Search

This study was undertaken to identify the mechanistic role of γδ T cells in the pathogenesis of experimental psoriatic arthritis (PsA).In this study, we performed interleukin-23 (IL-23) gene transfer in wild-type (WT) and T cell receptor δ-deficient (TCRδWe demonstrated that γδ T cells support systemic granulopoiesis, which is critical for murine PsA-like pathology. Briefly, γδ T cell ablation inhibited the expression of neutrophil chemokines CXCL1 and CXCL2 and neutrophil CD11b+Ly6G+ accumulation in the aforementioned PsA-related tissues. Although significantly reduced expression of granulocyte-macrophage colony-stimulating factor (GM-CSF) and IL-17A was detected systemically in TCRδOur findings do not support the notion that tissue-resident γδ T cells initiate the disease but demonstrate a novel role of γδ T cells in neutrophil regulation that can be exploited therapeutically in PsA patients.

Theoretical models for food drying commonly utilise an effective diffusivity solved through curve fitting based on experimental data. This creates models with limited predictive capabilities. Multiscale modeling is one approach which can help transition to a more physics-based model minimizing the empirical information required while improving a model's predictive capabilities. However, to enable an accurate scaling operation, multiscale models require diffusivity at a fine scale (microscale). Measuring these properties is experimentally costly and time consuming as they are often temperature and/or moisture dependent. This research conducts an inverse analysis on a multiscale homogenization food drying model to deduce the temporal diffusivity of intracellular water. A representation of the real cellular water breakdown was considered and appropriate assumptions to represent its cellular heterogeneity, in relation to time, were investigated. The work uncovered that a linear decrease in intracellular water content could be assumed and thus a function for its diffusivity was developed. The proposed function is in terms of sample temperature and intracellular water content opening the possibilities to be applied to various food materials.

Tuberculosis (TB) stays a major cause of death globally after COVID-19 and HIV. An early diagnosis to control TB effectively, needs a fast reliable diagnostic method with high sensitivity. Serodiagnosis involving polyclonal antibodies detection against an antigen of Mycobacterium tuberculosis (Mtb) in serum samples can be instrumental. In our study, Rv3874 and Rv3875 antigens were cloned, expressed, and purified individually and as a chimeric construct in Escherichia coli BL21. Enzyme-Linked Immunosorbent Assay (ELISA) based findings revealed that the Rv3874-Rv3875 chimeric construct was two-fold more sensitive (59.7%) than the individual sensitivities of Rv3874 (28.4%) and Rv3875 (24.9%) for 201 serum TB positive samples. Furthermore, the fusion construct was a little more sensitive (60.4%) for male subjects than that for females (58.8%). Lastly, our preliminary findings, molecular insights of secondary structure, and statistical and in silico analysis of each construct also advocate that CEP can be considered a better immunodiagnostic tool in addition to previously reported EC skin test.

Waste in the food supply chain is estimated to be about 30-40% of the total food production, which aggravates the world hunger and increases waste management burden and environmental impact. Despite the dire food scarcity, majority of this food waste takes place in developing countries because of the lack of appropriate and affordable preservation techniques. Traditional open sun drying is the most popular food-reservation technique to the local farmers due to near-zero capital cost and cheap labor cost. However, this method is highly energy intensive, unhygienic, and time demanding. The high energy consumption resulting from uncontrolled simultaneous heat, mass, and momentum transfer processes in traditional drying systems highlights the necessity of pursuing sustainability in drying process targeting reduced energy consumption, environmental and social impacts. This paper presents a comprehensive review on the sustainable food drying technologies based on renewable energy sources, with emphasis on the developing countries. It was observed that the integration of thermal energy storage with heat pump makes the integrated drying system more efficient, and dries food with better quality. Likewise, advanced integrated drying systems, such as, solar with microwave, and heat pump with microwave make the drying process more cost and quality competent. Finally, impact of resource distribution and governmental incentives for renewable energy use in sustainable drying is discussed.

