Abstract: A classification method based on representative rate and voting mechanism was established to monitor the end point of tablet coating by using near-infrared(NIR)technology. Firstly, the sample mean of each coating time point was clustered by hierarchical clustering, and establishing the classification model. Then, the representative rate proposed in this paper was used to verify the model at each time point. The method verified the mean clustering model through the representative rate to confirm the availability and feasibility of the model. Finally, the training model was used to predict the sample categories at each time point, and the final voting results at each time point were obtained through voting mechanism, which made up for the lack of model prediction in systematic clustering and avoided the wrong judgment caused by individual samples. The experimental data verified that the lowest representative rate was 73.33% for T100M, and the highest was 100.00% for T130M, with an average representative rate of 86.77%, The voting results show that the finishing points of the first batch of test data and the second batch of test data are 1T130M and 2T132M respectively. The voting mechanism can also monitor the process of coating at other time points.

Key words: near infrared technology, coating, representative rate, voting mechanism