Food Engineering Reviews最新文献

The quality of cocoa depends on both the origin of the cacao and the processing stages. The roasting process is critical because it develops the aroma and flavor, changing the beans’ chemical composition significantly by chemical reactions induced by thermal energy. Aspects have been identified as the main differences between bulk cocoa and fine cocoa, the effect of time and temperature on the formation of the flavor and aroma, and the differences between conductive heating in an oven, convective with airflow, and steam flow. Thermal energy initially causes drying, then non-enzymatic browning chemical reactions (Maillard reaction, Strecker degradation, oxidation of lipids, and polyphenols), which produce volatile and non-volatile chemical compounds related to the flavor and aroma of cocoa roasted. This review identified that the effect of the heating rate on the physicochemical conversion of cocoa is still unknown, and the process has not been evaluated in inert atmospheres, which could drastically influence the avoidance of oxidation reactions. The effect of particle size on the performance of product quality is still unknown. A more in-depth explanation of energy, mass, and chemical kinetic transfer phenomena in roasting is needed to allow a deep understanding of the effect of process parameters. In order to achieve the above challenges, experimentation and modeling under kinetic control (small-scale) are proposed to allow the evaluation of the effects of the process parameters and the development of new roasting technologies in favor of product quality. Therefore, this work seeks to encourage scientists to work under a non-traditional scheme and generate new knowledge.

In pressure-assisted thermal sterilization process (PATS), most of the microbial inactivation occurs under a combination of high temperature and pressure conditions for which meaningful experimental isothermal/isobaric (static) survival data are rarely if ever available. Therefore, a kinetic survival model for the targeted microbe, the magnitudes of its parameters, and the pressure’s direct contribution to the process lethality, i.e., besides the processing temperature elevation, must be determined mathematically from experimental survival ratios obtained after the food cooling at the end of dynamic treatments. At least in principle, this can be achieved with the endpoints method, explained and demonstrated with the aid of simulated realistic dynamic temperature and pressure profiles. The pressure’s net (unmediated) contribution to the process lethality can be expressed as equivalent time at the processing temperature, or as the added number of decades’ reduction to the treatment’s final survival ratio had it been reached in a purely thermal process having the same temperature profile. The pressure’s role is also manifested in the coefficients of a special pressure dependency term incorporated into the dynamic inactivation kinetics model. This term indicates whether the process lethality rises monotonically with temperature and pressure or there exists an optimal combination of the two. The expanded rate model can be used to simulate and examine the efficacy of existing or contemplated PATS processes by varying the temperature and pressure profiles, and/or by modifying the targeted microbe’s survival parameters.

Process engineering of food materials is strongly associated with their microstructure, which quantification allows proper selection of equipment operating conditions, monitoring sensorial attributes and consumer’s acceptance. This evaluation has been carried out by applying the fractal approach using microscopy images that depict complex, ragged and irregular forms. Therefore, this review aimed to summarise the fundamentals, calculation methods and applications of fractal dimension (F_D), lacunarity ((Lambda)) and multifractals for describing the microstructure of selected food systems including the calculation methods derived from the digital image analysis and recent investigations involving the fractal analysis in vegetal materials, animal food products, doughs, gels, starch, food-related micro- and nanoparticles, powders and fats. It was noted that several microscopy techniques were used broadly, and their selection depended on the sample type and specific region of interest. Regarding the calculation of fractal parameters, the box-counting method performed on images of the surface was prevalent in most of the revised pieces of research, finding F_D values from 1.60 (for binary images) to 2.99 (for grayscale images). Also, several relationships were found between F_D and temperature, composition and textural parameters. It was noted, however, that a specific trend was not detected given that variations in acquisition procedures and observation scale prevailed among the reported works. Besides, it was noteworthy that (Lambda) and multifractals were unexploited, notwithstanding that these fractal properties can aid to achieve a thorough examination of food microstructure. Based on the inspection of fractals in the imaged microstructure, the present review is helpful to improve the management and control of food engineering processes based on food microstructure for obtaining higher quality products.

Fruits and vegetables are very important agricultural products in daily life. Evaluating the quality attributes of fresh fruits and vegetables by nondestructive sensing techniques has been an intensive research topic over the past two decades. The research progress on the detection of internal and external quality attributes of fresh fruits and vegetables using various nondestructive spectroscopic and imaging techniques, including visible/near-infrared spectroscopy, time-resolved and space-resolved spectroscopy, machine vision, hyperspectral and multispectral imaging, fluorescence techniques, X-ray imaging, computed tomography scanning, magnetic resonance imaging, and Raman techniques, is presented and discussed in this review. Each kind of fruit or vegetable shows great variability in physical characteristics (including size, shape, color, and temperature) and biological characteristics (including cultivar, season, maturity level, and geographical origin). This physical and biological variability complicates the quality evaluation of fresh fruits and vegetables. To eliminate the influence of variability and improve the inspection accuracy, a lot of attempts, including pre-processing, light intensity transformation, global model, band math, model transfer, etc., have been made in image correction and spectral compensation methods. This review provides a detailed summary of the various methods for solving the problem of physical and biological variability, as well as their advantages and disadvantages. Additionally, the current problems to be solved in spectroscopic and imaging technologies and the research trends of nondestructive measurement of the quality of fresh fruits and vegetables in the future are also revealed.