Application of machine learning (ML)-based algorithms in food drying is an exciting and innovative approach to advance the drying technology. In order to appropriately develop this novel approach i...

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.

Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for accurately predicting the drying kinetics and understanding the morphological changes during drying. However, the mathematical modelling of drying processes is complex and computationally very expensive due to multiphysics and the multiscale nature of heat and mass transfer during drying. Physics-informed machine learning (PIML)-based modelling has the potential to overcome these drawbacks and could be an exciting new addition to drying research for describing drying processes by embedding fundamental transport laws and constraints in machine learning models. To develop such a novel PIML-based model for drying applications, it is necessary to have a fundamental understanding of heat, mass, and momentum transfer processes and their mathematical formulation of drying processes, in addition to data-driven modelling knowledge. Based on a comprehensive literature review, this paper presents two types of information: fundamental physics-based information about drying processes and data-driven modelling strategies to develop PIML-based models for drying applications. The current status of physics-based models and PIML-based models and their limitations are discussed. A sample PIML-based modelling framework for drying application is presented. Finally, the challenges of addressing simultaneous heat, mass, and momentum transport phenomena in PIML modelling for optimizing the drying process are presented at the end of this paper. It is expected that the information in this manuscript will be beneficial for further advancing the field.

High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We sought to determine the utility of “synthetic data” in training ML algorithms for the detection of tuberculosis (TB) from inflammatory biomarker profiles. A retrospective dataset (A) comprised of 278 patients was used to generate synthetic datasets (B, C, and D) for training models prior to secondary validation on a generalization dataset. ML models trained and validated on the Dataset A (real) demonstrated an accuracy of 90%, a sensitivity of 89% (95% CI, 83–94%), and a specificity of 100% (95% CI, 81–100%). Models trained using the optimal synthetic dataset B showed an accuracy of 91%, a sensitivity of 93% (95% CI, 87–96%), and a specificity of 77% (95% CI, 50–93%). Synthetic datasets C and D displayed diminished performance measures (respective accuracies of 71% and 54%). This pilot study highlights the promise of synthetic data as an expedited means for ML algorithm development.

Food materials, particularly plant-based food materials, are mostly cellular in structure with diverse structural heterogeneities and variable cellular properties. To uncover the fundamental drying process, the cellular-level understanding of simultaneous heat and mass transfer process and associated morphological changes is essential. Consequently, researchers have attempted to investigate microlevel transport and material properties and their spatial and temporal distribution using recent innovative and cutting-edge experimental methods and technologies. The aim of this paper is to review the latest experimental technologies that can be applied to investigate the micro-level features of food material during drying. A comprehensive overview of advanced experimental techniques including X-ray microtomography, nuclear magnetic resonance (NMR), Near Infrared (NIR) Spectroscopy, light microscopy, nanoindentation and atomic force microscopy (AFM) has been presented. Finally, a summary of the current challenges of micro-level investigation for food drying is presented at the end of this paper.

Polyunsaturated fatty acids are metabolized into regulatory lipids important for initiating inflammatory responses in the event of disease or injury and for signaling the resolution of inflammation and return to homeostasis. The epoxides of linoleic acid (leukotoxins) regulate skin barrier function, perivascular and alveolar permeability and have been associated with poor outcomes in burn patients and in sepsis. It was later reported that blocking metabolism of leukotoxins into the vicinal diols ameliorated the deleterious effects of leukotoxins, suggesting that the leukotoxin diols are contributing to the toxicity. During quantitative profiling of fatty acid chemical mediators (eicosanoids) in COVID-19 patients, we found increases in the regioisomeric leukotoxin diols in plasma samples of hospitalized patients suffering from severe pulmonary involvement. In rodents these leukotoxin diols cause dramatic vascular permeability and are associated with acute adult respiratory like symptoms. Thus, pathways involved in the biosynthesis and degradation of these regulatory lipids should be investigated in larger biomarker studies to determine their significance in COVID-19 disease. In addition, incorporating diols in plasma multi-omics of patients could illuminate the COVID-19 pathological signature along with other lipid mediators and blood chemistry.

