Perishable Items Research Articles

Crop Diversification in India (1990–2016): Implications for the Eastern and Northeastern States

This study utilises data from the Agriculture Census for 1990–1991, 2005–2006 and 2015–2016, employing the Herfindahl-Hirschman Index (HHI) to assess crop diversification in Indian states. The overall HHI declined from 1990–1991 to 2015–2016, indicating increased crop diversification nationally. However, the proportion of states exceeding the overall average HHI significantly decreased, indicating a shift towards specialisation or monoculture in some states. The analysis notes a decrease in the gross cropped area (GCA) under total food crops (TFC) and an increase in total non-food crops (TNFC). Despite the overall decline in TFC, high-value crops like horticultural produce, sugar crops, spices, and condiments have seen improvement. Focusing on the Eastern region (ER) and the Northeastern region (NER), the study reveals diversification in NER due to higher allocation of land to high-value crops, while economically disadvantaged states in the ER, such as Bihar and Odisha, show a trend towards crop specialisation, potentially affecting overall food security in the region. The study emphasises the need for government support to address persistent vulnerabilities in poverty, unemployment, and low yields in cereals, pulses, and oilseeds in these regions, recommending inputs, infrastructure development, insurance, and market linkages for perishable items to enhance food security.

Simulation model of perishable item picking policies in the buffer stock of a push-pull supply chain

Simulation model of perishable item picking policies in the buffer stock of a push-pull supply chain

Solving the green reverse logistics problem in e-commerce using a reinforcement learning based genetic algorithm

This study explores the two-phase green reverse logistics problem with time windows and a focus on perishable items that pose a significant challenge in the management of returned goods in e-commerce. We proposed a mixed integer programming model that considers carbon emissions, fuel consumption costs, facility establishment and operating costs, among other factors.We incorporated reinforcement learning concepts to adjust parameters in traditional genetic algorithms, which often have inflexible parameter settings, thereby enhancing both the efficiency and quality of the solutions. The Q-learning algorithm was adopted as the learning method, and various action combinations of reinforcement learning were explored and compared. We further evaluated the performance of different genetic algorithm variations. The results indicate that the proposed algorithm provides high-quality solutions, and that effective parameter configuration significantly impacts the algorithm’s overall performance.

Antibacterial evaluation of garlic extracts on chitosan/starch packaging film using response surface methodology and its application for shelf-life extension of bell peppers (Capsicum annuum).

In this study, garlic extract (GE) was assessed as a potential additive in chitosan/starch (Ch/De) coatings, focusing on phenolic and flavonoid content analyses and antibacterial properties. Using response surface methodology approach, an optimization method was employed to achieve the optimal antibacterial formulation, with Ch, De, and GE identified as key variables in the Design of Experiment. Fourier transform infrared spectroscopy and X-ray diffraction analyses elucidated interactions among these primary components within the films, while thermogravimetric analysis confirmed the enhanced thermal stability of GE-coated film formulations (Ch/De/GE). The Ch/De/GE exhibited antibacterial efficacy against Escherichia coli (ATCC 25922) with an inhibition zone of 7.2mm at optimized concentrations of 2% w/v Ch, 1.5% w/v starch, and 0.5% v/v GE. In silico molecular docking studies provided insights into GE's inhibitory role as an antibacterial agent. Evaluation of green and yellow bell peppers (Capsicum annuum) over 18 days showed that coated peppers maintained better visual appearance and mass stability, with a weight loss decrease of 40.54%-48.96%, compared to uncoated ones. Additionally, the Ch/De/GE coating effectively inhibited bacterial growth, reducing it by 1-1.23 log CFU, during the storage period. In conclusion, the Ch/De/GE coating effectively extends the shelf-life of bell peppers and maintains their quality, demonstrating its potential for use in food packaging to preserve perishable items. PRACTICAL APPLICATION: The optimized chitosan/starch/garlic extract (Ch/De/GE) film developed in this study shows promising potential for application in the food packaging industry, particularly in extending the shelf life of perishable items like bell peppers. Its enhanced antibacterial properties, along with its ability to maintain visual appearance and reduce weight loss, make it an effective natural preservative that could replace synthetic additives in food packaging. By incorporating this biodegradable film into packaging solutions, producers can offer safer, more sustainable products that meet consumer demand for natural and environmentally friendly options.

