A technique for measuring non-structural carbohydrate reserves in flag leaves of paddy rice using Fourier transform infrared spectroscopy (FTIR).

Abstract

The application of Fourier transform infrared (FTIR) spectroscopy for non-structural carbohydrates (NSC) prediction as a tool for pre-breeding screening has immense potential but remains to be unexplored, because of technical challenges associated with these measurements. This study investigated the potential of employing FTIR spectroscopy as a high-throughput tool for forecasting NSC content, including total soluble sugar (TSS) and starch content, of 30 rice accessions from the Rice Diversity Panel 1 (RDP1) germplasm and RiceTec hybrids grown in 2019 (320 genotypes) and 2020 cropping (312 genotypes). Partial Least Squares (PLS) regression analysis was used to construct predictive models to estimate NSC content in flag leaves and stem of rice exposed to elevated and ambient nighttime air temperature during the flowering stage of rice. The TSS model exhibited a coefficient of determination (R 2 ) value of 0.63 and root mean square error of prediction (RMSEP) values of 3.62 mg g - 1 . Notably, the NSC model demonstrated a superior metric performance, with R 2 = 0.66 and RMSEP of 5.58 mg g - 1 . The predictive model created in this research effectively measured the NSC composition present in the flag leaves of rice. Expanding the sample size and incorporating additional principal components may enhance the model's predictive accuracy. The FTIR technique can produce fast accurate results and resolve the high analytical costs. Overall, the use of FTIR in conjunction with PLS regression analysis provides a potential tool to advance our understanding of various rice genotypes, particularly concerning their ability to withstand abiotic stress such as HNT.

Plant phenotyping relevance

FTIRとPLS回帰を用いてイネ葉・茎の非構造性炭水化物含量を高速推定する手法を開発・評価しており、植物形質の取得法が研究の中心である。

Code and data availability

The supplied blocks describe FTIR spectral data collection and PLS model development in Unscrambler X, but contain no data availability statement, public repository deposit, or author-provided URL for the spectral/phenotype datasets, trained models, or analysis code. The only URL besides the DOI (http://wrchr.org/) is,

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