Simplified Approach to Predict Food Safety through the Maximum Specific Bacterial Growth Rate as Function of Extrinsic and Intrinsic Parameters

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

A Chromatic Enzymatic Time-Temperature Integrator Device Based on the Degradation of Phenolic Compounds for the Real-Time Prediction of the Quality and Shelf Life of Cherries

The particular characteristics of cherries, such as color, firmness, and palate increase their demand, as does, among other things, their antioxidant properties that benefit human health. However, their high perishability leads to a reduced shelf life and consequently generates undesirable changes in the cherry flow chain. To ensure food quality and safety and prevent food waste, a smart device prototype is proposed. The concepts related to the formulation and design of the enzymatic-type chromatic time-temperature integrator (TTI) device used to monitor the real-time quality of cherries are described. The kinetic parameters for thermal inactivation of cultivar Santina cherries were determined based on the degradation of phenolic compounds that are substrates of the polyphenol oxidase enzyme, whose hydroxylation reaction of a monophenol to o-diphenol leads to the oxidation in o-quinone. The proposed device concept aims to help retailers and consumers decide upon selling and buying according to the remaining shelf life, thus promoting sustainability related to food processes.

Fuzzy Logic Decision Support System to Predict Peaches Marketable Period at Highest Quality

Food waste occurs from harvesting to consumption. Applying procedures and technologies, changing attitudes, and promoting awareness have positive social, economic, and environmental impacts that can contribute to reducing food waste. The paper presents a decision support system (DSS) to predict the quality evolution of fruits and vegetables, particularly of peaches, and estimate its commercialization period at the highest overall perceived quality by consumers, thus contributing to reducing food waste. The Fuzzy Logic DSS predicts the evolution of the physical-chemical parameters of peaches (hardness, soluble solids content, and acidity) depending on the cultivar (Royal Summer and Royal Time), storage time, and temperature. As the range of the values of these physical-chemical parameters of peaches that consumers perceive to be at their highest quality are known, the DSS predicts the marketable period in days. Case studies were developed to analyze the influence of each physical-chemical parameter on the commercialization days (number and time to start). It is concluded that temperature is the most important parameter for fruit conservation. A low value of conservation temperature allows for the significant extension of the time that peaches can be sold at the highest quality. Hardness is used to determine the harvest date since it is an index of fruit ripeness. The same conclusion is obtained for the influence of the soluble solids content. The influence of acidity on marketable days is less than the other physical-chemical parameters. This DSS helps retailers to sell their peaches at the highest quality with benefits for all parties. It also helps in the decision-making concerning the actions to take when fruits are reaching the end of their highest quality by predicting the range of the commercialization days. This formulation can be extended to other fruits and vegetables and in the last instance contribute to the reduction of food loss and waste, consequently promoting social, economic, and environmental aspects of our daily life.

Decision Support System in Dynamic Pricing of Horticultural Products Based on the Quality Decline Due to Bacterial Growth

A decision support system (DSS) was developed to help reduce food waste at traditional food retailers while selling fresh horticultural products, but also to promote food safety and quality. This computational tool includes two major functions: (1) the prediction of the remaining shelf life of fresh horticultural product, namely lettuce, onion, carrot, and cabbage based on its microbial growth status, governed by extrinsic and intrinsic parameters (temperature, water activity and pH, respectively). The remaining shelf life of the studied horticultural products is determined by using the online predictive food microbiology tool— the Combined Database for Predictive Microbiology (Combase). The time to reach the infectious doses of bacteria considered in the study for each of the four horticultural products are predicted; (2) the calculation of the dynamic price of the produce that should be set each day, depending on the predicted end of the marketing period to increase the demand and potential for sale to the final consumer. The proposed dynamic pricing model assumes a linear relation with the remaining shelf life of the analyzed vegetable to set the selling price. The shelf life determined by the DSS for optimal storage conditions is, in general, conservative, ensuring food safety. The automatic dynamic pricing gives new opportunities to small retailers to manage their business, fostering profit and simultaneously contributing to reduce food waste. Thus, this decision support system can contribute to the sustainable value of reducing food waste by providing information to small grocers and retailers on the safety of their perishable status depending on storage conditions and allowing them to suggest a fair price depending on that quality.