Nondestructive grading test of rice seed activity using near infrared super-continuum laser spectrum
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摘要: 针对目前农业种植选种应用对于带稃壳水稻种子活力分级检测的迫切需求，以及现有通用的糙米检测技术存在的问题，本文提出一种基于近红外超连续激光光谱的水稻种子活力透射光谱检测方法。首先，设计了种子活力近红外吸收光谱检测系统，测量了3种不同年份的带稃壳的水稻种子的近红外吸收光谱，结果显示，水稻种子的活力梯度与近红外吸收光谱的特征吸收峰值相关。然后，采用归一化、二阶导数校正法和正交信号校正相结合优化了种子光谱的预处理算法。最后，建立主成分分析(PCA)模型，对光谱进行降维，确定最佳主成分数目，应用偏最小二乘判别分析(PLS-DA)建立了水稻种子活力分析鉴别模型。分析结果表明，本文设计的透射式吸收光谱检测系统结合PLS-DA判别模型可对不同活力的水稻种子进行分类，校正集和验证集的准确率分别为94.44%和95.92%，筛选后水稻种子的发芽率可达97.17%。研究结果表明，本文提出的基于近红外光谱信息实现水稻种子活力无损分级的方法可行，且具有较高的预测精度。Abstract: In view of the urgent need for seed selection technology in agriculture and for grading detection of the vigor of three different years of unpeeled rice seeds, we proposed a new method of detecting the vigor of rice seeds based on near-infrared super-continuous laser spectrum to overcome the significant issues in pre-existing universal brown rice detection technology. Firstly, we design a near-infrared absorption spectroscopy system with which we detect seed viability and measure the NIR spectra of three different years of unpeeled rice seeds. The results show that the activity gradient of the rice seeds is correlated with the characteristic absorption peak of their NIR absorption spectrum. Then, the spectrum of seed is optimized with a pretreatment algorithm of normalization, second derivative correction and orthogonal signal correction. Finally, a Principal Component Analysis (PCA) model is established to reduce the dimension of the spectrum and determine the optimal number of principal components. A Partial Least Squares Discriminant Analysis (PLS-DA) model is established. The analysis results show that the transmission absorption spectrum detection system designed in this paper combined with the PLS-DA discrimination model can classify rice seeds of different vigor with an accuracy of 94.44% and 95.92%. After screening, the germination rate of rice seeds can reach 97.17%. The results show that it is feasible to achieve non-destructive classification of rice seed activity using near-infrared spectroscopy with high accuracy.
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图 1 (a)反射式光谱检测方法与(b)透射式光谱检测方法测量原理示意图
Figure 1. Schematic diagram of (a) reflection type spectral detection method, and (b) transmission type spectral detection method
图 2 水稻种子活力的透射光谱检测系统
Figure 2. Transmission spectrum detection system for rice seed vigor
图 3 水稻种子的近红外吸收光谱图。（a）不同活力的水稻种子近红外光谱平均曲线；（b） 高活力水稻种子近红外吸收光谱
Figure 3. Near-infrared absorption spectrum of rice seeds. (a) The average curves of transmission spectra of rice seeds with different vigors. (b) Near-infrared absorption spectrum of high vigor seeds
图 4 二阶导数处理后的光谱信息
Figure 4. The absorption spectrum of rice seed after second derivative processing
图 5 两种预处理方法结果对比。（a）正交信号校正处理后的光谱；（b）标准正态变量校正后的光谱
Figure 5. Comparison of absorption spectrum of rice seed processed by two pretreatment methods. (a) Orthogonal signal correction; (b) standard normal variate correction
图 6 PLS-DA模型对2018年（a）、2017年（b）、2016年（c）及随机混合（d）的水稻种子活力判别结果
Figure 6. Evaluation results of vigor of the rice seed in 2018 (a)、2017 (b)、2016 (c) and random mixing (d) determined by PLS-DA model
表 1 筛选前水稻种子的活力情况
Table 1. The seed vigor parameters of rice seeds before selecting
年份 活力高 活力低 不发芽 发芽率 2018 192 52 44 84.72% 2017 154 71 63 78.13% 2016 106 93 89 69.09% 随机混合 133 84 71 75.351% 总计 585 300 267 —— 
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表 2 不同预处理方法对样品的活力鉴别情况
Table 2. The vitality identification results of samples by different pretreatment methods
预处理方法 光谱范围/nm 主成分数 准确率/% 未处理 1100～2100 5 68.19 MSC 1100～2100 3 75.63 SNV 1100～2100 3 71.78 OSC 1100～2100 3 77.05 归一化+MSC 1100～2100 3 70.33 SD+MSC 1100～2100 3 77.91 SD+SNV 1100～2100 3 79.85 SD+OSC 1100～2100 3 78.57 归一化+SD+MSC 1100～2100 3 83.97 归一化+SD+SNV 1100～2100 3 88.06 归一化+SD+OSC 1100～2100 3 94.13 归一化+SD+OSC 1100～2100 2 82.85 归一化+FD+OSC 1100～2100 3 89.39
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表 3 主成分数与模型贡献率
Table 3. Number of principal components and model contribution rate
主成分数 1 2 3 4 5 6 模型准确率/% 55.3 82.9 95.9 94.2 94.1 92.7 累积贡献率/% 62.4 85.7 93.5 96.1 98.2 99.6
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表 4 PLS-DA模型判别准确率及筛选后种子发芽率
Table 4. Total accuracy of PLS-DA model and seed germination of rice seeds after screening
年份 校正集准确率/% 验证集准确率/% 筛选后发芽率/% 2018 94.44 95.92 97.17 2017 93.98 94.69 96.52 2016 91.53 92.37 95.06 随机混合 92.74 93.16 96.07
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