基于自主研制的地面成像光谱辐射测量系统（field imaging spectrometer system, FISS）, 利用获取的可见/近红外波段成像光谱数据进行鲜猪肉和解冻猪肉的识别研究, 同时对鲜猪肉的新鲜度在类别和等级上分别进行识别研究。 通过最小噪声分离变换和一阶微分处理, 消除数据高频随机噪声和基线偏移, 改善多重共线性, 运用Wilks’lambda逐步法选择特征波长, 采用Fisher线性判别函数建立判别分析模型。 运用选择的前8个波段建立模型, 对鲜猪肉和解冻猪肉的识别即可高达99%； 运用选择的前6个波段, 鲜猪肉新鲜度类别总体正确识别率达到98%； 运用28个波段, 鲜猪肉新鲜度等级的总体正确识别率为93.6%。 研究结果表明, FISS在肉类食品品质识别分类方面具有较高的应用潜力。

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.