RationaleThe global public health is in serious crisis due to emergence of SARS-CoV-2 virus. Studies are ongoing to reveal the genomic variants of the virus circulating in various parts of the world. However, data generated from low- and middle-income countries are scarce due to resource limitation. This study was focused to perform whole genome sequencing of 151 SARS-CoV-2 isolates from COVID-19 positive Bangladeshi patients. The goal of this study was to identify the genomic variants among the SARS-CoV-2 virus isolates in Bangladesh, to determine the molecular epidemiology and to develop a relationship between host clinical trait with the virus genomic variants.MethodSuspected patients were tested for COVID-19 using one step commercial qPCR kit for SARS-CoV-2 Virus. Viral RNA was extracted from positive patients, converted to cDNA which was amplified using Ion AmpliSeq™ SARS-CoV-2 Research Panel. Massive parallel sequencing was carried out using Ion AmpliSeq™ Library Kit Plus. Assembly of raw data is done by aligning the reads to a pre-defined reference genome (NC_045512.2) while retaining the unique variations of the input raw data by creating a consensus genome. A random forest-based association analysis was carried out to correlate the viral genomic variants with the clinical traits present in the host.ResultAmong the 151 viral isolates, we observed the 413 unique variants. Among these 8 variants occurred in more than 80 % of cases which include 241C to T, 1163A to T, 3037C to T,14408C to T, 23403A to G, 28881G to A, 28882 G to A, and finally the 28883G to C. Phylogenetic analysis revealed a predominance of variants belonging to GR clade, which have a strong geographical presence in Europe, indicating possible introduction of the SARS-CoV-2 virus into Bangladesh through a European channel. However, other possibilities like a route of entry from China cannot be ruled out as viral isolate belonging to L clade with a close relationship to Wuhan reference genome was also detected. We observed a total of 37 genomic variants to be strongly associated with clinical symptoms such as fever, sore throat, overall symptomatic status, etc. (Fisher’s Exact Test p-valueConclusionTo our knowledge, this study is the first large scale phylogenomic studies of SARS-CoV-2 virus circulating in Bangladesh. The observed epidemiological and genomic features may inform future research platform for disease management, vaccine development and epidemiological study.

Bacterial phosphothreonine lyases, or phospholyases, catalyze a unique post-translational modification that introduces dehydrobutyrine (Dhb) or dehydroalanine (Dha) in place of phosphothreonine or phosphoserine residues, respectively. We report the use of a phospha-Michael reaction to label proteins and peptides modified with Dha or Dhb. We demonstrate that a nucleophilic phosphine probe is able to modify Dhb-containing proteins and peptides that were recalcitrant to reaction with thiol or amine nucleophiles under mild aqueous conditions. Furthermore, we used this reaction to detect multiple Dhb-modified proteins in mammalian cell lysates, including histone H3, a previously unknown target of phospholyases. This method should prove useful for identifying new phospholyase targets, profiling the biomarkers of bacterial infection, and developing enzyme-mediated strategies for bioorthogonal labeling in living cells.

