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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。
The feasibility of using Fourier transform near-infrared spectroscopy (FT-NIRS) to detect pea protein, starch, fat and total polyphenol content was discussed. The contents of protein, starch, fat and total polyphenols in 190 pea germplasms were determined by chemical methods. The NIR spectra of the seeds and the powder were collected. Partial least squares (PLS) was used to establish two prediction models of spectral and compositional contents. The result of pea powder model is better than that of particle model. The predicted residuals (RPDs) of protein powder and starch powder model are 5.88,5.82 and the correlation coefficient r2 is 0.99,0.99, which has good predictive performance. The quality characteristics of the pea germplasm and the origin of 150 pea germplasm whose production area information were well defined were analyzed by two-step cluster analysis. Three types of pea germplasm were identified, which were characterized as follows: low protein content of group 1, total high polyphenol content of group 2, 3 high protein, high starch and high fat content. Further analysis of the pea quality traits with sowing time, longitude, latitude, altitude changes. The results showed that near-infrared spectroscopy could rapidly identify and identify some quality traits of pea germplasm resources. The results of cluster analysis, the effect of geographical coordinates and sowing dates on the main quality characters of pea germplasm could be used to collect high-quality traits Pea germplasm resources provide a reliable basis.