利用近红外光谱与PCA-SVM识别热损伤番茄种子

为了研究近红外光谱技术用于热损伤种子快速无损识别的可行性,该文以120粒番茄种子为研究对象,其中60粒番茄种子通过高温加热处理的方式成为热损伤种子组,其他60粒番茄种子为正常种子组,利用实验室自主搭建的近红外光谱检测系统获取单粒番茄种子在980~1 700 nm范围内的光谱,分别采用偏最小二乘判别法(partial least squares discriminant analysis,PLS-DA)和支持向量机(support vector machines,SVM)建立了番茄种子热损伤的定性分析模型。试验结果表明:2种判别模型的验证集总正确率均大于96%,均可用于热损伤种子的判别。其中,基于主成分分析(principal component analysis,PCA)预处理的光谱数据构建的支持向量机模型的判别效果最好,其校正集和验证集的判别正确率均为100%,更适用于种子热损伤识别。因此,应用近红外光谱技术可快速无损识别热损伤番茄种子,为种子检验提供了一种新的方法。

Discrimination of heat-damaged tomato seeds based on near infrared spectroscopy and PCA-SVM method

Abstract: The problem of heat-damaged seeds frequently occurs because of improper storage of moist seeds or artificial drying of damp seeds at high temperature, thus influencing its sale and usability. It is therefore vital for seed companies and farmers to identify damaged seed. The current visual method for discriminating heat-damaged seeds is subjective and based on discoloration. However, heat damage does not always cause a color change in kernels while it could cause protein denaturation which may result in NIR absorption differences between native protein and denatured protein. In this study, we investigated the possibility of using near infrared spectroscopy to classify good and heat-damaged seeds. A group of 60 tomato seeds was heat-damaged with high temperature treatment while another group of 60 samples was good seeds without heating treatment. The laboratory self-constructed near infrared spectroscopy system, which measured reflectance spectra (log(1/R)) from 980 to 1700 nm, was used to obtain single seed spectra . In order to verify the difference of viability between heat-damaged seeds and good seeds, a standard germination experiment for 14 days was conducted after the spectra of samples were done. The germination rate, germination potential and germination index of heat-damaged seeds were significantly lower (p < 0.05?) than that of good seeds, and the average germination days was higher (p < 0.05?) than that of good seeds, which indicated that the viability of heat-damaged seeds was lower than that of good seeds. Then, the samples were divided into calibration set and validation set according to the ratio of 3:1 by Kennard-Stone method which is widely used in the qualitative analysis of spectral data. Methods of partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were applied to establish the discriminating models for heat-damaged tomato seeds. The results showed that those two discriminative models could be used to differentiate heat-damaged seeds and good seeds. The total accuracy of each validation set was higher than 96%. For PLS-DA model, total classification accuracy for both the calibration sample set and validation sample set were 100% and 96.7%, respectively when five PLS factors was selected by leave-one-out cross validation. The classification accuracy of the good seeds of the validation set was 100%, but one heat-damaged sample was misjudged as the good sample. SVM model yielded higher classification accuracy than PLS-DA model, which was more suitable for classifying heat-damaged tomato seeds according to near infrared reflectance spectra. The SVM model based on principal component analysis (PCA) of the preprocessed spectral data gave the best result, its classification accuracy of the calibration set and the validation set were 100%. Moreover, the prediction bias of PCA-SVM model was less than that of the PLS-DA model, and the average deviation of the validation set was 0.024, which was more conducive to the stability of the model. The overall results suggest that near infrared spectroscopy technique combined with proposed pattern recognition algorithm is accurate for classification of heat-damaged and sound seeds, and provides a new method for future research about nondestructive testing of seed quality.