Perishable Goods Research Articles

Overview of Food Adulteration: Its Definition, Causes, Impacts, and Detection

A tampered food, Food is any substance made up of carbohydrates, water, fats, and proteins that is consumed by humans and other animals to survive. It holds great significance for us. Nonetheless, it is not uncommon for food products to be tampered with, and dangerous situations can arise from poorly managed supply chains that handle perishable goods. Economic Customers who purchase and serve food are impacted in numerous ways; they might not receive the nutrients they are supposed to, contaminated food may be harmful to their health, and they might also suffer financial losses. So, every responsible individual, organization, and government should complete their role to protect the act of food adulteration and to reveal the recognized acts. Furthermore, there are numerous gaps in the literature that need to be filled by academics and researchers working in various research institutes. These gaps include the assessment of the situation, the identification of foods that are vulnerable to adulteration in the national context, the development and validation of detection methods, and much more. adulteration has long plagued the food business, negatively impacting both food quality and public health. The process of altering food by adding undesirable or dangerous components lowers its safety and quality, a practice known as food adulteration. Inadequate handling of this issue has led to the production of contaminated and dangerous food products, which has raised concerns throughout history. Food adulteration can harm our health and often does so without our knowledge. By addressing these issues, we may proactively work to create a better society. Food adulteration can involve adding, taking away, or replacing expensive food ingredients with less expensive (cheaper) ones in order to get unfair financial advantage. The manufacturer may profit financially from this act of food fraud, but the eventual consumers of the goods suffer losses. A variety of approaches, such as physical, chemical, biological, and other methods, are used to detect food adulteration. However, detecting adulterants can be hard when the adulterants closely mirror the food’s physiochemical properties. The right of consumers to safe and high-quality food is undermined by adulteration, hence it is imperative that people, groups, and governments take action to stop these practices.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

Leveraging Machine Learning to Optimize Pricing and Restocking Strategies for Fresh Vegetables

In the competitive and diverse retail market, restocking and pricing perishable vegetables is challenging. Using big data and machine learning to analyze sales data, this research can build predictive models to refine these strategies and predict trends[1][2]. This research analyzed sales data from 2020 to 2023, identifying key trends in sales volumes, seasonality, and consumer preferences. It incorporated these findings into a multi-objective programming model that also considered wholesale and loss rates. This research used statistical tests like the Kruskal-Wallis and Mann-Whitney U to detect significant sales differences across vegetable categories, while the Spearman correlation showed strong seasonal and culinary impacts on consumer buying behaviors. To optimize freshness and maximize sales and profits, this research utilized genetic algorithms and regression analyses, including an effective XGBoost model. This model had a Mean Absolute Percentage Error of 16.97 and an R² of 0.94, aiding in predicting daily sales and improving pricing strategies and restocking schedules. These methods helped enhance decision-making, ensuring vegetable freshness and maximizing profitability, with projected profits reaching ¥41,582.87. This study not only highlights the effectiveness of integrating big data and machine learning in the retail sector but also sets a benchmark for operational efficiency in managing perishable goods.

Edible coatings for enhancing the shelf-life of foods: meaningful or myth

In the current landscape, effective food management poses significant challenges, particularly in ensuring access to healthy, nutritious diets. Proper handling of food materials during post-harvest stages such as harvesting, packaging, transportation, and distribution are crucial for maintaining food quality, with packaging playing a pivotal role in extending the shelf life of perishable goods. However, the widespread use of conventional plastic packaging has detrimental effects on the environment. As a sustainable alternative, edible coatings have emerged as a promising solution for preserving food over extended periods. While these coatings are primarily used to prolong the shelf life of food products, this review delves deeper into the potential of edible coatings from various perspectives. Edible coatings not only offer multiple benefits such as environmental sustainability and improved food preservation but also present certain limitations. This article provides a comprehensive discussion of different types of edible coatings, their respective advantages and disadvantages, and their application to specific food products. Moreover, the current status and recent research developments surrounding edible coatings are explored, emphasizing their role as a sustainable, eco-friendly alternative to conventional packaging methods that helps reduce environmental pollution and promote a healthier planet.

