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

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 R² 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 R² 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 R² 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.