Food Engineering Reviews最新文献

Grain drying is a vital operation in preparing finished grain products such as flour, drinks, confectioneries and infant food. The grain drying kinetics is governed by the heat and mass transfer process between the grain and the environment. Incomplete, improper and over-drying are crucial to the grain quality and negatively influence the acceptance of the grain by the consumers. Dried grain moisture content is a critical factor for developing grain drying systems and selecting optimal performance by researchers and the grain processing industry. Many grain drying technologies such as fluidised bed dryers, fixed bed dryers, infrared dryers, microwave dryers, vacuum dryers and freeze dryers have been used in recent years. To improve the drying process of grain, researchers have combined some drying technologies such as microwave + hot air, infrared + hot air and microwave + a fluidised bed dryer. Also, they introduce some treatments such as ultrasound dielectric and dehumidification. These methods enhance the dryer performance, such as higher moisture removal, reduced processing time, higher energy efficiency and nutrient retention. Therefore, this review focused on the drying conditions, time, energy consumption, nutrient retention and cost associated with the reduction of moisture content in grain to a suitable safe level for further processing and storage.

Currently, statistical experimental design is employed as a quality control method to produce excellent products at the lowest possible cost in food processing, speeding up the process of developing new foods, cutting down costs associated with research, easing the process of moving food products from the research and development stage to the manufacturing stage, and troubleshooting manufacturing issues. In the extraction of bioactive compounds from food, the optimization procedure becomes more straightforward as the optimum conditions of the numerous evaluated responses converge to a similar section. However, the problem becomes more complicated when the optimum values for each response go further apart, making it harder to discover settings that satisfy all of them simultaneously. Using a mathematical function, multiple responses can be integrated into a single one in another manner. The generated response surface enables the calculation of optimum values for extraction parameter, which fulfills all responses whenever possible. Hence, this article reviewed multiple responses, desirability function, and graphical optimization solutions employed to extract food bioactive compounds in the last decade. These techniques’ benefits, drawbacks, potentials, and applications were discussed, including some of their uses in the extraction of bioactive compounds. This will help to address the actual mathematical and statistical issues that arise during the multiple response extraction of food bioactive compounds.

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Solar dryers have always been criticized for their lower performances. There are numerous ways to define the performance of a solar drying system such as thermal performance, drying kinetics, environmental aspects, economic evaluations, and quality of the dried product. Different modeling techniques have also been developed to design and analyze solar dryers and drying processes. This article presents a systematic, comprehensive, and state-of-the-art overview of various performance indicators and modeling techniques used for the evaluation and analysis of solar dryers, especially domestic and low cost solar dryers. Environmental analysis has severe global implications, and product quality is one of the biggest concerns of consumers. But the environmental impact and product quality assessments for domestic solar dryers are observed to be rarely reported in the literature. The use of modeling techniques in solar drying has changed the way of analyzing any thermal system. Here, an attempt is made to establish an overall assessment criterion for domestic solar dryers and to give a one-stop solution for researchers and users around the world.

Abstract

Dairy products, such as liquid, powdered, and fermented milk, provide an excellent source of nutrition and constitute part of a well-balanced diet. However, measures to prevent the deterioration of the quality of dairy products during thermal treatment and storage are lacking at present. Deterioration of milk quality is primarily related to reactions of oxidation, hydrolysis, and Maillard reaction. While multiple studies have focused on the factors influencing the deterioration of dairy products during processing and storage, systematic analysis of the underlying mechanisms is essential to improve our overall understanding of the process. This report presents a structured overview of the mechanisms and markers associated with deterioration reactions in marketed dairy products. The overall impacts of deterioration reactions on the quality and flavor of dairy products are additionally reviewed. Furthermore, potential markers of deterioration reactions and future development directions of the dairy industry are discussed, with a view to providing a reference for the quality and safety guarantee of dairy products.

Graphical Abstract

Quality evaluation of seafood is essential for consumer satisfaction. Hyperspectral imaging (HSI) has been introduced in the seafood industry for assessing seafood quality, safety, authenticity, and adulteration whilst maintaining sample integrity. However, there is limited information on multivariate analyses applied using the HSI for seafood quality. This review presents a comprehensive summary of the existing published research to describe the application of HSI coupled with multivariate analyses of seafood products. Applications of multivariate analyses for map distribution, spectral selection, and data extraction of the HSI system in the seafood industry are highlighted. Trends and challenges using HSI in the seafood industry are also discussed in this review. As a rapid and non-destructive tool, HSI technology shows great potential for evaluating the quality of seafood products by on-line or at-line detection. The ability to provide spatial and spectral information coupled with multivariate analyses makes the HSI system broadly in the seafood industry. Deep learning performed by artificial intelligence is a great solution recently for data classification of hyperspectral imaging with a shift-invariant feature of seafood products. HSI systems fitted with multivariate analyses software could be eased in the large-scale seafood industry to determine the chemical, biological, and physical quality traits of seafood products.

