[Fresh and frozen-thawed meat discrimination based on FISS imaging spectral data].

In the present paper, a self-developed Field imaging spectrometer system (FISS) was used to detect whether pork has been frozen and thawed. The preservation time of fresh pork has also been identified. Fresh and frozen-thawed pork was scanned and imaged and hyperspectral image cubes were acquired using FISS. To eliminate high-frequency random noise and baseline offset and improve the multi-collinearity, all samples were preprocessed by MNF (Minimum noise fraction) transform and first derivative. Multiple analysis models were built by using Wilks' lambda stepwise method to select proper wavelengths. Fisher LDA (linear discriminant analysis) was performed to discriminate fresh and frozen-thawed pork. Eight selected bands gave 99% correct results of fresh or frozen-thawed pork samples. For the freshness by the day, classification accuracy reached 98% with 6 selected bands, while for the freshness by the hour, classification accuracy reached 93.6% with all 28 selected bands. The results showed that FISS might be used as a screening method to identify the quality of meat.

[Study on discrimination of varieties of milk based on FISS imaging spectral data].

Using a self-developed field imaging spectrometer system (FISS), hyperspectral images of 14 typical kinds of milk were acquired, based on which the discrimination of varieties of milk was studied. Firstly, removing 2 abnormal samples, the remaining 12 kinds of milk were randomly sampled, a total of 1 200 pixel samples. To eliminating high-frequency random noises and baseline offset and decrease the multi-collinearity, all samples were preprocessed by smooth-moving average and first derivative. Secondly, multiple discriminant analysis models for milk were built using characteristic wavelengths selected by the stepwise method. Results demonstrated that the overall identification accuracy for 1 200 spectral samples put together reached 95.5%, of which the overall distinguishing rate of Mengniu, Yili and Guangming acidophilous milk was 88.3%. The discriminant models for the three kinds of acidophilous milk subset, 300 spectral samples in all, were built, with the overall distinguishing rate of 88.7%. This explicated that FISS would be useful for discriminating milk varieties, and to accomplish specific discrimination of milk varieties, it would be best for milk of the same type from different manufacturers to form a subset, which may not only reduce the model variables, improving operational efficiency and the stability of the model, but improve their overall discriminant accuracy.