An Exploratory Study of FT-NIR Spectroscopy and Class-Wise PCA for Quality Screening of Mee Rough Tea

To address the need for rapid evaluation of large batches of Mee rough tea during the acceptance stage, this study aims to explore the feasibility of using portable Fourier transform near-infrared (FT-NIR) spectroscopy for preliminary quality screening. The goal is to develop a rapid, non-destructive, and relatively objective assessment method that is applicable to practical acceptance scenarios. This work represents an exploratory proof-of-concept study rather than a finalized industrial grading solution. Spectral data of three reference categories and thirty-six test samples were collected in the wavelength range of 1350–2500nm using a portable FT-NIR spectrometer. The sample configuration was designed to simulate practical acceptance sampling conditions. The spectra were preprocessed using multiplicative scatter correction, first-order derivative transformation, and mean-centering. Independent principal component analysis (PCA) models were constructed for each reference category to achieve class-wise feature dimensionality reduction, with cumulative explained variance exceeding 95%. Distance thresholds were determined using the 3σ principle based on Euclidean distance and Mahalanobis distance. Classification was performed by distance-based matching between test samples and reference categories. Under optimized matching degree threshold settings of 0.9 and 0.7, the two distance models achieved classification accuracies of 86.11% and 83.33%, respectively, demonstrating the feasibility of the proposed approach. The main contribution of this study is the application of class-wise PCA combined with distance-based discrimination to the acceptance stage of Mee rough tea. The proposed framework provides a practical exploratory approach for rapid screening and offers a preliminary digital tool to support acceptance decisions. Further validation using larger and more diverse datasets will be necessary prior to large-scale industrial implementation.