Cold chain break detection and analysis: Can machine learning help?

Janus hydrogel loaded with a CO2-generating chemical reaction system: Construction, characterization, and application in fruit and vegetable preservation

In this study, we inserted a dynamic chemical reaction system that can generate CO2 into Janus hydrogel (JH) to develop a multidimensional preservation platform that integrates hygroscopicity, antibacterial activity, and modified atmospheric capacity. The double gel system developed using sodium alginate/trehalose at a 1:1 ratio effectively encapsulated 90% of citric acid. Furthermore, CO2 loss was avoided by separately embedding NaHCO3/cinnamon essential oil and citric acid microcapsules into a gelatin pad to develop JH. Freeze-dried JH exhibited a porous and asymmetric structure, very strongly absorbing moisture, conducting water, and rapidly releasing CO2 and essential oils. Furthermore, when preserving various fruits and vegetables in practical settings, JH provided several preservation effects, including color protection, microbial inhibition, and antioxidant properties. Our study findings broaden the application of JH technology for developing chemical reaction systems, with the resulting JH holding substantial promise for cold chain logistics.

Effects of temperature fluctuations on the quality and microbial diversity of beef meatballs during simulated cold chain distribution

BACKGROUND

Cold chain distribution with multiple links maintains low temperatures to ensure the quality of meat products, whereas temperature fluctuations during this are often disregarded by the industry. The present study simulated two distinct temperatures cold chain distribution processes. Quality indicators and high-throughput sequencing were employed to investigate the effects of temperature fluctuations on the quality and microbial diversity of beef meatballs during cold chain distribution.

RESULTS

Quality indicators revealed that temperature fluctuations during simulated cold chain distribution significantly (P < 0.05) exacerbated the quality deterioration of beef meatballs. High-throughput sequencing demonstrated that temperature fluctuations affected the diversity and structure of microbial community. Lower microbial species abundance and higher microbial species diversity were observed in the temperature fluctuations group. Proteobacteria and Pseudomonas were identified as the dominant phylum and genus in beef meatballs, respectively, exhibiting faster growth rates and greater relative abundance under temperature fluctuations.

CONCLUSION

The present study demonstrates that temperature fluctuations during simulated cold chain distribution can worsen spoilage and shorten the shelf life of beef meatballs. It also offers certain insights into the spoilage mechanism and preservation of meat products during cold chain distribution. © 2024 Society of Chemical Industry.

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

Effects of immersing treatment of curcumin and piperine combined with vacuum packaging on the quality of salmon (Salmo salar) during cold chain logistics

In order to study the effects of the compound preservatives (curcumin and piperine (CP)) and vacuum packaging (VP) on the quality of salmon during cold chain logistics suffered from temperature abuse, the physiochemical indexes (texture, water holding capacity (WHC), total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), free amino acids (FAA) contents), microbial indicators (total mesophilic bacteria count (MBC), total psychrotrophic bacteria count (PBC), H2S-producing bacteria count (HSBC)) were determined, and the moisture changes were explored by near-infrared (NIR) spectroscopy and low-field nuclear magnetic resonance (LF-NMR). The results showed that the treatment of curcumin and piperine in combination with vacuum packaging could maintain the quality of salmon suffered from temperature abuse most effectively. At the end of storage, the MBC of VP+CP was only 4.95 log CFU/g, which was about 1 log CFU/g lower than the control sample stored at the same condition. The combined treatment also retarded the increase of TVB-N, TBARS, and the decrease of hardness, springiness, and a* value, as well as water migration in salmon, contributing to higher water holding capacity and better appearance. Besides, VP+CP retarded the decrease of free glutamate, which contributed to umami taste. Due to the biological activity and safety of the preserves, the combined treatment could be a promising method for preservation of seafood.

Cold chain relevance in the food safety of perishable products

The food cold chain is an effective tool that allows food markets to maintain food quality and reduce losses. Poor logistics may result in foodborne disease outbreaks and greenhouse gas emissions caused by organic matter decay. The ongoing pandemic of COVID-19 makes it necessary to study the chances of SARS-CoV-2 transmissions in food products. This study reviews cold chain logistics as a handy tool for avoiding food safety risks, including COVID-19. The cold chain of perishables and its proper management make it possible to maintain quality and safety at any stage of the food supply chain. The technology covers each link of the food chain to prevent microbial spoilage caused by temperature fluctuations and the contamination with SARS-CoV-2 associated with perishable foods. Given the lack of knowledge in this field in Latin America, the region needs new research to determine the impact of the cold chain on perishable foodstuffs. The perishable cold chain is only as strong as its weakest link, and the national and international markets require new traceability protocols to minimize the effect of COVID-19.

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact

Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon. Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.