Food Processing: Techniques and Technology Food Processing: Techniques and Technology Техника и технология пищевых производств 2074-9414 2313-1748 110809 10.21603/2074-9414-2025-4-2602 WMZZOJ ОРИГИНАЛЬНАЯ СТАТЬЯ ORIGINAL ARTICLE ОРИГИНАЛЬНАЯ СТАТЬЯ Chemometrics in Laboratory Data Analysis Применение хемометрики в аналитике пищевых систем https://orcid.org/0000-0002-4079-6950 Лисицын Андрей Борисович Lisitsyn Andrey B. https://orcid.org/0000-0003-4298-0927 Чернуха Ирина Михайловна Chernukha Irina M. imcher@inbox.ru https://orcid.org/0000-0002-8313-4105 Никитина Марина Александровна Nikitina Marina A. https://orcid.org/0000-0001-8923-8661 Пчелкина Виктория Александровна Pchelkina Viktoriya A. Федеральный научный центр пищевых систем имени В. М. Горбатова Москва Россия V.M. Gorbatov Federal Research Center for Food Systems Moscow Russian Federation Федеральный научный центр пищевых систем имени В. М. Горбатова Москва Россия V.M. Gorbatov Federal Research Center for Food Systems Moscow Russian Federation Федеральный научный центр пищевых систем имени В. М. Горбатова Москва Россия V.M. Gorbatov Federal Research Center for Food Systems Moscow Russian Federation Федеральный научный центр пищевых систем имени В. М. Горбатова Москва Россия V.M. Gorbatov Federal Research Center for Food Systems Moscow Russian Federation 25 12 2025 25 12 2025 55 4 723 743 23 05 2025 05 08 2025 https://fptt.ru/en/issues/24078/24085/

Пищевой продукт – это сложная пищевая система, оценка качества которой требует целостного подхода. Хемометрика позволяет получить важную информацию при анализе пищевых продуктов. Цель исследования – показать перспективы применения хемометрических методов в обработке экспериментальных данных в пищевых системах. Объектами исследования являлись научные публикации отечественных и зарубежных ученых. Поиск научных источников осуществляли в базах данных Scopus, PubMed, MEDLINE, Web of Knowledge, Google Scholar, IEEE Xplore, Science Direct, eLIBRARY.RU (РИНЦ). Поисковые запросы включали следующие ключевые слова и словосочетания: хемометрика (chemometrics); хемометрические методы (chemometric methods); метод главных компонент (principal component analysis); PLS (projection on latent structures); искусственная нейронная сеть, ИНС (artificial neural network, ANN); многомерная классификация (multivariate classification); многомерный анализ данных (multivariate data analysis). Рассмотрены основные инструменты хемометрики, используемые при анализе пищевых систем: иерархический кластерный анализ (HCA), метод главных компонент (РСА), дискриминантный анализ с помощью регрессии на латентные структуры (PLS-DA), методы проекции на латентные структуры (PLS), квадратичная проекция на латентные структуры PLS (QPLS), множественная линейная регрессия (MLR), искусственная нейронная сеть (ANN), метод опорных векторов (SVM), классификация по k-ближайшим соседям (KNN), методы ансамблей (RF, XGBoost). Из всего разнообразия хемометрических методов наиболее востребованным является PCA. Анализ научных публикаций показал, что для каждого вида пищевой продукции лучше использовать не один метод, а их сочетание. Методы классификации в каждом отдельном случае показывают разные результаты. Исследования показали, что наиболее оптимально применять хемометрические методы не по отдельности, а в совокупности, например PCA + PLS-DA + ANN или PCA + PLS-DA + KNN. Сочетание инструментальных и хемометрических методов не только улучшает точность анализов, но и трансформирует подходы к управлению качеством для обеспечения устойчивого производства в пищевой промышленности.

Food is a complex system that requires holistic quality assessment. Chemometrics uses mathematical, statistical, and computer science methods to analyze and interpret chemical data, which means good prospects for food quality evaluation. This review covered Russian and international publications indexed in Scopus, PubMed, MEDLINE, Web of Knowledge, Google Scholar, IEEE Xplore, Science Direct, and eLIBRARY.RU (RSCI). The search queries included such keywords as chemometrics; chemometric methods; principal component analysis; PLS (projection to latent structures); artificial neural network (ANN); multivariate classification; multivariate data analysis. The main chemometric tools applied to food systems included hierarchical cluster analysis (HCA), principal component analysis (PCA), latent structures-discriminant analysis (PLS-DA), projections to latent structures (PLS), quadratic projection to latent structures (QPLS), multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and ensemble model prediction (RF, XGBoost). The PCA proved to be the most popular chemometric method applied in the food industry. However, combinations of methods were always more effective than a single one. The KNN methods appeared to be quite unreliable. Combinations of chemometric methods demonstrate the best prospects, e.g., PCA + PLS-DA + ANN or PCA + PLS-DA +KNN. If combined with instrumental tools, they are able to improve analytical accuracy and provide effective management approaches, thus ensuring sustainable food industry.

 Хемометрика пищевые продукты метод главных компонент проекция на латентные структуры классификация регрессия Chemometrics food principal component analysis projections to latent structures classification regression Статья подготовлена в рамках выполнения темы НИР № FGUS‑2024–0002 государственного задания ФГБНУ «ФНЦ пищевых систем имени В. М. Горбатова» РАН. The research was part of State Assignment No. FGUS-2024-0002 to the Federal Research Center for Food Systems, Russian Academy of Sciences.

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