Classification models for identifying Pterocarpus santalinus L.f. using NIR spectroscopy data

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.