Qualitative analysis of broccoli (Brassica oleracea var. italica) glucosinolates: Investigating the use of mid-infrared spectroscopy combined with chemometrics

Abstract

Glucosinolates are phytochemicals with important health and nutritional benefits. This study reports the use of high-performance liquid chromatography (HPLC) and mid-infrared (MIR) spectroscopy to characterise and differentiate between broccoli varieties and systems of production (organic vs. non-organic) depending on their glucosinolate content and infrared fingerprint. Broccoli samples (n = 53) from seven varieties were analysed using MIR spectroscopy and HPLC. Differences in the MIR spectra of the individual broccoli varieties were observed in the carbohydrate fingerprint region (950–1100 cm-1) and between 1340 and 1615 cm-1 assigned to specific glucosinolates. Principal component analysis (PCA) of the MIR fingerprint spectra enabled the differentiation between samples with relatively high (200–500 mg/100 g DW) and low (< 200 mg/100 g DW) glucobrassicin content. Linear discriminant analysis (LDA) and PCA-LDA were used to classify broccoli varieties according to the system of production (organic vs. non-organic) and variety (common vs. Tenderstem® broccoli). The classification rates indicated that > 70 % of the samples were correctly classified as organic and non-organic, while > 90 % of the samples were correctly classified as common broccoli and Tenderstem®. This study demonstrates that MIR spectroscopy could be used as a potential tool to classify and monitor broccoli samples according to their variety and system of production.