Dataset on Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM)

This study aimed to develop and apply the NIRS prediction models for quantifying amino acids in biofortified Quality Protein Maize (QPM). Sixty-three (63) QPM maize genotypes were used as the calibration set, and another twenty (20) genotypes were used as the validation set. The Microwave Hydrolysis system coupled with post-column derivatization with 6-Amino-quinoline- succinimidyl-carbamate as the derivatization reagent and the HPLC method were used to generate the reference data set used for the calibration development. The calibration models were developed for essential and non-essential amino acids using WINSI Foss Software. Good coefficient of determination in calibration (R2cal) of 0.91, 0.93, 0.93, and 0.91 and low standard error in calibrations (SEC) of 0.62, 0.71, 0.26, and 1.75 were obtained for glutamic acids, alanine, proline, and leucine respectively, while aspartic acids, serine, glycine, arginine, tyrosine, valines, and phenylalanine had a fairly good R2Cal of 0.86, 0.71,0.81,0.78, 0.68, 0.79. and 0.75 . In contrast, poor (R2cal) were obtained for histidine (0.07), cystine (0.09), methionine (0.09), lysine (0.20), threonine (0.51) and isoleucine (0.09) respectively. The models’ prediction performances (R2 pred) and standard error of prediction (SEP) were reasonably good for certain amino acids such as aspartic acid (0.90), glycine (0.80), arginine (0.94), alanine (0.90), proline (0.80), tyrosine (0.83), valine (0.82), leucine (0.90) and phenylalanine (0.88) with SEP of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20 and 0.77 rsepectively. However, certain amino acids had their R2 pred below 0.50, which could be improved to become useful for screening purposes for those amino acids. Further work is recommended by including a training set representing the sample population’s variance to improve the model’s performance.

本研究旨在开发并应用近红外光谱（Near Infrared Spectroscopy, NIRS）预测模型，以量化生物强化优质蛋白玉米（Quality Protein Maize, QPM）中的氨基酸含量。本研究共纳入63个优质蛋白玉米基因型作为校正集，另有20个基因型作为验证集。本研究采用微波水解系统结合柱后衍生化法，以6-氨基喹啉-琥珀酰亚胺基氨基甲酸酯作为衍生化试剂，并结合高效液相色谱（High Performance Liquid Chromatography, HPLC）方法，生成用于构建校正模型的参考数据集。研究使用WINSI Foss软件构建了必需氨基酸与非必需氨基酸的校正模型。针对谷氨酸、丙氨酸、脯氨酸与亮氨酸，其校正决定系数（R²cal）分别为0.91、0.93、0.93与0.91，校正标准误（SEC）分别为0.62、0.71、0.26与1.75；而天冬氨酸、丝氨酸、甘氨酸、精氨酸、酪氨酸、缬氨酸及苯丙氨酸的校正决定系数分别为0.86、0.71、0.81、0.78、0.68、0.79与0.75，表现较为良好。与之相反，组氨酸（0.07）、胱氨酸（0.09）、蛋氨酸（0.09）、赖氨酸（0.20）、苏氨酸（0.51）及异亮氨酸（0.09）的校正决定系数较低。部分氨基酸的模型预测性能（R²pred）与预测标准误（SEP）表现较为理想，其中天冬氨酸（0.90）、甘氨酸（0.80）、精氨酸（0.94）、丙氨酸（0.90）、脯氨酸（0.80）、酪氨酸（0.83）、缬氨酸（0.82）、亮氨酸（0.90）与苯丙氨酸（0.88），其对应的预测标准误分别为0.24、0.39、0.24、0.93、0.47、0.34、0.78、2.20与0.77。然而，部分氨基酸的预测决定系数低于0.50，可通过优化进一步提升其性能，以满足氨基酸筛选的应用需求。建议后续研究纳入能够覆盖样本群体方差的训练集，以进一步改善模型的预测性能。