Data_Sheet_1_High-throughput near-infrared spectroscopy for detection of major components and quality grading of peas.PDF

Pea (Pisum sativum L.) is a nutrient-dense legume whose nutritional indicators influence its functional qualities. Traditional methods to identify these components and examine the relationships between their contents could be more laborious, hindering the quality assessment of the varieties of peas. This study conducted a statistical analysis of data about the sensory and physicochemical nutritional attributes of peas acquired using traditional techniques. Additionally, 90 sets of spectral data were obtained using a portable near-infrared spectrometer, which were then integrated with chemical values to create a near-infrared model for the basic ingredient content of peas. The correlation analysis revealed significant findings: pea starch displayed a substantial negative correlation with moisture, crude fiber, and crude protein, while showing a highly significant positive correlation with pea seed thickness. Furthermore, pea protein exhibited a significant positive correlation with crude fiber and crude fat. Cluster analysis classified all pea varieties into three distinct groups, successfully distinguishing those with elevated protein content, high starch content, and low-fat content. The combined contribution of PC1 and PC2 in the principal component analysis (PCA) was 51.2%. Partial least squares regression (PLSR) and other spectral preprocessing methods improved the predictive model, which performed well with an external dataset, with calibration coefficients of 0.89–0.99 and prediction coefficients of 0.71–0.88. This method enables growers and processors to efficiently analyze the composition of peas and evaluate crop quality, thereby enhancing food industry development.

豌豆（Pisum sativum L.）是一种营养丰富的豆科作物，其营养指标会影响自身的功能品质。传统的组分鉴定与含量间关联探究方法往往耗时费力，制约了豌豆品种的品质评价工作。本研究针对传统手段获取的豌豆感官与理化营养属性数据开展了统计分析；此外，借助便携式近红外光谱仪获取了90组光谱数据，并将其与化学测定值相结合，构建了豌豆基础组分含量的近红外预测模型。相关性分析得到了极具价值的研究结果：豌豆淀粉与水分、粗纤维及粗蛋白均呈显著负相关，而与豌豆籽粒厚度呈极显著正相关；豌豆蛋白则与粗纤维及粗脂肪呈显著正相关。聚类分析将所有供试豌豆品种划分为3个独立类群，成功区分出高蛋白、高淀粉以及低脂肪含量的豌豆品种。主成分分析（PCA）中PC1与PC2的累计贡献率为51.2%。偏最小二乘回归（PLSR）结合其他光谱预处理方法对预测模型进行优化，优化后的模型在外部验证集上表现优异，其校准系数介于0.89~0.99之间，预测系数介于0.71~0.88之间。该方法可帮助种植者与加工企业高效分析豌豆组分、评价作物品质，进而推动食品工业的发展。