Nondestructive grading test of rice seed activity using near infrared super-continuum laser spectrum
-
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.
-
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 —— 
下载: 导出CSV
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
下载: 导出CSV
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
下载: 导出CSV
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
下载: 导出CSV
-
杨振发, 肖航, 张雷, 等. 基于近红外光谱的水泥生料氧化物含量快速测定方法研究[J]. 分析化学,2020,48(2):275-281.YANG ZH F, XIAO H, ZHANG L, et al. Rapid determination of oxides content in cement raw meal based on near infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2020, 48(2): 275-281. (in Chinese) AMBROSE A, LOHUMI S, LEE W H, et al. Comparative nondestructive measurement of corn seed viability using fourier transform near-infrared (FT-NIR) and raman spectroscopy[J]. Sensors and Actuators B:Chemical, 2015, 224: 500-506. 李茂刚, 闫春华, 薛佳, 等. 近红外光谱结合小波变换—随机森林法快速定量分析甲醇汽油中甲醇含量[J]. 分析化学,2019,47(12):1995-2003.LI M G, YAN CH H, XUE J, et al. Rapid quantitative analysis of methanol content in methanol gasoline by near infrared spectroscopy coupled with wavelet transform-random forest[J]. Chinese Journal of Analytical Chemistry, 2019, 47(12): 1995-2003. (in Chinese) 何龙生. 水稻种子活力测定方法的初步研究[D]. 杭州: 浙江农林大学, 2018: 1-2.HE L SH. The preliminary study on methods for determination of rice seed vigor[D]. Hangzhou: Zhejiang A&F University, 2018. (in Chinese) 王岳含. 我国种子质量可追溯系统研究[D]. 北京: 中国农业科学院, 2016.WANG Y H. Seed quality tracing system of china research[D]. Beijing: Chinese Academy of Agricultural Sciences, 2016. (in Chinese) 牟致远. 小麦种子活力及其遗传分析[J]. 西南农业大学学报,1987,9(4):421-425.MU ZH Y. Seedling vigor and genetic analysis in wheat varieties[J]. Journal of Southwest Agricultural University, 1987, 9(4): 421-425. (in Chinese) 王青峰, 宫庆友, 沈凌云, 等. 超甜玉米种子活力研究[J]. 种子,2007,26(6):4-7. doi: 10.3969/j.issn.1001-4705.2007.06.002WANG Q F, GONG Q Y, SHEN L Y, et al. Study of combining ability of seed vigor in super sweet corn[J]. Seed, 2007, 26(6): 4-7. (in Chinese) doi: 10.3969/j.issn.1001-4705.2007.06.002 朱银, 颜伟, 杨欣, 等. 基于近红外光谱的小麦种子发芽率测试[J]. 江苏农业科学,2015,43(12):111-113.ZHU Y, YAN W, YANG X, et al. Test of germination rate of wheat seeds based on near infrared spectroscopy[J]. Jiangsu Agricultural Sciences, 2015, 43(12): 111-113. (in Chinese) 李欢欢, 卢伟, 杜昌文, 等. 基于光声光谱结合LS-SVR的稻种活力快速无损检测方法研究[J]. 中国激光,2015,42(11):1115003. doi: 10.3788/CJL201542.1115003LI H H, LU W, DU CH W, et al. Study on rapid and non-destructive detection of rice seed vigor based on photoacoustic spectroscopy combined with LS-SVR[J]. Chinese Journal of Lasers, 2015, 42(11): 1115003. (in Chinese) doi: 10.3788/CJL201542.1115003 李美凌, 邓飞, 刘颖, 等. 基于高光谱图像的水稻种子活力检测技术研究[J]. 