Kinetic Study of Water and Total Soluble Solid Changes of Black Cherry Tomato (Solanum lycopersicum cv. OG) Sauce Using Rotary Vacuum Evaporation.

Kinetics of water removal and total soluble solid (TSS) content change of black cherry tomato (cv. OG) sauce by rotary vacuum evaporation (RVE) were investigated. The effect of different vacuum conditions (vacuum levels and boiling temperatures of 500 mmHg - 80° C, 550 mmHg - 75°C, 600 mmHg - 70°C a...

Full description

Bibliographic Details
Published in:Current Research in Nutrition & Food Science Vol. 8; no. 3; pp. 1037 - 1046
Main Authors: HO THI NGAN HA, NGUYEN MINH THUY
Format: Journal Article
Published: Current Research in Nutrition & Food Science Dec2020
Online Access:View this record in EBSCOhost
Description
Summary:Kinetics of water removal and total soluble solid (TSS) content change of black cherry tomato (cv. OG) sauce by rotary vacuum evaporation (RVE) were investigated. The effect of different vacuum conditions (vacuum levels and boiling temperatures of 500 mmHg - 80° C, 550 mmHg - 75°C, 600 mmHg - 70°C and 650 mmHg - 65°C) during evaporation /concentration was examined. Tomatoes puree with an initial TSS of 13.47±0.18° Brix was concentrated to 39.83±0.30° Brix. There was a linear relationship between water removal and time during the concentration of black cherry tomato sauce by RVE. The TSS change of tomato sauce during the concentration was applied to three exponential mathematical models (two-parameter, three-parameter, and four-parameter). In studying the consistency of all models, some statistical indicators, namely the coefficient of determination (R² ), the chi-square (χ² ) as well as the root mean square error (RMSE) were considered. Among the models, the three-parameter exponential model was proven to best describe the concentration behavior of the tomato sauce using rotary vacuum evaporation with the highest R², the lowest χ², and the lowest RMSE. The validation with the experimental data at other vacuum levels had also confirmed the consistency of the selected model. This knowledge is very important for process optimization and product quality improvement.