Fourier-Transform Mid-Infrared (FT-MIR) Spectroscopy as a High-Throughput Phenotyping Tool for Measuring Total Fatty Acids in Chickpea ( Cicer arietinum L.) Flour

Abstract

Fourier-transform mid-infrared (FT-MIR) spectroscopy with an attenuated total reflectance (ATR) sampling interface is a robust technique applicable to high-throughput phenotyping (HTP). This technique is cost-effective in breeding programs compared with wet chemistry techniques (i.e., GC-MS) due to minimal labor and chemical costs. This study aims to probe the applicability of FT-MIR spectroscopy to phenotype total fatty acids in chickpea flour with partial least-squares (PLS) regression as the principal chemometric model. Using two spectral regions (2845.82–3035.92 cm –1 and 1720.17–1763.03 cm –1 ), three PLS models were built to predict total fatty acids (TFA), total unsaturated fatty acids (TUSFA), and total saturated fatty acids (TSFA) in chickpea flour. These regression models had R 2 values of 0.97, 0.97, and 0.91 and root-mean-square error of prediction (RMSEP) values of 12.49, 6.21, and 5.89 mg/100 g, respectively. Predictions using these models support the implementation of a high-throughput workflow to phenotype fatty acids from chickpea flours in an optimized fashion with minimal labor costs, sample preparation, and chemicals, thus eliminating hazardous wastes supporting plant breeding and food processing industries.

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