Non-destructive identification of cashmere and wool fibers based on PLS-DA and LDA using NIR spectroscopy

Manual identification of cashmere and wool fibers is often laborious, subjective, and time-consuming due to their extremely similar features. In order to non-destructively and accurately detect these animal fibers, this study proposes a novel detection method based on machine learning algorithms by near-infrared (NIR) spectroscopy. Building upon the preprocessing of NIR spectroscopy data of cashmere and wool fibers, both partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) classifiers are used to distinguish cashmere and wool fibers. First, four data preprocessing methods are applied: mean normalization (MN), z-score standardization (ZSS), mahalanobis distance (MD), and discrete wavelet transform (DWT). Second, following the preprocessing, PLS-DA is used for feature extraction of the spectral data. Finally, based on the criterion of cumulative contribution rate of 80%, determine the number of principal components (PCs) and use the selected PCs as input for LDA. This study compares three feature extraction methods, principal component analysis (PCA), factor analysis, and sparse principal component analysis (SPCA), and two identification models, k-nearest neighbor (KNN) and decision tree (DT). Experimental results indicate that the proposed PLS-DA-LDA model outperforms the other 11 models, offering a new method for the identification of cashmere and wool fibers using NIR spectroscopy.