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

Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint

The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.

Beyond Traditional Methods: Deep-Learning Machines Empower Fingerroot (Boesenbergia rotunda)-Extract Production with Superior Antioxidant Activity

This study investigated the impact of drying parameters on the quality of fingerroot (Boesenbergia rotunda) extract, focusing on phenolic compounds, flavonoids, and antioxidant activity. A Box-Behngen design was employed to evaluate the effects of maltodextrin concentration, inlet temperature, and outlet temperature on the extract's properties. The highest total phenolic content (18.96 µg of GAE/mg extract) and total flavonoid content (33.52 µg of GE/mg extract) were achieved using 20% maltodextrin, a 160 °C inlet temperature, and an 80 °C outlet temperature. Antioxidant activity, measured by DPPH and FRAP assays, was also influenced by drying parameters. Stepwise regression analysis revealed that maltodextrin concentration significantly affected all responses, while the inlet temperature had no significant effect. The outlet temperature significantly influenced FRAP activity. The developed mathematical models accurately predicted experimental values, validating the effectiveness of the RSM and Deep-Learning Machine. Optimal drying conditions for maximizing phenolic compounds were determined to be 20% maltodextrin, a 150 °C inlet temperature, and a 70 °C outlet temperature, resulting in TPC 15.33 µg of GAE/mg extract, TF 28.75 µg of GE/mg extract, IC50 value of 3.99 µg/mL, FRAP value at 4.44 µmoL Fe2+/mg extract of phenolic content, and 18.96 µg of the GAE/mg extract. Similar conditions were found to be optimal for maximizing flavonoid content. These findings provide valuable insights for optimizing the drying process of fingerroot extract to preserve its bioactive compounds and enhance its potential applications.

Recent advances in artificial intelligence towards the sustainable future of agri-food industry

Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Artificial neural networks in the modeling of the catalytic activity of a biosensor composed of conjugated polymers and urease

Thin films of conjugated polymer and enzyme can be used to unravel the interaction between components in a biosensor. Using artificial neural networks (ANNs) improves data interpretability and helps construct models with great capacity for classifying and processing information. The present work used kinetic data from the catalytic activity of urease immobilized in different conjugated polymers to create ANN models using time, substrate concentration, and absorbance as input variables since the models had absorbance in a posterior instant as output value to explore the predictivity of the ANNs. The performance of the models was evaluated by Pearson's correlation coefficient (ρ) and mean squared error (MSE) values. After the learning process, a series of new experiments were performed to verify the generality of the models. As the main results, the best ANN model presented 0.9980 and 3.0736 × 10-5 for ρ and MSE, respectively. For the simulation step, intermediary values of substrate concentration were used. The mean absolute percentage error (MAPE) values were 3.34, 3.07, and 3.78 for 12 mM, 22 mM, and 32 mM concentrations, respectively. Overall, with the simulations, it was possible to ascertain the interpolatory capacity of the model, which has a learning mechanism based on absorbance and time as variables. Thus, the potential of ANNs would be in their use in pre-evaluations, helping to determine the substrate concentration at which there is higher catalytic activity or in determining the linear range of the sensor.

Mathematical Modeling Approach and Simulation in Food Drying Applications

Recent developments in the branch of food drying involve advancements in the development of mathematical models [...].

Natural deep eutectic solvents for the extraction of lentinan from shiitake mushroom: COSMO-RS screening and ANN-GA optimizing conditions

Using natural deep eutectic solvents (NDES) for green extraction of lentinan from shiitake mushroom is a high-efficiency method. However, empirical and trial-and-error methods commonly used to select suitable NDES are unconvincing and time-consuming. Conductor-like screening model for realistic solvation (COSMO-RS) is helpful for the priori design of NDES by predicting the solubility of biomolecules. In this study, 372 NDES were used to evaluate lentinan dissolution capability via COSMO-RS. The results showed that the solvent formed by carnitine (15 wt%), urea (40.8 wt%), and water (44.2 wt%) exhibited the best performance for the extraction of lentinan. In the extraction stage, an artificial neural network coupled with genetic algorithm (ANN-GA) was developed to optimize the extraction conditions and to analyze their interaction effects on lentinan content. Therefore, COSMO-RS and ANN-GA can be used as powerful tools for solvent screening and extraction process optimization, which can be extended to various bioactive substance extraction.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