White-light driven sterilization based on chlorine-doped cuprous oxide for antibacterial food packaging

Microbial spoilage and pathogen contamination present significant challenges to food quality and safety. Incorporating photodynamic sterilization (PDT) into active packaging films, especially using white light-driven methods, is recognized as one of the most effective strategies to combat microbial contamination and extend shelf-life. Cuprous oxide (Cu2O) shows promise due to its suitable band gap for absorbing visible light, although its photocatalytic effectiveness is hindered by electron-hole recombination. In this study, a chlorine (Cl) doping strategy was investigated to enhance Cu2O’s PDT capabilities under visible light. This approach aimed to improve Cu2O’s band structure for generating reactive oxygen species (ROS) and to enhance charge carrier separation efficiency, thereby inhibiting recombination. The resulting Cl-doped Cu2O (Cl-Cu2O) exhibited enhanced antibacterial activity, which was integrated into a gelatin (Gel) and carboxymethylcellulose (CMC)-based food packaging film, referred to as the GC/Cl-Cu2O film. This innovative film provides a white light-driven active packaging solution designed to extend the shelf life of fruits by suppressing microbial growth. This approach not only addresses food safety concerns but also mitigates economic losses associated with food waste, offering a sustainable solution to enhance the quality and safety of perishable goods.

Frieght forwarding strategies for perishable goods with reference to Global Tide Shipping and Logistics in Cochin

Frieght forwarding strategies for perishable goods with reference to Global Tide Shipping and Logistics in Cochin

Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation

Monitoring the temperature of perishable goods during transport and storage is essential to prevent waste and maintain product quality. Exploiting the unique property of phase-change materials (PCM), altering their physical state at specific temperatures, we optimize a capacitive sensor design based on a copper on polyimide interdigitated spiral (IDE) structure coated with a PCM to irreversibly detect temperature thresholds. The effect of the sensor dimensioning on its response is analyzed using a finite element model simulation. The model predicted up to 51% capacitance variation for optimal coverage of the PCM after spreading over the IDE, which was validated experimentally within a 5% error. Two melting concepts utilizing the spreading or the removal of the melted PCM over the IDE are investigated based on a capillary retention mechanism to maintain sensor sensitivity under inclination. Finally, an eco-friendly implementation of the capacitive structure and its wireless operation at 460 MHz is demonstrated on paper with a printed zinc transducer passivated with beeswax and covered with jojoba oil. Melting of the oil at a threshold temperature of 12.3 °C resulted in an irreversible shift in resonance frequency of 14 MHz. This study provides guidelines for the design and implementation of irreversible temperature monitoring capacitive sensors.

Achieving Off-Grid Refrigeration in Remote Areas: A Solar-Powered Vapor Compression Refrigerator Prototype with PCM Integration

The availability of vaccines, medicines, and perishable goods in remote or off-grid areas remains a formidable challenge. Integrating solar photovoltaic (PV) systems with refrigeration technology has emerged as a promising solution to address this critical need. This paper presents an autonomous solar-powered refrigerator prototype for off-grid refrigeration in remote areas utilising renewable energy. The system comprises a 160 W photovoltaic module, a 12/24 V DC compressor refrigerator, a lead-acid battery, and a Maximum Power Point Tracking (MPPT) controller. Its main feature is complete autonomy from the electricity grid, thanks to its standalone configuration. An experimental campaign evaluated the system's behaviour in the laboratory for 24 hours at different set-point temperatures. A water-based Phase Change Material (PCM) was implemented to improve its autonomy in severe outdoor conditions. A further experimental campaign emulated the functioning of the prototype while managing the temperature of a sample solution whose melting temperature was equal to – 21°C to ensure its liquid state during the tests. The target range for preserving the sample was defined at ± 1°C. Several real conditions have been considered, such as higher cooling loads realised through 21 litres of additional thermal mass in the refrigerator and pick and place conditions for simulating the opening and closing of the door. The results demonstrate that the solar refrigerator prototype achieves complete autonomy from the electricity grid, paving the way for solutions for preserving perishable goods such as medicines and food in unelectrified areas.

