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Updated: Aug 12, 2026

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar
Published on: August 6, 2018
Amino Acid Quantification from Maize Tissues
Huda Ansaf1, Abou Yobi1, Ruthie Angelovici2
1Christopher S. Bond Life Sciences Center, Division of Biological Sciences, Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA.
Abstract:
Amino acid analysis is a vital part of analytical biochemistry. The increasing demand for low nitrogen fertilization and for plant-based diets with balanced amino acid levels and composition have made it crucial to develop reliable, fast, and affordable methods for analyzing amino acids in plants. As maize accounts for 43% of global cereal production, improving the amino acid composition of its kernels (i.e., seeds) is critically important for meeting the dietary requirements of humans and livestock. Moreover, amino acid quantification in maize leaves is necessary for improving yield prediction, stress sensing, and nitrogen use efficiency. Many amino acid quantification methods use reverse-phase high-pressure liquid chromatography and gas chromatography approaches to assess the amino acid content of maize tissues. Historically, these techniques involved the use of chemical derivatization, a chemical reaction that alters the properties of a compound to make it detectable or more sensitive to detection. Although accurate, these methods are time-consuming, expensive, and unsuitable for large populations. Here, we introduce two high-throughput methods for quantifying amino acids from large maize populations, such as those used for quantitative trait locus mapping, genome-wide association studies, and large mutant populations. Both methods use an ultraperformance liquid chromatography-tandem mass spectrometry instrument to quantify all 20 proteogenic amino acids in a maize tissue in a short run time. A dependable, affordable, and high-throughput method for quantifying amino acids in maize has important implications for assessing kernel quality, yield, and management efficacy, such as fertilizer usage and watering.
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