基于DPLS-CSM优化的NIRS杂交水稻种子真伪快速无损鉴定

Rapid and Non-destructive Identification of Hybrid Rice Seeds Using DPLS-CSM Optimized Near Infrared Reflectance Spectroscopy

近红外光谱分析技术(NIRS)具有快速、无损、低成本、准确、无污染的优点,有望应用于杂交水稻种子真实性的高通量鉴定及筛选。本研究采用近红漫反射光谱结合一种新的化学计量学方法——判别式偏最小二乘分类筛选法(DPLS-CSM),对单粒水稻种子‘新两优6号’与其父本、母本和其他假种子进行了区分。通过与判别式偏最小二乘(DPLS)法的对比,使用DPLS-CSM建模时的灵敏度Sn、命中率Pr和马修斯相关系数Mcc分别为97.92%、97.58%和95.51%,高于DPLS法建模的92.01%、93.97%和90.68%;在对验证集进行检验时,DPLS-CSM模型的Sn、Pr和Mcc值分别为98.96%、95%和93.83%,高于后者的94.79%、88.35%和88.57%。结果表明,使用DPLS-CSM结合NIRS对‘新两优6号’的真伪鉴定是可行的,该方法为水稻杂交种真伪的快速无损鉴别与筛选提供了新的选择。

With the advantages of being quick, non- destructive, cheap, accurate and pollution- free, Near Infrared Spectroscopy（NIRS） can be applied in high throughput identification and screening of hybrid rice seeds in the future. This research utilized Near Infrared Reflectance Spectroscopy combining with a new chemometric method, Discriminant Partial Least Squares Classification Screening Method（DPLS- CSM）, todifferentiate single hybrid rice seed‘Xinliangyou 6＇from its male parent, female parent and other fake seeds.According to comparison with Discriminant Partial Least Squares（DPLS） method, it showed that the Sensitivity（Sn）, Precision（Pr） and Matthews Correlation Coefficient（Mcc） were 97.92%, 97.58% and 95.51% respectivelyby using DPLS-CSM method for modeling, which exceeded the values of 92.01%, 93.97% and 90.68% byusing DPLS method. When validation set samples were being inspected, the Sn, Pr and Mcc values of DPLS-CSM models were 98.96%, 95% and 93.83% respectively, which exceeded the values of 94.79%, 88.35% and88.57% by the latter. The results indicated that it was feasible to identify the authenticity of rice seed‘Xinliangyou 6＇by using DPLS-CSM combined with NIRS. This method provided people with a new choice to identify and screen hybrid rice seeds rapidly and nondestructively.