Fresh Food Research Articles

RESEARCH ON CONSUMERS’ INTENDED USAGE OF COLD CHAIN LOGISTICS SERVICE THROUGH FRESH-FOOD APPS BASED ON THE STRUCTURAL EQUATIONS MODEL

By expanding the theory of planned behavior with Structural Equation Modeling, the objective of the study is to investigate consumer behaviors in the purchasing of fresh food through fresh-food apps and cold chain logistics services usage in Shanghai and Beijing, China. The results showed that the usefulness of the fresh-food apps has a positive impact on consumers’ attitudes to enjoying apps’ cold chain logistics services. However, the ease of use of apps has never had a positive impact on consumers’ attitudes towards enjoying cold chain logistics services. Furthermore, consumers’ attitudes, perceived behavioral control and subjective norm have a positive impact on their intention to use cold chain logistics services via fresh food apps. Findings confirmed that attitude plays a part of mediating role in usefulness and behavioral intention.

Development of Semolina Starch/Agar-Based Intelligent Films by Incorporating Butterfly Pea Flower Anthocyanins to Monitor the Freshness of Prawns

The natural plant-derived pH indicator film which monitors food freshness during storage has gained attention among researchers due to consumer awareness of food quality, freshness, and food safety. This study aims to develop a semolina/agar-based film (S/A) incorporated with anthocyanins from butterfly pea flower extract (BPE) as a freshness indicator for tiger prawns and freshwater prawn during 4 days of storage at 4±1 °C. The semolina/agar-based butterfly pea extract (S/A/BPE) films were developed with different concentrations of BPE (3, 6, and 9%) by casting method. Increasing BPE up to 9% in the S/A film increased the thickness and moisture content, lowering the films’ water permeability. The film also had a smoother surface structure with a darker colour. Different pH solutions (pH 2.0-12.0) also changed the colour of films, depending on the BPE concentrations. The application of S/A/BPE films on tiger prawns and freshwater prawns caused colour changes from dark blue at day 0 (fresh stage) to intense green at day 2 (beginning of spoilage) indicating the spoilage of samples. The pH values of both prawns were gradually increased. However, no major changes were observed in the texture profile analysis. These results indicate that S/A/BPE film has the potential to be used as a pH indicator film to detect the freshness of prawn samples.

Preparation and characterization of pH-sensitive intelligent packaging films based on cassava starch/polyvinyl alcohol matrices containing Aronia melanocarpa anthocyanins

Intelligent packaging films have sparked interest due to their capacity to indicate food freshness. In this study, a pH-sensitive packaging film was developed using the cast film process, with cassava starch and polyvinyl alcohol (PVA) as substrates. Aronia melanocarpa anthocyanins (AMA) were incorporated as visual indicators of pH changes. The study assessed the impact of the cassava starch/PVA ratio and AMA content on the films' physical, structural, and pH-sensitive properties. It evaluated their potential for monitoring milk freshness. The results revealed that films with a starch/PVA ratio 3/7 achieved optimal tensile strength (TS) and elongation at break (EAB). The incorporation of AMA into the films enhanced their UV barrier properties. Notably, the compatibility between starch and PVA was improved when the film contained 1 g of AMA per hectogram (hg) of the total mass of starch and PVA. Specifically, the enhanced compatibility was evident through differential scanning calorimetry (DSC) and scanning electron microscopy (SEM) analysis. Furthermore, the films demonstrated notable color changes within a pH range of 2–11, indicating their potential for monitoring milk freshness. These findings suggested that the films containing AMA hold great promise for applications in intelligent packaging within the food industry.

ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness

The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability.

Perishable food procurement decision making in emergency scenarios: a case study from China

ABSTRACT The frequent occurrence of natural disasters and public health and safety events bring considerable risk and uncertainty to the supply of materials, especially for perishable fresh food. Currently, there is limited scholarly research addressing the procurement decisions related to perishable fresh food during emergencies. In this paper, the emergency food procurement problem is addressed by considering cost, supply risk, and other factors, and a procurement decision optimisation model is constructed with the dual objectives of minimizing both total cost and supply risk. Additionally, the urban emergency is used as the case study, which provides background description and the actual data that enable better understanding of what actually occurs during emergencies, as well as validating the applicability of the model. The findings could support the decision of supply chain participants engaged in emergency fresh food procurement and enrich the body of theory in the field of emergency fresh food procurement decisions.

