Refractance window (RW) concentration of milk‐Part II: Computer vision approach for optimizing microbial and sensory qualities

Innovative date syrup processing with ohmic heating technology: Physiochemical characteristics, yield optimization, and sensory attributes

The present study aimed to investigate the application of the ohmic heating (OH) technique in the production of date syrup from the date fruit of the Sukkary variety at different electric field strengths (EFS) (9, 10, and 11 V/cm). The results were compared to the conventional heating method (CH). The response surface methodology was used to optimize yield. The results showed that the time to reach the boiling point of dates and water mixture using OH was less than the CH by 80% for extracting and 900% for evaporation. In addition, the productivity of date syrup using OH at EFS of 11 V/cm was higher than the CH by 86.11%. There is no significant effect between OH at EFS of 11 V/cm and CH in moisture content, refractive index, density, TSS, and viscosity. The optimum level of EFS was 11.5 V/cm, which gave a higher yield (64.93%). OH, save consumed power and cost. The OH gave the highest scores of sensory characteristics compared to CH. Total sugars, monosaccharides, and ketone monosaccharides were detected in the date syrup, and the result was positive, while the quintuple sugars and multiple sugars were negative for all treatments. The OH reduced the cost by 85.78% compared with CH.

Assessing Compositional and Quality Parameters of Unconcentrated and Refractive Window Concentrated Milk Based on Color Components

In this study, a multiple linear regression equation was developed to measure and predict quality parameters of unconcentrated and concentrated milk based on color components. The viscosity, density, pH, moisture, and fat content could be measured using image processing technology. The multiple linear regression model had a good fitting on experimental data considering the limited errors (0.00–1.12%), standard deviation (0.000–0.043), and root mean square error (0.0007–0.3721). Therefore, these models can be used to predict the quality parameters of milk, including fat percentage, pH, viscosity, density, and moisture content, based on color components of unconcentrated and concentrated milk. The maximum and minimum of color change were 12.28 and 5.96, respectively. The values of browning index were also well-predicted and were within the standard limits. The non-destructive and quick procedure that proposed in this study showed a percentage of accuracy in assessing and predicting the quality parameters milk based on color components. Overall, the color correlates with different compositional and physical characteristics, and provide a possible internet of things (IoT)-based approach to accompany the conventional approaches in the future after further evaluation at large scale for various types of milks subjected to various processes.