Quality changes and shelf-life prediction model of postharvest apples using partial least squares and artificial neural network analysis

Zhang, Yueyi; Zhu, Danshi; Ren, Xiaojun; Shen, Yusi; Cao, Xuehui; Li, He; Lǐ, Jiànróng

doi:10.1016/j.foodchem.2022.133526

Cited by 28 publications

(10 citation statements)

References 32 publications

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“…As a result, the efficiency of logistics operations is improved. Based on environmental information about timestamps, shelf-life predictions [ 153 ] or blockchain-based smart contract triggers can be developed to avoid food waste and loss, and provide better quality products (Q5). Known how important big data is for model learning in artificial intelligence.…”

Section: Literature Review and Discussionmentioning

confidence: 99%

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Lei

Liu

et al. 2022

Foods

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Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made.

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Section: Literature Review and Discussionmentioning

confidence: 99%

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Lei

Liu

et al. 2022

Foods

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“…GAN can solve the problem of poor model training due to an insufficient dataset. Also, PLSR can show better results than the neural network model only when the value of R 2 is higher (Siripatrawan & Makino, 2018; Zhang, Zhu, et al., 2022).…”

Section: Applications In the Food Sectormentioning

confidence: 99%

“…All the aforementioned models can be implemented and debugged in MATLAB (The Mathworks, Natick, MA, USA) (Shao et al., 2021; Zhang, Zhu, et al., 2022).…”

Section: Introduction and Mechanism Of Shelf Life Modelmentioning

confidence: 99%

Recent advances in shelf life prediction models for monitoring food quality

Cui

Zheng

Wang

et al. 2023

Comp Rev Food Sci Food Safe

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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.

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“…Partial Least Squares Regression (PLSR) is a multivariate statistical data analysis approach that combines the benefits of PCA and regression analysis, which can establish predictive models with high accuracy when there are linear correlations between variables [28].…”

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%

“…This technique is very useful when there is a large number of independent variables that can influence a characteristic of the food matrix. Because of this, this method has been used in several studies to evaluate the impact of different features (temperature, pH, light, microbiological growth, among others) on the shelf-life of food products and in the development of regression models that simulate food degradation [12,[28][29][30][31].…”

Section: Partial Least Squares Regression (Plsr)mentioning

confidence: 99%