Research on Pricing and Replenishment Decision of Vegetable Products Based on Optimization Algorithms

With the improvement of people's living standards, people's requirements for the freshness and variety richness of food are also constantly increasing. Due to the short shelf life of fresh vegetable products, reasonable prediction and formulation of pricing and replenishment strategies have a significant impact on supermarket revenue. This article first cleans the data and uses Excel and Grabbs statistics to determine the missing and abnormal values of the data. The missing values are supplemented by 0, and the abnormal values are determined and eliminated through manual intervention. Conduct descriptive statistics on the preprocessed data to obtain partial data features of categories classified by day and month. Analyze numerical features to determine the distribution pattern of data using monthly classification, and then use SPSS PRO to conduct Spearman correlation analysis to obtain the relationship between vegetable categories. Next, this article quantifies six data indicators, including daily sales volume of each category, average unit price during normal sales, and discount degree. Then, MATLAB is used for regression fitting analysis to obtain the relationship expressions between each indicator and the total sales volume. Excel analysis is used to summarize the data to obtain the available variety information of each item of vegetables in the supermarket from June 24th to 30th, And use Python software to establish a time series prediction model to analyze and process the information of supermarket products, and obtain the demand for individual products on July 1st. Using the single product selected by the supermarket as the 0-1 decision variable, and the total operating profit and supply demand relationship of the supermarket as the dual objective variables, a multiple linear regression model is constructed to obtain the final replenishment policy and pricing decision. The data in this paper is from the National College Students Mathematical Modeling Competition C question.

Analysis of Factors Influencing Consumers' Satisfaction with Online Fresh Food Shopping

With the development of Internet technology, more and more people choose online shopping, resulting in an increasing amount of online review data. It is crucial to mine information about users' feelings from these reviews to obtain factors that affect customer satisfaction. This article uses crawler technology to obtain the review data of JD supermarket hairy crabs. After data preprocessing, it extracts five characteristic factors that affect satisfaction, including logistics, specifications, taste, freshness and quality. Then, it uses a Likert scale to quantify and score the data, and establishes a customer satisfaction factor model based on Bayesian network, which is compared with a neural network model. Evaluation criteria include accuracy, precision, and recall. The research results show that the customer satisfaction model based on Bayesian network has the best measurement effect, which can output the influence degree and correlation of each factor on satisfaction, and provide relevant suggestions for producers and other stakeholders.

Research on Pricing and Replenishment Strategies for Vegetable Commodities Based on an Enhanced Nonlinear Objective Programming Model

With the increasing consumption of fresh food ingredients in the Chinese market and the perishable nature of vegetable commodities, which represent the largest proportion, optimizing procurement strategies plays a crucial role in enhancing the profitability of supermarkets. This has garnered significant attention from scholars and practitioners. Drawing on principles from microeconomics, this study employs multiple linear regression to describe the relationship between pricing and sales volume. Additionally, time series forecasting methods are utilized. By considering both supermarket profitability and social welfare maximization as optimization objectives, single-objective optimization and multi-objective nonlinear programming models are established. Various decision strategies for supermarkets under different scenarios are discussed, and a satisfactory decision is obtained by solving the models using differential evolution algorithm. Through research on the expected conditions and data provided by real supermarkets, this paper provides guidance for intelligent supermarket procurement strategies and supply chain management in practical settings.

Research on Vegetable Product Pricing and Replenishment Decision Based on Linear Regression and ARIMA Model

With the rise of fresh food supermarkets, how to automatically price and restock vegetable products has become a major challenge for many businesses. To develop a restocking and pricing plan for vegetable products in supermarkets, we first calculate the cost profit margin, standardize the profit margin and total sales volume data, and then incorporate vegetable categories into a linear regression model using dummy variables, We found a significant negative correlation between profit margin and total sales volume, and obtained the product pricing strategy using the linear equation of profit margin. After conducting ADF testing, the number of autoregressive terms and the average number of sliding terms of the Time Series (ARIMA) model were determined based on the test set results, and the model was used to predict the total daily replenishment of each vegetable category in the next week. This study has reference value for vegetable restocking and pricing decisions.