Advances in efficient extraction of essential oils from spices and its application in food industry: A critical review

With the increase of people's awareness of food safety, it is crucial to find natural and green antimicrobial agents to replace traditional antimicrobial agents. Essential oils of spices (SEOs) are low toxicity or nontoxic, which exhibited antioxidants and antimicrobial activity according to many in vitro and in situ experiments. Spices are widely available and low cost as a plant raw material for the extraction of SEOs. This review summarized highly efficient extraction techniques for SEOs, such as physical field assisted extraction technology, supercritical fluid extraction, and biological-based techniques. Furthermore, purification of SEOs and components were also recapitulated. Purification techniques of SEOs improve their utilization value due to the increased content of bioactive components. Finally, the review concentrated on the applications of SEOs in food industry, including food preservation, food active packaging by means of films or coatings, antioxidant properties. In addition, addressing the problem of unstability of SEOs and its role to inhibit the pathogenic bacteria, the encapsulation of SEOs for use in the food industrial sectors reduces the safety risk to human health.

Applicability and Freshness Control of pH-Sensitive Intelligent Label in Cool Chain Transportation of Vegetables

Freshness is one of the main factors affecting consumers' purchase of food. The freshness indicator labels of packaged fresh green bell pepper (Capsicum annuum L.) and greengrocery (Brassica chinensis L.) were constructed, and pH-sensitive indicator labels based on the dye of anthocyanin and the mixing dye of methyl red and bromothymol blue were prepared in this study. At the same time, the color, chlorophyll content and vitamin C content of vegetables were measured in order to explore the applicability of indicator labels in the cool chain transportation of vegetables. Compared with the nature dye, the chemical dye-type indicator labels are more sensitive to pH changes. The results showed that the mixed indicator intelligent label had the best indication effect, and the MB 2 (mixing 1 g/L methyl red and bromothymol blue solutions at a ratio of 3:2 with a concentration of 70 mL/L in indicator film solution) indicator label could effectively indicate the freshness changes in vegetables during storage. Meanwhile, the color changes of the MB 2-type indicator label were correlated with the colors change of the sample, changes in nutrients, and changes in CO2 content inside the packaging. In addition, freshness detection models for green bell pepper and greengrocery by using color information of MB 2 intelligent labels were established. Hence, this pH-sensitive label can be applied as a promising intelligent packaging for non-destructively monitoring the freshness of respiratory and non-respiratory climacteric vegetables.

Sensing materials for fresh food quality deterioration measurement: a review of research progress and application in supply chain

Fresh food are consumed in large quantities worldwide. During the supply chain, microbial growth in fresh food can lead to the production of a number of metabolites, which make food highly susceptible to spoilage and contamination. The quality of fresh food changes in terms of smell, tenderness, color and texture, which causes a decrease in freshness and consumers acceptance. Therefore, the quality monitoring of fresh food has become an essential part in the supply chain. As traditional analysis methods are highly specialized, expensive and have a small scope of application, which cannot be applied to the supply chain to realize real-time monitoring. Recently, sensing materials have received a lot of attention from researchers due to the low price, high sensitivity and high speed. However, the progress of research on sensing materials has not been critically evaluated. The study examines the progress of research in the application of sensing materials for fresh food quality monitoring. Meanwhile, indicator compounds for spoilage of fresh food are analyzed. Moreover, some suggestions for future research directions are given.

Basic sensory properties of essential oils from aromatic plants and their applications: a critical review

With higher standards in terms of diet and leisure enjoyment, spices and essential oils of aromatic plants (APEOs) are no longer confined to the food industry. The essential oils (EOs) produced from them are the active ingredients that contribute to different flavors. The multiple odor sensory properties and their taste characteristics of APEOs are responsible for their widespread use. The research on the flavor of APEOs is an evolving process attracting the attention among scientists in the past decades. For APEOs, which are used for a long time in the catering and leisure industries, it is necessary to analyze the components associated with the aromas and the tastes. It is important to identify the volatile components and assure quality of APEOs in order to expand their application. It is worth celebrating the different means by which the loss of flavor of APEOs can be retarded in practice. Unfortunately, relatively little research has been done on the structure and flavor mechanisms of APEOs. This also points the way to future research on APEOs.Therefore, this paper reviews the principles of flavor, identification of components and sensory pathways in humans for APEOs. Moreover, the article outlines the means of increasing the efficiency of using of APEOs. Finally, with respect to the sensory applications of APEOs, the review focuses on the practical application of APEOs in food sector and in aromatherapy.

Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks

The effects of temperature, air velocity, and infrared radiation distances on the drying characteristics and quality of apple slices were investigated using infrared-assisted-hot air drying (IRAHAD). Drying temperature and air velocity had remarkable effects on the drying kinetics, color, total phenol content, total flavonoid content, and vitamin C content (VCC) of apple slices. Infrared radiation distance demonstrated similar results, other than for VCC and color. The shortest drying time was obtained at 70 °C, air velocity of 3 m/s and infrared radiation distance of 10 cm. A deep neural network (DNN) was developed, based on 4526 groups of apple slice drying data, and was applied to predict changes in moisture ratio (MR) and dry basis moisture content (DBMC) of apple slices during drying. DNN predicted that the coefficient of determination (R2) was 0.9975 and 1.0000, and the mean absolute error (MAE) was 0.001100 and 0.000127, for MR and DBMC, respectively. Furthermore, DNN obtained the highest R2 and lowest MAE values when compared with multilayer perceptron (MLP) and support vector regression (SVR). Therefore, DNN can provide new ideas for the rapid detection of apple moisture and guide apple processing in order to improve quality and intelligent control in the drying process.

Recent application of artificial neural network in microwave drying of foods: a mini-review

The microwave-assisted thermal process is a high-efficiency drying method and is promising to be applied in the food industry. However, the prediction of the thermal treatment results from such a dynamic and complicated process can be difficult. Additionally, the determination of the optimal drying parameters, such as drying temperature, microwave power, and drying time for optimized performance can also be hard. Recently, extensive research has been focusing on the use of artificial neural network (ANN) models in the laboratory-scale microwave drying processes and has shown the feasibility of such application. As a regression tool, the ANN models have been widely used in predicting drying performance; when integrated with additional optimizing algorithms, the ANN models could be used for drying parameter optimization; and when combined with real-time measuring techniques (e.g. nuclear magnetic resonance), the ANN models could be used for monitoring and controlling the drying process in a dynamic sense. Future research could focus on testing the developed ANN models in industrial-scale microwave drying processes and applying the ANN models in microwave drying kinetics research for optimizing the dynamic drying processes. © 2022 Society of Chemical Industry.

Advances in prepared dish processing using efficient physical fields: A review

Prepared dishes are increasingly popular convenience food that can be eaten directly from hygienic packaging by heating. Physics field (PF) is food processing method built with physical processing technology, which has the characteristics of high efficiency and environmental safety. This review focuses on summarizing the application of PFs in prepared dishes, evaluating and comparing PFs through quality changes during processing and storage of prepared dishes. Currently, improving the quality and extending the shelf life of prepared dishes through thermal and non-thermal processing are the main modes of action of PFs. Most PFs show good potential in handing prepared dishes, but may also react poorly to some prepared dishes. In addition, the difficulty of precise control of processing conditions has led to research mostly at the laboratory stage, but as physical technology continues to break through, more PFs and multi-physical field will be promoted for commercial use in the future. This review contributes to a deeper understanding of the effect of PFs on prepared dishes, and provides theoretical reference and practical basis for future processing research in the development of various enhanced PFs.

Insight into the incredible effects of microwave heating: Driving changes in the structure, properties and functions of macromolecular nutrients in novel food

Microwave heating technology performs the characteristics of fast heating, high efficiency, green energy saving and easy control, which makes it deeply penetrate into the food industry and home cooking. It has the potential to alter the appearance and flavor of food, enhance nutrient absorption, and speed up the transformation of active components, which provides an opportunity for the development of innovation foods. However, the change of food driven by microwave heating are very complex, which often occurs beyond people's cognition and blocks the development of new food. It is thus necessary to explore the transformation mechanism and influence factors from the perspectives of microwave technology and food nutrient diversity. This manuscript focuses on the nutritional macromolecules in food, such as starch, lipid and protein, and systematically analyzes the change rule of structure, properties and function under microwave heating. Then, the flavor, health benefits, potential safety risks and bidirectional allergenicity associated with microwave heating are fully discussed. In addition, the development of new functional foods for health needs and future market based on microwave technology is also prospected. It aims to break the scientific fog of microwave technology and provide theoretical support for food science to understand the change law, control the change process and use the change results.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