Optimizing Byproduct Processing for Clean Label Foods: Avocado and MSM‐Tambaqui With a Focus on Zero Waste

ABSTRACTAgricultural byproducts, often discarded, possess significant nutritional value and technological potential. This study investigates the efficacy of ethanolic‐water extracts from avocado (Persea americana Mill.) byproducts, obtained with minimal solvent use through optimized extraction, in enhancing the stability of mechanically separated meat‐tambaqui (Colossoma macropomum) burgers, a high‐fat Amazonian fish. 2000 ppm of avocado seed and peel extract were applied in shelf‐life tests conducted under refrigeration and freezing conditions to evaluate the stability of the burgers. The results demonstrated that the bioactive compounds from avocado byproducts resulted in lower TBARS values, indicating strong antioxidant properties, reduced formation of volatile nitrogen compounds, and color maintenance than sodium erythorbate. Monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) were preserved using bioactive ethanolic‐water extracts, and the protein content was improved compared to the control, enhancing nutritional quality. This study highlights the potential of using agri‐food byproducts, especially for application in highly perishable items such as fish, promoting the development of clean label products, thus supporting a more efficient and environmentally friendly food industry focusing on the circular economy.

Sustainable inventory, pricing, and marketing strategies for imperfect quality perishable items with inspection errors and advertisement-, expiration date- and price-dependent demand under carbon emissions policies

The demand for perishable products is affected by freshness levels, advertising efforts and selling prices, among other factors. By virtue of being consumed mostly by humans, quality control is also an important factor in perishable production systems. Moreover, inventory management related operations in such production systems release sizeable quantities of carbon emissions that are often regulated by carbon policies. To study the interactions of all these attributes in the context of a perishable inventory system, this paper proposes four sustainable inventory models for a perishable product with imperfect quality, inspection errors and whose demand depends on the advertising frequency, expiration date and selling price. The emissions released are assumed to be governed by carbon tax and carbon cap policies. Two of the models are developed under the assumption that the quality inspection process is 100% effective while the other two models consider the possibility of committing inspection errors, and additionally, for each pair of models, one is developed under a carbon tax policy and the other under a carbon cap policy. All the models are aimed at jointly optimising the perishable product’s lot-size, advertising frequency and selling price. The numerical results show that the presence of inspection errors leads to 28% and 23% lower profits under carbon tax and cap policies, respectively. Moreover, profit can be maximised by either stocking perishable products with longer shelf lives or targeting customers that engage with advertising mediums and those that are not price-conscious, while emissions can be minimised via the enforcement of carbon policies.

Integrated production and outbound distribution scheduling problem with multiple facilities/vehicles and perishable items

Integrated production and distribution operations are crucial and necessary for companies to outperform their competitors. In this paper, we study an integrated problem includes heterogeneous facilities and capacitated vehicles. Our goal is to produce and distribute products in batches that meet customer demands at the lowest possible cost. We first give the formal definition of the problem, then propose a Mixed Integer Programming (MIP) formulation and strengthen with several polynomial size valid inequalities. Furthermore, we develop Memetic Algorithm (MA) and finally present 2592 test instances to illustrate the efficiency of both MA and MIP formulation. The optimal or feasible solutions for 1283 of 2592 test instances are obtained by the proposed MIP formulation. MA finds optimal or near optimal solutions for all these instances. When comparing the best solutions obtained by MA with the solutions of the MIP formulation, MA produces a superior result. Besides that, the time to obtain for optimal solutions are approximately 9 min and for all test instances approximately 82 min using MIP formulation. The time needed to solve all test instances with MA is only 3 sec. These findings clearly demonstrate the speed and accuracy of MA in producing results. These results also demonstrate that, it is possible to achieve solutions that are either equal or very close to those found by the MIP formulation, in an extremely short computation time with MA.