Background A series of (E)-5-(4-((Z)-4-substitutedbenzylidene-2-thienylmethylene-5-oxo- 2-phenyl-4,5-dihydro-1H-imidazol-1-yl) benzylidene)thiazolidine-2,4-diones were synthesized and evaluated for antimycobacterial and antimicrobial activity. All these ligands were docked against protein (InhA) Enoyl-ACP reductase of the type II fatty acid syntase (FAS-II) system, (PDB ID: 4COD). Objective In this report, we have designed and synthesized azole scaffolds with good antitubercular activities as there is a real need to develop new candidates with less toxicity and more efficiency toward pathogen. The obtained antimycobacterial activity data have been validated in the terms of ligand-protein interaction and were also analyzed for ADME properties to determine their potential to build up as good oral drug candidates. Methods All the synthesized compounds have been established by elemental analysis, IR, 1H NMR, 13C NMR and Mass spectral data. In vitro antimycobacterial activity was carried out against (M. tuberculosis) H37Rv strain using Lowenstein-Jensen medium and antimicrobial activity against two gram-positive bacteria (S. aureus, S. pyogenes), two gram-negative bacteria (E. coli, P. aeruginosa) and three fungal species (C. albicans, A. niger, A. clavatus) using the broth microdilution method. In silico molecular docking studies were carried out using Glide (grid-based ligand docking) program incorporated in the Schrodinger molecular modeling package by Maestro 11.0 and ADME properties of synthesized compounds was performed using DruLito software. Results Compounds 3a, 3b, 3d, 3g, 3i and 3n exhibited promising antimicrobial activity whereas compound 3n showed very good antimycobacterial activity along with Gilde docking score (-8.864) and with the one violation in Lipinski's rule of five. Conclusion The wet lab result for compound 3n along with Glide XP docking score and the calculated ADME parameters give the best choice for the preparation of new derivatives in order to improve antitubercular activity in future with more improved potency.

Free to read at publisher's site. Plant‐based food materials are mostly porous in nature and heterogeneous in structure with huge diversity in cellular orientation. Different cellular environments of plant‐based food materials, such as intercellular, intracellular, and cell wall environments, hold different proportions of water with different characteristics. Due to this structural heterogeneity, it is very difficult to understand the drying process and associated morphological changes during drying. Transport processes and morphological changes that take place during drying are mainly governed by the characteristics of and the changes in the cells. Therefore, to predict the actual heat and mass transfer process that occurs in the drying process and associated morphological changes, development of multiscale modeling is crucial. Multiscale modeling is a powerful approach with the ability to incorporate this cellular structural heterogeneity with microscale heat and mass transfer during drying. However, due to the huge complexity involved in developing such a model for plant‐based food materials, the studies regarding this issue are very limited. Therefore, we aim in this article to develop a critical conceptual understanding of multiscale modeling frameworks for heterogeneous food materials through an extensive literature review. We present a critical review on the multiscale model formulation and solution techniques with their spatial and temporal coupling options. Food structure, scale definition, and the current status of multiscale modeling are also presented, along with other key factors that are critical to understanding and developing an accurate multiscale framework. We conclude by presenting the main challenges for developing an accurate multiscale modeling framework for food drying.

The structural heterogeneities of fruits and vegetables intensify the complexity to comprehend the interrelated physicochemical changes that occur during drying. Shrinkage of food materials during drying is a common physical phenomenon which affects the textural quality and taste of the dried product. The shrinkage of food material depends on many factors including material characteristics, microstructure, mechanical properties, and process conditions. Understanding the effect of these influencing factors on deformation of fruits and vegetables during drying is crucial to obtain better-quality product. The majority of the previous studies regarding shrinkage are either experimental or empirical; however, such studies cannot provide a realistic understanding of the physical phenomena behind the material shrinkage. In contrast, theoretical modeling can provide better insights into the shrinkage that accompanies simultaneous heat and mass transfer during drying. However, limited studies have been conducted on the theoretical modeling of shrinkage of fruits and vegetables. Therefore, the main aim of this paper is to critically review the existing theoretical shrinkage models and present a framework for a theoretical model for the shrinkage mechanism. This paper also describes the effect of different drying conditions on material shrinkage. Discussions on how the diverse characteristics of fruits and vegetables affect shrinkage propagation is presented. Moreover, a comprehensive review of formulation techniques of shrinking models and their results are also presented. Finally, the challenges in developing a physics-based shrinkage model are discussed.

