High throughput FT-MIR indirect analysis of sugars and acids in watermelon

Abstract

Indirect measurements of taste-related compounds are required when a high number of samples has to be analyzed in a short period of time, with a minimum cost. For this purpose, FT-MIR partial least square (PLS) regression models for the prediction of total soluble solids, sugars and organic acids have been developed using three sample sets including breeding lines and commercial varieties of watermelon. Specific models with excellent performance were obtained only for sugars. Nevertheless, a general model supposed a compromise between the best and worse models and offered %RMSEP values of 11.3%, 11.1% and 11.7% for fructose, glucose and sucrose respectively. The model was applied to the selection of high content samples (selection pressure 20% and 30%) obtaining good sensitivity levels and mean percentile of selected samples close to the expected values (100% sensitivity). The robustness of FT-MIR models was assessed with predictions of external assays, obtaining reasonable performances.