A colorimetric sensor array for the discrimination of glucosinolates.

Area of Science:

• Analytical Chemistry
• Food Science
• Plant Biochemistry

Background:

• Glucosinolates are key bioactive compounds in cruciferous vegetables, influencing flavor and health benefits.
• Accurate discrimination of glucosinolates is crucial for food quality assessment and nutritional analysis.

Purpose of the Study:

• To develop and validate a novel colorimetric sensor array (CSA) method for differentiating 10 types of glucosinolates.
• To assess the CSA technique's ability to classify different cruciferous vegetable samples based on their glucosinolate profiles.

Main Methods:

• A colorimetric sensor array (CSA) was employed for glucosinolate detection.
• Chemometric methods including principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), and partial least squares regression (PLSR) were utilized.
• Assays were performed over a concentration range of 0-150 μM for glucosinolates.

Main Results:

• The CSA method demonstrated good linearity (r² ≥ 0.97) for 10 glucosinolates.
• CSA coupled with PCA and HCA successfully distinguished most glucosinolate samples by type.
• CSA combined with LDA achieved 94% accuracy in classifying 8 types of cruciferous vegetables.
• PLSR indicated strong correlations (Rp range: 0.813–0.964) between CSA responses and glucosinolate concentrations.

Conclusions:

• The developed CSA technique provides a rapid and effective approach for the discrimination of various glucosinolates.
• This method shows significant potential for application in food analysis, quality control, and nutritional profiling of cruciferous vegetables.