浙江农业学报,2015,27(1):1-6. doi: 10.3969/j.issn.1004-1524.2015.01.01LI M L, DENG F, LIU Y, et al. Study on detection technology of rice seed vigor based on hyperspectral image[J]. Acta Agriculturae Zhejiangensis, 2015, 27(1): 1-6. (in Chinese) doi: 10.3969/j.issn.1004-1524.2015.01.01 宋乐, 王琦, 王纯阳, 等. 基于近红外光谱的单粒水稻种子活力快速无损检测[J]. 粮食储藏,2015,44(1):20-23. doi: 10.3969/j.issn.1000-6958.2015.01.005SONG L, WANG Q, WANG CH Y, et al. Qualitative analysis of single rice seed vigor using near infrared reflectance spectroscopy[J]. Grain Storage, 2015, 44(1): 20-23. (in Chinese) doi: 10.3969/j.issn.1000-6958.2015.01.005 ZHOU X B, ZHAO J W, POVEY M J W, et al. Variables selection methods in near-infrared spectroscopy[J]. Analytica Chimica Acta, 2010, 667(1-2): 14-32. doi: 10.1016/j.aca.2010.03.048 史永刚, 栗斌, 田高友, 等. 化学计量学方法及MATLAB实现[M]. 北京: 中国石化出版社, 2010.SHI Y G, LI B, TIAN G Y, et al.. Chemometrics Method and MATLAB Implementation[M]. Beijing: China Petrochemical Press, 2010. (in Chinese) 郭帅, 苏杭, 黄星灿, 等. 光学无创血糖浓度检测方法的研究进展[J]. 中国光学,2019,12(6):1235-1248. doi: 10.3788/co.20191206.1235GUO SH, SU H, HUANG X C, et al. Research progress in optical methods for noninvasive blood glucose detection[J]. Chinese Optics, 2019, 12(6): 1235-1248. (in Chinese) doi: 10.3788/co.20191206.1235 王纯阳. 基于近红外光谱的单籽粒水稻种子品质检测的方法研究[D]. 合肥: 中国科学技术大学, 2017.WANG CH Y. The nondestructive quality analysis of single rice seed using near infrared spectroscopy[D]. Hefei: University of Science and Technology of China. (in Chinese) 高升, 王巧华, 李庆旭, 等. 基于近红外光谱的红提维生素C含量、糖度及总酸含量无损检测方法[J]. 分析化学,2019,47(6):941-949.GAO SH, WANG Q H, LI Q X, et al. Non-destructive detection of vitamin C, sugar content and total acidity of red globe grape based on near-infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2019, 47(6): 941-949. (in Chinese) 付丹丹, 王巧华, 高升, 等. 不同品种鸡蛋贮期S—卵白蛋白含量分析及其可见/近红外光谱无损检测模型研究[J]. 分析化学,2020,48(2):289-297.FU D D, WANG Q H, GAO SH, et al. Analysis of S-Ovalbumin content of different varieties of eggs during storage and its nondestructive testing model by visible-near infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2020, 48(2): 289-297. (in Chinese) 刘宁武, 许林广, 周胜, 等. 量子级联激光光谱在土壤生态系统中的应用[J]. 光学学报,2019,39(11):1130001. doi: 10.3788/AOS201939.1130001LIU N W, XU G L, ZHOU SH, et al. Application of quantum-cascade laser spectroscopy to soil ecosystems[J]. Acta Optica Sinica, 2019, 39(11): 1130001. (in Chinese) doi: 10.3788/AOS201939.1130001 谢臣瑜, 翟文超, 李健军, 等. 超连续激光单色仪系统级光谱响应度定标比对验证[J]. 红外与激光工程,2020,49(2):0205005. doi: 10.3788/IRLA202049.0205005XIE CH Y, ZHAI W CH, LI J J, et al. System-level spectral responsivity calibration comparison and validation of supercontinuum laser and monochromator[J]. Infrared and Laser Engineering, 2020, 49(2): 0205005. (in Chinese) doi: 10.3788/IRLA202049.0205005 曹栋栋, 阮晓丽, 詹艳, 等. 