Decision-Making for The Selection of Vegetable Suppliers for Foods Distributors uses AHP and TOPSIS

The evaluation of supplier selection is an important aspect that needs to be considered in supply chain management. The best suppliers contribute to a more efficient supply chain system. Companies operating in the food distribution sector are particularly attentive to their suppliers. One of the products that receives significant attention is spinach, a perishable item, making supplier evaluation crucial. This research aims to evaluate the supplier selection of spinach products based on several criteria, such as delivery, quality, price, service, and location. Weighting is done using the Analytical Hierarchy Process (AHP). In this case, assessment or weighting is carried out by distributing questionnaires to three respondents in the field. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed to determine the rankings of each alternative. The results of this study indicate that the price criterion has the highest weight with a weight of 0.39. The recommended selected alternative is alternative 4, with the highest average value (Vi) based on the assessments of three experts. This model proves capable of addressing the issues of supplier selection evaluation, leading to the selection of alternative or supplier 4 as the spinach supplier for the food distribution company.

Optimal profit in two-level trade credit eoq model with default risk and reminder cost under finite time horizon having time-dependent demand and deterioration

Trade credit is a type of promotional activity that generally increases demand and revenue but also invites default risk due to dishonest customers. Due to default risk, revenue is lost, and to overcome this, an arrangement is made to remind the defaulters. A retailer dealing with a perishable item wants to exhaust the stock quickly after a certain deterioration level. Demand and deterioration of the products are normally dynamic. The business period of seasonal products is uncertain. Considering these facts, we formulate and analyze a two-level trade credit inventory model, where the wholesaler and retailer give credit periods to their corresponding downstream customers. After a certain level of deterioration, the retailer increases the credit period for the customers for early stock exhaustion, and to reduce default risk, a reminder cost is introduced. These activities increase the profit. The mathematical models under different circumstances are formulated for different time horizons. Some existing results are deduced. The models are numerically solved using a parametric study and the Generalised Reduced Gradient method through LINGO 19.0 software. Some lemmas and theorems are deduced to establish the analytical outcomes. Trade-offs between the number of the business cycle, trade credit, and reminder cost against optimum profit are separately demonstrated. The results with and without reminder cost are compared, and it is shown that the model with reminder cost fetches more profit. Profit under different uncertain environments are evaluated, and they differ marginally. Some beneficial impacts are discussed.

Uninformed Choices in Perishables

We study consumer choices of perishable items and their effects on retail waste using a data set that tracks expiration date–level consumer choices and on-shelf inventory.

Fresh Fruit Detection Using Deep Learning

The Fresh Fruit Identification System constitutes a notable advancement in the meals industry. Employing sophisticated technologies along with machine-getting-to-know and computer imagination and prescient, it complements the great evaluation and optimization of perishable items, mainly focusing on culmination and greens. This contemporary system employs advanced gadgets gaining knowledge of models, especially convolutional neural networks (CNNs), to meticulously analyze high-decision pics of processed meals. By rapidly and appropriately identifying objects based totally on attributes that include shade, texture, and shape, the machine guarantees no longer the simplest precision in categorization but also an in-intensity best evaluation. This entails defect detection and the assessment of ripeness, making sure that the most effective rate-satisfactory merchandise reaches customers. The implementation of sturdy and scalable systems has adeptly addressed demanding situations associated with facts, model education, and real-time processing. Consequently, the Fresh Food Identification System emerges as a pivotal contributor to raising the standards of the perishable meals supply chain, embodying a dedication to excellence and performance. Keywords— Fresh Fruit Detection, Machine Learning, Computer Vision, Fruit Quality Assessment, Food Safety, Image Processing,Classification Algorithms, Object Detection, Feature Extraction, Deep Learning,Convolutional Neural Networks (CNN).

