Performance of calibration models for evaluation of apples sensory texture with contact acoustic emission detector (CAED) was studied. For model evaluation and testing, 2500 apples of 19 cultivars were harvested over two seasons. Apples were stored at normal atmosphere (NA), controlled atmosphere (CA) for different periods or were treated with 1-methylcyclopropene (1-MCP) in order to obtain a high variability of texture and fruit maturity. Apples were tested simultaneously in two distinct laboratories. The models were created and validated on averaged values from 10 fruits using simple linear regression, multiple linear regression (MLR) and principal component regression (PCR). Performance statistics of the models were expressed in terms of determination coefficient (R(2)), root mean square errors of cross validation (RMSECV) or prediction (RMSEP) and ratio of prediction to deviation (RPD). Firmness and total acoustic emission counts were predictors of sensory texture in the models. MLR and PCR models show better performance for prediction of sensory data than simple linear regression models however PCR models show the best results among models tested in this study. Common PCR models for several cultivars allow for successful prediction of hardness (RPD > 2.0), crispness and overall texture (1.5 < RPD < 2.0). The single-cultivar PCR models, constructed on data sets containing 26-39 averaged values, reveal significantly better performance (RPD > 2.0 for most of the cases) than the common PCR models for many varieties. (C) 2011 Elsevier Ltd. All rights reserved.