Converting Apple Textural Parameters Obtained from Penetrometers and Their Relationships with Sensory Attributes

Abstract

Textural attributes of apple impact consumers’ acceptance of the fruit, and are frequently measured by researchers and industry experts to evaluate the fruit quality at different stages of production and marketing. Various instruments are used to conduct these textural evaluations in research and industry settings. The application of different instruments makes the comparison and integration of results extremely difficult. The main objectives of this study were to compare data obtained from three widely used textural instruments, investigate their relationships with each other and with sensory evaluations, and develop models to convert data among instruments. Three penetrometers were included in the study: (1) Fruit Texture Analyzer (FTA); (2) Mohr Digi-Test-2 (MDT-2); and (3) TA.XTplus Texture Analyzer (TA.XTplus). Eight apple varieties with a range of textural attributes were selected. Eleven sensory judges evaluated three apple slices (1/8 apple) from each variety. The instrumental measurements were conducted on 10 apples per instrument from each variety, with two measurements on each apple. Results of principal component analysis indicated that 95.82% of the variation in the texture data could be explained using only two principal components. Linear and nonlinear regression models were developed to convert data obtained from an instrument to those from other instruments.