A Model for Analyzing Teaching Quality Data of Sports Faculties Based on Particle Swarm Optimization Neural Network

In this paper, we use a particle swarm optimization neural network algorithm to analyze the teaching data of physical education faculties and evaluate the quality of teaching in physical education faculties. By studying and analyzing the optimization problem of the weight parameters of convolutional neural network training, this paper designs a hybrid algorithm combining the improved PSO algorithm and the traditional gradient descent in the framework of the BP algorithm by using the gradient information of the loss function and the principle of group cooperative search through PSO algorithm. The hybrid algorithm takes the loss function as the objective function, based on the principle of the PSO algorithm, and optimizes the objective function by combining the gradient information of the loss function of the convolutional neural network. The convergence speed and global search ability of the algorithm are effectively improved while ensuring an acceptable increase in computation. The weight values of the three-level indicators of teacher teaching behavior, student learning behavior, and teaching environment relative to the teaching quality of physical education classroom are 0.106, 0.634, and 0.260, respectively, which shows that the dimension of student learning behavior has the highest weight value in the evaluation of physical education classroom teaching quality, followed by teaching environment and finally teacher teaching behavior. Teachers' teaching ability will affect the effect of teaching methods, and the stronger the teaching ability is, the better the selection and utilization of teaching methods can be optimized.

Extraction techniques in food industry: Insights into process parameters and their optimization

This review presents critical evaluation of the key parameters that affect the extraction of targeted components, giving due consideration to safety and environmental aspects. The crucial aspects of the extraction technologies along with protocols and process parameters for designing unit operations have been emphasized. The parameters like solvent usage, substrate type, concentration, particle size, temperature, quality and storage of extract as well as stability of extraction have been elaborately discussed. The process optimization using mathematical and computational modeling highlighting information and communication technologies have been given importance aiming for a green and sustainable industry level scaleup. The findings indicate that the extraction processes vary significantly depending on the category of food and its structure. There is no single extraction method or universal set of process conditions identified for extracting all value-added products from respective sources. A comprehensive understanding of process parameters and their optimization as well as synergistic combination of multiple extraction processes can aid in enhancement of the overall extraction efficiency. Future efforts must be directed toward the design of integrated unit operations that cause minimal harm to the environment along with investigations on economic feasibility to ensure sustainable extraction systems.

Applications of knowledge graphs for food science and industry

The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.

Application of Artificial Neural Network in the Baking Process of Salmon

The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method.

Application of carbon dots in food preservation: a critical review for packaging enhancers and food preservatives

Carbon dots (CDs) have two unique advantages: one is ease of synthesis at low price, the other is desirable physical and chemical properties, such as ultra-small size, abundant surface functional groups, nontoxic/low-toxicity, good biocompatibility, excellent antibacterial and antioxidant activities etc. These advantages provide opportunities for the development of new food packaging enhancers and food preservatives. This paper systematically reviews the studies of CDs used to strengthen the physical properties of food packaging, including strengthen mechanical strength, ultraviolet (UV) barrier properties and water barrier properties. It also reviews the researches of CDs used to fabricate active packaging with antioxidant and/or antibacterial properties and intelligent packaging with the capacity of sensing the freshness of food. In addition, it analyzes the antioxidant and antibacterial properties of CDs as preservatives, and discusses the effect of CDs applied as coating agents and nano-level food additives for extension the shelf life of food samples. It also provides a brief review on the security and the release behavior of CDs.

Machine learning-based modeling in food processing applications: State of the art

Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better-quality products. The development of a machine learning (ML)-based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better-quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML-based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step-by-step procedure to develop an ML-based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML-based techniques to hybrid food processing operations. The potential of physics-informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML-based technology for food processing applications.

