NIRS chemometrics for rapid nutritional profiling of vegetable pea (Pisum sativum L.) germplasm.

Abstract

Vegetable pea (Pisum sativum L.) is a nutritionally rich food source with a balanced profile of macronutrients and micronutrients, contributing multiple health benefits and plays a crucial role in combating nutritional deficiencies. Its nutritional diversity encompassing high range of protein, starch, soluble sugars, and phenolic content, renders it an ideal candidate for nutritional profiling, which is essential for mining Nutri-dense accessions. Near-infrared reflectance spectroscopy (NIRS) is a valuable alternative to conventional methods for nutritional profiling, offering rapid, accurate, less laborious, cost-effective, and non-destructive analysis with the capability to measure multiple parameters simultaneously for large-scale germplasms. This investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid with spectral pre-processing done by standard normal variate (SNV) and detrending (DT) using 90 vegetable pea (both marketable and mature stages) dried seed flour. The best-performing models were developed for moisture content (0.938, 0.469, 3.989), protein (0.931, 0.709, 3.063), starch (0.814, 1.312, 2.317), amylose (0.847, 0.646, 2.556), TDF (0.932, 0.652, 3.473), phenol (0.925, 0.078, 3.538), TSS (0.918, 0.231, 3.494), and phytic acid (0.898, 0.095, 2.358) corresponding to coefficient of determination (RSQ), corrected standard error of prediction (SEP(C)), and ratio of performance to deviation (RPD), respectively. This study presents the first report on the development of NIRS based prediction models using MPLS method for multi-trait assessment across different developmental stages in diverse vegetable pea germplasm, exhibiting high-throughput capability of the models in an economical and precise way.

Plant phenotyping relevance

NIRSとmPLSによる植物種子の栄養形質を非破壊・高スループットに推定する予測モデルを開発し、性能指標で評価しているため、植物形質取得法が中心です。

Code and data availability

The paper's NIRS spectral data, wet-chemistry reference measurements, and Win ISI calibration models are not publicly deposited; the authors state the datasets are available only from the corresponding author on reasonable request. The supplementary DOCX is not described as containing the phenotype/spectral datasets or

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