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.

Innovative risk management techniques in food safety: A review of best practices and emerging trends

Effective risk management in food safety is critical to ensuring consumer protection and maintaining the integrity of global food supply chains. Traditional approaches, such as Hazard Analysis and Critical Control Points (HACCP) and Good Manufacturing Practices (GMP), have long been the foundation of food safety risk management. However, with the increasing complexity of food production and distribution, new challenges demand more advanced techniques. This review explores innovative risk management methods, focusing on best practices and emerging trends that are transforming food safety. Technological advancements are driving much of this innovation. Predictive analytics and machine learning now allow for real-time monitoring and hazard detection, improving the accuracy of risk assessments. Blockchain technology is being leveraged for enhanced traceability and transparency in the supply chain, ensuring faster recall management and improved accountability. The integration of IoT (Internet of Things) sensors provides continuous monitoring of environmental factors like temperature and humidity, which are critical in preventing contamination, especially in perishable goods. Additionally, risk-based inspection systems, supported by regulatory frameworks such as the Food Safety Modernization Act (FSMA), are becoming more prevalent, prioritizing resources where risks are highest. Emerging trends such as artificial intelligence (AI) for predictive risk management, big data analytics, and sustainability-driven risk approaches are reshaping the landscape. These innovations not only enhance risk mitigation but also align with consumer demands for greater transparency and environmental responsibility. Despite challenges such as cost, data privacy, and regulatory hurdles, the future of food safety lies in the continued development and adoption of these advanced techniques. This review highlights the potential of these innovative practices to improve food safety standards and protect public health in an increasingly interconnected world. Keywords: Innovative, Risk Management, Food Safety, Review.

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.

An EOQ Model for Delayed Deteriorating Items: Analysis of Three-Stage Consumption, Dual Storage Facilities, and Variable Partial Backlogging Rates

Study’s Excerpt An Economic Order Quantity (EOQ) model that accounts for items with delayed deterioration by integrating three distinct stages of consumption rates, dual storage facilities, and partially backlogged shortages was proposed. The model can minimize overall variable costs and establish the conditions for the existence and uniqueness of the optimal solution by optimizing cycle length, order quantity, and depletion timing. The model could help in inventory management of perishable goods, particularly in scenarios involving multiple storage options and consumption phases. Full Abstract This paper presents an Economic Order Quantity (EOQ) model for items with delayed deterioration, incorporating three distinct stages of consumption rates, dual storage facilities, and shortages. The model assumes that the average consumption rates before and after the onset of deterioration, as well as during shortages, are constant but differ from one another. The shortages are partially backlogged and vary according to the waiting time until the next replenishment. The model aims to minimize the overall variable cost per unit of time by optimizing the cycle length, order quantity, and the timing at which the inventory in the owned warehouse is depleted. The paper establishes the necessary and sufficient conditions to ensure the existence and uniqueness of the optimal solution. A sensitivity analysis is conducted, and the findings are used to derive managerial insights.

Dynamic pricing for perishable goods: A data-driven digital transformation approach

In supermarkets, rapid pricing adjustments are crucial due to the short shelf life of products. Adopting a multi-case study approach, this study examines the application of dynamic pricing strategies for perishable goods through the lens of a data-driven digital transformation (DD-DT) approach. We introduce a three-phase model of DD-DT for dynamic pricing: initiation, facilitation, and strategic adaptation. In the initiation phase, we identify essential frameworks for robust data collection and analytical processes, which form the backbone of informed pricing decisions. During the facilitation phase, the study integrates sophisticated algorithms and real-time analytics to process and interpret the collected data, facilitating its seamless integration into pricing strategies. The strategic adaptation phase is critical as it focuses on the ongoing refinement and enhancement of pricing strategies, enabling supermarkets to adapt swiftly to market fluctuations and consumer behaviour changes. By presenting a comprehensive DD-DT framework, this research significantly augments the existing literature on dynamic pricing and offers actionable insights for practitioners seeking to optimize pricing strategies in a digitally transforming marketplace.