Highlights • Mathematical formulation with the fractional viscoelastic model for material characterization. • Development of correlation of fractional order (ψ) and stiffness ( χ ) with moisture content (Mt). • Experiments conducted on food samples for determining strain rate dependence. • Developed an easy and accurate method to characterize hygroscopic food materials during drying. Abstract Mechanical properties, specifically, viscoelastic properties of food materials are the most important characteristics which change significantly while drying is in progress. These changes of the viscoelastic properties have a great impact on the deformation of food material during drying. Understanding of the nature of this changing viscoelastic property is crucial for developing a realistic mathematical model for deformation during food drying. The viscoelastic properties of fresh food samples have been predicted using classical models such as Maxwell model and Burgers model. However, due to the diverse nature of food material, these models cannot predict various viscoelastic properties accurately (relaxation modulus, relaxation time, stiffness, coefficient of viscosity and creep compliance) during drying as conditions are continuously changed. In this context, the fractional viscoelastic model is proposed in this study as a new approach for predicting the accurate viscoelastic behaviour for food materials during drying. Therefore, the main aim of this paper is to develop and validate a viscoelastic model for characterizing viscoelastic properties of food material during drying using fractional viscoelastic model. The proposed model is used to capture the changes of the viscoelastic properties of the food materials during drying. Proposed model is validated by comprehensive experiments as predicted results very closely matched with experimental results. It is found that the fractional order (ψ)(ψ) changes exponentially with the moisture content, whereas the stiffness (χ)(χ) shows third order polynomial change with the moisture content. Interestingly, it is found that the material behaviour is dependent on strain rate while applying the monotonic compression load on the fresh apple tissue at different constant strain rates.

In most drying processes, several physical, chemical and nutritional modifications take place in food products. Innovative drying techniques such as intermittent drying can enhance the quality of dehydrated products effectively and efficiently. Intermittent drying is a technique where drying conditions are changed through varying the drying air temperature, humidity, velocity, pressure, or even mode of heat input. This drying technique has been successfully applied to overcome the problems of conventional drying systems such as longer time consumption, case hardening, lower energy efficiency and poor-quality attributes. However, as the effect of intermittent drying on food quality is not yet well understood, a comprehensive study of quality change during intermittent drying is crucial. The main aim of this paper is to present a thorough review of the potential effect of intermittent drying methods on physical, chemical, nutritional, and stability characteristics of plant-based food material. It is found that application of intermittency using different drying systems has a significant effect on product quality and its stability. In addition, a comprehensive review on existing models of physio/biochemical kinetics for food drying is presented. Finally, the paper is concluded with the discussion of the current challenges and future directions of intermittent drying for producing high-quality dried food products.

Hygroscopic food materials contain free (FW) and bound (BW) water in different cellular environments. In-depth understanding of the mechanisms of moisture migration from different cellular environments during drying is crucial for optimising heat and mass transfer as well as for obtaining better quality dried foods. Therefore, the main aim of the present work is to investigate the transportation mechanisms of FW and BW during drying. Experiments were performed on the potato tissue using 1H-NMR T2 relaxometry to uncover the mechanisms involved in FW and BW transportation. The results have confirmed the view that BW migrates after the rupture of the cell membranes. It is interesting to highlight that the cell membranes rupture at different stages of drying rather than collapsing at one time. The membrane collapse depends predominantly on the penetration rate of heat energy and the pressure gradient between intracellular and intercellular environments. All test results suggest that most of the cell membranes rupture at the middle stage of drying where the moisture content is about 2–4 kg/kg (db.). Furthermore, the moisture distribution profile confirmed that some moisture remained around the centre of the dried sample although the surface of the sample became dry.

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

Fruits and vegetables have a heterogeneous microstructure which dynamically changes during drying. Currently, food drying models approximate the material's structure through utilizing a representative elementary volume which adopts a larger scale of description and considers the fluid phases as fictitious continuums. This assumption limits the ability of the models to capture the heterogeneity of food material. The multiscale modeling is considered to have the ability to overcome this limitation and advance the understanding of the micro level transport processes. This research aims to develop a multiscale homogenization model for food drying. The homogenization was performed on the cellular structure of apple tissue considering intracellular (bound) water and free water separately to calculate the effective diffusivity for convective drying. The simulation was performed at three different drying temperatures (45 °C, 60 °C and 70 °C), and the results were validated using experimental data. The homogenized diffusivity approach described the experimental data well and provides new insight into how to consider the heterogeneous structure of food material utilizing knowledge of the microstructural evolution.

