zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-2101Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESОнлайн-определение качества воздуха в помещениOnline Detection of the Indoor Air EnvironmentZhouW.ZhouWentao

Цзиань; Нанкин

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion NUIST 

Ji'an; Nanjing

ZhaoL.ZhaoLei

Цзиань; Нанкин

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion NUIST 

Ji'an; Nanjing

ZengG.ZengGuangxu

Цзиань

Ji'an

TianD.TianDongpeng

Цзиань; Нанкин

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion NUIST 

Ji'an; Nanjing

ZhuJ.ZhuJunyi

Цзиань; Нанкин

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion NUIST 

Ji'an; Nanjing

LiuY.LiuYuzhu

Цзиань; Нанкин

State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion NUIST 

Ji'an; Nanjing

yuzhu.liu@nuist.edu.cnЛаборатория экологической метеорологии; Нанкинский университет информационных наук и технологийJi’an Key Laboratory of Ecological Meteorology; Nanjing University of Information Science and TechnologyЛаборатория экологической метеорологииJi’an Key Laboratory of Ecological Meteorology202622012026931148148Copyright © Zhou W., Zhao L., Zeng G., Tian D., Zhu J., Liu Y., 20262026Zhou W., Zhao L., Zeng G., Tian D., Zhu J., Liu Y.Zhou W., Zhao L., Zeng G., Tian D., Zhu J., Liu Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/2101

Для определения качества воздуха в помещениях создана модель классификации, основанная на лазерно-индуцированной эмиссионной спектроскопии (LIBS) и машинном обучении. В качестве образцов взяты резиновое и деревянное покрытия, мебельная краска и интерьерная краска. Анализ спектров показывает, что в составе воздуха присутствуют различные металлические элементы, в том числе Ti, Ca и Na. Для летучих органических соединений, присутствующих в покрытиях или красках, сравниваются интенсивности C, H и O, которые являются основными ингредиентами летучих органических соединений. Установлено, что LIBS может быть использована для обнаружения различных составляющих воздуха в помещениях. Для различения типов воздуха в помещении использован метод главных компонент, обучающий набор данных сохранен для дальнейшей идентификации. Создана модель классификации на основе усовершенствованной искусственной нейронной сети с обратным распространением ошибки (BP-ANN) с точностью распознавания 93.4%. После обучения модели ее производительность протестирована с использованием спектров различных покрытий и красок, точность распознавания достигла 98.2%. Показано, что данный метод, сочетающий LIBS и машинное обучение, обладает большим потенциалом для определения качества воздуха в помещении.

Nowadays, people spend a significant amount of time living and working indoors. For that, online detection and classification of different indoor air environments is of great importance, which is very challenging because of the lack of unified standards. In this study, a novel classification model based on laser-induced breakdown spectroscopy (LIBS) and machine learning was established to detect indoor air environments. To study different indoor air environments, four different coatings or paints (rubber coating, wood coating, furniture paint, and interior paint) were taken as samples to ascertain the gas composition. The analysis of their spectra shows there are various metal elements in these gas compositions, including Ti, Ca, and Na. For volatile organic compounds (VOCs) present in coatings or paints, the intensities of C, H, and O, which are VOCs’ main ingredients, are compared to determine if there is a certain difference. The results verify that LIBS could be used to detect different indoor air environments. Principal component analysis was used to distinguish the four indoor air environments, and the training data set was stored for further identification. Furthermore, a classification model was established based on the improved error back propagation artificial neural network (BP-ANN), achieving a recognition accuracy of 93.4%. After model training, the model’s performance was tested using the spectra of different coatings or paints, and the recognition accuracy reached 98.2%. These results indicate that this method, combining LIBS and machine learning, has great potential for detecting the quality of the indoor air environments.

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