A Post-Harvest Prediction Mass Loss Model for Tomato Fruit Using A Numerical Methodology Centered on Approximation Error Minimization

Francisco Javier Bucio; Cesar Isaza; Jose Amilcar Rizzo Sierra; Jonny Zavala de Paz; Ely Karina Anaya Rivera; Enrique Gonzalez Gutierrez

A Post-Harvest Prediction Mass Loss Model for Tomato Fruit Using A Numerical Methodology Centered on Approximation Error Minimization

2017

Due to its nutritional and economic value, the tomato is considered one of the main vegetables in terms of production and consumption in the world. For this reason, an important case study is the fruit maturation parametrized by its mass loss in this study. This process develops in the fruit mainly after harvest. Since that parameter affects the economic value of the crop, the scientific community has been progressively approaching the issue. However, there is no a state-of-the-art practical model allowing the prediction of the tomato fruit mass loss yet. This study proposes a prediction model for tomato mass loss in a continuous and definite time-frame using regression methods. The model is based on a combination of adjustment methods such as least squares polynomial regression leading to error estimation, and cross validation techniques. Experimental results from a 50 fruit of tomato sample studied over a 54 days period were compared to results from the model using a second-order polynomial approach found to provide optimal data fit with a resulting efficiency of ~97%. The model also allows the design of precise logistic strategies centered on post-harvest tomato mass loss prediction usable by producers, distributors, and consumers.

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