Application of Wavelet Analysis to Spectral Data for Categorization of Lamb Muscles

Application of wavelet analysis to near-infrared (NIR) hyperspectral imaging data was exploited for categorization of lamb muscles in this study. A variety of common wavelet transforms was investigated to identify the best wavelet features for categorization of lamb muscles. The fifth-order Daubechies wavelet ("db5") was found to be the best wavelet function for decomposition of lamb spectral signal. Features of wavelet coefficients extracted from db5 wavelet at the fifth decomposition level were then used as the inputs of least-squares support vector machine (LS-SVM) for developing classification models. Principal component analysis (PCA) was used for dimensionality reduction. Classification performance of LS-SVM classifiers in tandem with wavelet transform and PCA was compared with the LS-SVM models based on original, first derivative, second derivative, smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC) spectral data; then, the overall correct classification performance for the training and test sets using combination with wavelet approximation and detail coefficients in fifth decomposition scale and PCA was 100 and 96.15 %, respectively. In addition, the developed classification models were successfully applied to the hyperspectral images for obtaining classification maps and the kappa coefficient of 0.83 was obtained for the visual classification. The results revealed that the application of wavelet analysis has a great potential for categorization of lamb muscles in tandem with multivariate analysis and image processing.