Reverse Logistics Analysis of Chips Products Towards Green Supply Chain Management in MSMEs

The food industry has the nature of perishable goods, so there is always the possibility of products being returned to the manufacturer, namely reverse logistics. Reverse logistics include collection, sorting, recycling, redistribution, and disposal. One of the main challenges MSMEs face is accumulating unsold or damaged products. Chip products not sold out in the stores where they are kept often have to be recalled, resulting in a buildup of products in the warehouse. This study aims to analyze the practice of reverse logistics of banana chip products in one MSME called A3 and evaluate the extent to which they are based on Green Supply Chain Management (GSCM). The research method used is descriptive qualitative; data collection is done through interviews, direct observation, and analysis of related documents. The results showed that A3 had implemented reverse logistics practices by considering environmental aspects such as production waste management. However, some areas can still be improved further to maximize the contribution to Green Supply Chain Management. The implication of this study is to provide a deeper understanding of reverse logistics practices in the context of MSMEs and provide recommendations for other MSMEs to improve the conformity of their practices with the concept of Green Supply Chain Management.

Agent-Based Modelling for the Cost-Benefit Analysis of Adaptation Strategies: A Case Study from Inuit Nunangat

This paper presents a case study of how agent-based modelling can be utilized to conduct a cost-benefit analysis of two differing adaptational strategies to resource insecurity. Using Inuit Nunangat (the Canadian Arctic) as the setting, models are developed to represent two adaptational strategies in response to the onset of the Little Ice Age: exchange with other communities via long-distance trade and intensification of local resource procurement. After determining the average kilograms of resources acquired through a model of local resource procurements, two models were then developed to determine under what scenarios long-distance journeys to procure perishable food goods would be more productive than hunting locally. Ultimately, the results showed that while there are scenarios where undertaking a trading journey would result in a higher average amount of resources acquired, those scenarios would not have been realistic for most Thule communities, leaving hunting locally as the more beneficial adaptational strategy on an economic basis.

IMPROVEMENT OF THE TECHNOLOGICAL PROCESS OF PERISHABLE GOODS DELIVERY IN THE CITY OF KHARKIV

The article analyses the existing methods for building perishable goods delivery technologies and identifies strategies to study the prospects for developing perishable goods delivery systems, which allowed the authors to formulate the research objective in this development. The chosen topic is quite relevant since the formation of rational transport and technological schemes for the delivery of perishable goods allows for increasing the efficiency of resource use, decreasing the cost of their delivery, and reducing the delivery time of perishable goods. One of the ways to improve the efficiency of perishable goods delivery and reduce delivery time is to determine rational distribution routes and rational brands of rolling stock. The study established that foreign scientists have proposed solutions related to the construction of transport and technological schemes for the delivery of perishable goods, taking into account the choice of vehicles and routes for the delivery of goods. Most of these solutions focused on determining the appropriate loading capacity of rolling stock and the optimal delivery routes for perishable goods. A result of the conducted research was a proposed methodology for a comprehensive improvement of the perishable goods delivery process, consisting of determining rational distribution routes for a selected set of vehicles with different loadings. The authors implemented the software for the algorithm for choosing routes for vehicles of various capacities with the definition of the core technical and operational and technical and economic indicators of efficiency of the delivery process of perishable goods. The software implementation of the algorithm for selecting routes for vehicles of different capacities with the determination of the core technical and operational and technical and economic indicators of the efficiency of the delivery process of perishable goods allows one to quickly, in real-time, perform calculations and determine the rational parameters of the transport process and choose the appropriate routes and rolling stock. Keywords: perishable goods, cargo delivery, distribution routes, truck capacity, costs.

A multi-level multi-product supply chain network design of vegetables products considering costs of quality: A case study.

