurement and hyperspectral images. The matrix of spectral data was formed by two methods: random selection of spectral signatures of HSIs from the whole surface of fruits or the image surface of HSIs of fruits (initial HSIs) as a region of interest (ROI). Based on the data of moisture measurement and selection of spectral signatures of hyperspectral images, calibration models were built for detection of moisture and dry matter of avocado fruits. Using sequential simulation by the projection to latent structures (PLS) method, accurate calibration models were developed to detect moisture (RP2 = 0.89) and dry matter (RP2 = 0.92) in the composition of avocado fruits. When building calibration models by the initial HSIs, models were obtained to predict moisture (RС2 = 0.99) and dry matter (RС2 = 0.99) in the composition of avocado fruits. It is proposed to use calibration models by the initial HSIs to determine moisture and dry matter in the intervals of the acceptable values according to the acting standard UNECE STANDARD FFV-42:2019.

Показано применение методов гиперспектрального изображения (HSI) в диапазоне 400–1000 нм и многомерного анализа для сортировки плодов авокадо Хасс. Методом главных компонент осуществлена декомпозиция матрицы данных HSI плодов авокадо и выявлены полосы отражения в видимой и ближней инфракрасной областях спектра, взаимосвязанные с процессом созревания и содержанием влажности плодов авокадо. Установлено, что при использовании факторных нагрузок в качестве псевдоцвета возможна визуализация при поточной сортировке плодов авокадо по влажности. Построение калибровочных моделей определения влажности и сухого вещества плодов авокадо проведено на основе данных измерений влаж ности и гиперспектральных изображений. Формирование матрицы спектральных данных осуществляли двумя способами: посредством отбора спектральных сигнатур HSI случайным образом со всей поверхности плодов или поверхность изображения HSI плодов (исходные HSI) как области интереса (ROI). На основе данных измерений влажности и отбора спектральных сигнатур гиперспектральных изображений проведено построение калибровочных моделей определения влажности и сухого вещества плодов авокадо. При последовательном моделировании методом PLS (проекция на латентные структуры) разработаны точные калибровочные модели для определения влажности (RP2 = 0,89) и сухого вещества (RP2 = 0,92) в составе плодов авокадо. При построении калибровочных моделей по исходным HSI получены модели для прогнозирования влажности (RС2 = 0,99) и сухого вещества (RС2 = 0,99) в составе плодов авокадо. Предлагается использование калибровочных моделей по исходным HSI для определения влажности и сухого вещества в интервалах допустимых значений в соответствии с действующим стандартом UNECE STANDARD FFV-42:2019.

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