Feed Friend: Fostering Community Involvement in Food Assistance

According to the Food and Agriculture Organization (FAO) of the United Nations, approximately one-third of all food produced for human consumption worldwide is wasted annually. Food wastage occurs at various stages of the supply chain, including production, processing, distribution, and consumption. FeedFriend is an innovative website which was designed to combat food-wastage by fostering a vibrant community of donors and recipients. Donor Registration Individuals or Businesses can sign up as Donors on FeedFriend, providing details about the surplus food they have available for donation. This includes restaurants with excess inventory, grocery stores with perishable items nearing expiration, or individuals with leftover food from events or meals. Food Listings Donors can create listings within the website, specifying the type and quantity of food they wish to donate, include relevant details such as expiration dates etc. In Feed Friend, Users can filter the Donation requests based on their location. This type of service is used to match donors with nearby recipients in need. Recipients may include food banks, shelters, community centers, or individuals facing food insecurity. Feed Friend facilitates communication between donors and recipients, coordinates pickup or delivery logistics directly by providing donor’s contact details. So that it enables a better communication between Donors and Receivers.

Multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse

One of the main challenges of food bank warehouses in developing countries is to determine how to allocate perishable products to beneficiary agencies with different expiry dates while ensuring food safety, meeting nutritional requirements, and minimizing the shortage. The contribution of this research is to introduce a new multi-objective, multi-product, and multi-period perishable food allocation problem based on a single warehouse management system for a First Expired-First Out (FEFO) policy. Moreover, it incorporates the temporal aspect, guaranteeing the dispatch of only those perishable products that meet the prescribed minimum quality standards. A weighted sum approach converts the multi-objective problem of minimizing a vector of objective functions into a scalar problem by constructing a weighted sum of all the objectives. The problem can then be solved using a standard constrained optimization procedure. The proposed mixed integer linear model is solved by using the CPLEX solver. The solution obtained from the multi-objective problem allows us to identify days and products experiencing shortages. In such cases, when there is insufficient available inventory, the total quantity of product to be dispatched is redistributed among beneficiaries according to a pre-established prioritization. These redistributions are formulated as integer programming problems using a score-based criterion and solved by an exact method based on dynamic programming. Computational results demonstrate the applicability of the novel model for perishable items to a real-world study case.

Influence of Lactobacillus curvatus and Lactococcus lactis subsp. lactis on the shelf life of sausages in vacuum packaging

Boiled meat products, including sausages, are perishable items, making them potentially hazardous and requiring the use of preservatives. Promising preservatives for sausages include starter cultures of lactic acid microorganisms, which act as antagonists to pathogenic and opportunistic bacteria. Therefore, this study aimed to determine the types of microbiota causing spoilage of sausages during storage in vacuum packaging and the effectiveness of their treatment with a starter culture of Lactobacillus curvatus or a mixture of starter cultures of Lactobacillus curvatus + Lactococcus lactis subsp. lactis. On the first day of storage, the dominant microorganisms in the sausages were Klebsiella variicola and Bacillus amyloliquefaciens plantarum. By the 21st day of storage, signs of sausage spoilage were observed, including the appearance of cloudy juice and the separation of the vacuum packaging. The main microorganisms causing sausage spoilage were Moellerella wisconsensis, Proteus mirabilis, and Bacillus cereus. In the control sausages on the 18th day, the number of mesophilic aerobic and facultative anaerobic microorganisms approached the allowable limit, and they were not suitable for further storage. Treatment of sausages with a starter culture or a mixture of Lactobacillus curvatus + Lactococcus lactis subsp. lactis led to an increase in the number of mesophilic aerobic and facultative anaerobic as well as lactic acid microorganisms practically throughout the entire storage period. However, signs of spoilage in both treatment groups appeared on the 36th day. Both treatments with starter cultures extended the shelf life of sausages in chilled conditions by 12 days. The use of a mixture of starter cultures for treating sausages during storage in vacuum packaging was not advisable. The obtained results can be used to extend the shelf life of boiled meat products, taking into account the species composition of spoilage microbiota.

