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.%利用自主研制的地面成像光谱辐射测量系统(field imaging spectrometer system,FISS)获取了14种典型牛奶样品的高光谱图像数据,并基于此做了牛奶品种识别研究.首先剔除2个异常样品,对剩余12种牛奶样品随机取样,共取1 200个像元样本;为消除高频随机噪声和基线偏移,改善多重共线性,对所有样本做移动平均平滑和一阶微分预处理,再运用逐步回归法选择的特征波长建立牛奶多类判别分析模型.结果表明,对12种牛奶样本混合识别,总体判别精度高达95.5%,其中蒙牛、伊利和光明三种原味酸奶的总体正确识别率为88.3%;对这三种原味酸奶构成的样本子集单独识别,其总体正确识别率为88.7%.这说明FISS能够用于牛奶品种识别研究,还发现为实现有针对性的牛奶品种鉴别,同类型不同厂家生产的牛奶最好单独识别,这不仅能减少模型变量,提高模型运算效率和稳定性,也能提高判别的总体精度.
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