基于DPLS-CSM优化的NIRS杂交水稻种子真伪快速无损鉴定
|
摘要
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.
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.
引文
[1]陈金节,张从合,周桂香,等.提高两系杂交水稻种子质量的对策[J].杂交水稻,2009,24(4):1-4.
[2]张凤,陈伟.杂交水稻种子纯度鉴定方法概述[J].种子,2004,23(9):55-58.
[3]王兆贤,高前宝,王友存,等.两系杂交水稻种子纯度检测方法的比较[J].杂交水稻,2011,26(6):28-30.
[4]严衍禄,赵龙莲,李军会,等.现代近红外光谱分析的信息处理技术[J].光谱学与光谱分析,2000,20(6):777-780.
[5]Delwiche S R,Norris K H.Classification of hard red wheat by nearinfrared diffuse reflectance spectroscopy[J].Cereal Chemistry,1993,70(1):29-35.
[6]Delwiche S R,Graybosch R A,Amand P S,et al.Starch waxiness in hexaploid wheat(Triticum aestivum L.)by NIR reflectance spectroscopy[J].Journal of Agricultural and Food Chemistry,2011,59(8):4002-4008.
[7]梁亮,杨敏华,刘志霄,等.杂交稻种品系与真伪的可见-近红外光谱鉴别[J].激光与红外,2009,39(4):407-410.
[8]李晓丽,唐月明,何勇,等.基于可见/近红外光谱的水稻品种快速鉴别研究[J].光谱学与光谱分析,2008,28(3):578-581.
[9]梁剑,刘斌美,陶亮之,等.基于水稻种子近红外特征光谱的品种鉴别方法研究[J].光散射学报,2013,25(4):423-428.
[10]张世辉,赵正洪,周斌,等.杂交稻及亲本种子混杂的原因与对策[J].杂交水稻,2002,17(5):16-19.
[11]Chen L Z,Xue X F,Ye Z H,et al.Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy[J].Food Chemistry,2011,128(4):1110-1114.
[12]施丰成,李东亮,冯广林,等.基于近红外光谱的PLS-DA算法判别烤烟烟叶产地[J].烟草化学,2013(4):56-59.
[13]Luna A S,da Silva A P,Pinho J S A,et al.Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2013,100(1):115-119.
[14]史波林,赵镭,刘文,等.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,41(2):132-137.
[15]闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10):1205-1209.
[16]Haaland D M,Thomas E V.Partial least-squares methods for spectral analysis.1.Relation to other quantitative calibration methods and the extraction of qualitative information[J].Analytical Chemistry,1988,60:1193-1202.
[17]祝诗平,梁晶,屠大伟,等.基于近红外光谱与DPLS的潲水油快速鉴别方法[J].西南大学学报:自然科学版,2012,34(5):1-6.
[18]郝勇,孙旭东,高荣杰,等.基于可见/近红外光谱与SIMCA和PLSDA的脐橙品种识别[J].农业工程学报,2010,26(1):373-377.
[19]汤守鹏,姚鑫锋,姚霞,等.基于主成分分析和小波神经网络的近红外多组分建模研究[J].分析化学,2009,37:1445-1450
[20]Peng B Z,Ge N,Cui L,et al.Monitoring of alcohol strength and titratable acidity of apple wine during fermentation using nearinfrared spectroscopy[J].LWT-Food Science and Technology,2016,66:86-92.
[21]张景芳,蒋芦荻,张燕玲.支持向量机在中药肾毒性研究中的应用[J].中国中药杂志,2015,40(6):1134-1138.
[22]Lee S,Kang Y M,Park H.Human nephrotoxicity prediction models for three types of kidney injury based on data sets of pharmacological compounds and their metabolites[J].Chem Res Toxicol,2013,26:1652.
[23]黄艳艳,朱丽伟,李军会,等.应用近红外光谱技术快速鉴别玉米杂交种纯度的研究[J].光谱学与光谱分析,2011,31(3):661-664.
