Field pea is an essential crop in Canada, and it possesses high value from nutritional, functional, and economic aspects. As a member of the pulse family, the protein and amino acid contents are key nutritional parameters for field peas, which are susceptible to their genotypes and environmental conditions. Advanced methods are needed to overcome traditional wet chemistry's time and cost inefficiency. An optical technique, near-infrared reflectance spectroscopy (NIRS) provides a potential solution by being fast, chemical free and non-destructive. This study focused on the development and evaluation of calibration models in NIRS to predict moisture, crude protein and 18 amino acids in field pea using two scanning systems: DA7250 and FT9700. A total of 480 pea samples cultivated in Saskatchewan, Canada, were selected for comprehensive chemical analysis to form the calibration set. Calibration models were developed using partial least squares (PLS) regression equation. Overall, the NIRS calibration models exhibit an ideal performance in predicting protein and amino acids, excluding cysteine, methionine, and tryptophan. Since NIRS is an analytical method based on the reflection and absorption of light, the form of the sample and type of NIRS platform could influence the accuracy and precision of spectrum data. Calibration models based on DA7250 indicate that whole seeds are more suitable than ground seeds in NIR analysis. Regarding the performance difference between DA7250 and FT9700, significant differences were observed in analyzing protein and most amino acids, except Arg, Asp, Lys, Met, Thr, Trp and Tyr. Overall, DA7250 possessed a superior predictive capability over FT9700, which is advised in future applications. To clarify the potential impact of external factors on the quality of pea protein, more focus was put on the influence brought by the environment and genotype, as well as their interactions (G×E). NIRS models based on whole seeds from chapter one were used to predict crude protein, moisture, and amino acids in 8210 field peas from two breeding programs. A subsample of 2207 samples of 99 genetic lines from 2 harvesting years and 2 locations were carried out for G×E analysis. The observed results indicated that the genotype×environment interactions had significant influences on pea protein and amino acid contents. The year×location and year x genotype provided the majority of the variance in addition to the genotype's dominating influence. Additionally, protein content in peas was more sensitive to external factors than amino acid concentration.