Abstract: Abstract: Pale, soft, and exudative (PSE) and dark, firm, and dry (DFD) are degraded grades of pork. Reddish- pink, firm, and non-exudative (RFN) are considered superior grades of pork. The meat color, pH value, and water holding capacity for PSE, DFD, and RFN have different ranges which directly influence the purchasing decision of customers, thus affecting the economic benefits of the pork processors. With growing demand for quality meat and increasing state-of the-art meat processing technologies, pork industries are in need of a reliable technology for rapid, accurate, and non-destructive detection of meat quality. Spectral technology has gained importance in agricultural research and the meat industry. Spectral technology has also shown its massive potential in the meat industry. However, full wave band contains huge amount of spectral information, which possess a severe drawback to spectroscopic technology in terms of its accuracy and detection speed. This study proposes a pork comprehensive quality classifying method based on partial least squares (PLS) projection algorithm and support vector machine (SVM). The acquired spectral data of sample meat were first normalized using standard normal variation (SNV) transformation method. Next, the whole sample was randomly classified into two sets: the calibration set (for developing prediction model using 75% of total samples) and the validation set ( to validate the model using remaining 25% of the total samples). Mean filter was used to smooth the normalized spectral signal. Partial least square projection method was then used to obtain projection coefficients for each wave band. Classification models were developed using 1 to 20 different wave bands, and root mean square errors (RMSE) of each model were calculated by cross validation. The lowest RMSE was obtained using 13 wave bands and it was observed that it became stable with more wavelengths. The spectral wave lengths of 371, 388, 425, 456, 473, 562, 578, 607, 696, 764, 772, 813 and 927 nm were chosen based on the RMSE value to develop a pork quality prediction and classification model. Particle swarm optimization (PSO) algorithm optimized penalty parameter and radial basis core function parameter, was 4.939 and 0.01 for the best. Pork comprehensive quality SVM classifier was established using special wavelengths and optimized parameters. It was shown that the back-to-recognition rate of pale, soft, and exudative (PSE), reddish, firm, and non-exudative (RFN), and dark, firm, and dry (DFD) in training sets were 88.46%, 94.11%, and 92.31% respectively. For testing sets, predictive accuracy rates of three kinds were 84.62%, 94.11%, and 84.62% respectively. The study revealed the advantages of the established classifier, such as based on simple model and achieving high prediction accuracy, and so on. This study shows a simple, rapid, and non-destructive method by optical instrument to detect and classify pork based on its comprehensive quality. The study can be a milestone to develop a fast, accurate, and reliable technique for non-destructive detection of pork in slaughtering house, super markets and other required areas where quality is a concern.