No abstract

We investigate the mathematical correlation between weight loss and texture characteristics of modified atmosphere packaging (MAP) for cherry tomatoes (Solanum lycopersicum). In this study, the Slogstic1 model showed that the weight loss of cherry tomatoes with MAP increased with prolonged storage time at room temperature.Moreover, this weight loss had a negative influence on all the texture parameters.Taking weight loss as a dependent variable, the texture parameters of firmness, cohesiveness, gumminess, chewiness, and resilience were significantly related to weight loss as independent variables and used to establish the fitting functions. With an increase in weight loss, the changes in firmness, cohesiveness, gumminess, chewiness, and resilience were consistent with the Log3P1, Gauss, Logistic, Boltzmann, and GaussAmp models, respectively. The result will provide useful information for predicting the weight loss and evaluating the texture of MAP fruit quantitatively. Novelty impact statement• The weight loss of MAP cherry tomatoes conformed to the Slogstic1 growth model.• The weight loss had the strongest negative correlation with firmness.• With increasing weight loss, the changes in firmness, cohesiveness, gumminess, chewiness, and resilience matched the Log3P1, Gauss, Logistic, Boltzmann, and GaussAmp models, respectively.

Weight loss associated with fruit texture during storage has received numerous reports; however, no research has been conducted on the mathematical relationships between weight loss and textural traits of table grapes stored at cold and ambient temperatures. In this study, it was found that the weight loss of ‘Red Globe’ was in the range of 0 to 0.0487, 0 to 0.0284 and 0 to 0.0199 compared to 0 to 0.0661, 0 to 0.0301 and 0 to 0.028 of ‘Wink’ at 13 °C, 3 °C, and 0 °C of storage for 13 days. Stored for 13 days at 13 °C, 3 °C, and 0 °C, the range of the textural traits of failure force, strain and penetration work in ‘Red Globe’ were 6.274 to 3.765, 6.441 to 3.867, 6.321 to 4.014; 51.931 to 11.114, 51.876 to 13.002, 51.576 to 20.892; 21.524 to 13.225, 21.432 to 14.234, 21.321 to 15.198 in contrast to in ‘Wink’ of 4.4202 to 2.2292, 4.4197 to 2.653, 4.4371 to 2.8199 and 15.674 to 2.7881, 15.776 to 4.1431, 15.704 to 5.702 and 12.922 to 7.754, 12.909 to 8.021, 12.915 to 8.407. Meanwhile, the weight loss and textural traits of two table grapes were examined using time-dependent and weight loss-dependent modeling at 13 °C, 3 °C, and 0 °C of storage. The Logistic, ExpDec1, and ExpDec2 models, as well as the Boltzmann model, were identified as the best fit for the obtained data. The equations proved to be more effective in characterizing the change in weight loss and texture of ‘Red Globe’ and ‘Wink,’ with the best equations suited to the weight loss and textural parameters having an average mean standard error of 2.89%. The viability of the established models was evaluated, and parametric confidence intervals of the equations were proposed to fit different grape cultivars. According to the findings, the weight loss and texture of the two grape cultivars could be accurately predicted by the established models; additionally, the results showed that cold storage is better for the quality of table grapes and that weight loss can predict the textural quality of table grapes. This study provides a theoretical framework for optimum storage temperature together with a significantly convenient and quick approach to measure the texture of grapes for fruit dealers and enterprises.

Texture is an important indicator to evaluate the quality of table grapes. However, there is no convenient and fast method to estimate the texture on grapes. Hence, this study evaluated the texture of two table grapes (cvs. Red Globe and Wink) through weight loss-dependent modeling under ambient storage. The weight loss and textural properties of the Polynomial, Boltzmann, and ExpDec2 models were found to better fit the obtained data. The equations were proved to be more efficient in describing the change in weight loss and texture of 'Red Globe' and 'Wink', with the best equations fitted to the weight loss and textural parameters having an average mean standard error of 3.78%. The feasibility of the established models was evaluated, and parametric confidence intervals of the equations were proposed to fit the different varieties of grapes. According to our findings, the textural quality of table grapes can be predicted by weight loss. The results may offer a critically convenient and fast method to estimate the texture on grapes for industry.