Reliable classification of nutmeg fruits from some geographical origins using SWNIR spectroscopy associated with PLS-DA and SVM methods: Case Study in Aceh Province, Indonesia

Abstract

Essential oils of nutmeg (Myristica fragrans) can have different qualities due to the soil and industrial process, as Indonesia’s main nutmeg production areas are enormous, especially in Aceh province. The lack of information on detecting chemical constituents and classifying nutmeg from Aceh province makes the price of nutmeg from Aceh unable to compete. Therefore, this study aims to reliably classify nutmeg fruits of some geographical origins in Aceh Province using shortwave near-infrared (SWNIR) spectroscopy associated with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). The SWNIR used in this study has a wavelength of 381 to 1065 nm at a resolution of 3 nm and a diffuse reflectance mode. The fruit comes from four geographical areas of origin in the province of Aceh. A total of forty SWNIR spectral data were further classified using two algorithms, namely PLS-DA and SVM. The results show that SWNIR can correctly classify nutmeg from some sub-districts in Aceh province with the help of the PLS-DA and SVM algorithms. From this study, the following SVM algorithm is recommended to be used to classify nutmeg from the province of Aceh with an outstanding level of precision.