[Identification of egg freshness using near infrared spectroscopy and one class support vector machine algorithm].
[Identification of egg freshness using near infrared spectroscopy and one class support vector machine algorithm].
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发表时间：
2010-04
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通讯作者：
Hao Lin;Jiewen Zhao;Quansheng Chen;Jianrong Cai;Ping Zhou
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作者：
Hao Lin;Jiewen Zhao;Quansheng Chen;Jianrong Cai;Ping Zhou
利用近红外光谱技术结合模式识别技术对鸡蛋的新鲜度进行鉴别。采用单类支持向量机（OC-SVM）算法解决训练样本数量不均衡的分类问题。采用傅里叶变换近红外光谱法对86个鸡蛋样品（71个鲜蛋和15个不鲜蛋）进行了分析。首先，获得了鸡蛋在10 000-4 000 cm（-1）波数范围内的原始光谱;然后，利用主成分分析（PCA）从原始光谱数据中提取有用信息，并优化PC的数量。最后，利用OC-SVM对判别模型进行校正，并将最优的PC作为模型的输入特征向量。为了获得较好的性能，在建模过程中对OC-SVM核函数的正则化参数v和参数sigma进行了优化。最佳OC-SVM模型得到nu = 0.5和sigma 2 = 20.3。实验结果表明，在相同条件下，OC-SVM比传统的两类SVM模型具有更好的分类性能。OC-SVM模型在独立预测集上对鲜蛋和不鲜蛋的识别率均达到80。两类SVM模型对鲜蛋的识别率为100%。然而，当两类SVM模型用于区分不新鲜的鸡蛋，识别率为0%的独立预测集。与传统的两类SVM模型相比，OC-SVM模型在少数不新鲜鸡蛋样本的判别中表现出了上级的性能。实验结果表明，利用近红外光谱技术对鸡蛋新鲜度进行识别是可行的，OC-SVM是解决训练集样本数不均衡问题的一个很好的选择。
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 imbalanced 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 regularization parameter v and parameter sigma of the kernel function in OC-SVM model were optimized in building model. The optimal OC-SVM model was obtained with nu = 0.5 and sigma2 = 20.3. Experimental result shows that OC-SVM got better performance than conventional 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.