Plant-based food materials are porous and hygroscopic in nature; therefore, it contains three water environments, namely, intercellular, intracellular water and cell wall water. The intercellular water is known as capillary water or free water which is less constrained than intracellular water, considered as loosely bound water (LBW), and cell wall water, which is recognised as strongly bound (SBW). During food processing such as drying, frying, heating and cooking, optimisation of heat and mass transfer is crucial. The existing heat and mass transfer models for food processing are developed based on the concept that all of the water inside the food material is bulk water, which can act as free water that can be easily transported. This simplistic assumption has been made due to a lack of sufficient data to enable consideration of the proportion of free and bound water in plant-based food materials. Therefore, the aim of the present study is to investigate the proportion of different types of water such as free, LBW and SBW in 11 different plant-based food materials. The water proportion was investigated using 1H NMR T2 relaxometry. The experimental results uncovers that plant-based food materials contain about 80 to 92% LBW, 6 to 16% free water and only about 1 to 6% SBW. This investigation also confirms that among the five different fruits, kiwi contains the lowest percentage of LBW while Apple contains the highest percentage of LBW. Among the vegetables, eggplant comprises the largest amount of LBW while cucumber contains least amount of SBW. An attempt was made to establish a relationship between physical properties of fruits and vegetables and the proportion of the different types of water. Interestingly, it was found that SBW strongly depends on the proportion of solid in the sample tissue whereas FW depends on the porosity of the material.

Temperature fluctuation inside the cabinet of a household refrigerator significantly affects the quality of preserved food. Phase change material (PCM) is a latent heat storage system that can store and release the heat energy by changing its phase from liquid to solid and solid to liquid respectively. Therefore, use of PCM inside the refrigerator cabinet has the potential for minimizing the temperature fluctuation during the door opening and the power failure. However, very few studies in the literature were dedicated to investigating the role of PCM to reduce the temperature fluctuation. The aim of this work is to experimentally investigate the effects of PCM on temperature fluctuation inside the cabinet of a household refrigerator during the door opening and power failure. The results found that a significantly lower temperature fluctuation can be obtained using PCM. It was found that during the door opening condition the air temperature in the cabinet rose rapidly. However, when a PCM container was used, temperature variation was reduced to 3–5°C. During the power failure, the system with PCM maintained a lower temperature inside the storage chamber for a long period of time (about 2 hours). Moreover, the test results indicate that PCM maintains more stable temperature in the foodstuffs inside the refrigerator. This reduction of temperature fluctuation ultimately improves the quality of preserved food.

Effective diffusivity is the most important key parameter needed in the analysis, design and optimization of heat and mass transfer during food drying process. Generally, two types of effective diffusivities are used to develop the mathematical modelling of food drying namely, moisture dependent effective diffusivity (MDED) and temperature dependent effective diffusivity (TDED). However, no study has extensively investigated which effective diffusivity is more accurate in predicting drying kinetics. The main goal of this study is to determine the appropriate effective diffusivity for predicting the drying kinetics. Drying models were developed for different fruits and vegetables based on moisture dependent and temperature dependent effective diffusivities. COMSOL Multiphysics, a finite element based engineering simulation software is used to solve the coupled heat and mass transfer equations. 3D moisture profiles were developed to investigate the spatial moisture distribution during drying. Extensive experimental investigation on five types of fruit and vegetables were conducted and results were compared with the simulated results. The experiments were repeated three times, and the average of the moisture content at each value was used for constructing the drying curves. Close agreement between experimental and simulated results validates the models developed. It was observed that the moisture profile and temperature profile in case of MDED were more closely fitted with the experimental results. For all fruits and vegetables, the moisture ratio with MDED was significantly lower than moisture ratio with TDED. This finding confirms that the MDED is more accurate for predicting kinetics in food drying. Moreover, the moisture ratio of apple was lowest whereas pear showed the highest moisture ratio. On the hand, carrot showed a considerably lower moisture ratio compared to potato.

