Perishable Products Research Articles

Impact of High Hydrostatic Pressure and UV-C Radiation on Total Aerobic Bacteria in Minas Frescal Cheese.

Minas Frescal cheese (MFC) is a perishable product with high water activity and neutral pH, conditions that favor the development of microorganisms. Total aerobic bacteria (TAB) can deteriorate the cheese, negatively affecting its sensory characteristics. By controlling TAB, the shelf life of the product is extended and its quality is maintained, contributing to meeting consumer expectations. This study aimed to evaluate the individual and combined effect of technological treatments of high hydrostatic pressure (HHP) and ultraviolet C radiation (UV-C) on the TAB count present in the natural microbiota of MFC, as an alternative to control the microbiological quality of this product. After production, MFC were subjected to treatments with different levels of HHP (100 to 400 MPa/10 min) and UV-C (0.097 to 0.392 J/cm2.s-1). The combinations of HHP and UV-C doses were determined by a central composite rotational design (CCRD). The model efficiently described the individual and combined effect of HHP and UV-C on TAB, demonstrating that gradually increasing HHP levels reduces TAB counts in MFC, regardless of the UV-C dose applied. This study contributes significantly to the literature by providing new insights into how these technologies can be used to improve the microbiological quality of fresh cheeses.

Collaboration for the sustainable food supply chain: A bibliometric analysis

AbstractThere is increasing attention on the topic of collaboration for sustainable food supply chains (SFSCs), with increasingly contributing journals and publications every year. The urgency of this topic is even more highlighted due to the COVID‐19 pandemic and the more recent energy and food crisis. Hence, the field needs to have a good portrait of the ongoing research in this area and to better understand future research directions to enable optimized future strategic plans and problem‐solving capability of effective collaboration for SFSCs. This paper reviews, analyses, and synthesizes the current state of research into collaboration for SFSCs. We examine a sample of 528 articles identified from the Scopus and Web of Science databases using bibliometric analysis methodology. We identify four research clusters: collaboration and sustainable supply chain management, emerging markets and resilience, digital technologies, and perishable food products. This paper clarifies interrelated themes and identifies a range of topic areas that still demand further investigation.

Lagrangian-based heuristics for production planning with perishable products, scarce resources, and sequence-dependent setup times

Lagrangian-based heuristics for production planning with perishable products, scarce resources, and sequence-dependent setup times

Nanoemulsion edible coating for shelf-life improvement and quality control in perishable products

Nanoemulsion edible coating for shelf-life improvement and quality control in perishable products

Dynamic pricing and inventory model for perishable products with reference price and reference quality

Reference price (quality) is a benchmark point used by consumers to make price (quality) judgements. Combining reference price and reference quality with dynamic pricing and inventory management, this paper applies differential equations theory to construct a optimal control model for perishable products when the quality of products declines exponentially. The demand of products depends on price, quality, reference price and reference quality, among which reference price and reference quality are influenced by consumers’ past memory. The aim is to maximize the retailer’s profit during the period. The conclusions are as follows. Firstly, a optimal dynamic pricing and inventory model for perishable products with reference price and reference quality is constructed, and the model is extended to dual decision variables, stochastic demand, time-dependent effects, competitive and infinite planing horizon setups. Secondly, through the Pontryagin maximum principle, the analytical expression of the optimal dynamic price is derived. Thirdly, a linear search method to solve the optimal static price is proposed. Finally, the sensitivity of the main parameters is analyzed and the corresponding management enlightenments are given. By comparison of dynamic and static pricing, we find that dynamic pricing can achieve more profits and take a shorter selling period. In addition, for the sales problem of perishable products affected by both reference price and reference quality, retailers should adopt skimming pricing strategy (the optimal price decreases with time). Furthermore, to obtain more profit, retails should strive to increase the sensitivity coefficients of two types of reference, and the reference quality memory coefficient, while to decrease the reference price memory coefficient.

The latest research progress on polysaccharides-based biosensors for food packaging: A review

In recent years, polysaccharide-based biosensors have emerged as promising technologies for intelligent food packaging, offering innovative solutions to enhance food quality and safety. This review highlights advancements in designing, developing, and applying these biosensors, particularly those utilizing polysaccharides such as chitosan, cellulose and alginate. Engineered with nanomaterials like ZnO, silver, and carbon nano-tubes demonstrated high sensitivity in real-time monitoring of food spoilage indicators, including pH changes, volatile nitrogen compounds and microbial activity. We discuss the electrochemical properties of these biosensors, highlighting how the integration of electrochemical methods significantly improves their detection capabilities within packaging environments, leading to sensor sensitivity enhancement, greater accuracy, and spoilage detection, ultimately extending the shelf life of perishable food products. Additionally, the review addresses the practical challenges of industrial implementation and explores future research directions for optimizing sensor functionality and scalability. The findings underscore the potential of polysaccharide-based intelligent packaging as a sustainable and effective alternative to conventional methods, paving the way for broader commercial adoption.

