Fourier‐transform mid‐infrared spectroscopy for high‐throughput phenotyping of total dietary fiber in pulse crops

Abstract

Abstract This study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes. The standard analytical approach for TDF analysis is based on the Association of Official Analytical Collaboration method 985.29, which requires extensive sample preparation with extended analysis times of up to 30 h. The FT‐MIR approach was developed to enhance rapid and non‐destructive analysis and minimize the traditional workload associated with phenotyping TDF in pulse crops by accomplishing the same task in a shorter time and at minimal cost. Partial least squares regression (PLSR) was applied with chemometric modeling in MIR regions (650–1480 and 2771–3700 cm −1 ), encompassing spectral bands associated with undigested polysaccharides and partially or undigested protein and fatty acid methyl ester fractions that fingerprint TDF. K‐fold cross‐validation was used for PLSR modeling to enhance computational speeds with large‐scale data processing. These PLSR models for chickpea, dry pea, and lentil have coefficients of determination ( R 2 ) as 0.91, 0.96, and 0.94 with root mean square errors of prediction in the range of 0.05–0.5 g/100 g. This technique supports rapid phenotyping of TDF from raw flour in <1 min. The FT‐MIR technique can relieve the phenotyping bottleneck in pulse breeding and pulse‐based food and feed industries, targeting the measurements of TDF and ensuring a rapid and high‐throughput pipeline for plant breeding and cultivar development.

Plant phenotyping relevance

FT-MIR分光法とPLSRモデルを開発・検証し、パルス作物のTDFという植物形質を高速・非破壊測定する手法が研究の中心である。

Code and data availability

The supplied blocks describe FT-MIR spectral collection and PLSR modeling (Scikit-learn, SciPy, Agilent MicroLab) for TDF in pulse crops, but contain no public deposit of spectral data, phenotype datasets, trained models, or author analysis code. The only availability statement is generic Supporting Information hosted

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