探讨了利用显微近红外成像技术识别精料补充料中肉骨粉的可行性.分别采集奶牛精料补充料和肉骨粉样品,制备沉淀颗粒,规则排列于聚四氟乙烯( PTFE)背景底板上,进行显微近红外图像采集.设置像素点大小为50 μm×50 μm,采集面积为5 000μm×5 000 μm,100×100个像素(共计10 000条光谱).光谱范围为7 800 ～4 000 cm -1,光谱分辨率为8 cm-1.采用主成分分析和模糊聚类分析,对显微近红外图像数据集进行信息提取与处理.结果显示,肉骨粉与精料补充料可依据在图像上不同的主成分得分进行区分；在主成分分析的基础上,通过模糊聚类方法可以进一步细化样本类别.研究表明,显微近红外成像方法可应用于肉骨粉快速检测中.%The possibility of using near-infrared microscopic imaging to discriminate meat and bone meal ( MBM ) in concentrate supplement was investigated. Samples of MBM and dairy concentrate supplement were collected, and the sediment particles were prepared and arranged on polytetrafluoroethene ( PTFE ) background plate for near infrared imaging. Image size was 5 000 μm x 5 000 p,m, using 50 μm pixel resolution (10 000 spectra were obtained). Each spectrum was across the wavelength range 7 800 ~ 4 000 cm-1 , with 8 cm-1 data resolution. Both principal component analysis and fuzzy clustering analysis were used to extract and present relevant information from NIR microscopic imaging data sets. The results showed that MBM could be distinguished from dairy concentrate supplement by the scores from principal component analysis, and the samples were subdivided by using fuzzy C-means clustering based on the PCA analysis. It is demonstrated that NIR microscopic imaging approach is one of the most promising methods for detecting MBM.
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