Forecasting superstore fruit and vegetable sales based on ARIMA-LSTM

In the context of a fresh food supermarket, managing perishable vegetables, which have a notably limited shelf life, maintaining optimal stock levels becomes imperative for safeguarding profitability. This research paper intricately explores the analysis of numerous datasets using Power Query and Power BI, with a pivotal focus on devising a succinct coding system. To ensure data quality, outliers are meticulously eliminated using advanced techniques like linear interpolation and Z-Score processing. The ARIMA time series model is employed to predict the sales volume for each category in July, having been trained on data from June 2022 to June 2023, adeptly capturing historical sales patterns and emerging trends. Additionally, a parameter-tuned LSTM recurrent neural network, optimized with Adam's algorithm, is utilized to refine predictions, considering its inherent memory capabilities. A thoughtfully designed weighted average approach combines outputs from both models, strategically minimizing overfitting and enhancing prediction accuracy. The resultant research fortifies inventory management optimization, bolstering the supermarket's profitability while effectively meeting customer demands.

Feeding behaviour and food choice in three populations of the millipede Glomeris hexasticha Brandt, 1833 (Diplopoda: Glomerida: Glomeridae): intra- and interpopulation laboratory study

Abstract Foraging behaviour, determined by both internal and external factors, requires investment of time and energy. Optimizing the nutritional decisions also influences efficiency during the feeding process and fitness. Feeding behaviour has been poorly investigated in millipedes. Hence, the aims of this study were to test food choice and to explore intra- and interpopulation variability in several sequences of feeding behaviour in millipede Glomeris hexasticha Brandt, 1833 under laboratory conditions. Individuals of G. hexasticha and leaf litter were collected from three populations within Belgrade, Serbia. A feeding experiment was conducted using a cross-shaped experimental maze with oak leaves, small-leaved linden leaves, and moss as potential food sources. The individuals most often chose oak leaves, subsequently moss, while linden leaves were the least preferred. Sexual dimorphism was detected only for the average time spent in maze arms that contained food items in the Ada New Belgrade population and the Hyde Park population. Intrapopulation differences that depended on feeding status and interpopulation differences in some behavioural sequences were detected. We assume that sexual dimorphism in most of the analysed behavioural traits was not detected, because both sexes had to satisfy basic nutritive requirements after the same starvation period. However, research timing may be responsible, at least partially, for the obtained results. Repeating experiments in different periods of the year or using more fresh food or food sources in different phases of the decomposition may give different results. Further, in our experimental design, some unknown preference towards particular maze arms could not be completely excluded.

Intelligent double-layer films based on gellan gum/mica nanosheets/anthocyanin/konjac glucomannan/carrageenan for food real-time freshness monitoring

In this work, double-layer intelligent films were designed and synthesized based on Broussonetia papyrifera fruit anthocyanin (BPFA), mica nanosheets (MN), konjac glucomannan (KG), carrageenan (CA) and gellan gum (GG) using layer-by-layer casting strategy for real-time and in situ food freshness monitoring. The KG/CA/BPFA layer served as the indicator layer, while the GG/MN layer acted as the protective outer layer. It was observed that the water vapor permeability value of the GG-MN/KG-CA-BPFA double-layer film was reduced by 4.7 times, and the tensile strength was increased by 4.1 times compared with the GG film, which could be related to the reinforcing effect and proper barrier property of MN. Notably, the double-layer film blocked most of UV-light and oxygen, and its water contact angle reached 102.4° ± 1.18°. In particular, the films showed favorable visually detectable colorimetric responses to different pH buffer solutions and volatile ammonia. More importantly, visual assessment using shrimps as test specimens demonstrated distinct color alteration from red to blue-grey on spoilage. The color change of the film was also highly correlated with TVB-N (R2 = 0.964) and pH (R2 = 0.999). In conclusion, the newly designed double-layer film exhibited excellent performance, which guides an innovative approach for advanced food-packing material development.

Exploratory Study of Romanian Generation Z Perceptions of Green Restaurants

The hospitality industry has taken advantage of consumers’ increasing concerns regarding environmental issues. Worldwide, green restaurants have increased their profitability by using environmental attributes as a competitive advantage. In Romania, restaurants could profit from this new trend. This study aims to determine the perception of Generation Z consumers regarding green restaurants, since these younger consumers seem to have adopted more eco-friendly behavior compared to previous generations. Another objective of the study refers to assessing the importance of attributes relating to environmental protection and organic food when choosing a restaurant, consequently revealing a suitable market segment for this type of restaurant. In order to determine their degree of knowledge related to the concept of green restaurant, potential consuming behavior if implemented, and identifying the factors considered important when choosing a restaurant, this research compiles information from individuals belonging to Generation Z. Using principal component analysis, five factors were obtained, among which the “health and fresh food” was the most important. Based on the five dimensions, K-means cluster analysis led to three profiles of consumers. “Pro-environmental and convenience seekers” represent the largest cluster and should be considered the main target for future green restaurant practitioners. Additionally, our study finds an overall positive attitude toward green restaurants among the respondents and, more specifically, opinions on how these restaurants could address environmental issues and offer organic food, both important factors for Generation Z. This translates into a strong incentive for future entrepreneurs considering this business model in Romania.