Role of dehydration technologies in processing for advanced ready-to-eat foods: A comprehensive review

Advanced ready-to-eat foods, which can be consumed directly or only need simple processed before consumption, refer to the products that processing with cutting-edge food science and technology and have better quality attribute. Cold chain and chemical addition are commonly used options to ensure microbial safety of high moisture advanced ready-to-eat foods. However, this requires freezing/thawing processing at high cost or has undesirable residue. Dehydration treatment has the potential to compensate those shortcomings. This article reviewed the positive effects of dehydration on advanced ready-to-eat foods, current application status of dehydration technologies, novel dehydration related technologies and the pathogenic bacteria control of products. It is observed that dehydration treatment is receiving increasing attention for ready-to-eat foods including space foods, 3 D-printed personalized foods and formula foods for special medical purposes. Recently developed drying technologies such as pulsed spouted microwave freeze-drying and infrared freeze-drying have attracted much interest due to their excellent drying characteristics. Finally, intelligent drying, dehydration-nano-hybridization and dehydration-induced multi-dimensional modification technology are some of the emerging R and D areas in this field.

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.

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network

Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R² > 0.926, RMSEC < 0.481) compared with partial least-squares model (R² > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying.

Mathematical modeling of nutritional, color, texture, and microbial activity changes in fruit and vegetables during drying: A critical review

Retention of quality attributes during drying of fruit and vegetables is a prime concern since the product's acceptability depends on the overall quality; particularly on the nutritional, color, and physical attributes. However, these quality parameters deteriorate during drying. Food quality changes are strongly related to the drying conditions and researchers have attempted to develop mathematical models to understand these relationships. A better insight toward the degradation of quality attributes is crucial for making real predictions and minimizing the quality deterioration. The previous empirical quality models employed kinetic modeling approaches to describe the quality changes and therefore, lack the realistic understanding of fundamental transport mechanisms. In order to develop a physics based mathematical model for the prediction of quality changes during drying, an in-depth understanding of research progress made toward this direction is indispensable. Therefore, the main goal of this paper is to present a critical review of the mathematical models developed and applied to describe the degradation kinetics of nutritional, color, and texture attributes during drying of fruit and vegetables and microbial growth model during storage. This review also presents the advantages and drawbacks of the existing models along with their industrial relevance. Finally, future research propositions toward developing physics-based mathematical model are presented.

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.

Link optimization of the new generation instant messaging network based on artificial intelligence technology

The current network environment is dynamic, open and extensible. In order to better ensure the needs of users, higher requirements are placed on link resource allocation. Based on the research and analysis of the instant communication protocol, this paper studies an intelligent routing evolution algorithm and related fault recovery strategy for the instant communication network. Research on instant messaging intelligent algorithms for routing evolution is mainly based on routing algorithms and artificial intelligence intelligent algorithms. When a link failure occurs in the communication network, the routing algorithm performs route reconstruction and optimization on the entire instant communication network. Considering that there may be evolutionary needs of large-scale routing networks in practical applications, this paper introduces artificial intelligence intelligent algorithms to optimize intelligent algorithms to improve efficiency. A cognitive routing protocol based on MIMO (Multiple Input Multiple Output) technology is proposed. By using MIMO technology, a lot of gain is brought to the communication link under multiple antennas. These gains correspond to different link types. The protocol realizes cognition through intelligent routing evolution algorithm and predicts the state of the network. Setting the routing life and hello period according to the perceived network status can optimize the performance of the network.

Modelling Volume Change and Deformation in Food Products/Processes: An Overview

Volume change and large deformation occur in different solid and semi-solid foods during processing, e.g., shrinkage of fruits and vegetables during drying and of meat during cooking, swelling of grains during hydration, and expansion of dough during baking and of snacks during extrusion and puffing. In addition, food is broken down during oral processing. Such phenomena are the result of complex and dynamic relationships between composition and structure of foods, and driving forces established by processes and operating conditions. In particular, water plays a key role as plasticizer, strongly influencing the state of amorphous materials via the glass transition and, thus, their mechanical properties. Therefore, it is important to improve the understanding about these complex phenomena and to develop useful prediction tools. For this aim, different modelling approaches have been applied in the food engineering field. The objective of this article is to provide a general (non-systematic) review of recent (2005-2021) and relevant works regarding the modelling and simulation of volume change and large deformation in various food products/processes. Empirical- and physics-based models are considered, as well as different driving forces for deformation, in order to identify common bottlenecks and challenges in food engineering applications.