Low-moisture foods such as spices, grains, and seeds constitute an important part of the human diet. Increased consumer concern for food safety and food quality has focused on the decontamination technologies required for low-moisture foods. Cold plasma treatment has been a promising novel technology in the food processing industry due to its advantages in safety, efficiency, versatility, and environmentally friendly nature. It has shown various capabilities on safety and quality control in low-moisture foods. This paper comprehensively reviewed the application of cold plasma in low-moisture foods, including inactivation of microorganisms, degradation of mycotoxins, influences on the quality of low-moisture foods, and promotion of seed germination. Cold plasma can inactivate the pathogenic microorganisms on the surface of low-moisture foods, by generating active species, ultraviolet radiation, and electric fields, which helps to extend the shelf life of foods while having minimal impact on food quality. Cold plasma technology is also an effective approach to detoxify mycotoxin-contaminated low-moisture foods by degrading various mycotoxins. With the manipulation of parameters for cold plasma generation, target functional properties of food products may be obtained. In addition, the application of cold plasma in seed germination is promising and could be of great significance to the global food crisis. This review also suggests that more systematic studies are needed to employ cold plasma in the low-moisture foods industry for selected applications.

Fruits and vegetables are essential for overall human health and nutrition, and there is a high quest for fruits and vegetables of superior quality. Nonetheless, the preservation of fruit is still challenging due to the high moisture content. Pretreatments have assisted fruit and vegetable drying in improving shelf-life and maintaining quality. However, conventional pretreatments affect the physicochemical properties and product qualities. Therefore, thermal and nonthermal pretreatments followed by drying have been researched to improve and enhance fruits and vegetables’ physicochemical properties. This article evaluates sequential thermal (ohmic, electrohydrodynamic, infrared, etc.) or  nonthermal (high-pressure processing, ultrasonic, pulsed-electric field, etc.) pretreatment technologies and drying in the last 5 years, underscoring their efficiency in augmenting the product qualities of fruits and vegetables. In addition, details of nonthermal and thermal pretreatment technologies are explained, and their success stories, drawbacks, and future studies on improving these technologies are provided. Besides, the safety evaluations of various pretreatments are also delved in. Finally, it is recommended that the next 5 years of research should also explore pulsed light, manothermosonication, ultraviolet light, oscillating magnetic field, thermosonication, and ionization radiation pretreatment to augment dried fruits and vegetables’ product qualities and physicochemical properties. This will help better comprehend the impact of emerging technologies on fruits and vegetables’ physicochemical properties.

There are several published empirical mathematical models to describe the water content vs. time relationships in dry or dried-food spontaneous hydration, or in deliberate rehydration, similar to or the same as those originally proposed for water vapor sorption kinetics. Most of these models come in one of two forms: for non-sigmoid and for sigmoid relationships. Some, notably the stretched exponential (“Weibullian”) with an adjusted shape parameter, can describe both. All the empirical hydration models are rarely, if ever, unique, and most of them can be used interchangeably for a given set of experimental data. It is proposed to add an expanded version of a particular popular hydration model of non-sigmoid curves so that it, too, can describe both kinds of hydration patterns. Either model would facilitate the mathematical description of systems or processes where a non-sigmoid hydration pattern turns into a sigmoid one, or vice versa. In principle, variants of these two model types can be used to describe water loss or drying curves, at least qualitatively.

During microbial inactivation by a lethal agent (ignoring damage/injury, recovery from damage/injury, etc.), an individual spore or cell can be either viable/alive and countable, or already inactivated/dead and uncountable. But since we only count the survivors’ total number at successive times, the fates of the individual microbes remain unknown. This makes the process probabilistic and creates the need for a stochastic model to describe it. The familiar continuous deterministic models such as the loglinear or Weibullian, which are only applicable to large populations, can be viewed as the mathematical limits of underlying discrete stochastic models. When the targeted microbial population is initially small, its survival curve is inherently irregular and irreproducible. But when the targeted population is large, its survival curve is initially smooth and reproducible but inevitably becomes irregular and irreproducible as the number of survivors diminishes. Perhaps the most important difference between the deterministic loglinear model, or the Weibullian with a shape factor > 1, and their fully stochastic versions is that the latter predict complete elimination of the targeted microbe in a realistic finite time. A stochastic survival model is derived from the individual microbes’ Markov chains (or trees) and the character of the underlying survival probability rate’s time-dependence. Various types of such dependencies are presented, and their different manifestations in the stochastic survival curves shapes are demonstrated. Also discussed are ways to extract (estimate) the stochastic model’s parameters from the regular and reproducible part of static experimental survival data, which in some cases requires unconventional regression techniques.

Several investigations have been made at lab scale considering the quality by design (QbD) and process analytical technology (PAT) approaches. Nonetheless, such applications have been focused on the analyzers or multivariate tools used at small scales. This comprehensive review presents the state of the art of both QbD and PAT. In addition, the key historical events since 1940 which have influenced the development of the QbD/PAT system are also highlighted. Moreover, the application of the recommended PAT tool of multivariate tools for design, data acquisition, and analysis (design of experiments, multivariate data analysis, and multivariate process control) is revised for the food and food-related biotechnology industries and describes the applications reported over the last 20 years. On this subject, only 34 studies were found in literature whose relation was close with both industries at industrial or pilot plant scales; a description of each of them focusing on multivariate tools is presented. Finally, some conclusions and future perspectives on this topic are given, with the aim of initiating a change in the field.