Modeling of drying kiwi slices and its sensory evaluation

Evolution of Quality Parameters and Bioactivity of Actinidia chinensis cv. Sungold (Kiwifruit) Slices Subjected to Different Drying Conditions Storage for 4 Months

The present study aimed to investigate the impact on nutritional and functional properties of dried kiwifruit (Actinidia chinensis cv. Sungold) slices during conservation for 120 days in sealed containers in the dark at 25 °C. For this purpose, kiwifruits slices were dried at two different temperatures, 40 and 55 °C, for 30 and 25 h, respectively. Fresh and dried kiwi slices were analyzed for their pH, activity water, total solid soluble (TSS), color, titratable acidity, total phenols (TPC) and flavonoids content (TFC), organic acids, and radical scavenging activities. Analysis carried out on the dehydrated samples showed a good aptitude of kiwi material towards the drying process. Particularly, it has been observed that the drying treatment at low temperature helped to preserve the nutraceutical properties of the fruits. In fact, samples treated at 40 °C (KLT) showed at day 0 (T0) the highest TPC and TFC with values of 979.42 Gallic Acid Equivalents (GAE)/100 g of dried weight (dw) and 281.84 mg catechin equivalents (CTE)/100 g dw even if compared with fresh kiwi slices sample (FKF). Moreover, KLT also exhibited the highest values of antioxidant activity (1657 mmol Trolox/100 g dw). After 120 days storage, all dried samples showed a high ascorbic acid content (429-339 mg/100 g dw fruits) and only a slight variation of physicochemical parameters. Textural Parameters (hardness, springiness, cohesiveness, gumminess, and chewiness), apart from resilience results, showed significant differences between kiwifruit dried at 55 °C and at 50 °C (KLT and KHT, respectively). Color and aroma intensity were the main sensory descriptors with higher scores.

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.

Influence of Drying Methods on Jackfruit Drying Behavior and Dried Products Physical Characteristics

Drying processes including solar, oven, and refractance window were studied to determine their influence on the drying behavior of jackfruit slices and properties of resultant jackfruit powders. The loss of sample mass, converted to the ratio between the water content at time t and the initial water content (moisture ratio), was used as the experimental parameter for modelling drying processes. Fifteen thin layer drying models were fitted to the experimental data using nonlinear regression analysis. Based on the highest R² and lowest SEE values, the models that best fit the observed data were Modified Henderson and Pabis, Verma et al., and Hii et al. for RWD, oven, and solar drying, respectively. The effective moisture diffusivity coefficients were 5.11 × 10⁻⁹, 3.28 × 10⁻¹⁰, and 2.55 × 10⁻¹⁰ for RWD, oven and, solar drying, respectively. The solubility of freeze-dried jackfruit powder (75.7%) was not significantly different from the refractance window dried powder (73.2%) and was higher than oven-dried jackfruit powder (66.1%). Oven-dried jackfruit powder had a lower rehydration ratio and porosity. Differences in rehydration ratio and porosity under different drying methods could be explained by the microstructure. Fractal dimension (FD) and lacunarity were applied to study the structure and irregularities of jackfruit dried with the different methods. FD was significantly (P < 0.05) affected by the drying method. FD ranged from 1.809 to 1.837, while lacunarity ranged between 0.258 and 0.404.

Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil

In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (E)-2-decenal, (E,E)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil.

Thermodynamic and Quality Performance Studies for Drying Kiwi in Hybrid Hot Air-Infrared Drying with Ultrasound Pretreatment

The present study examined the effect of ultrasonic pretreatment at three time the levels of 10, 20 and 30 min on some thermodynamic (effective moisture diffusivity coefficient(Deff), drying time, specific energy consumption (SEC), energy efficiency, drying efficiency, and thermal efficiency) and physical (color and shrinkage) properties of kiwifruit under hybrid hot air-infrared(HAI) dryer at different temperatures (50, 60 and 70 °C) and different thicknesses (4, 6 and 8 mm). A total of 11 mathematical models were applied to represent the moisture ratio (MR) during the drying of kiwifruit. The fitting of MR mathematical models to experimental data demonstrated that the logistic model can satisfactorily describe the MR curve of dried kiwifruit with a correlation coefficient (R2) of 0.9997, root mean square error (RMSE) of 0.0177 and chi-square (χ2) of 0.0007. The observed Deff of dried samples ranged from 3.09 × 10−10 to 2.26 × 10−9 m2/s. The lowest SEC, color changes and shrinkage were obtained as 36.57 kWh/kg, 13.29 and 25.25%, respectively. The highest drying efficiency, energy efficiency, and thermal efficiency were determined as 11.09%, 7.69% and 10.58%, respectively. The results revealed that increasing the temperature and ultrasonic pretreatment time and decreasing the sample thickness led to a significant increase (p < 0.05) in drying efficiency, thermal efficiency, and energy efficiency, while drying time, SEC and shrinkage significantly decreased (p < 0.05).

Cold plasma: An emerging pretreatment technology for the drying of jujube slices

Jujube slices were pretreated by cold plasma for 15, 30, and 60 s on each side, followed by hot air drying at 50, 60, and 70 °C. Scanning electron microscopy investigation indicated that the application of cold plasma significantly changed the surface topography of jujube slice by etching larger cavities, which can facilitate moisture transfer and consequently enhance drying rate and effective diffusivity. Modified Henderson & Pabis model and Two-term model were the two most recommended models for describing the drying kinetics of jujube slices. Cold plasma pretreatment improved the contents of procyanidins, flavonoids, and phenolics by 53.81%, 33.89%, and 13.85% at most, respectively, and thereby enhanced antioxidant capacity by 36.85% at most. Besides, cold plasma pretreatment can reduce the production of 5-hydroxymethylfurfural by 52.19% at most. In summary, cold plasma can be used as a promising pretreatment tool for drying processes of jujube slices.

