title: [Quantitative analysis of hybrid maize seed purity using near infrared spectroscopy].
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 600-100% 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.
authors.affiliation: 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. huangyucute@163.com
authors.forename: Yan-Yan
authors.initials: YY
authors.lastname: Huang
authors.forename: Li-Wei
authors.initials: LW
authors.lastname: Zhu
authors.forename: Han-Xu
authors.initials: HX
authors.lastname: Ma
authors.forename: Jun-Hui
authors.initials: JH
authors.lastname: Li
authors.forename: Bao-Qi
authors.initials: BQ
authors.lastname: Sun
authors.forename: Qun
authors.initials: Q
authors.lastname: Sun
mesh_terms: D002138:Calibration; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D012639:Seeds; D019265:Spectroscopy, Near-Infrared; D003313:Zea mays