Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Oyster mushroom drying in tray dryer: Parameter optimization using response surface methodology, drying kinetics, and characterization

In this study, the drying of oyster mushrooms (P. ostreatus) in a tray dryer was optimized. The parameters used to optimize the drying process were drying temperature, airspeed, mass loading, and moisture content. Its drying kinetics were investigated at the optimum drying parameters. A quadratic equation was obtained to predict the moisture content of mushrooms at the given drying temperature, airspeed, and mass loading, and it was validated against experimental results. A minimum moisture content (9.99 wt%) was obtained at the optimum conditions of 60 °C, 3 m/s airspeed, and mass loading of 200 g using a tray dryer. Proximate analysis, shelf-life analysis, inorganic elemental analysis, and functional group analysis were done as a characterization method for mushrooms after drying at the optimum drying conditions. About 27.8 wt% protein and 50.2 wt% carbohydrates were found in proximate results. Besides, potassium and sodium were the dominant elements as estimated by spectrophotometry analysis. The induction period (IP) of dried mushrooms at room temperature is 3520:47 (hour: minute) from the oxidation stability analysis, and the water activity of dried mushrooms was found to be 0.36. The drying kinetics of oyster mushrooms were studied at various temperatures (50-75 °C), optimum airspeed (3 m/s), and mass loading (200 g). The best-fit model describing the mushrooms drying kinetics was found to be Midilli et al., with the lowest RMSE (0.008749), X2 (0.0014), and the highest R2(0.9993) values. The kinetic triplet activation energy, effective diffusivity, and diffusivity constant (Ea, Deff, D0) for oyster mushrooms drying were determined and found to lay in the general range for foodstuffs. The value of Deff results lies within the range of 10-8 to 10-12 m2/s, with Ea of 15.32 kJ/mol and D0 value 2.263 × 10-6 m2/s.

The Application of Artificial Intelligence and Big Data in the Food Industry

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Effective moisture diffusivity prediction in two Portuguese fruit cultivars (Bravo de Esmolfe apple and Madeira banana) using drying kinetics data

Air convective dehydration was performed at various temperatures (35 °C, 40 °C, 45 °C and 50 °C) using two types of fruits cultivars produced in different regions of Portugal: the Bravo de Esmolfe apple, from the Beiras province, and the Cavendish banana, from Madeira Island. The data collected were used to predict the effective moisture diffusion, which is a crucial input parameter in drying modeling and design. As expected, the values obtained in both falling drying rate periods detected for apples increased with an increase in drying temperature. The effective moisture diffusion in apples varied from 1.968 × 10^-10 m² s^-1 at 35 °C to 4.013 × 10^-10 m² s^-1 at 50 °C, for the first falling drying rate period, and from 0.9567 × 10^-10 m² s^-1 at 35 °C to 3.328 × 10^-10 m² s^-1 at 50 °C, for the second period. The dependence of effective moisture diffusion on temperature for bananas is similar, ranging from 1.572 × 10^-10 to 2.627 × 10^-10 m² s^-1 as the drying temperature changed from 35 to 50 °C.

Impact of ultrasonication and blanching as a pre-treatment on quality parameter of dried and rehydrated bitter gourd

Vegetables are owed to the restriction of seasonal availability and regional abundance; it becomes essential that vegetables are preserved safely during the off-season. These existing demands look for dried products with high nutritional and organoleptic properties similar to fresh products. This study aimed to investigate the effect of ultrasonication and blanching before hot air drying on the quality attributes of bitter gourd (Momordica charantia). Dried samples were rehydrated to find the efficiency of pre-treatment and physicochemical properties. M. charantia slices were pre-treated with ultrasonication and blanched and dried at two different temperatures, 50 °C and 60 °C. M. charantia pre-treated using ultrasonication and dried at 60 °C reduces the drying time to 50% and rehydration time to 40% compared to untreated samples. Physico-chemical analysis of ultrasonicated samples revealed to have better retention of moisture (dried - 3.6%, rehydrated - 88%), Colour ΔE (dried - 9.07, rehydrated - 1.6), ascorbic acid (dried - 513, rehydrated - 310 mg/100 g), phenol (dried - 302, rehydrated - 231 GAE mg/100 g) and β-carotene (dried - 68 µg/100 g, rehydrated - 39 µg/100 g) compared to blanching.

