花生籽仁蔗糖含量近红外模型构建及在高糖品种培育中的应用

Development and application of a near infrared spectroscopy model for predicting high sucrose content of peanut seed

Sugar content is an important factor affecting the flavor and processing characteristics of peanut (Arachis hypogaea L.) kernel and the sucrose content accounts for more than 90% of the total sugar content in peanut kernel. High-efficiency detection approach for sucrose content is crucial in developing high sucrose peanut varieties. In this study, 185 peanut genotypes with diversified sucrose contents were scanned in wave length of 1100-2500 nm for constructing a near-infrared spectrum of naturally dried seeds. The sucrose contents were determined by high performance liquid chromatography (HPLC) combined with standard curve method. A near-infrared calibration model of peanut seed sucrose content was established by partial least square method (PLS). For sucrose content, the coefficient of determinations (R²) was 0.962 with mean square deviation of 0.383. The coefficient of correlation between the predicted values and chemically tested value were 0.947 in 20 external samples, indicating that the model could predict sucrose content of peanut kernel. Six new sweet peanut lines with sucrose content over 7%, oleic acid over 78%, oil content less than 48% and desirable agronomic characters were selected among the hybrid progenies of “Jihua 02-1-4 × Zhonghua 26”.