Effective logistics management is crucial for the distribution of perishable agricultural products to ensure they reach customers in high-quality condition. This research examines an integrated, multi-echelon supply chain for perishable agricultural goods. The supply chain consists of four stages: supply, processing, storage, and customers. This study investigates the quality-related costs associated with product perishability to maximize supply chain profitability. Key factors considered include the network design, location of processing and distribution centers, the ability to process raw products to minimize post-harvest quality degradation, the option to sell the excess produce to a secondary market due to unpredictable yields, and the decision not to fulfill demand from distant customers where significant quality loss and price drops would be involved, instead diverting those products to the aforementioned secondary market. Quantitative methods and linear mathematical programming are employed to model and validate the proposed supply chain using actual data from a real-world case study on vegetable supply chains. The main contribution of this research is the incorporation of quality costs into the objective function, which allows the supply chain to prioritize meeting nearby customers' demands with minimal quality loss over serving distant customers where high quality loss is unavoidable. Additionally, deploying a faster transportation fleet can significantly improve the overall profitability of the perishable product supply chain.

Hurdle Concept and the Role of Lactic Acid Bacteria

The hurdle idea is a food preservation technique that includes building up a number of barriers to prevent microbes from growing and prolong the shelf life of perishable goods. The idea of using lactic acid bacteria (LAB), which are essential for food preservation, is one that is significant. Through a variety of processes, including fermentation, competitive exclusion, metabolite generation, biofilm formation, nutritional competition, and metabolic activity, LAB support the hurdle concept. Lactic acid bacteria (LAB) ferment glucose to produce lactic acid, which results in an acidic environment that prevents infections and spoiling germs from growing.

Hurdle Concept and The Role of Lactic Acid Bacteria

The hurdle idea is a food preservation technique that includes building up a number of barriers to prevent microbes from growing and prolong the shelf life of perishable goods. The idea of using lactic acid bacteria (LAB), which are essential for food preservation, is one that is significant. Through a variety of processes, including fermentation, competitive exclusion, metabolite generation, biofilm formation, nutritional competition, and metabolic activity, LAB support the hurdle concept. Lactic acid bacteria (LAB) ferment glucose to produce lactic acid, which results in an acidic environment that prevents infections and spoiling germs from growing. Additionally, LAB generates antimicrobial substances such as hydrogen peroxide, organic acids, and bacteriocins, which work by competitively excluding harmful germs from growing. Furthermore, metabolites of lactic acid bacteria (LAB) such acetic acid, diacetyl, and carbon dioxide enhance the flavor of fermented foods and serve as organic preservatives. Moreover, LAB have the ability to create biofilms that shield them from antimicrobial agents and environmental stressors, increasing their survival and persistence in food matrices. By means of nutritional competition, LAB outcompetes pathogenic or spoilage bacteria in the consumption of available resources, hence restricting their growth. In addition, LAB produces enzymes and antibacterial substances, among other metabolic activities that further impede the growth of infections.

Optimizing perishable food products across states: A multi objective evolutionary algorithm for surplus to deficit transportation

Ensuring the seamless flow of goods from farmer to stakeholder is paramount especially considering the facts such as demand fluctuations, quantity availability, vehicle capacity, inventory limits and truck preferences. In this paper, we proposed a mixed integer linear programming (MILP) model for distributing perishable food goods from surplus states to surplus and deficit state wholesalers in the Indian subcontinent, with the goal of minimizing total cost (fixed, variable, and operational) and transportation time. To ensure product delivery on time, we looked at various scenarios including a case with real-life constraints. Solving this computationally complex task with conflicting objectives in a reasonable time is difficult. To handle the aforementioned multi-objective problem, we proposed an evolutionary algorithm based modified non-dominated sorting genetic algorithm II (MNSGA-II). Furthermore, the suggested algorithm’s performance is tested against the most extensively used multi-objective algorithm, Non-dominated Sorting Genetic method-II (NSGA-II). According to the findings, the proposed MNSGA-II outperforms practically all instances.