为研究一种快速有效的猪肉质量检测方法，解决传统检验方法耗时、成本高等问题，从市场上收集了109组猪肉样品，通过理化方法将其区分为健康猪肉和病死猪肉。使用FT-NIR光谱仪采集了样品的光谱，并对光谱进行去噪和降维处理。结果显示，光谱数据经过标准正态变换，结合多元散射校正处理和傅里叶变换降维后，建立了基于支持向量机的分类模型，5-折交叉验证准确率达到94.5%。结果表明，利用该方法可以很好地进行不同品质猪肉样品的分类，为用近红外光谱技术检测猪肉质量提供了依据和基础。

To study a rapid and effective pork quality inspection method which could solve the problems of time-consuming and high cost in traditional inspection methods, 109 groups of pork samples were collected from markets and farms. The samples were divided into healthy pork and diseased or dead pork by physical and chemical methods. A FT-NIR spectrometer was used to acquire the spectra of the samples. The spectra acquired were processed in noise reduction and dimensionality reduction. The results showed that after processing, a prediction model based on support vector machine algorithm was established. The accuracy of 5-fold cross validation was up to 94.5%. The results showed that the method could be used to classify the pork of different quality and provide the basis for the application of near-infrared spectroscopy in pork quality detection.