基于可见-近红外光谱及随机森林的鸡蛋产地溯源
Discrimination of origin of eggs using visible-near-infrared spectroscopy and random forest
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摘要: 为了研究快速无损鉴别鸡蛋产地的可行性,利用可见-近红外光谱技术,采集4种湖北不同产地鸡蛋的透射光谱（500900 nm）,利用中心化、归一化、标准正态变量（SNV）、Savitzky-Golay平滑滤波（SG）和多元散射校正（MSC）、直接正交信号校正（Direct Orthogonal Signal Correction,DOSC）算法对光谱数据进行预处理,采用t分布式随机邻域嵌入（t-distributed stochastic neighbor embedding,t-SNE）、主成分分析（PCA）方法对预处理后的数据降维,并将降维后的数据分别输入极限学习机（extreme learning machine,ELM）和随机森林（random forest,RF）,建立鸡蛋产地溯源模型。比较两种方法建立的模型,发现运用DOSC预处理及t-SNE提取的光谱特征信息建立的RF模型鉴别效果最好,训练集和预测集的鉴别正确率分别为100%和98.33%。研究结果表明基于可见-近红外光谱技术对鸡蛋产地溯源是可行的,为进一步研究与开发鸡蛋产地溯源便携式仪器提供技术支持。Abstract: In order to study the feasibility of rapid and nondestructive identification of the origin of eggs.Visible-near-infrared spectroscopy was used to obtain the spectral transmittance of eggs ( 500900 nm) , which were the origin of four areas of Hubei.Combining with centering, normalization, standard normal variables, Savitzky-Golay smoothing filter, multiple scatter correction and the direct orthogonal signal correction method were to preprocessing the spectral data, the t-distributed stochastic neighbor embedding ( t-SNE) and PCA were to reduce the dimension of the preprocessed data.The processed data was transmitted as the input of extreme learning machine and random forest.The models were established to discriminate the origin of eggs.Compared the two methods, the RF model based on DOSC and t-SNE obtained the best results. The correct rates of calibration and prediction were 100%, 98.33% respectively.The results showed that the visible-near-infrared spectroscopy technology could be competent for the discrimination of the origin of eggs, which would provide a technical support for further research and development of portable devices for the discrimination of the origin of eggs.
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