Chemometric evaluation of alfalfa sprouting impact on its metabolic profile using HPTLC fingerprint-efficacy relationship analysis modelled with partial least squares regression

Sprouting is a commonly applied food processing practice specially in Western countries. Tracking the impact of sprouting of Medicago sativa L. (alfalfa) seeds on their phytochemical composition and curative efficacy was implemented in the current study. Sprouting of alfalfa seeds under controlled conditions for eleven days was performed in a biochemical incubator and three samples were randomly taken each day. A total of thirty-six samples (three ungerminated seeds and thirty-three sprouts samples) were collected, extracted and their cytotoxic, antioxidant and antimicrobial activities against five pathogenic microbial strains were measured. Samples were subjected to High performance thin layer chromatography (HPTLC) as a pattern-oriented strategy for metabolite fingerprinting to discover the fluctuations occurring during the sprouting process accompanied by multivariate chemometric analysis. Unsupervised pattern recognition was carried out using Principal Component Analysis (PCA) after extracting the chromatographic fingerprints from HPTLC chromatograms using ImageJ® software. PCA- loading plots demonstrated that luteolin-7-O-glucoside, ferulic acid and P-coumaric acid were the metabolically significant markers. Thus, simultaneous quantification of these crucial three markers in different aged alfalfa seeds/ sprouts extracts was performed using a newly developed and validated HPTLC-image analysis method. The results of the biological activities together with the quantitative data were further subjected to a Partial Least Squares Regression (PLSR) model for implementing HPTLC fingerprint-efficacy relationship analysis. The results obtained from metabolic pool profiling revealed that sprouting can cause remarkable changes in the phytochemical, nutritional and efficacy characteristics of alfalfa seeds.