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To achieve rapid and accurate identification of valuable wood products, an analytical method was developed by combining electric soldering iron cauterization with soft ionization by chemical reaction in transfer-mass spectrometry (SICRIT-MS). SICRIT is a flow-through dielectric barrier discharge ionization technique pioneered by Zenobi et al. in 2016. The electric soldering iron cauterization-SICRIT-MS method requires no sample pretreatment, easy operation and a single analysis in less than 5 s, meeting the demands of rapid analysis. Operating parameters for the soldering iron and SICRIT ion source were optimized to achieve maximum total ion current intensity under soldering iron temperature of 450 ℃, ion source AC voltage amplitude of 2 000 V, and sample transfer line temperature of 150 ℃. With the optimized parameters, the SICRIT-MS method was applied to analyze valuable wood samples, including 29 certified standard wood samples and 6 online-purchased real samples, resulting in a dataset of 210 sets of mass spectral fingerprint data. Based on the mass spectral fingerprint data acquired under positive ion mode, a predictive model was trained using the random forest algorithm. The random forest model underwent optimization for the number of decision trees, max feature algorithm, and feature selection criteria, was evaluated through out-of-bag and 10-fold cross-validation. The results showed the error rates of out-of-bag and 10-fold cross-validation are 4.76% and 4.74%, respectively. The established random forest model can accurately distinguish wood samples from the genera Dalbergia, Guibourtia, and Pterocarpus with a classification accuracy of larger than 95%. The importance of features in distinguishing the three wood genera was investigated through binary classification modeling, revealing features 269.1, 270.1, 255.1, 159.0, 182.1, 102.1 and 83.1 as crucial in classification. These features may correspond to characteristic compounds in different wood species or differences in the content of the same compound across species. The predictive model was successfully applied to rapid identification of genera in valuable wood products sold online. Three purchased Guibourtia samples are confirmed as authentic, while the other three are not identified as the claimed genera. This method provides a scientific basis and experimental reference for authenticity identification and quality evaluation.

To achieve rapid and accurate identification of valuable wood products, an analytical method was developed by combining electric soldering iron cauterization with soft ionization by chemical reaction in transfer-mass spectrometry (SICRIT-MS). SICRIT is a flow-through dielectric barrier discharge ionization technique pioneered by Zenobi et al. in 2016. The electric soldering iron cauterization-SICRIT-MS method requires no sample pretreatment, easy operation and a single analysis in less than 5 s, meeting the demands of rapid analysis. Operating parameters for the soldering iron and SICRIT ion source were optimized to achieve maximum total ion current intensity under soldering iron temperature of 450℃, ion source AC voltage amplitude of 2000 V, and sample transfer line temperature of 150 ℃. With the optimized parameters, the SICRIT-MS method was applied to analyze valuable wood samples, including 29 certified standard wood samples and 6 online-purchased real samples, resulting in a dataset of 210 sets of mass spectral fingerprint data. Based on the mass spectral fingerprint data acquired under positive ion mode, a predictive model was trained using the random forest algorithm. The random forest model underwent optimization for the number of decision trees, max feature algorithm, and feature selection criteria, was evaluated through out-of-bag and 10-fold cross-validation. The results showed the error rates of out-of-bag and 10-fold cross-validation are 4.76% and 4.74%, respectively. The established random forest model can accurately distinguish wood samples from the genera Dalbergia, Guibourtia, and Pterocarpus with a classification accuracy of larger than 95%. The importance of features in distinguishing the three wood genera was investigated through binary classification modeling, revealing features 269.1, 270.1, 255.1, 159.0, 182.1, 102.1 and 83.1 as crucial in classification. These features may correspond to characteristic compounds in different wood species or differences in the content of the same compound across species. The predictive model was successfully applied to rapid identification of genera in valuable wood products sold online. Three purchased Guibourtia samples are confirmed as authentic, while the other three are not identified as the claimed genera. This method provides a scientific basis and experimental reference for authenticity identification and quality evaluation. [ABSTRACT FROM AUTHOR]