杂交水稻种子不同活力测定方法与其田间成苗率的相关性[J]. 浙江农业学报,2014,26(5):1145-1150. doi: 10.3969/j.issn.1004-1524.2014.05.01CAO D D, RUAN X L, ZHAN Y, et al. Relativity analysis between seedling percentage in field and different seed vigor testing methods of hybrid rice seeds[J]. Acta Agriculturae Zhejiangensis, 2014, 26(5): 1145-1150. (in Chinese) doi: 10.3969/j.issn.1004-1524.2014.05.01 刘燕德, 叶灵玉, 孙旭东, 等. 基于光谱指数的蜜橘成熟度评价模型研究[J]. 中国光学,2018,11(1):83-91. doi: 10.3788/co.20181101.0083LIU Y D, YE L Y, SUN X D, et al. Maturity evaluation model of tangerine based on spectral index[J]. Chinese Optics, 2018, 11(1): 83-91. (in Chinese) doi: 10.3788/co.20181101.0083 董盈红. 油菜籽的傅里叶变换红外光谱鉴别[J]. 保山学院学报,2018,37(2):38-41. doi: 10.3969/j.issn.1674-9340.2018.02.011DONG Y H. Identification of rapeseed by fourier transform infrared spectroscopy[J]. Journal of Baoshan Teachers College, 2018, 37(2): 38-41. (in Chinese) doi: 10.3969/j.issn.1674-9340.2018.02.011 褚小立. 化学计量学方法与分子光谱分析技术[M]. 北京: 化学工业出版社, 2011.CHU X L. Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications[M]. Beijing: Chemical Industry Press, 2011. (in Chinese) 王昕, 吕世龙, 李岩, 等. 基于基线漂移模型的气体光谱自动基线校正[J]. 光谱学与光谱分析,2018,38(12):3946-3951.WANG X, LV SH L, LI Y, et al. Automatic baseline correction of gas spectra based on baseline drift model[J]. Spectroscopy and Spectral Analysis, 2018, 38(12): 3946-3951. (in Chinese) 王凡, 李永玉, 彭彦昆, 等. 基于可见/近红外透射光谱的番茄红素含量无损检测方法研究[J]. 分析化学,2018,46(9):1424-1431. doi: 10.11895/j.issn.0253-3820.181164WANG F, LI Y Y, PENG Y K, et al. Nondestructive determination of lycopene content based on visible/near infrared transmission spectrum[J]. Chinese Journal of Analytical Chemistry, 2018, 46(9): 1424-1431. (in Chinese) doi: 10.11895/j.issn.0253-3820.181164 高升, 王巧华, 付丹丹, 等. 红提糖度和硬度的高光谱成像无损检测[J]. 光学学报,2019,39(10):355-364.GAO SH, WANG Q H, FU D D, et al. Nondestructive detection of sugar content and firmness of red globe grape by hyperspectral imaging[J]. Acta Optica Sinica, 2019, 39(10): 355-364. (in Chinese) 范雪婷, 朱明东, 杨晨光, 等. 利用近红外吸收光谱对水稻种子活力的判别方法[J]. 杂交水稻,2019,34(4):62-67.FAN X T, ZHU M D, YANG CH G, et al. Assessment of rice seed vigor using near infrared spectroscopy[J]. Hybrid Rice, 2019, 34(4): 62-67. (in Chinese) SAMPAIO P S, SOARES A, CASTANHO A, et al. Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms[J]. Food Chemistry, 2018, 242: 196-204. doi: 10.1016/j.foodchem.2017.09.058 LIU D L, WU Y X, GAO Z M, et al. Comparative non-destructive classification of partial waxy wheats using near-infrared and raman spectroscopy[J]. Crop &Pasture Science, 2019, 70(5): 437-441. -
下载:
图(6) / 表(4)
计量
- 文章访问数: 3136
- HTML全文浏览量: 693
- PDF下载量: 164
- 被引次数: 0
