Tablets Classification Method Based on Bayesian Decision and Near Infrared Spectroscopy

Lack of samples and uneven distribution in different sample sets is the problem at tablets classification using near infrared spectroscopy that cause the classification error. A classification method based on Bayesian decision making is proposed. The method estimate the priori probability density of calibration set and class conditional probability density of unknown tablets spectral using different kinds of calibration set. The posterior probability is calculated by Bayesian full probability formula. The unknown tablet is classified according to its posterior probability. The experiment selects four categories of citalopram tablets with different amount in each category. The Bayesian decision model based on calibration set of 70 spectra is used to classify 20 tablets in the validation set. Comparing to PLSDA method, the sensitivity and specificity of the Bayesian model is 100%, validating the Bayesian decision taxonomy can improve the classification accuracy and adaptability in the distribution based on NIR spectral.