近红外光谱结合一类支持向量机算法检测鸡蛋的新鲜度

研究利用近红外光谱技术结合模式识别方法识别鸡蛋的新鲜度,在识别模型建立过程中,引入一类支持向量机(OC-SVM)算法解决新鲜蛋和非新鲜蛋训练样本数量不平衡问题。首先获取鸡蛋在10 000~4 000 cm-1范围内的近红外漫反射光谱,通过主成分分析方法提取光谱数据中的特征信息,优选了3个主成分作为模型的输入向量,然后采用OC-SVM区分新鲜蛋和非新鲜蛋。在模型建立过程中,对相关参数进行了优化,试验结果显示在相同条件下,OC-SVM模型识别结果较传统的支持向量机模型好。最优OC-SVM模型对新鲜蛋和非新鲜蛋的识别率均为80%,传统的支持向量机对新鲜度的识别率为100%,对非新鲜度的识别率却为0%。研究结果表明利用近红外光谱快速识别鸡蛋新鲜度方法是可行的;OC-SVM算法为训练样本数量不平衡提供了一种有效的解决方法。

Identification of Egg Freshness Using Near Infrared Spectroscopy and One Class Support Vector Machine Algorithm

Near infrared (NIR) spectroscopy combined with pattern recognition was attempted to discriminate the freshness of eggs. The algorithm of one-class support vector machine (OC-SVM) was employed to solve the classification problem due to im-balanced number of training samples. In this work, 86 samples of eggs (71 samples of fresh eggs and 15 samples of unfresh eggs) were surveyed by Fourier transform NIR spectroscopy. Firstly, original spectra of eggs in the wave-number range of 10 000-4 000 cm~(-1) were acquired. And then, principal component analysis (PCA) was employed to extract useful information from original spectral data, and the number of PCs was optimized. Finally, OC-SVM was performed to calibrate discrimination model, and the optimal PCs were used as the input eigenvectors of model. In order to obtain a good performance, the regulariza-tion parameter v and parameter σ of the kernel function in OC-SVM model were optimized in building model. The optimal OC-SVM model was obtained with v=0. 5 and σ~2 =20. 3. Experimental result shows that OC-SVM got better performance than con-ventional two-class SVM model under the same condition. The OC-SVM model was achieved with identification rates of 80 for both fresh eggs and unfresh eggs in the independent prediction set. The identification rates of fresh eggs were 100% in two-class SVM model. However, when the two-class SVM model was used to discriminate the unfresh eggs of, the identification rates were 0% in the independent prediction set. Compared with conventional two-class SVM model, the OC-SVM model showed its superior performance in discrimination of minority unfresh eggs samples. This work shows that it is feasible to identify egg freshness using NIR spectroscopy, and OC-SVM is an excellent choice in solving the problem of imbalanced number of samples in training set.