IMPROVING THE PREDICTION FOR SENSORY TEXTURE ATTRIBUTES FOR MULTICOMPONENT SNACK BARS BY OPTIMIZING INSTRUMENTAL TEST CONDITIONS

Abstract

ABSTRACT Ten instrumental variables derived from a variety of five test probes (three‐point bending, needle, 2‐mm/6‐mm cylinder compression and small blade shear) and five deformation rates ranging from 5 to 100 cm/min were used to predict the sensory texture attributes for 16 commercial snack bars. Hardness, crispness, brittleness and hand‐evaluated hardness were selected as relevant texture attributes and evaluated from a descriptive analysis by 10 trained panelists. To determine the optimal instrumental test conditions (test probes and deformation rate) for the prediction of each sensory attribute, various model statistics based on partial least squares regression were used. The blade exhibited high predictive abilities, showing validation coefficient determination (Rval) greater than 0.8 for all the sensory attributes evaluated. The results display that, provided the correct instrumental conditions are set, a valuable improvement in the prediction of sensory attributes by instrumental variables can be achieved. PRACTICAL APPLICATIONSTextural attributes are important drivers of liking for snack bar products. The food industry has made a great deal of efforts in optimizing instrumental methodologies for their routine quality control, especially for texture measurements to maintain consumer acceptance. Therefore, a reliable prediction of sensory attributes derived from the instrumental variables is a crucial issue for the successful launch of instrumental analysis in industrial applications. To satisfactorily predict sensory texture attributes, the optimization of instrumental settings is of uttermost importance.