Hyperbaric storage (HS) is a developing food preservation technology based on the application of moderate hydrostatic pressure. Having a quasi-zero energetic cost, this technology has been proposed as sustainable alternative to refrigeration. However, despite HS was conceived in 1972, it has not attracted interest of researchers and industries until few years ago. Hence, literature, technical knowledge, and working unit design are still lacking. The purpose of the present review is to provide an overview on hyperbaric storage, highlighting its potentialities as a sustainable food storage technology. Moreover, process constraints and unexplored applications of HS conditions are envisaged. Finally, critical aspects that still need to be investigated are highlighted to provide the foundations for future research. The review of the literature indicates that HS is a promising technology, which could extend food microbiological stability and boost the metabolism of microorganisms involved in biotechnological processes, such as fermentations. HS also affects food matrix biomolecules, with particular reference to protein structures and activity, and lipid physical properties. In the investigated matrices (i.e. plant derivatives, meat, fish, and dairy products), HS produced minor sensory changes. On the other hand, lipid oxidation was significantly increased. Proteins and fat structure modification might be used to tailor food ingredient functionality, opening the way for pioneering HS applications. Nevertheless, still several issues, such as poor technical knowledge, scarcity of investigated food matrices, and lack of appropriate packaging solutions, need to be overcome to make HS industrially viable.

Impregnation of fruits and vegetables is an effective approach to enrich their porous texture with functional solutions. High-pressure impregnation (HPI) is a newly developing impregnation technique based on imposing a high hydrostatic pressure on porous media soaked in a liquid phase. HPI can provide a high mass intake within a considerably short time. This paper reviews the current development of HPI in terms of its applications in food processing. The current paper also emphasizes fundamental approaches that have been developed to characterize and model the mass transfer during HPI. Moreover, a systematic review covering the general background and theoretical basis of pressure-driven impregnation into porous media is provided, which is necessary for future research developments in this field. The HPI process has a considerably higher mass transfer yield than vacuum impregnation and osmotic dehydration. However, due to the existing challenges in monitoring the process parameters such as internal pressure profile and mass transfer, specific approaches have been developed and applied to model and characterize the process. Thus, the ability to model the process highly depends on obtaining enough knowledge about the physics of the flow into the porous matrix under high-pressure and fluid/solid interactions. It is expected that by development in understanding the process and modelling it, HPI will be a highly reliable, controllable, and efficient process to (pre)treat porous foods such as fruits and vegetables for various applications.

The first-order kinetics for microbial inactivation was derived more than 100 years ago and is still used, although more and more researchers are aware of very common non-linear survival curves. The Weibull model is just a simple alternative to the linear model and can be used to describe convex, concave, and linear survival curves. The objective of this review is not to criticize the first-order kinetics or to praise the Weibull model. In this review, the Peleg and Mafart versions of the Weibull model were compared with emphasis on the parameters of those model expressions, the scaled sensitivity coefficients of the model parameters, and the dependency of the model parameters to each other. Secondary modeling was also considered. It was concluded that both Peleg and Mafart versions can be safely used to describe linear and non-linear (convex or concave) survival curves under constant conditions. However, concerning the secondary model, predictions under dynamic conditions, and availability of an Excel®-based workbook for this purpose, the Peleg model seemed to be one step ahead. One of the parameters of the Weibull model and the secondary model derived from this parameter could be used as an alternative to the conventional D value. This work confirms that the Weibull model can serve as the microbial survival model instead of the now obsolete linear model whatever the lethal treatment is.

Berries are delicious and nutritious, making them among the popular fruits. There are various types of berries, the most common ones include blueberries, strawberries, raspberries, blackberries, grapes, and currants. Fresh berries combine high nutritional value and perishability. The processing of berries ensures high quality and enhanced marketability of the product. Sorting, disinfection, and decontamination are essential processes that many types of fruits such as citrus fruits, berries, pomes, and drupes must undergo to ensure improved quality, uniformity, and microbiological safety of the product. Drying and freezing are excellent processing methods to extend the shelf life of berries which also provide new options to the consumer of a wide variety of berries. With the demand for high quality and automatic high-throughput detection of the quality of fruit products, intelligent and rapid detection of various parameters during processing has become the development direction of modern food processing. Therefore, this paper reviews the application of advanced detection technologies, artificial intelligence-based methods for detection and prediction during berry sorting, drying, disinfecting, sterilizing, and freezing processing. These advanced detection techniques include computer vision system, near infrared, hyperspectral imaging, thermal imaging, low-field nuclear magnetic resonance, magnetic resonance imaging, electronic nose, and X-ray computed tomography. These artificial intelligence methods include mathematical modeling, chemometrics, machine learning, deep learning, and artificial neural networks. In general, advanced detection techniques incorporating artificial intelligence have not yet penetrated into all aspects of commercial berry processing, which include drying, disinfecting, sterilizing, and freezing processes.

Based on the design of printing models, three-dimensional (3D) and four-dimensional (4D) printing technologies have shown extensive and promising application potential in the food industry. The majority of previous researches on printing models focus on single or multiple models to test the performance of printers and inks, assess the influence of printing parameters on product performance, and print new products. This review compares the differences between the recently proposed 3D/4D printing models and summarizes the key factors needing to be considered in model design. The solid models are mainly used to explore printing parameters, while the filling models are used to study the texture characteristics of food. Models with changing shapes or colors reflect the importance of model structural design. The reasons for distortion in the process of transition from digital models to food models are analyzed, and the corresponding solutions are proposed. In the future, it is necessary to expand model database and develop cloud platform services so as to facilitate the sharing of related resources and strengthen the personalized nutrition of different consumer groups.