Improving the Prediction for Sensory Texture Attributes for Multicomponent Snack Bars by Optimizing Instrumental Test Conditions

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 (R val ) 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. CorrespondingTextural 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.

Noninvasive Deformation Test for the Tenderness Classification of Broiler Breast Meat

“…Consequently, a trained sensory panel or a group of consumers is the most efficient tool to characterize meat tenderness (Greve et al . ). However, due to the extensive time and financial considerations required to maintain a sensory panel, a great deal of effort has been exerted to develop instruments capable of assessing meat texture (Lee et al .…”

Texture of Hot-Air-Dried Persimmon (Diospyros kaki) Chips: Instrumental, Sensory, and Consumer Input for Product Development

“…Thus, an additional instrumental texture measurement is needed to fully characterize this product. Greve et al [ 9 ] used multiple instrumental variables derived from a variety of five test probes (three-point bending, needle, 2 mm/6 mm cylinder compression, and small blade shear) to predict the sensory texture attributes for commercial snack bars. Of the tested probes, the blade exhibited the highest overall predictive ability, showing a validation coefficient determination greater than 0.8 for all the sensory attributes evaluated (Hardness, Crispness, Brittleness, Hand-Evaluated Hardness).…”

Prediction of sensory texture of broiler breast meat using instrumental spectral stress–strain analysis

“…As tenderness is recognised as a multidimensional sensory attribute, it can be best perceived and measured by humans (Szczesniak, 1963). With respect to this view, it would be relevant to speculate that a trained sensory panel is the most efficient tool to characterise the tenderness of meat (Greve et al, 2010). However, due to the fact that sensory evaluation is costly, extensive and excessively time-consuming as well as variability between human subjects exists, a great deal of effort has been devoted to the development of instruments for the prediction of meat texture (Lee et al, 2009;Gilbert et al, 2013).…”

“…Poor or low correlations between sensory and instrumental meat texture attributes could be in part due to the anisotropic or heterogeneous structure of meat and assessing meat texture from instrumental parameters could not be constantly reproducible. Another possible reason might be ascribed to the difficulty in designing instrumental methods or the lack of understanding of the meat texture to be multidimensional aspects (Greve et al, 2010).…”