Recent processing of fruits and vegetables using emerging thermal and non-thermal technologies. A critical review of their potentialities and limitations on bioactives, structure, and drying performance

Fruits and vegetables have rich bioactive compounds and antioxidants that are vital for the human body and prevent the cell from disease-causing free radicals. Therefore, there is a growing demand for high-quality fruits and vegetables. Nevertheless, fruits and vegetables deteriorate due to their high moisture content, resulting in a 40-50% loss. Drying is a common food preservation technique in the food industry to increase fruits and vegetables' shelf-life. However, drying causes chemical modifications, changes in microstructure, and bioactives, thus, lowering the final product's quality as a considerable amount of bioactives compounds and antioxidants are lost. Conventional pretreatments such as hot water blanching, and osmotic pretreatment have improved fruit and vegetable drying performance. However, these conventional pretreatments affect fruits' bioactive compounds retention and microstructure. Hence, emerging thermal (infrared blanching, microwave blanching, and high-humidity hot-air impingement blanching) and non-thermal pretreatments (cold plasma, ultrasound, pulsed electric field, and edible films and coatings) have been researched. So the question is; (1) what are the mechanisms behind emerging non-thermal and thermal technologies' ability to improve fruits and vegetables' microstructure, texture, and drying performance? (2) how do emerging thermal and non-thermal technologies affect fruits and vegetables' bioactive compounds and antioxidant activity? and (3) what are preventing the large-scale commercialization of these emerging thermal and non-thermal technologies' for fruits and vegetables, and what are the future recommendations? Hence, this article reviewed emerging thermal blanching and non-thermal pretreatment technologies, emphasizing their efficacy in improving dried fruits and vegetables' bioactive compounds, structural properties, and drying performance. The fundamental mechanisms in emerging thermal and non-thermal blanching pretreatment methods on the fruits and vegetables' microstructure and drying performance were delved in, as well as what are preventing the large-scale commercialization of these emerging thermal and non-thermal blanching for fruits and vegetables, and the future recommendations. Emerging pretreatment approaches not only improve the drying performance but further significantly improve the retention of bioactive compounds and antioxidants and enhance the microstructure of the dried fruits and vegetables.

Kinetic Model of Moisture Loss and Polyphenol Degradation during Heat Pump Drying of Soursop Fruit (Annona muricata L.)

The aim of this study is to investigate the impact of time and temperature of the heat pump drying process of soursop slices at different levels on moisture content and total polyphenol content (TPC). Twelve types of classical kinetic models have been used in this work to describe the suitability of experimental data with models. The conformity is assessed based on statistical values (e.g., coefficient of determination (R2), Chi–square value (X2), etc.). The loss of moisture in the material is described in accordance with Fick’s diffusion law. Value of moisture rate (MR), and effective moisture diffusivities (Deff) have been identified. Experimental results show that MR value depends on the time and drying temperature, Deff increases when increasing the drying temperature from 20–50 °C with values of 1.24 × 10−9, 1.85 × 10−8, 7.69 × 10−8, and 5.54 × 10−7 m/s2. The Singh et al. model is the best option to describe the moisture of the sliced soursop drying process at 30 °C (R2 = 0.97815). The largest TPC decomposition occurs at a temperature of 50 °C. The ability to decompose TPC is proportional to the drying temperature. The TPC decomposition dynamic model follows a first–order reaction when drying at 20 °C with a determinant coefficient R2 = 0.9693.

