Chemometrics has been widely applied in near-infrared (NIR) spectroscopic analysis for quantitative detection and discrimination. However, new methods are still needed to simplify data processing and modeling to speed up the analysis and improve the convenience in practical uses. As a new type of technique for spectroscopic measurement and computation, the multivariate optical computing (MOC) technique is employed in spectroscopic analysis. The technique uses multivariate information in the spectrum to achieve quantitative computation and discrimination through the designed filters. In this work, the filters for discrimination analysis of near-infrared spectroscopy was designed based on principal component analysis (PCA) and Fisher's discrimination criterion. The spectra of the calibration samples can be projected into a two-dimensional space by the two filters to achieve an optimized classification, and a confidence ellipse can be obtained for each class of the samples. The ellipse can be used as a model for the discriminating the prediction samples. The distance of a prediction sample to the model is a good measurement of its classification. The samples with a distance less or equal to 1 are classified into the same class of the model, but those with a distance larger than 1 is excluded from the class, and the larger the distance, the bigger the dissimilarity. The proposed method was tested with the NIR spectra of 460 samples of tobacco leaf in three different parts of the plant and 73 samples of the medicinal capsules (amoxicillin granules) produced by four producers. The true positive rate can be higher than 90%, except for the tobacco samples and even higher than 95% for the capsule samples. However, the false-positive rate of the tobacco samples is still not so satisfactory due to the similarity of the NIR spectra. Using near infrared spectroscopy, the proposed method may provide a good technique for quality control, product detection and production monitoring in different fields.