豌豆品质性状近红外模型建立及区域差异分析

Using Fourier Transform Near-Infrared Spectroscopy for the Evaluation and Regional Analysis of Pea( Pisum sativum L. )

探讨了傅立叶变换近红外光谱技术(FT-NIRS)检测豌豆蛋白质、淀粉、脂肪和总多酚含量的可行性。用化学方法测定190份豌豆种质的蛋白质、淀粉、脂肪以及总多酚含量,采集其子粒与粉末的近红外光谱,采用偏最小二乘法(PLS)分别建立两种光谱与成份含量预测模型。豌豆粉末模型结果优于子粒模型,其中蛋白质和淀粉的粉末模型的预测残差(RPD)为5.88、5.82,相关系数r2达到0.99、0.99,具有很好的预测性能。对其中产地信息详细明确的150份豌豆种质的品质性状与产地进行两步聚类分析,明确得到3种类型,其特点分别为:类群1低蛋白质含量,类群2高总多酚含量,类群3高蛋白质、高淀粉和高脂肪含量。进一步分析了豌豆品质性状随播种期、经度、纬度、海拔高度的变化情况。结果表明,近红外光谱技术可对豌豆种质资源的部分品质性状进行快速筛选鉴定,聚类分析结论、地理坐标与播期对豌豆种质主要品质性状的影响规律,都可为收集高品质性状豌豆种质资源提供可靠依据。

Pea（ Pisum sativum L.） is an important edible legume. Feasibility of the Fourier Transform Near-Infrared Spectroscopy（ FT-NIRS） on estimating quality traits in pea was evaluated in current study. A total of 190 pea accessions involved with their protein,starch,oil,and total polyphenol content were chemically analyzed. After obtaining spectra of the samples in milled powder and intact seed forms,partial least squares（ PLS） regression was applied for model development. Models of powder were generally superior to models in intact seed. The optimal models were powder-based for protein and starch with residual predictive deviation（ RPD） of 5. 88 and 5. 82 as well as coefficients of correlation（ r2） of 0.99 and 0.99,respectively. High values of correlation coefficient（ r2） revealed that models had good predictive capacities for rapid germplasm analysis of pea. To explore the relationship between quality traits and producing regions,150 pea varieties with specific information were analyzed by two-step cluster analysis. Three distinct groupings were obtained with obvious features. Group 1 was in low protein content. Group 2 was in high total polyphenol content. Group 3 was in high protein,starch,and oil content. The nutrition contents were affected by longitude,latitude,and altitude of planting location as well as seeding date. These results could provide reliable information for the collection of excellent germplasm resources with good quality traits.