Alamu Emmanuel Oladeji, Menkir Abebe, Adesokan Michael, Fawole Segun, Maziya-Dixon Busie
Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture, Southern Africa Research and Administration Hub (SARAH) Campus, Lusaka 10101, Zambia.
Food and Nutrition Sciences Laboratory, International Institute of Tropical Agriculture (IITA), Ibadan 20001, Nigeria.
Foods. 2022 Sep 9;11(18):2779. doi: 10.3390/foods11182779.
The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to be a fast, cost-effective, and non-destructive method. Thus, 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 coefficients of determination in calibration (R) of 0.91, 0.93, 0.93, and 0.91 and low standard errors 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 fairly good R values of 0.86, 0.71, 0.81, 0.78, 0.68, 0.79, and 0.75. In contrast, poor (R) was 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 (R) 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 values of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20, and 0.77, respectively. However, certain amino acids had their R 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.
在玉米育种项目中，由于使用高效液相色谱法（HPLC）和其他传统方法进行分析的成本高昂，准确量化氨基酸具有挑战性。在育种过程中使用近红外光谱（NIRS）方法筛选多种基因型已被证明是一种快速、经济高效且无损的方法。因此，本研究旨在开发并应用NIRS预测模型来量化生物强化优质蛋白玉米（QPM）中的氨基酸。六十三（63）个QPM玉米基因型用作校准集，另外二十（20）个基因型用作验证集。采用微波水解系统并以6-氨基喹啉-琥珀酰亚胺基-氨基甲酸酯作为衍生试剂进行柱后衍生，结合HPLC方法生成用于校准开发的参考数据集。使用WINSI Foss软件为必需氨基酸和非必需氨基酸建立校准模型。谷氨酸、丙氨酸、脯氨酸和亮氨酸在校准中的决定系数（R）分别为0.91、0.93、0.93和0.91，校准标准误差（SEC）分别为0.62、0.71、0.26和1.75，而天冬氨酸、丝氨酸、甘氨酸、精氨酸、酪氨酸、缬氨酸和苯丙氨酸的R值相当不错，分别为0.86、0.71、0.81、0.78、0.68、0.79和0.75。相比之下，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）和异亮氨酸（0.09）的R值较差。对于某些氨基酸，如天冬氨酸（R = 0.90）、甘氨酸（R = 0.80）、精氨酸（R = 0.94）、丙氨酸（R = 0.90）、脯氨酸（R = 0.80）、酪氨酸（R = 0.83）、缬氨酸（R = 0.82）、亮氨酸（R = 0.90）和苯丙氨酸（R = 0.88），模型的预测性能（R）和预测标准误差（SEP）相当不错，SEP值分别为0.24、0.39、0.24、0.93、0.47、0.34、0.78、2.20和0.77。然而，某些氨基酸的R值低于0.50，对于这些氨基酸，可通过进一步改进使其适用于筛选目的。建议开展进一步工作，纳入代表样本群体方差的训练集以提高模型性能。
