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Brand traceability of several different kinds of milk powder was studied by combining near infrared spectroscopy diffuse reflectance mode with soft independent modeling of class analogy (SIMCA) in the present paper. The near infrared spectrum of 138 samples, including 54 Guangming milk powder samples, 43 Netherlands samples, and 33 Nestle samples and 8 Yili samples, were collected. After pretreatment of full spectrum data variables in training set, principal component analysis was performed, and the contribution rate of the cumulative variance of the first three principal components was about 99.07%. Milk powder principal component regression model based on SIMCA was established, and used to classify the milk powder samples in prediction sets. The results showed that the recognition rate of Guangming milk powder, Netherlands milk powder and Nestle milk powder was 78%, 75% and 100%, the rejection rate was 100%, 87%, and 88%, respectively. Therefore, the near infrared spectroscopy combined with SIMCA model can classify milk powder with high accuracy, and is a promising identification method of milk powder variety.

The near infrared spectrum atlases of milk powders mingled with different adjuvant are the object for cluster analysis. Drawing assistance from the disparity in infrared fingerprint atlas that change according to the contents of chemical constituent, and making mingled component models, the milk powders mingled with different adjuvant were taken for a rapid sorting test using SIMCA clustering analytical method. In the experiment, two hundred fifty sorts of milk powders in the markets from different manufacturers were scanned by near infrared ray, and were tested with reproducibility determination. It was found difficult to extract fingerprint characters just from the external appearance of the near infrared spectrum atlases from milk powders mingled with different adjuvant, and it is needed to adopt pattern recognition technique to determine intelligently. One hundred sixty atlases were drawn out randomly for cluster analysis, and unknown samples were pretested. Results showed that the milk powders mingled with different adjuvant can be identified by near infrared spectrum analysis associated with cluster analysis methods, notwithstanding the similar near infrared spectrum atlases of different sample were difficult to identify directly. No overlapping phenomenon was found among milk powders mingled with different adjuvant, and they did not interfere with each other. The results from clustering spectra between samples were satisfactory, and the correct ratios of blind detections were over 90%. In addition, the correct ratios of this method may be elevated remarkably with sufficient number of samples, increasing training set sample quantity and sampling representation, and strengthening the standard degree of manipulation. It is concluded that the designed model to determine milk powders mingled with different adjuvant is rational, and the determination capability is fine.