A New Vendor Managed Inventory for Perishable Products Considering Supplier Selection

A New Vendor Managed Inventory for Perishable Products Considering Supplier Selection

Decarbonization and waste reduction in a disrupted food supply chain: a step towards achieving food sustainability

PurposeThis study aims to reduce carbon emissions and minimize waste in the event of disruptions in a short and fast-food perishable such as fruits, vegetables, packaged food items, etc. supply chain through optimal investment in green and preservation technologies.Design/methodology/approach This study utilized a Hessian matrix approach to optimize decision variables with an objective to maximize the profit function.FindingsThe study demonstrates that investing in both green and preservation technology within a short and fast-food supply chain is highly beneficial for decarbonization and waste reduction and it leads to profit maximization. It has been shown with the help of a numerical experiments with investment in both green and preservation technology that total profit is 3.09% higher than without investment made in either technology.Practical implicationsThis study aids the industry in achieving food sustainability by minimizing waste of perishables and also minimizes carbon emissions which is essential for environmental protection. It assists industries in determining the optimal investment in preservation technology to minimize waste and in green technology to reduce emissions, thereby maximizing profits.Originality/valueThe current study formulates an inventory model that helps in decarbonization and waste reduction in food supply chain with the consideration of machine learning, demand disruption, preservation technology investment, screening of purchased items, waste disposal, a double triangular distribution deterioration rate, green technology investment, carbon emissions from various supply chain activities, carbon tax policy and fuel price variation over time for perishable food products in a two-warehouse system.

Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics

AbstractOptimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.

The influence of nanochitosan-incorporated edible coating on the characteristics of Pangasius (Pangasius sp.) fillet

Nanochitosan is known to have the potential to protect fish fillets, highly perishable products, as it can penetrate deep into the meat tissue and has high antibacterial activity. This study aimed to analyze the effect of nanochitosan incorporation to edible coating in inhibiting the degradation of Pangasius fillet quality during storage. The fillets were soaked with 2 % chitosan and nanochitosan concentrations (1 %, 2 %, and 3 %), stored for 15 days, and its quality was assessed periodically. On the 10th day of storage, the fillet coated with 2 % nanochitosan-incorporated edible coating (NEC) showed the lowest weight loss (1.41 ± 0.01 %) and maintained the pH close to neutral (6.92 ± 0.08), along with low TVB-N (33.55 mg N/100 g). and TPC (4.7 × 105 CFU/g) value, fulfilling the standard set by Indonesian National Standardization Agency. Based on the results, nanochitosan proved to extended the storage duration of Pangasius fillet while maintaining its quality.

Active Modified Atmosphere Packaging Helps Preserve Quality of Edible Flowers

Edible flowers are becoming increasingly popular as food products, since they give aroma, color, and visual appeal and are also health-promoting compounds. However, they are a highly perishable product, thus post-harvest technologies are needed to extend their marketability. In this study, active (N2: 100%) and passive modified atmosphere packaging (MAP) technologies were applied to three edible flower species, namely Begonia grandiflora ‘Viking’, Tropaeolum majus, and Viola cornuta, stored at 4 °C. Even if the flowers’ quality decay occurred differently according to the species, active MAP better maintained petal colors and slowed down the edible flowers’ decay than passive MAP by decreasing flower respiration in all three species and sugars consumption in begonia; there was weight loss in nasturtium, and better preserved total phenolic content in begonia and viola. Coupling cold storage with active MAP can be an effective method to extend edible flowers’ post-harvest life.

Impact of Harvest Method on Development of European Sea Bass Skin Microbiome during Chilled Storage

