Predicting the Quality of Pasteurized Vegetables Using Kinetic Models: A Review

A resurgence in interest examining thermal pasteurization technologies has been driven by demands for “cleaner” labeling and the need of organic and natural foods markets for suitable preventive measures to impede microbial growth and extend shelf life of minimally processed foods and ready-to-eat foods with a concomitant reduction in the use of chemical preservatives. This review describes the effects of thermal pasteurization on vegetable quality attributes including altering flavor and texture to improve consumer acceptability, stabilizing color, improving digestibility, palatability and retaining bioavailability of important nutrients, and bioactive compounds. Here, we provide kinetic parameters for inactivation of viral and bacterial pathogens and their surrogates and marker enzymes used to monitor process effectiveness in a variety of plant food items. Data on thermal processing protocols leading to higher retention and bioactivity are also presented. Thermal inactivation of foodborne viruses and pathogenic bacteria, specifically at lower pasteurization temperatures or via new technologies such as dielectric heating, can lead to greater retention of “fresh-like” properties

Storage stability of vitamin C fortified purple mashed potatoes processed with microwave-assisted thermal sterilization system

Quality changes in ready-to-eat, shelf-stable foods, during storage can be influenced by many factors, such as processing, storage conditions, and the barrier properties of the packaging. This research investigated retention of vitamin C and anthocyanin in purple mashed potatoes as influenced by packaging barrier properties and encapsulation during storage after microwave assisted thermal sterilization. Purple mashed potatoes fortified with encapsulated (EVC) or non-encapsulated vitamin C (NVC) were packaged in two high-barrier polymer pouches (TLMO and PAA), processed with a pilot-scale microwave assisted thermal sterilization (MATS) system (F0 = 10.7 min), and stored at 37.8 °C for 7 months. MATS processing caused a significant increase (P < 0.05) in the oxygen transmission rates (OTRs) of PAA pouches but did not affect the barrier properties of TLMO pouches. PAA film also had a significantly higher (P < 0.05) water vapor transmission rate (WVTRs) than TLMO films, which resulted in a significantly higher (P < 0.05) weight loss in the samples packaged in PAA pouches than TLMO pouches. Purple mashed potatoes containing encapsulated vitamin C in both TLMO and PAA pouches showed the highest retention over 2 months of storage at 37.8 °C than non-encapsulated vitamin C. Additionally, purple mashed potatoes exposed to 700 lumens light showed a significantly higher (P < 0.05) deterioration in the anthocyanin, total phenolic content, color, and vitamin C. Overall, MATS processed purple mashed potatoes in high barrier polymeric packaging can minimize the quality changes when stored in dark conditions during storage and have an extended shelf life

Predicting the Quality of Pasteurized Vegetables Using Kinetic Models: A Review

A resurgence in interest examining thermal pasteurization technologies has been driven by demands for “cleaner” labeling and the need of organic and natural foods markets for suitable preventive measures to impede microbial growth and extend shelf life of minimally processed foods and ready-to-eat foods with a concomitant reduction in the use of chemical preservatives. This review describes the effects of thermal pasteurization on vegetable quality attributes including altering flavor and texture to improve consumer acceptability, stabilizing color, improving digestibility, palatability and retaining bioavailability of important nutrients, and bioactive compounds. Here, we provide kinetic parameters for inactivation of viral and bacterial pathogens and their surrogates and marker enzymes used to monitor process effectiveness in a variety of plant food items. Data on thermal processing protocols leading to higher retention and bioactivity are also presented. Thermal inactivation of foodborne viruses and pathogenic bacteria, specifically at lower pasteurization temperatures or via new technologies such as dielectric heating, can lead to greater retention of “fresh-like” properties

A new method of producing date powder granules: Physicochemical characteristics of powder

A mixing technique was developed to produce free flowing powder granules from date. This method involved preparation of the paste from raw date, mixing with maltodextrin powder followed by an oven drying. In order to determine an optimum proportion of date paste and maltodextrin (DE 6) to produce stable granules, mixing was carried out at various levels of maltodextrin (MD) ranging between 0.54 kg and 1.0 kg per kg of date paste (dry weight basis). The date paste dried with 1.0 kg of maltodextrin/1.0 kg of date paste produced non sticky and free flowing powder. Several physicochemical parameters such as water activity, bulk density, color, hygroscopicity and glass transition temperatures of date powders were measured. The caking of date powders during storage at room temperature was explained using the concept of glass transition temperature

A Novel Machine Learning–Based Approach for Characterising the Micromechanical Properties of Food Material During Drying

Plant-based food materials (PBFMs) such as fruits and vegetables contain various irregular cellular compartments. Like other engineering materials, the characterisation of micromechanical properties (MMPs) of PBFMs is intensely important for accurately estimating the functionality of dried food products. The application of a machine learning (ML)–based approach to characterise the MMPs is a promising idea. However, no intensive research in this regard has been attempted yet. Therefore, we proposed an ML-based modelling framework to characterise the MMPs of PBFMs during drying. A feed-forward artificial neural network (ANN) model with a backpropagation algorithm was developed and optimised with a genetic algorithm (GA)–based optimisation tool for characterising PBFMs, specifically carrots. Moreover, the accuracy of the ANN model was compared with a multiple nonlinear regression (MNLR) model. It was found that the developed network model agreed very well with the experimental data when predicting the elastic modulus, stiffness and hardness, with an accuracy of the goodness of fit (R2) values of 0.992, 0.993 and 0.802, respectively. It is expected that the developed model has incredible potential to characterise the MMPs of similar food products.</p

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

Polyesters Incorporating Gallic Acid as Oxygen Scavenger in Biodegradable Packaging

Biodegradable polyesters polybutylene succinate (PBS) and polybutylene adipate-co-terephthalate (PBAT) were blended with gallic acid (GA) via cast extrusion to produce oxygen scavenging polymers. The effects of polyesters and GA contents (5 to 15%) on polymer/package properties were investigated. Increasing GA formed non-homogeneous microstructures and surface roughness due to immiscibility. GA had favorable interaction with PBAT than PBS, giving more homogeneous microstructures, reduced mechanical relaxation temperature, and modified X-ray diffraction and crystalline morphology of PBAT polymers. Non-homogenous dispersion of GA reduced mechanical properties and increased water vapor and oxygen permeability by two and seven folds, respectively. Increasing amounts of GA and higher humidity enhanced oxygen absorption capacity, which also depended on the dispersion characteristics of GA in the matrices. PBAT gave higher oxygen absorption than PBS due to better dispersion and higher reactive surface area. GA blended with PBAT and PBS increased oxygen scavenging activity as sustainable active food packaging using functional biodegradable polymers

Application of machine learning-based approach in food drying:opportunities and challenges

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 in all aspects of food drying field, significant scientific research is required. The main aspects of food drying research are the determination of material properties, microstructural characterization, mathematical modeling, and process optimization. It is essential to express this fundamental information through ML-based algorithms to advance the food drying research. This paper aims to present a comprehensive review of the application of machine learning-based approaches in food drying modeling, property prediction, microstructural characterization, and process parameters optimization. Moreover, this paper discusses the possibilities and challenges to apply ML-based algorithms in multiscale modeling and microwave-based hybrid drying. It is expected that this review paper will be beneficial in advancing the machine learning-based food drying technology

Optimal molar gas composition of selected gas mixtures with helium that maximize turbulent free convection along vertical plates

With the goal of intensifying turbulent free convection from heate