Hyperspectral Imaging for the Dynamic Mapping of Total Phenolic and Flavonoid Contents in Microgreens

This study investigates the application of hyperspectral imaging (HSI) combined with machine learning (ML) models for the dynamic mapping of total phenolic content (TPC) and total flavonoid content (TFC) in sunflower microgreens. Spectral data were collected across different cultivation durations (Days 5, 6, and 7) to assess the secondary metabolite distribution in leaves and stems. Overall, the results indicate that TFC in leaves peaked on Day 5, followed by a decline on Days 6 and 7, while stems exhibited an opposite trend. However, TPC did not show a consistent pattern. Spectral reflectance analysis revealed higher near-infrared reflectance in leaves compared to stems. The variation in trait and spectral data among the collected samples was sufficient to develop models predicting the TPC and TFC content. K-nearest neighbours provided the highest predictive accuracy for TPC (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.95 and 1.6 mg GAE/100 g) and ridge regression performed best for TFC (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.97 and 6.1 mg QE/100 g). Dimensionality reduction via principal component analysis (PCA) proved effective for TPC and TFC prediction, with PC1 alone achieving performance comparable to the full spectral dataset. This integrated HSI-ML approach offers a non-destructive, real-time method for monitoring bioactive compounds, supporting sustainable agricultural practices, optimising harvest timing, and enhancing crop management. The findings can be further developed for smart microgreen farming to enable real-time secondary metabolite quantification, with future research recommended to explore other microgreen varieties for broader applicability.