[24]朱大洲,王坤,周光华,等.单粒大豆的近红外光谱特征及品种鉴别研究[J].光谱学与光谱分析,2010,30(12):3217-3221.
[2]张凤,陈伟.杂交水稻种子纯度鉴定方法概述[J].种子,2004,23(9):55-58.
[3]王兆贤,高前宝,王友存,等.两系杂交水稻种子纯度检测方法的比较[J].杂交水稻,2011,26(6):28-30.
[4]严衍禄,赵龙莲,李军会,等.现代近红外光谱分析的信息处理技术[J].光谱学与光谱分析,2000,20(6):777-780.
[5]Delwiche S R,Norris K H.Classification of hard red wheat by nearinfrared diffuse reflectance spectroscopy[J].Cereal Chemistry,1993,70(1):29-35.
[6]Delwiche S R,Graybosch R A,Amand P S,et al.Starch waxiness in hexaploid wheat(Triticum aestivum L.)by NIR reflectance spectroscopy[J].Journal of Agricultural and Food Chemistry,2011,59(8):4002-4008.
[7]梁亮,杨敏华,刘志霄,等.杂交稻种品系与真伪的可见-近红外光谱鉴别[J].激光与红外,2009,39(4):407-410.
[8]李晓丽,唐月明,何勇,等.基于可见/近红外光谱的水稻品种快速鉴别研究[J].光谱学与光谱分析,2008,28(3):578-581.
[9]梁剑,刘斌美,陶亮之,等.基于水稻种子近红外特征光谱的品种鉴别方法研究[J].光散射学报,2013,25(4):423-428.
[10]张世辉,赵正洪,周斌,等.杂交稻及亲本种子混杂的原因与对策[J].杂交水稻,2002,17(5):16-19.
[11]Chen L Z,Xue X F,Ye Z H,et al.Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy[J].Food Chemistry,2011,128(4):1110-1114.
[12]施丰成,李东亮,冯广林,等.基于近红外光谱的PLS-DA算法判别烤烟烟叶产地[J].烟草化学,2013(4):56-59.
[13]Luna A S,da Silva A P,Pinho J S A,et al.Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2013,100(1):115-119.
[14]史波林,赵镭,刘文,等.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,41(2):132-137.
[15]闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10):1205-1209.
[16]Haaland D M,Thomas E V.Partial least-squares methods for spectral analysis.1.Relation to other quantitative calibration methods and the extraction of qualitative information[J].Analytical Chemistry,1988,60:1193-1202.
[17]祝诗平,梁晶,屠大伟,等.基于近红外光谱与DPLS的潲水油快速鉴别方法[J].西南大学学报:自然科学版,2012,34(5):1-6.
[18]郝勇,孙旭东,高荣杰,等.基于可见/近红外光谱与SIMCA和PLSDA的脐橙品种识别[J].农业工程学报,2010,26(1):373-377.
[19]汤守鹏,姚鑫锋,姚霞,等.基于主成分分析和小波神经网络的近红外多组分建模研究[J].分析化学,2009,37:1445-1450
[20]Peng B Z,Ge N,Cui L,et al.Monitoring of alcohol strength and titratable acidity of apple wine during fermentation using nearinfrared spectroscopy[J].LWT-Food Science and Technology,2016,66:86-92.
[21]张景芳,蒋芦荻,张燕玲.支持向量机在中药肾毒性研究中的应用[J].中国中药杂志,2015,40(6):1134-1138.
[22]Lee S,Kang Y M,Park H.Human nephrotoxicity prediction models for three types of kidney injury based on data sets of pharmacological compounds and their metabolites[J].Chem Res Toxicol,2013,26:1652.
[23]黄艳艳,朱丽伟,李军会,等.应用近红外光谱技术快速鉴别玉米杂交种纯度的研究[J].光谱学与光谱分析,2011,31(3):661-664.
[24]朱大洲,王坤,周光华,等.单粒大豆的近红外光谱特征及品种鉴别研究[J].光谱学与光谱分析,2010,30(12):3217-3221.