Logistic regression analysis of marketability of tomato fruit harvested at different maturity stages and subjected to disinfection, storage condition and storage period treatments

Marketability Probability Study of Cherry Tomato Cultivars Based on Logistic Regression Models

“…The commercial quality was evaluated for each fruit. The storage time at the optimal temperature and the firmness of the stored fruit are the factors with the greatest influence on the marketability probability of tomatoes [19,20]. Therefore, the measured parameters were the firmness as determined by an AGROSTA 100 digital durometer (Durofel DFT 100, Serqueux, France) specific to fruits and vegetables.…”

“…where π (x) is the marketability probability of the tomato given a set of n explanatory variables x i (i = 1,..., n), which can be categorical variables (e.g., cultivar) or continuous ones (e.g., days of storage (DOS)), α is the intersection parameter, and β i are the slope parameters for the primary effects of each explanatory variable (i = 1,..., n) [19,32]. We used the multiple logistic regression model to evaluate significant predictors of marketability probability for all the explanatory variables considered in the study.…”

“…Regarding the primary extrinsic factors, those that produce the greatest change in quality are the ripening state and the time and conditions of transport and storage. Of the intrinsic and extrinsic factors, those that have the greatest influence on the tomato marketability probability are the time and storage conditions, together with the fruit firmness [19,20].…”

“…To understand the postharvest behavior of new tomato cultivars, it is necessary to study the commercial life in response to the current needs. Therefore, it is necessary to develop study models that holistically integrate all the causes of commercial loss (Figure 1), such as the studies performed by Melesse et al [20] on the marketability of tomatoes subjected to different pre-and postharvest treatments and Tolesa et al [19] on the marketability of tomato fruits harvested at different stages of maturity and subjected to different disinfection and storage conditions.…”

“…Logistic regression models are statistical models that determine the relationship between a dichotomous qualitative dependent variable (binary or binomial logistic regression) and one or more independent explanatory variables or covariables, whether qualitative or quantitative (multiple logistic regression) [32]. The logistic regression model is presented as a novel approach for calculating the marketability probability of fresh products that have been subjected to multiple agro-economic practices [19,20,33]. This logistic regression model has not been applied in comparative studies of tomato cultivars (Figure 1).…”