Using near-infrared reflectance spectroscopy (NIRS) and chemometrics for non-destructive estimation of the amount and composition of seed tocopherols in Brassica juncea (Indian mustard).

Abstract

Tocopherol content and composition ( α- , γ- ) in Brassica juncea seeds are normally determined using wet chemistry methods, which are time-consuming, labor-intensive, and hazardous to human health. We attempted the development and validation of the first near-infrared reflectance spectroscopy (NIRS) model as a quick alternative. A total of 356 B. juncea seed samples were collected from a germplasm diversity set of 178 B. juncea genotypes. These were evaluated over the course of two crop seasons (2019-20 and 2020-21) and scanned by NIRS. Modified Partial least square (MPLS) method was used to regress their reference values against spectral transformations. The development of a reliable NIRS calibration equation was made possible by the availability of a wide range of variation for α -tocopherol (11.18-84.6 mg/kg) and γ -tocopherol (57.27-255.5 mg/kg) in the seeds of diversity panel. A model with the highest coefficient of determination (RSQ) was identified for strong association between NIRS-predicted values and ultra-performance liquid chromatography (UPLC)-based reference values. The newly developed model exhibited RSQ of 0.786, 0.896, 0.906 for α- , γ- , and total tocopherols, respectively. This model was further validated using external set of samples and the results confirmed the robustness of the equation with high RSQ values. Supplementary information The online version contains supplementary material available at 10.1007/s13197-025-06204-3.

Code and data availability

The paper's NIRS spectral dataset, UPLC reference tocopherol measurements, and calibration model code are not publicly deposited; the authors state both data and code are available only from the corresponding author upon request. No public URL or repository is provided.

No evidence-backed public reproduction asset is currently recorded.