Adaptive neuro fuzzy inference system modeling of Synsepalum dulcificum L. drying characteristics and sensitivity analysis of the drying factors

The requirement for easily adoptable technology for fruit preservation in developing countries is paramount. This study investigated the effect of pre-treatment (warm water blanching time—3, 5 and 10 min at 60 °C) and drying temperature (50, 60 and 70 °C) on drying mechanisms of convectively dried Synsepalum dulcificum (miracle berry fruit—MBF) fruit. Refined Adaptive Neuro Fuzzy Inference System (ANFIS) was utilized to model the effect and establish the sensitivity of drying factors on the moisture ratio variability of MBF. Unblanched MBF had the longest drying time, lowest effective moisture diffusivity (EMD), highest total and specific energy consumption of 530 min, 5.1052 E−09 m²/s, 22.73 kWh and 113.64 kWh/kg, respectively at 50 °C drying time, with lowest activation energy of 28.8589 kJ/mol. The 3 min blanched MBF had the lowest drying time, highest EMD, lowest total and specific energy consumption of 130 min, 2.5607 E−08 m²/s, 7.47 kWh and 37 kWh/kg, respectively at 70 °C drying temperature. The 5 min blanched MBF had the highest activation energy of 37.4808 kJ/mol. Amongst others, 3—gbellmf—38 epoch ANFIS structure had the highest modeling and prediction efficiency (R² = 0.9931). The moisture ratio variability was most sensitive to drying time at individual factor level, and drying time cum pretreatment at interactive factors level. In conclusion, pretreatment significantly reduced the drying time and energy consumption of MBF. Refined ANFIS structure modeled and predicted the drying process efficiently, and drying time contributed most significantly to the moisture ratio variability of MBF.

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey

BACKGROUND

Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi-Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the number of neurons (3 to 20), the activation functions and the addition of a second hidden layer. Different numbers (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy, as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40 °C, 50 °C and 60 °C temperatures at 0.5, 0.75 and 1 ms^-1 airflow velocity.

RESULTS

The best performed ANFIS model, comprised by 5-2-2 of π-shaped andtriangular membership functions for time, temperature and airflow velocityinputs respectively, was able to describe the moisture ratio evolution of E. amoenum more precisely than the best ANN topology, achieving higher values of coefficientof determination (R² ), root mean square error (RMSE) and sum of squared errors(SSE). The best thin-layer model involving six adjustable parameters, managedto describe experimental data most accurately with R² = 0.9996, RMSE = 0.0057and SSE = 7.3·10^-4 .

CONCLUSION

The results of the comparative study indicate that empirical regression models with increased numbers of adjustable parameters, constitute a simpler and more accurate modeling approach for estimating the moisture ratio of E. amoenum Fisch. & C. A. Mey under fluidized bed drying. © 2021 Society of Chemical Industry.

Osmotic, osmovacuum, sonication, and osmosonication pretreatment on the infrared drying of Ginkgo seed slices: Mass transfer, mathematical modeling, drying, and rehydration kinetics and energy consumption