Modelling and kinetic study of microwave assisted drying of ginger and onion with simultaneous extraction of bioactive compounds

Onion and ginger are rich sources of bioactive compounds which are lost during conventional drying process. The present study was designed to optimize the novel Microwave Assisted Drying and Extraction technique (MADE) for simultaneous drying and extraction/recovery of bioactive compounds from model food products. The time required for drying of samples was 11 (onion) and 16 (ginger) minutes with recovery yield of 87% (onion) and 85% (ginger). The drying time was reduced to 100 times compared to hot air drying and moisture ratio of dried samples was best described by Midilli model. The diffusivities of onion and ginger slices were 1.27 e^-11 and 1.43 e^-11 m²/s, respectively. Moreover, microwave-based extraction was compared with conventional one. The results of antioxidant activity and total phenolic contents of condensates obtained through MADE were higher compared to conventional method. In short, MADE exhibited better yield of extraction and drying properties compared to conventional methods.

Influence of the Freeze-drying Conditions on the Physicochemical Properties and Grinding Characteristics of Kiwi

The aim of this work was to study the influence of freeze-drying conditions of kiwi (Actinidia deliciosa) on physicochemical properties and grinding characteristics of dried fruits. Whole kiwi fruits were freeze-dried with the different pressure: 12, 20, 42, 63, 85, and 103 Pa. Dried fruit properties that underwent evaluation included color, texture, rehydration, total phenolics content, antioxidant properties and sensory analysis. Moreover, the grinding energy indices of dried kiwi were determined. The results showed that an increase in the pressure caused decreased lightness, but increased yellowness and greenness of freeze-dried kiwi. The force of kiwi penetration increased and the rehydration ability decreased with the increase in the pressure. The specific grinding energy of dried kiwi ranged from 10.1 to 13.6 kJ⋅kg−1, whereas the average particle size of kiwi powder changed from 0.331 to 0.337 mm. The highest values of these parameters were obtained for kiwi freeze-dried with the highest pressure.

Modelling and Kinetic Study of Novel and Sustainable Microwave-Assisted Dehydration of Sugarcane Juice

Sugarcane juice is a perishable food with a good nutritional profile. Thus, there is a need to increase its shelf life by reducing water content which facilitates storage and transportation. In this study, process conditions were optimized to concentrate the sugarcane juice at various microwave powers (30, 50, 80, 100 W). A central composite design was applied to optimize the process conditions (power and time). The overall evaporation time depends on microwave powers; increase in power reduced the processing time. The results showed that at 100 W sugarcane juice was concentrated to 75° brix for 15 min which reduced the energy consumption to 1.3 times compared to other powers. Moreover, microwave processing better retained the sensory properties of concentrate and preserved its antioxidant activity. Thus, 100 W was most energy efficient in concentrating sugarcane juice. In general, microwave processing reduced the processing time and cost making it a sustainable approach to concentrate juices.

Thin-layer drying of parchment Arabica coffee by controlling temperature and relative humidity

This paper presents thin-layer drying of parchment coffee (Coffea arabica). Thin-layer drying of parchment coffee was conducted under controlled temperatures (50°C, 60°C, and 70°C) and relative humidities (10%-30%). The temperature of the drying air was important for drying at a high temperature, which results in the rapid removal of moisture and reduced time for drying. Nine thin-layer drying models (Newton, Page, Henderson and Pabis, logarithmic, two-term, modified Henderson and Pabis, two-term exponential, approximation diffusion, and modified-Midilli) were fitted to the experimental data for parchment coffee. The drying parameters of parchment coffee were related to temperature and relative humidity. The best model was the modified-Midilli model, which can be used to design the optimal dryer. The effective moisture diffusivity of parchment coffee drying was determined by minimizing the sum of squares of the deviations between the experimental data for the moisture content and the predicted values of thin-layer drying. The effective moisture diffusivity as a function of the temperature at each relative humidity was expressed by the Arrhenius-type equation.

Recent developments of artificial intelligence in drying of fresh food: A review

Intellectualization is an important direction of drying development and artificial intelligence (AI) technologies have been widely used to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in different food drying technologies due to the advantages of self-learning ability, adaptive ability, strong fault tolerance and high degree robustness to map the nonlinear structures of arbitrarily complex and dynamic phenomena. This article presents a comprehensive review on intelligent drying technologies and their applications. The paper starts with the introduction of basic theoretical knowledge of ANN, fuzzy logic and expert system. Then, we summarize the AI application of modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products in artificial biomimetic technology (electronic nose, computer vision) and different conventional drying technologies. Furthermore, opportunities and limitations of AI technique in drying are also outlined to provide more ideas for researchers in this area.

Modeling of drying kiwi slices and its sensory evaluation

In this study, monolayer drying of kiwi slices was simulated by a laboratory-scale hot-air dryer. The drying process was carried out at three different temperatures of 50, 60, and 70°C. After the end of drying process, initially, the experimental drying data were fitted to the 11 well-known drying models. The results indicated that Two-term model gave better performance compared with other models to monitor the moisture ratio (with average R² value equal .998). Also, this study used artificial neural network (ANN) in order to feasibly predict dried kiwi slices moisture ratio (y), based on the time and temperature drying inputs (x₁, x₂). In order to do this research, two main activation functions called logsig and tanh, widely used in engineering calculations, were applied. The results revealed that, logsig activation function base on 13 neurons in first and second hidden layers were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R² value .997. Furthermore, kiwi slice favorite is evaluated by sensory evaluation. In this test, sense qualities as color, aroma, flavor, appearance, and chew ability (tissue brittleness) are considered.