Classification models for identifying <i>Pterocarpus santalinus</i> L.f. using NIR spectroscopy data

Abstract Pterocarpus santalinus L.f. ( P. santalinus ), protected under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), is a high-priced, slow-growing, and scarce wood primarily used in crafting high-end furniture. The international timber trade currently faces issues of counterfeit P. santalinus , with commonly used substitutes including Dalbergia louvelii R.Viguier, Pterocarpus tinctorius Welw., Gluta renghas L. and Baphia nitida Lodd. This study aims to develop a P. santalinus authenticity identification model based on near-infrared spectroscopy (NIRS) technology. The NIR spectral pretreatment involved the use of four methods, either individually or in combination: multiplicative scatter correction (MSC), moving average smoothing (MAS), Savitzky-Golay (S-G), autoscaling (AUTO) and standard normal variate (SNV). An authenticity identification model for P. santalinus based on long short-term memory (LSTM) was established and compared with commonly used support vector machines (SVM) and random forest (RF) models. The results indicate that the accuracy of the MSC-LSTM model is 97.1 %, with precision, recall, and F1 score all exceeding 0.85. In identifying P. santalinus in the test set, the MSC-LSTM model has an error rate of only 4.8 %. LSTM performs outstandingly across multiple indicators, demonstrating its ability to identify P. santalinus authenticity. The developed MSC-LSTM P. santalinus authenticity identification model shows enhanced accuracy compared to SVM and RF, significantly reducing misidentification of P. santalinus .