Modelling effects of pre-storage treatments, maturity stage, low-cost storage technology environment and storage period on the quality of tomato fruit

Abstract

To cite this article: Getachew Neme Tolesa, Tilahun Seyoum Workneh & Sileshi Fanta Melesse (2018) Modelling effects of pre-storage treatments, maturity stage, low-cost storage technology environment and storage period on the quality of tomato fruit, CyTA -Journal of Food, 16:1, 271-280, ABSTRACTThe aim of this study was to develop a model for prediction of the quality of tomato during storage. Two storage conditions (evaporatively cooled and ambient), four disinfection treatments (tap, anolyte, hot and chlorinated water) and three maturity stages (green, pink and red) were employed in this experiment. Using multivariate analysis, the principal components (PC) (PC1, PC2) were analysed. The PC1 and PC2 contributed 47.9% and 26.0% of total variation, respectively. Maturity stages and the disinfection treatments had significant (p < 0.05) influence on PC1 and PC2. Nonlinear fractional and polynomial models were fitted to the experimental data to screen the best models. Hue angle, firmness, titratable acidity (TA) and total soluble solid:TA demonstrated nonlinear relationship with days of storage. TA, hue angle and firmness of tomatoes fitted well to the models developed. The models are recommended for use by tomato farmers to predict changes in the quality parameters.Efectos modelados en la calidad del tomate a partir de tratamientos previos al almacenamiento, etapas de madurez, ambiente tecnológico de almacenamiento de bajo costo y periodos de almacenamiento RESUMEN El presente estudio se propuso crear un modelo que permitiera pronosticar la calidad del tomate durante su almacenamiento. Para este cometido, en el experimento se utilizaron dos condiciones de almacenamiento (con enfriamiento por evaporación y a temperatura ambiente), cuatro tratamientos de desinfección (agua de la llave, electrolizada, caliente y clorada) y tres etapas de madurez (verde, rosado y rojo). Mediante análisis multivariado se analizaron los componentes principales (PC1, PC2). El PC1 y el PC2 explican 47,9% y 26,0% de la variación total, respectivamente. Las distintas etapas de madurez y los diferentes tratamientos de desinfección influyeron de manera significativa (P<0,05) en PC1 y PC2. A fin de cribar los mejores modelos se ajustaron modelos fraccionales no lineales y polinomiales a los datos experimentales. Se comprobó que el ángulo del tono, la firmeza, la acidez valorable (TA) y TSS:TA tienen una relación no lineal con los días de almacenamiento (DOS). A su vez, se constató que la TA, el ángulo del tono y la firmeza de los tomates se ajustaron bien a los modelos desarrollados. En conclusión se recomienda a los productores de tomate el uso de estos modelos, ya que permitirán pronosticar los cambios que puedan producirse en los parámetros de calidad.

Physicochemical Quality Changes in Tomatoes during Delayed Cooling and Storage in a Controlled Chamber

“…IS-treated tomato varieties showed lower decreases in firmness, while among the cultivars, the Dabol (large) variety showed the lowest decrease in firmness, from 28.83 to 20.49 N after 15 days of storage. Tolesa and Workneh [40] reported declines in the firmness of tomatoes harvested at the green, pink, and red maturity stages from 29 to 10 N, 25 to 13 N, and 20.5 to 10 N, respectively, over a storage period of 21 days inside an evaporative cooler, which is in agreement with the present findings. Majidi et al [41] reported that controlled atmosphere storage maintained tomato firmness better than cold storage during the first 10 days of storage, and that a steeper decline in firmness (65 N to 59 N) was observed from 10 to 20 days of storage.…”

“…Pinheiro et al [49] also found a decrease in the hue angle values over a storage period of 30 days and recommended a storage temperature of 10 °C to better preserve tomato quality, avoid chilling injuries, and extend shelf life, which is in agreement with our findings. The slowing down of color development could be due to physiological respiration, which could influence the quality of stored tomatoes throughout the storage period [40]. Srinivasa et al [48] observed a decrease of hue angle values during the storage of tomato fruits using eco-friendly films.…”

Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things

“…Fruits of different usages should be picked at different stages of maturity. The picking time needs to be properly advanced or delayed, according to the specific usage [4][5][6][7]. Due to the limitations of warehousing and logistics, fruits should not be picked when they are too mature or raw.…”