European sea bass (Dicentrarchus labrax) is one of the most significant species farmed in the Mediterranean, yet a very perishable product. Its quality deteriorates rapidly as a result of three mechanisms: microbial activity, chemical oxidation, and enzymatic degradation. Microbial spoilage is the mechanism that contributes most to the quality deterioration of fresh and non-processed fish. To this end, our study aims to identify for the first time the combined effect of aquatic environment and harvest method on the composition and trajectory at storage at 0 °C of the European sea bass skin microbiome. Sampling was performed in two commercial fish farms in Western (WG) and Central Greece (CG) where fish were harvested using different methods: direct immersion in ice water or a mixture of slurry ice; application of electro-stunning prior to immersion in ice water. Samples were collected on harvest day and one week post-harvest. To profile the bacterial communities in the fish skin, 16S ribosomal RNA gene sequencing was used. The results and the following analyses indicated that the aquatic environment shaped the original composition of the skin microbiome, with 815 ASVs identified in the WG farm as opposed to 362 ASVs in the CG farm. Moreover, Pseudomonas and Pseudoalteromonas dominated the skin microbiome in the WG farm, unlike the CG farm where Shewanella and Psychrobacter were the dominant genera. All these genera contain species such as Shewanella putrefaciens, Pseudomonas aeruginosa, Pseudoalteromonas spp., and Psychrobacter sp., all of which have been implicated in the deterioration and spoilage of the final product. The different harvest methods drove variations in the microbiome already shaped by the aquatic environment, with electro-stunning favoring more diversity in the skin microbiome. The aquatic environment in combination with the harvest method appeared to determine the skin microbiome trajectory at storage at 0 °C. Although Shewanella had dominated the skin microbiome in all samples one week post-harvest, the diversity and the relative abundance of genera were strongly influenced by the aquatic environment and the harvest method. This study sheds light on the hierarchy of the factors shaping the fish skin microbiome and their importance for controlling post-harvest quality of fresh fish.

Implementation of an app-based time-temperature-indicator system for the real-time shelf life prediction in a pork sausage supply chain

Time-temperature-indicators (TTIs) combined with predictive modeling are helpful tools for avoiding the increasing amount of food waste and the associated waste of resources along the food supply chain. Successful implementation of these systems in practice is still absent due to missing digital technologies for real-time shelf life prediction. This study aimed to validate a novel app system developed for the digital read-out of OnVu™ TTIs and the shelf life prediction of perishable products along the raw pork sausage supply chain. Therefore, a kinetic shelf life model of raw pork sausage was developed based on microbial parameters. A dynamic TTI model was developed based on app-measured TTI data and validated on a laboratory scale to prepare for the pilot study. In the pilot study, the shelf life prediction of TTIs based on app measurements was validated under practical conditions. Results showed that the spoilage kinetics of raw pork sausage could, in general, be reflected by the OnVu™ TTI kinetics based on the app's color measurements. The pilot study showed that predicted and measured TTI color values by the app were in good agreement with accuracy factors of 1.02–1.03; however, slight differences in shelf lives revealed that the prediction model must be further improved by integrating more data. Although variances in the hourly range could be seen between the predicted shelf life based on TTI app measurements and the real shelf life of raw pork sausage, the study serves as a proof of concept for the general useability of the app for shelf life prediction because it showed that TTI and product kinetics were highly comparable. Further technical adjustments to the app and the adaptation of the charging time may further improve the shelf life prediction by the app along the raw pork sausage supply chain.

Standardization of vacuum frying techniques for the development of low-fat beetroot finger chips

Beetroot (Beta vulgaris L. ssp), is a popular root crop grown for its fleshy roots and is a nutritious and mostly consumed fresh vegetable. It is a perishable product of just two to three days after the mature stage. The beetroot can be used for the preparation of value-added products like juice, dehydrated powder, soup mix, chips, etc. The chips out-turn of beetroot finger chips under vacuum frying was about 20 %. The vacuum-fried beetroot finger chips with corn powder coating absorbed relatively less frying oil (16.79 %) when compared to those without corn powder coating chips (18.25 %). The tristimulus color of vacuum-fried beetroot finger chips with corn powder coating chips were improved. Among the vacuum-fried chips prepared under various combinations of vacuum (constant 640 mm Hg), frying temperature (3 levels), and frying duration (3 levels), it was found that the beetroot finger chips prepared with corn coating at 110 °C frying temperature for 15 minutes frying time were the best in terms of all quality parameters. The beetroot finger chip manufacturing cost was determined to be Rs. 235 per kg, and the benefit-cost ratio of production was found to be 1:1.70.

Dynamic modeling and real-time performance analysis of multi-product batch manufacturing systems with perishable products

Production lines handling perishable goods in industries such as petrochemical and food processing demand meticulous precision in timing. These systems are governed by stringent timeframes, where products must strictly adhere to defined residence times. Exceeding these time constraints leads to product scrapping, posing a significant threat to overall productivity. Modeling such intricate systems presents a formidable challenge, and success hinges upon a deep understanding of the complex interplay between production and batch scrapping dynamics. In response to this challenge, this paper presents a novel model for a multiproduct batch production line with perishable products. Furthermore, the real-time performance metric of Permanent Production Loss (PPL) is extended to this system using a method of PPL back-adjustment in time. To validate the fidelity of the proposed mathematical model, numerical case studies are conducted, comparing the developed model to a discrete event simulation. The results demonstrate that the model accurately represents the system at hand. The expressions for PPL evaluation and attribution are also validated using case studies.

