Predicting the shelf life of Trachinotus ovatus during frozen storage using a back propagation (BP) neural network model

Effects of immersion with electro-activated alkaline water on gel properties, biochemicals and odor characteristics of myofibrillar proteins

This study aimed to investigate the effects of alkaline water electrolysis (AWE) - assisted washing on gelling properties, microstructure, protein conformation, and flavor composition of golden pompano surimi. The experimental results showed that rinsing yield, water holding capacity (WHC), gel strength, and textural properties were effectively improved with AWE for 1 and 3 min. The rheological properties indicated a highly elastic gel with a dense and compact microstructure after soaking in AWE for 1 and 3 min, indicating that short-term alkaline immersion enhances cross-linking between myofibrillar proteins. Additionally, increasing the electrolysis time from 0 to 3 min effectively retained immobilized water due to increased aggregation of neighboring proteins, leading to a higher proportion of α-helix structures. Furthermore, AWE treatment for 3 min markedly reduced undesirable volatile compounds, including 1-butanol, hexanal, and 1-octen-3-ol, enhancing the hardness, chewiness, gumminess, and WHC of surimi. In conclusion, AWE-assisted washing emerges as an effective and reliable approach for upgrading the quality of surimi gel.

Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform

At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorithms were used for the first time to develop a multi-objective model that can simultaneously predict the shelf-life of five marine fish species at multiple storage temperatures using 14 features such as species, temperature, total viable count, K-value, total volatile basic‑nitrogen, sensory and E-nose-GC-Ms/Ms. as inputs. Among them, the radial basis function model performed the best, and the absolute errors of all test samples were <0.5. With the optimal model as the base layer, a real-time prediction platform was developed to meet the needs of practical applications. This study successfully realized multi-objective real-time prediction with accurate prediction results, providing scientific basis and technical support for food safety and quality.

Quality prediction of whole-grain rice noodles using backpropagation artificial neural network

BACKGROUND

Whole-grain rice noodles are a kind of healthy food with rich nutritional value, and their product quality has a notable impact on consumer acceptability. The quality evaluation model is of great significance to the optimization of product quality. However, there are few methods that can establish a product quality prediction model with multiple preparation conditions as inputs and various quality evaluation indexes as outputs. In this study, an artificial neural network (ANN) model based on a backpropagation (BP) algorithm was used to predict the comprehensive quality changes of whole-grain rice noodles under different preparation conditions, which provided a new way to improve the quality of extrusion rice products.

RESULTS

The results showed that the BP-ANN using the Levenberg-Marquardt algorithm and the optimal topology (4-11-8) gave the best performance. The correlation coefficients (R2) for the training, validation, testing, and global data sets of the BP neural network were 0.927, 0.873, 0.817, and 0.903, respectively. In the validation test, the percentage error in the quality prediction of whole-grain rice noodles was within 10%, indicating that the BP-ANN could accurately predict the quality of whole-grain rice noodles prepared under different conditions.

CONCLUSION

This study showed that the quality prediction model of whole-grain rice noodles based on the BP-ANN algorithm was effective, and suitable for predicting the quality of whole-grain rice noodles prepared under different conditions. © 2024 Society of Chemical Industry.

A BPNN-based ecologically extended input-output model for virtual water metabolism network management of Kazakhstan

In this study, a back-propagation-neural-network-based ecologically extended input-output model (abbreviated as BPNN-EIOM) is developed for virtual water metabolism network (VWMN) management. BPNN-EIOM can identify key consumption sectors, simulate performance of VWMN, and predict water consumption. BPNN-EIOM is then applied to analyzing VWMN of Kazakhstan, where multiple scenarios under different gross domestic production (GDP) growth rates, sectoral added values, and final demands are designed for determining the optimal management strategies. The major findings are (i) Kazakhstan typically relies on net virtual water import (reaching 1497.9 × 10⁶ m³ in 2015); (ii) agriculture is the major exporter and advanced manufacture is the major importer; (iii) by 2025, Kazakhstan's water consumption would increase to [19322, 22016] × 10⁶ m³ under multiple scenarios; (iv) when Kazakhstan's GDP growth rate, manufacturing's added value, and final demand are scheduled to 5.5%, 8.5%, and 5.8%, its VWMN can reach the optimum. The findings are useful for decision makers to optimize Kazakhstan's industrial structure, mitigate the national water scarcity, and promote its socio-economic sustainable development.

Recent advances in shelf life prediction models for monitoring food quality

Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real-time food quality monitoring.