Non-destructive quantification of lutein and beta-carotene in spinach by Raman spectroscopy under optimized conditions for linear discriminant analysis

Leafy vegetables present challenges for Raman-based carotenoid analysis due to strong fluorescence from chlorophyll and the coexistence of complex biomolecules. This study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels. Arabidopsis thaliana mutants with controlled carotenoid levels were used to build and validate the model, which was then applied to cultivated Spinacia oleracea. Various spectral preprocessing methods and Raman shift subsets were tested to optimize model performance. The LDA model successfully distinguished lutein and β-carotene concentration levels, achieving up to 95.45 % accuracy in Arabidopsis and 90.91 % in spinach. This classification-based strategy offers practical advantages over continuous quantification, particularly in food quality monitoring and nutritional labeling. The findings demonstrate the potential of LDA-assisted Raman spectroscopy as a selective and reliable tool for carotenoid analysis in chlorophyll-rich vegetables, with strong applicability for non-destructive quality control across the food production and distribution chain.