Construction and optimization of quantitative analysis models for pigments in broccoli ( Brassica oleracea L. var. italica ) based on near-infrared spectroscopy technology.

Abstract

Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R 2 of 0.992, RMSEC of 0.478 mg g -1 DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R 2 of 0.976, RMSEC of 0.098 mg g -1 DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R 2 of 0.790, RMSEC of 1.777 units g -1 DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.

Plant phenotyping relevance

ブロッコリーの色素という植物形質をNIRSで非破壊・高スループット推定するモデルを構築・評価しており、表現型取得法が研究の中心である。

Code and data availability

The paper's NIRS spectral data, pigment measurements, and TQ Analyst models are not publicly deposited; the data availability statement says data will be made available on request, and no authors' public code or dataset URL is provided. Supplementary materials are referenced but contain no explicit dataset/code deposit

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