Nondestructive Dropped Fruit Impact Test for Assessing Tomato Firmness

• Published in: Chemical engineering transactions Vol. 58
• Main Authors: K. Vursavus, Z. Kesilmis, B. Oztekin
• Format: Journal Article
• Language: English
• Published: AIDIC Servizi S.r.l 01-06-2017
• ISSN: 2283-9216
• DOI: 10.3303/CET1758055

A nondestructive method for assessing the firmness of tomato fruit was developed based on the mechanical properties of the fruit under the dropped fruit impact test. The tests were carried out on Bandita F1 greenhouse tomato variety at six maturity stages for getting a wide range of firmness stage in 2016 season. In the nondestructive dropped fruit impact measurements, impact force and contact time were sensed by a force sensor attached under the impact plate. Other impact parameters were derived from the impact force-contact time curves. Force-deformation ratio at rupture point was used in the measurements of destructive reference parameter and, it was expressed to be tomato firmness (FT). These nondestructive impact parameters were compared with destructive reference parameter for estimating FT. Ten nondestructive impact parameters were used and, the number of impact parameters being processed were reduced with correlation matrix and stepwise regression analyses. After these processes, simple linear regression (SLR) and multiple linear regression (MLR) were used for model development. Root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) were also used for performance evaluation of modelling approaches used to estimate the tomato firmness. The firmness levels of tomato samples were classified with cluster analysis and, classification performance of developed modelling approaches were tested for classification of tomato samples into three firmness levels. Average firmness values of 135 tomato samples were primarily separated to two groups. 70% and 30% of destructive reference and nondestructive impact parameters were used for calibration and validation data set, respectively. According to results of SLR and MLR statistical analysis, MLR model was found to be the most accurate model for firmness estimation with a RMSE of 0.19 N, MAPE of 5.35%, MAE of 0.10 N and R2 of 0.85 after validation. Therefore, it can be applied for firmness estimation of Bandita F1 greenhouse tomatoes with highest accuracy and success rate of 82.93% compared to SLR model in this study.