Москва, Россия
Москва, Россия
Москва, Россия
Москва, Россия
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.
Мед, фальсификация, идентификация, сахаросодержащие добавки, экзогенные сахара, изотопная масс-спектрометрия, ботаническое происхождение, географическое место происхождения
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