Field pea is a cool-season annual crop. It is noted for its high content of protein, vitamins, and minerals. The cool weather and high soil fertility in the Canadian prairies make it one of the best regions to grow peas. Although wheat and canola are the dominant crops in production, Canada is the largest producer of dry peas. Field pea is of high importance in global agricultural systems due to its wide application and nutritional value. In general, whole pea seeds contain 20–25% protein with a well-balanced amino acid profile. Rising demand of high-quality plant-based protein, requires efforts to better define the quality. Traditional chemical analysis for protein costs tremendously in both time and financial resources. Therefore, an alternative detection method for AA profiles that combines efficiency with a non-destructive process for measuring protein and amino acid contents in limited volume samples is urgently needed. Near infrared reflectance spectroscopy (NIRS) may offer such a tool. By detecting the reflectance of light applied on food, NIRS can measure various chemical components with little preparation and good reproducibility. What’s more, the predicting model applied in NIRS technique is based on the correlation between chemical composition and spectrum data, which can be promoted in accuracy and representativity by infusing sample data retrieved from different years. Hence it can be considered a robust tool for analyzing the nutritional contents in the future food industry. This study aims to assess the potential of two types of NIRS platforms for the determination of the protein and amino acid content of peas drawn from well-defined breeding programs. The resultant NIR models will be compared for accuracy and precision as well as testify the potential influence brought by morphology of pea seeds during scanning.