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Применение машинного обучения в аналитическом контроле препаратов лекарственных растений

https://doi.org/10.26896/1028-6861-2018-84-10-67-78

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Аннотация

Несмотря на то что объем мирового рынка лекарственных растений составляет сотни миллиардов долларов, государственный контроль за качеством подобных препаратов в большинстве стран мира практически отсутствует. Отчасти это объясняется сложным составом растительного сырья: традиционная аналитическая методология основана на применении стандартных образцов сравнения для каждого определяемого вещества. При этом препараты на основе лекарственных растений могут содержать десятки и сотни физиологически активных компонентов. Выделение данных соединений в чистом виде на практике осуществляют с помощью препаративной хроматографии, что приводит к их высокой стоимости. Более того, варьирование химического состава растительных препаратов в зависимости от географического происхождения сырья делает малореальным установление строгих диапазонов допустимых содержаний для всех физиологически активных компонентов. Совокупность вышеперечисленных факторов ограничивает возможности использования традиционных подходов к анализу, требующих строгой стандартизации, списка соединений для каждого типа растения, уровней содержаний и наличия стандартных образцов сравнения. Это привело к исследованию возможностей внедрения различных математических подходов как вспомогательной методологии. В отличие от традиционной методологии, подходы с использованием машинного обучения основаны на правильном сборе выборок данных. В такой выборке должны присутствовать группы образцов, отвечающие состояниям объекта, которые должен будет различить разрабатываемый алгоритм: аутентичный/поддельный, чистый/содержащий примеси, действенный/не содержащий определенного уровня активных компонентов и т.д. Данный обзор посвящен рассмотрению приложения машинного обучения к задачам химического анализа и производственного контроля сырья лекарственных растений и препаратов на его основе за последние 15 лет.

Об авторах

Д. В. Назаренко
Московский государственный университет имени М. В. Ломоносова
Россия
Дмитрий Владимирович Назаренко
Москва


И. А. Родин
Московский государственный университет имени М. В. Ломоносова
Россия
Игорь Александрович Родин
Москва


О. А. Шпигун
Московский государственный университет имени М. В. Ломоносова
Россия
Олег Алексеевич Шпигун
Москва


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Для цитирования:


Назаренко Д.В., Родин И.А., Шпигун О.А. Применение машинного обучения в аналитическом контроле препаратов лекарственных растений. Заводская лаборатория. Диагностика материалов. 2018;84(10):67-78. https://doi.org/10.26896/1028-6861-2018-84-10-67-78

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Nazarenko D.V., Rodin I.A., Shpigun O.A. The use of machine learning in the analytical control of the preparations of medicinal plants. Industrial laboratory. Diagnostics of materials. 2018;84(10):67-78. (In Russ.) https://doi.org/10.26896/1028-6861-2018-84-10-67-78

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