Abstract: In order to accurately and quickly identify cigarette ash from different brands and manufacturers, 58 types of cigarettes, 26 different interfering substances, and mixed cigarette ash samples from different brands and manufacturers were collected. The corresponding time-of-flight mass spectrometry data was obtained through a time-of-flight mass spectrometer, and then the time-of-flight mass spectrometry data was classified through system clustering. The characteristic peaks of the mass spectrometry were compared. Finally, principal component analysis and partial least squares discriminant analysis methods of machine learning are used to establish discriminant analysis for different methods. The results of principal component analysis indicate that this model has good reliability and predictive ability; partial least squares discriminant analysis indicates that the model is reliable and has good ability to predict cigarettes and interfering substance smoke ash. In addition, the model was verified for 200 times, and the results showed that the partial least squares discriminant model did not have overfitting when it was established. Therefore, combining time-of-flight mass spectrometry with two machine learning algorithms can help surveyors accurately and quickly identify and detect cigarette ash samples.

Key words:  cigarette ash, time-of-flight mass spectrometry, machine learning, principal component analysis, partial least squares discriminant analysis