Preparation of microwave‐processed oil‐free potato snacks with assorted natural flavours: Application of advanced chemometrics

Microwave-vacuum extraction cum drying of tomato slices: Optimization and functional characterization

Fruits and vegetables have shorter shelf life due to their perishable nature. Tomato, being a nutritionally rich fruit needs to be preserved for a longer period. In this context, this study was designed to dry the tomato slices through microwave-vacuum drying. This process was optimized for moisture ratio and drying rate using response surface methodology (RSM). The process was investigated at different power levels (30, 50, 80, and 100 W), pressure (0, 15, 20, and 25 inHg), and time (0, 4, 6, and 10 min) using Box-Behnken design. Results indicated that color, energy efficiency, and drying characteristics were significantly affected by changing power, vacuum levels, and processing time. Besides, nine mathematical models were applied on experimental data to deeply understand the moisture ratio of tomato slices. Amongst, Midilli model was found best to describe the drying process at 100 W and 25 inHg supported by R ² (0.9989), RMSE (0.001), and X ² (1.34e^-4). This study was focused on finding the optimal combinations of power, vacuum pressure, and time for better drying and reduced wastage of the fruit owing to its perishable nature. From all the microwave powers, higher microwave power and vacuum level showed better energy consumption, energy efficiencies, color retention, and rehydration capacity.

Discrimination of Four Cinnamomum Species with Physico-Functional Properties and Chemometric Techniques: Application of PCA and MDA Models

Discrimination of highly valued and non-hepatotoxic Cinnamomum species (C. verum) from hepatotoxic (C. burmannii, C. loureiroi, and C. cassia) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four Cinnamomum species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different Cinnamomum species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of Cinnamomum species to prevent food fraud.