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Development of Near-infrared Reflectance Spectroscopy Equations of Fatty Acid Composition, Glucosinolates & Oil Content

Author: YangYanYu
Tutor: ChenSheYuan
School: Hunan Agricultural University
Course: Crop Genetics and Breeding
Keywords: Near-infrared Quality Analysis Cole Fatty acid Oil content
CLC: S565.4
Type: Master's thesis
Year: 2007
Downloads: 234
Quote: 0
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Abstract


Analysis of near infrared reflectance spectroscopy (NIRS) has a non-destructive, fast analysis and low cost many advantages, and is ideal for high-volume screening of crop breeding quality traits, has become a breeding work is very important to select the means. Purchased by the National Oil Improvement Center Hunan sub-centers FOSS NIRSystem 5000-type NIR analyzer used in rapeseed quality analysis has been five years, has been using the original model, and the model has not been to maintain or create a new model. Has been difficult to determine the reliability of the data obtained. Collected in this study and to elect different sources of Brassica napus of 670 parts by analysis with the national standard methods, as well as the ISO standard method in which 557 parts of a fatty acid of the sample composition, 255 parts of the amount of rapeseed oil and 90 parts of rapeseed glucosinolate content, the establishment of a new rapeseed fatty acid composition, oil content and glucosinolate content of near-infrared analysis model. Fatty acid composition analysis model, oleic acid and erucic acid calibration decided coefficient (RSQ) and cross-validation coefficient of determination (1-VR) 0.98 (closer to 1, the more ideal). You can make 90% of the oleic and erucic forecast samples predicted absolute error limit control in the 4.25 and 5.83 percentage points, compared with the instrument Old greatly improved model of 16.90 and 17.61. Low erucic acid model established for low erucic acid sample can accurately distinguish erucic acid content of less than 4%, and the average absolute error is less than 0.5. Oil content model RSQ and 1-VR 0.97 and 0.96, can guarantee that 90% of the sample forecast absolute error limit and the relative error limit to 1.26% and 3.18%. The glucosinolate model, RSQ and 1-VR reached 0.99 and 0.97,90% sample forecast absolute error limit and the relative error is limited to less than 7.62 percent and 21.78 percent. In the modeling process, the research also drawn the following main conclusions: (1) segmentation model of oleic acid and erucic acid, can significantly improve the the concentration predicted effects in the two regional concentration within, but not the middle region ; in low erucic Sectional modeling, the maximum content should be controlled at around 10%, and reduce the number of samples of extremely low erucic acid, otherwise all samples may be predicted for the very low concentrations, up to less than the purpose to distinguish low erucic . (2) the oil content of the model prediction ability to expand beyond the scope of the sample, the sample average error of three percentage points above the predicted correction range can be controlled in less than 1.1 percent. Oleic acid model predictions correction range of more than five percent of the sample, the average error can be controlled within 2.5 percent; forecast is lower than the calibration range of the sample poor, but distinguished content towards. Erucic model almost do not have to expand the predictive ability.

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CLC: > Agricultural Sciences > Crop > Economic crops > Oil crops > Rapeseed ( Brassica )
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