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基于 SVM 和高光谱成像全尺度特征的香肠关键品质检测研究
食品检测
2026/09/21
摘要：本文提出了一种快速准确的检测方法，可以识别香肠中酸价、过氧化值和菌落面积是否超标。利用高光谱图像测 量和基于支持向量机SVM的多信息融合，建立了香肠含量模型。为了提高高光谱图像测量预测模型的准确性，减少测量 波动，引入图像信息和近红外值数据作为高光谱图像含量模型的参数输入。本研究检测模型的 RMSECV 和 r2 分别为 0.251和0.972。研究认为该理论和方法可以进一步推广到其他相关肉类农产品的检测。
关键词：高光谱成像，支持向量机（SVM），模式识别
期刊：2017 29th Chinese Control And Decision Conference (CCDC)
DOI：10.1109/CCDC.2017.7979378
设备：GaiaSorter
Research on the detection of key quality of sausages based on SVM and hyperspectral imaging full-scale characteristics
Abstract：In this paper a quick and accurate detection method is proposed, which can identify whether the sausage contains excessive acid value, peroxide value and area of colony. By using hyperspectral image measurement and multi-information fusion based on support vector machine SVM , the sausage content model is established. In order to improve the accuracy of hyperspectral image measurement predicted model and to reduce the measurement turbulence,the image information and the NIR value data that are input as the parameters of the hyperspectral image content model are introduced. The detection model’s RMSECV and r2 of the research are 0.251 and 0.972. The study concludes that the theory and method can be further extended to the detection of other related meat agricultural products.
Keywords：Hyperspectral imaging, Support vector machine (SVM), Pattern recognition
