Machine Learning-Based Species Classification Methods Using DART-TOF-MS Data for Five Coniferous Wood Species

Abstract

Various problems worldwide are caused by illegal production and distribution of timber, such as deception about timber species and origin and illegal logging. Numerous studies on wood tracking are being conducted around the world to demonstrate the legitimacy of timber. Tree species identification is the most basic element of wood tracking research because the quality of wood varies greatly from species to species and is consistent with the botanical origin of commercially distributed wood. Although many recent studies have combined machine learning-based classification methods with various analytical methods to identify tree species, it is unclear which classification model is most effective. The purpose of this work is to examine and compare the performance of three supervised machine learning classification models, support vector machine (SVM), random forest (RF), and artificial neural network (ANN), in identifying five conifer species and propose an optimal model. Using direct analysis in real-time ionization combined with time-of-flight mass spectrometry (DART-TOF-MS), metabolic fingerprints of 250 individual specimens representing five species were collected three times. When the machine learning models were applied to classify the wood species, ANN outperformed SVM and RF. All three models showed 100% prediction accuracy for genus classification. For species classification, the ANN model had the highest prediction accuracy of 98.22%. The RF model had an accuracy of 94.22%, and the SVM had the lowest accuracy of 92.89%. These findings demonstrate the practicality of authenticating wood species by combining DART-TOF-MS with machine learning, and they indicate that ANN is the best model for wood species identification.