The microstructure of plant-based food materials (PBFM) is mostly composed of assemblies of irregular cellular compartments. Dynamically changing mechanical properties play a vital role in the drying kinetics, deformation, and the quality attributes of PBFM. Therefore, understanding the micro-level mechanical properties and their relationships with the moisture loss during food drying is essential. However, research on this filed has rarely been attempted. This research aims to investigate the micro-level mechanical properties of PBFM during drying and establish a set of relationships between the elastic modulus, hardness, and stiffness and the moisture content. Nanoindentation experiments were performed using Hysitron TI-950 TriboIndenter at different stages of drying. It was found that the mechanical properties remained quite steady at early stages of drying, which were significantly increased at the later stages of drying. This study established the essential relationships among different micro-mechanical properties of PBFM and moisture content during drying.

Drying is a complex simultaneous heat and mass transfer process where water is migrated from the sample to the drying air through continuous evaporation. The rate of evaporation can be accelerated by the intermittent application of microwave energy with convective drying, which is called intermittent microwave convective drying (IMCD). However, theoretical aspects of IMCD process is not clearly understood yet, which is required to obtain a better -quality product and for designing energy efficient IMCD drying systems. Therefore, several researchers have attempted to develop a theoretical modelling framework for IMCD. However, most of the existing models do not consider the spatial distribution of air velocity around the product, which is important as it significantly affects the heat and mass transfer coefficient throughout the sample and eventually alters the drying kinetics. This paper aims to address this research gap by developing an IMCD modelling framework through integrating a simultaneous heat and mass transfer model with a computational Fluid Dynamics (CFD) model (drying air flow) under the condition of volumetric heating. A 3D model was developed, and the simulated results were validated with the experimental results. It was found that the integration of fluid flow (CFD) with an IMCD model significantly affected the drying kinetics as heat and mass transfer coefficients spatially varied throughout the sample. Drying models may thus overpredict the drying kinetics like moisture content if the spatial distribution of air velocity is not taken into account. It was also found that the nonuniform microwave power absorption caused an inhomogeneous distribution of the sample temperature. The new findings and knowledge from this study will lead to the development of more accurate drying models.

Bio-food materials are heterogeneous in structure with cellular diversity, where the majority of the water is located in the intracellular spaces. Understanding of the nature of the microscopic behaviour of water transport is crucial to enhance the energy efficiency in food processing and obtain the better quality of processed food. In this research, apoplastic and symplastic transport of cellular water in the bio-food material during drying was investigated using 1H-NMR-T2 relaxometry. We found that intracellular water (ICW) migrates from intracellular spaces to the intercellular spaces by progressive rupturing the cell membranes while drying at a higher temperatures (60 °C–70 °C). In this case, apoplastic process dominates the transport process. However, at lower temperature (45 °C), cell membranes do not rupture and therefore ICW migrates from cell to the neighbouring cell through micro-capillaries, where the symplastic process dominates the mass transfer at different stages of drying.

[EN] This study aims to investigate the complex microstructural changes in plant-based food materials during drying by using X-ray micro-computed tomography (X-ray µCT) along with the image analysis. The apple samples were dried at 60 °C and tested using X-ray µCT at different stages of drying. The porosity, cell and pore size distribution were determined from the micro-CT data set. It was observed that significant changes in porosity, cell and pore size distribution take place at different drying times and moisture contents. X-ray µCT can serve as a very promising tool to elucidate the evolution of the food microstructure during the drying process., This research is supported by Queensland University of Technology Postgraduate Award (QUTPRA) and Advanced Queensland Fellowship (AQF). The authors acknowledge the facilities and the technical assistance of the Institute of Health and Biomedical Innovation (IHBI). The authors also gratefully acknowledge the support of Dr. Marie-Luise Wille.