Quantitative Analysis of Hybrid Maize Seed Purity Using Near Infrared Spectroscopy
HUANG Yan-yan1, ZHU Li-wei1, MA Han-xu1, LI Jun-hui2, SUN Bao-qi1, SUN Qun1*
1. Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology, China Agricultural University /Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture／Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China 2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100193, China
Abstract：A quantitative identification model for testing the purity of hybrid maize seeds was built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). The NIR spectra of 123 seeds powder samples (Nongda108 and mother178) with the purity of 60%～100% were collected using MPA spectrometer. All samples were divided into two groups: calibration set (82 samples) and validation set (41 samples). Synergy interval partial least squares (SiPLSu) was used for selecting effective spectral regions and building models. The influences of different spectral regions and different calibration samples on the prediction results and different main components were compared. The result showed that the spectral regions 6 000～8 000, 6 000～9 000 and 6 000～10 000 cm-1 all had better prediction results (R2 over 95%). Spectral region 6 000～10 000 cm-1 was regarded the optimum spectral region for building the model with less main components(8), and the determination coefficient (R2) of calibration and validation sets were 96.61% and 97.67% respectively, SEC (standard error of calibration) and SEP (standard error of prediction) were 2.15% and 1.78% respectively, RSDs (relative standard deviation) were 2.04% and 1.94% respectively. Even with different calibration samples, the average determination coefficients (R2) of calibration and validation sets were 96.21% and 95.75%, SEC (standard error of calibration) and SEP (standard error of prediction) were 2.29% and 2.23% respectively, RSDs (relative standard deviation) were 2.81% and 2.73% respectively, which further proved the model’s stability. With the increase in the number of main components, the identification rates in calibration set and validation set gradually increased, when the number of main components reached 8, the model determination coefficients reached the best (96.61% and 97.67%), and related coefficients of true value and predicted value were 98.29% and 98.87% respectively. The results have important value for rapid and accurate testing of hybrid maize seed purity.
黄艳艳1，朱丽伟1，马晗煦1，李军会2，孙宝启1，孙 群1* . 应用近红外光谱技术定量分析杂交玉米纯度的研究[J]. 光谱学与光谱分析, 2011, 31(10): 2706-2710.
HUANG Yan-yan1, ZHU Li-wei1, MA Han-xu1, LI Jun-hui2, SUN Bao-qi1, SUN Qun1* . Quantitative Analysis of Hybrid Maize Seed Purity Using Near Infrared Spectroscopy . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(10): 2706-2710.
[1] WAN Pu-lin(万普林). Modern Agricultural Science and Technology(现代农业科技), 2005, 7: 13. [2] WANG Xi, ZHANG Bao-shi(王 玺, 张宝石). Seed(种子), 2002, (1): 43. [3] Delwiche S R. Journal of Cereal Science, 1998, 27: 241. [4] Farkas J, Dalmadi I. Progress in Agricultural Engineering Sciences, 2009, 5(1): 1. [5] Sgrulletta D, Stefanis E De. Journal of Food Science, 1997, 9(4): 295. [6] ZHAO Huan-huan, YAN Yan-lu(赵环环, 严衍禄). Journal of Maize Sciences(玉米科学), 1999, 7(3): 77. [7] BAI Qi-lin, CHEN Shao-jiang, YAN Yan-lu(白琪林, 陈绍江, 严衍禄). Scientia Agricultura Sinica(中国农业科学), 2006, 39(7): 1346. [8] KANG Yue-qiong, HAO Feng(康月琼, 郝 风). Seed(种子), 2004, 23(7): 10. [9] Paul Williams, Paul Geladi, Glen Fox, et al. Analytica Chimica Acta, 2009，653(2): 121. [10] Esteban-Diez I, Gonzalez-Saiz J M, Pizarro C. Analytica Chimica Acta, 2004, 525(2): 171. [11] Tallada J G, Palacios-Rojas N, Armstrong P R. Journal of Cereal Science, 2009, 50(3): 381. [12] Wu J G, Shi C H. Field Crops Research, 2004, 87(12): 13. [13] LIN Xin, NIU Zhi-you(林 新, 牛智有). Journal of Huazhong Agricultural University(华中农业大学学报), 2009, 28(4): 487. [14] Malley D F, Findlay D L, Zippel B. Journal of Paleolimnology, 1999, 21(5): 295. [15] Chen Q, Zhao J, Liu M, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46(3): 568.
