Chandan Kumar

Bio: Chandan Kumar is an academic researcher from Queensland Government. The author has contributed to research in topics: Mass transfer & Shrinkage. The author has an hindex of 25, co-authored 66 publications receiving 1806 citations. Previous affiliations of Chandan Kumar include Queensland University of Technology & Michigan Technological University.

Intermittent drying of food products : a critical review

Abstract: Drying is very energy intensive process and consumes about 20-25% of the energy used by food processing industry. The energy efficiency of the process and quality of dried product are two key factors in food drying. Global energy crisis and demand for quality dried food further challenge researchers to explore innovative techniques in food drying to address these issues. Intermittent drying is considered one of the promising solutions for improving energy efficiency and product quality without increasing the capital cost of the drier. Intermittent drying has already received much attention. However, a comprehensive review of recent progresses and overall assessment of energy efficiency and product quality in intermittent drying is lacking. The objective of this article is to discuss, analyze and evaluate the recent advances in intermittent drying research with energy efficiency and product quality as standpoint. Current available modelling techniques for intermittent drying are reviewed and their merits and demerits are analyzed. Moreover, intermittent application of ultrasound, infrared (IR) and microwave in combined drying technology have been reviewed and discussed. In this review article the gaps in the current literature are highlighted, some important future scopes for theoretical and experimental studies are identified and the direction of further research is suggested.

Microwave-convective drying of food materials: A critical review

Abstract: Microwave convective drying (MCD) is gaining increasing interest due to its unique volumetric heating capability and ability to significantly reduce drying time and improve food quality. The main objective of this paper is to discuss, critically analyze and evaluate the recent advances in MCD and suggest the future directions in this field. The main focus of this paper is the mathematical modeling and experimental investigations in microwave convective drying of food materials. Recent developments in mathematical modeling of MCD is discussed and existing experimental setup and their advantages and disadvantages are discussed and analysed. Long drying time is a concern in food industries. Reductions in drying time by applying MCD compared to convection drying are calculated and discussed. It was apparent that the proper integration of mathematical modeling and experimental technique is the best way to maximize the advantages of this drying method. Although a plethora of research is being carried out on this topic, there is still need for research to develop fundamental modeling to optimize the process parameters and scale up this technology for the industrial application. Overall, the review provides an in-depth insight into the latest development of MCD and its mathematical modeling approaches and will hopefully serve to inspire future work in the field.

Shrinkage of Food Materials During Drying: Current Status and Challenges.

Abstract: 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.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Modeling the Thin‐Layer Drying of Fruits and Vegetables: A Review

Abstract: The drying of fruits and vegetables is a complex operation that demands much energy and time. In practice, the drying of fruits and vegetables increases product shelf-life and reduces the bulk and weight of the product, thus simplifying transport. Occasionally, drying may lead to a great decrease in the volume of the product, leading to a decrease in storage space requirements. Studies have shown that dependence purely on experimental drying practices, without mathematical considerations of the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product. Thus, the use of mathematical models in estimating the drying kinetics, the behavior, and the energy needed in the drying of agricultural and food products becomes indispensable. This paper presents a comprehensive review of modeling thin-layer drying of fruits and vegetables with particular focus on thin-layer theories, models, and applications since the year 2005. The thin-layer drying behavior of fruits and vegetables is also highlighted. The most frequently used of the newly developed mathematical models for thin-layer drying of fruits and vegetables in the last 10 years are shown. Subsequently, the equations and various conditions used in the estimation of the effective moisture diffusivity, shrinkage effects, and minimum energy requirement are displayed. The authors hope that this review will be of use for future research in terms of modeling, analysis, design, and the optimization of the drying process of fruits and vegetables.