An Inventory Model for Perishable Items with Cubic Deterioration Rate Having Shelf-Life Price, Stock, and Time-Dependent Demand Rate

An Inventory Model for Perishable Items with Cubic Deterioration Rate Having Shelf-Life Price, Stock, and Time-Dependent Demand Rate

Projected Inventory-Level Policies for Lost Sales Inventory Systems: Asymptotic Optimality in Two Regimes

Inventory Projection and Asymptotic Optimality van Jaarsveld and Arts propose a new policy for the canonical periodic review lost sales inventory problem. Under this policy, orders are placed in each period such that the expected inventory level at the moment of replenishment order arrival is at a fixed level. This single-parameter policy is called the projected inventory-level (PIL) policy. The PIL inherits asymptotic optimality properties from the constant-order policy for long lead time and from the base-stock policy when the cost of losing a sale is large. The PIL policy has lower cost than competing heuristics in a numerical study. The PIL seems to be a promising approach for other inventory systems with a high-dimensional state space, such as the perishable item inventory management problem.

Identification of Formalin in Meatball Sold at Palopo City Food stalls using Schryver Method Approach

Formalin, a solution containing 30-50% formaldehyde gas (CH2O), finds applications in various sectors such as corpse preservation, disinfection, and the production of plastics and anti-foaming agents. Despite its industrial benefits, formalin is often misused as a preservative in the food industry, particularly in perishable items like meatballs. This research employs a descriptive method to provide an overview of formaldehyde identification in meatballs. Ten meatball samples were collected from permanent stalls in Palopo City for analysis. Laboratory tests conducted qualitatively using the Test Kit method revealed negative results (-) for formalin content in all samples. No color changes were observed in the tested meatballs, indicating the absence of formalin.

A multi-objective optimization model for the location of cold chain logistics distribution center based on trust domain optimization algorithm

Cold chain transportation refers to a logistics method that transports fresh, perishable, and perishable items from the place of production, processing or warehouse to the place of consumption under certain temperature conditions. As consumers have higher and higher requirements for food safety and quality, the cold chain logistics industry has also developed rapidly. However, the high cost of cold chain logistics, the difficulty of technology, the difficulty of ensuring service quality and other problems have also arisen, and how to optimize cold chain transportation has become a hot issue in the cold chain logistics industry. Cold chain transportation optimization refers to the premise of ensuring the quality and safety of goods, through reasonable transportation paths, transportation methods, temperature control and other measures, to minimize the cost of cold chain transportation, improve efficiency and service quality. In order to achieve the optimization of cold chain transportation, it is necessary to establish a reliable cold chain logistics distribution center to ensure the safety and quality of goods during transportation. In order to solve the problem of location selection of cold chain logistics distribution center, this paper first defines the service reliability and calculation method of cold chain distribution center. Then, the location model of the cold chain logistics distribution center and the trust domain optimization algorithm model were established. The model aims to minimize the total cost, while considering multiple factors such as transportation distance, temperature control, facility construction, etc., to ensure the reliability and operational efficiency of the cold chain logistics distribution center. Finally, this paper introduces an example for calculation to prove the feasibility and universality of the model in the location problem of cold chain logistics distribution center. Through the calculation of examples, it can be seen that the model can effectively reduce the cost of cold chain transportation and improve the service quality and efficiency of cold chain logistics distribution center. In summary, this paper proposes a reliable location model and trust domain optimization algorithm model for cold chain logistics distribution centers to solve the challenges faced by the cold chain logistics industry. This model can provide guidance for cold chain logistics companies to gain a greater advantage in a highly competitive market.

Warehouse Inventory Model for Perishable Items with Hybrid Demand and Trade Credit Policy

Warehouse Inventory Model for Perishable Items with Hybrid Demand and Trade Credit Policy

Integrated Inventory Model with Inflation for Deteriorating Items

The integrated inventory model works like a multi-tier supply chain involving a manufacturer, buyer, supplier, and customer. In the proposed article, integrated inventory management for perishable items has been developed. It is an essential responsibility of every supply chain member to ensure that any inventory can be delivered to the customer smoothly and on time. The model of inflation fluctuations takes into account inflation as one of its factors. This model considers the movement of inventory from production to supply in real data; Numerical examples have been explored to facilitate a practical understanding of this model in real-life situations, and the aggregate cost from the entire supply chain has been calculated. The model concludes with a sensitivity analysis, revealing the effects on the overall model stemming from alterations in key parameters that exert significant influence.