Antimicrobial Properties of Fennel By-Product Extracts and Their Potential Applications in Meat Products.

Background: Beef burgers are perishable meat products, and to extend their shelf life, EU Regulation 1129/11 permits the use of certain additives. Objectives: However, given the concerns of health-conscious consumers and the potential toxicity of synthetic substances, this study aimed to explore the use of fennel waste extracts as natural preservatives. Methods: This study characterized the bioactive compounds (phenolic content), the antioxidant activity (ABTS+ and DPPH assay), and the antimicrobial properties (against Salmonella enterica serotype Enteritidis, Escherichia coli, Staphylococcus aureus, Bacillus cereusi, and Pseudomonas aeruginosa) of different fennel waste extracts (LF, liquid fraction; SF, solid fraction and PF, pellet fraction). Additionally, the potential use of the best fennel extract was evaluated for its impact on beef burger shelf life (up to 18 days at 4 ± 1 °C) in terms of microbiological profile, pH, and activity water (aw). Results: The PF extract, which was rich in flavones, hydroxybenzoic, and hydroxycinnamic acids, demonstrated the highest antioxidant and antimicrobial activities. Microbiological analyses on beef burgers with PF identified this extract as a potential antimicrobial substance. The aw and pH values did not appear to be affected. Conclusions: In conclusion, fennel extracts could be proposed as natural compounds exploitable in beef burgers to preserve their quality and extend their shelf-life.

Use of non-thermal treatment methods to ensure the quality and safety of fish and other hydrobionts. Overview of the subject field

Products of aquatic origin are among the most perishable products. Heat processing, including sterilization, drying and evaporation, can lead to undesirable changes in food products, such as a decrease in their nutritional value or deterioration in organoleptic characteristics. The purpose of the paper is to review the results of scientific research on non-thermal methods of processing fish and other aquatic organisms to inhibit bacteria, ensure microbiological safety and maintain quality. The groups of primary spoilage bacteria vary among fish, crustaceans and molluscs depending on storage conditions and duration. Non-thermal processing methods such as ultra-high pressure, irradiation, pulsed electric field and low-temperature plasma have shown significant results in inhibiting microbial growth and increasing the shelf life of aquatic products. However, uncertain processing parameters and characteristics of the technology itself can lead to adverse effects such as lipid oxidation and protein degradation during sterilization. Non-thermal processing can be used in combination with antioxidant composite coatings (membranes) to delay the oxidation of lipids and proteins and improve the physical, chemical and sensory properties of fish products. It is possible to combine several methods of non-thermal treatment, which will make it possible to compensate for the shortcomings of one technology by the action of another method of non-thermal exposure. Further scientific research should be aimed at establishing optimal processing modes, the possibility of combining non-thermal methods with other technologies, such as modified atmosphere packaging in order to determine the mechanisms of spoilage and improve the quality of storage of products of aquatic origin, as well as further industrial implementation of modern processing methods. BELROSAKAVA

Research on Pricing and Dynamic Replenishment Planning Strategies for Perishable Vegetables Based on the RF-GWO Model

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model’s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization.

Note on “Joint dynamic pricing and freshness-keeping effort strategy for perishable products with price-, freshness-, and stock-dependent demand”

Note on “Joint dynamic pricing and freshness-keeping effort strategy for perishable products with price-, freshness-, and stock-dependent demand”

Integrating the triple bottom line of sustainability, resilience strategies, and product perishability consideration to design a pharmaceutical supply chain network: A COVID-19 case study

This paper aims to design a resilient and sustainable pharmaceutical supply chain network under the perishability of medicine in which a multi-objective nonlinear mathematical model is formulated. To this end, four objective functions seek to minimize total cost, maximize the social indicators, minimize CO2 emission and minimize de-resilience measures. Moreover, the three main categories of resilience strategies are integrated to mitigate the severe impacts of disruption. In order to solve the model, lexicographic goal programming is applied for small-scale problems, and NSGA-II is utilized for large-scale problems. The applicability of the proposed model is demonstrated by implementing it in a real case study during the COVID-19 situation. Also, a set of sensitivity analyses is conducted to validate the model and show the behavior of the objective functions. The results reveal the superiority of the resilient model with integrated strategies. Eventually, the Pareto front solutions are provided to quantify the trade-offs in satisfying the conflicting objective functions.