This study evaluated the mass transfer, drying, and rehydration kinetics (drying and rehydration curve, moisture diffusivity [D_eff ]), energy consumption (specific energy consumption [SEC], moisture extraction rate (MER), and specific moisture extraction rate [SMER]), and mathematical modeling of infrared dried Ginkgo biloba seed (GBS) using the various nonthermal pretreatments namely: osmotic (OS), osmovacuum (V + OS), ultrasound (US, ginkgo seed immersed in a distilled water with US), and osmosonication (US + OS, ginkgo seeds immersed in an OS solution with US). Results showed that various pretreatments affected mass transfer, drying, and rehydration characteristics, and energy consumption, which was confirmed by principal component analysis. In terms of mass transfer, US pretreatment recorded the highest weight loss while the osmosonication pretreatment registered the highest solid gain. The entire drying process occurred in the falling-rate period. The D_eff values were within the normal range of agroproducts (10^-11 to 10^-8 m² /s). The modified Page-I and Weibull model best fitted the drying and rehydration kinetics, respectively, with the coefficient of determination (R² ) > 0.991, root mean square error, residual sum of squares, and reduced chi-square closer to zero, compared with the other models. The untreated GBS (control) had the lowest energy efficiency (lowest SMER and MER) and the highest SEC than the pretreated GBS. Among the various pretreatments, the US pretreatment of GBS was superior, with the highest D_eff , MER, SMER, and drying rate, and lowest drying time and SEC. Based on the findings, sequential US pretreatment and infrared drying is a feasible drying technique for GBS that could be used commercially. PRACTICAL APPLICATION: Ginkgo tree cultivation in China has exceeded market needs with 60,000 tons per annum of GBS produced. Hence, there is a compelling need to explore new chances to use GBS availability irrespective of the seasonality and address the problem where GBS utilization is limited to the early phases of home-cooked dishes. Although drying increases the shelf life of ginkgo seeds, there is a higher operation cost. Thus, pretreatment can reduce energy consumption and augment the product quality is ideal. This research reported the impact of nonthermal pretreatments on ginkgo seeds' mass transfer, drying, and rehydration characteristics. The present results will provide a comprehensive understanding of the engineering application of ginkgo seed pretreatment, allowing for the best technique to be selected.

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Drying Kinetics and Quality of Whole, Halved, and Pulverized Tiger Nut Tubers (Cyperus esculentus)

The objective of this study was to provide the optimum drying conditions to produce high-quality dried tiger nuts using hot-air drying. For this, we evaluated the effect of the whole, halved, and pulverized tiger nuts and air temperature (50 to 70°C) on the drying kinetics and quality of tiger nuts. The drying process generally followed a constant rate in the first 3 hours and a falling regime. We found the optimum drying conditions for tiger nuts to be crushed before convective hot-air drying at a temperature of 70°C. At this optimum condition, the predicted drying time, vitamin C content, reducing sugars, browning, brightness, redness, and yellowness was 780 min, 22.9 mg/100 mg dry weight, 157.01 mg/100 g dry weight, 0.21 Abs unit, 56.97, 1.6, and 17.0, respectively. The tiger nut's reducing sugars increased from the 130.8 mg/100 dry weight in the raw tiger nuts to between 133.11 and 158.18 mg/100 dry weight after drying. The vitamin C degradation rate was highest in the uncut tiger nuts (32-35%) while in the halved and the pulverized samples, it was between 12 and 17%. The crushed samples' effective moisture removal increased between 5.6- and 6.75-fold at the different air temperatures than that of the intact tiger nuts. The activation energy was 18.17 kJ/mol for the unbroken, 14.78 kJ/mol for the halved, and 26.61 kJ/mol for the pulverized tiger nut samples. The model MR = 0.997 exp(−0.02t^1.266) + 0.0000056t was the most suitable thin-layer drying model among the models examined for convective hot-air drying of tiger nuts. It is advisable to crush tiger nut before hot-air drying to produce better-quality flour for making milk beverages, cakes, biscuits, bread, porridge, and tiger nut-based breakfast cereals.

Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer

Drying characteristics of stevia leaves were investigated in an infrared (IR)-assisted continuous-flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time (p < .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R2), root mean squared error (RMSE), and chi-squared error (χ2) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best-fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous-flow IR-assisted hybrid solar dryer.

Application of Computational Intelligence in Describing the Drying Kinetics of Persimmon Fruit (Diospyros kaki) During Vacuum and Hot Air Drying Process

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

Drying characteristics of yam slices (Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics

This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices (Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 °C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R²) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m²/s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59–54.93 KJ/mol). Rehydration ratio was highest at air velocity×air temperature×thickness (1.5 m/s×70 °C × 3 mm), and lowest at air velocity × air temperature×thickness (0.5 m/s×70 °C × 3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam (Dioscorea rotundata) slices.