College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
Molecules. 2018 Nov 25;23(12):3078. doi: 10.3390/molecules23123078.
Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874⁻1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine-SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.
种子在储存过程中的老化是不可逆转的，因此，对于种子公司和农民来说，找到一种快速、准确的种子活力检测方法来检测种子老化非常重要。本研究采用人工加速老化处理来模拟玉米籽粒的老化过程，并应用 874-1734nm 光谱范围内的高光谱成像作为一种快速准确的技术，来识别不同加速老化时间条件下的种子活力。采集了两个玉米品种在经过 8 个不同老化时间（0、12、24、36、48、72、96 和 120h）处理后的高光谱图像。采用主成分分析（PCA）对不同加速老化时间条件下的玉米籽粒进行定性分析。应用二阶导数选择特征波长。建立基于全谱和最优波长的分类模型（支持向量机-SVM）。结果表明，未经处理的玉米籽粒很少出现误分类，而经过 12h 和 24h 短老化时间处理的玉米籽粒则出现了一些误分类。总的来说，较短老化时间（0、12 和 24h）下的玉米籽粒分类准确率较高，在 61%至 100%之间。而老化时间较长（36、48、72、96、120h）的玉米籽粒分类准确率较低。根据 SVM 模型混淆矩阵的结果，每个玉米品种的 8 个类别可分为三组：第 1 组（0h）、第 2 组（12 和 24h）和第 3 组（36、48、72、96、120h）。同一组内的不同类别之间的玉米籽粒更容易相互误分类，而不同组之间的玉米籽粒之间误分类的样本较少。进行发芽试验验证分类模型，结果表明，标准发芽试验揭示的玉米籽粒活力的显著差异通常与 SVM 模型的分类准确率相匹配。对两个玉米品种的高光谱成像分析结果相似，表明可以使用高光谱成像技术结合化学计量学方法来评估种子活力和种子老化程度。