Research on Supply Chain Management Countermeasures for Fresh Agricultural Products

Due to the rapid growth of China's economy and the popularization of the Internet, Online platforms are becoming increasingly popular for consumers to purchase products, online shopping platforms can provide consumers with a richer range of products and cheaper prices, and therefore favored by consumers. Fresh products, which are considered a special type of consumer goods, are subject to more restrictions on transportation and sales due to their characteristics, which solves the development dilemma of fresh products. In the face of this situation, this article mainly analyzes the problems of the cold chain logistics service system of fresh agricultural products in the context of e-commerce and puts forward the corresponding countermeasures on the basis of which, provides suggestions and guidelines for the development of the relevant logistics and fresh food industry, and then better promote the healthy and sustainable development of fresh agricultural products cold chain logistics under the background of e-commerce.

Thermochemical Co-Liquefaction of Fruit Pomace’s Blends in a Binary Solvent System Toward Value-Added Bioproducts

Abstract Poland belongs to the most meaningful producers of fresh food products in the European Union, e.g., apples, cherries, carrots, as well as currants. A significant part of these products are subjected to further processing, which results in the generation of an abundant amount of wet residues, such as pomace. The present paper aims to investigate the possibility of co-processing various industrial residues in the form of fruit pomaces through thermochemical liquefaction toward high-energy-density biocrude and biochar. More specifically, industrial wastes received from commercial juice production, i.e., blackcurrant, apple, and cherry pomaces, were converted under subcritical conditions of the water-isopropyl alcohol solvent system at fixed conditions. Particular focus was put on testing binary and ternary mixture feedstock systems and the possibility of predicting the bioproduct yield. Furthermore, the quality of the resultant biocrudes was analyzed by means of FTIR, GC-MS, and elemental analysis. For all separately studied raw materials and their binary and ternary mixtures, the dominant group fraction was biocrude with its yield ranging between 45.8 and 54.5 wt%. Produced liquids exhibit high energy density (HHV between 30.1 and 32.4 MJ/kg); thus, all of them pose perspective alternative fuels for generating heat and electricity. Also, the solid biochars presented a high energy density (HHV around 24–26 MJ/kg) and can be considered as an alternative energy carrier. The postulated approach aims to increase the flexibility and profitability of future technology of processing useless waste toward value-added bioproducts for the chemical and energy sectors.

Food miles on the shelves: the share of local food products in the Hungarian retail sector

Share of national food products in retail is a frequent subject of policy debates. Local food is often associated with national security, sustainability, and support of local economy, contributing to value-added production, employment, rural development, and preservation of local food culture. Despite its importance, relevant academic literature about the proportion of national food in retail is basically non-existent. This paper presents a unique study that fills this gap and gives an account of the proportion of the main national food products in the Hungarian retail sector. The study presents a comprehensive picture of the food supply situation of the 10 largest retail chains in Hungary in the second half of 2020 for 16 key product categories (representing 67% of total food sales in value in the country), based on the experience of research that covered the physical examination of nearly 40 thousand individual food products. The study found that 70.85% of the analysed food products were supplied by domestic companies. Proportion of national products was higher for fresh food (meat, milk, perishable meat and milk products, eggs, fruits, and vegetables) and honey, while imports were dominant in higher value-added categories. Domestic chains had 15.37% points more local products in stock than international ones. The exact results can be used for benchmarking between companies and sectors, indicating comparative advantages and disadvantages, and provide solid basis for economic development plans.

Research On Vegetable Replenishment and Pricing Based on LSTM Neural Network Prediction Modeling

Vegetables in fresh food supermarkets face challenges with pricing and replenishment since the majority of vegetable varieties have a short shelf life. Fresh food supermarkets must replenish goods based on sales history and demand. In this paper, the pricing and replenishment decisions of fresh supermarkets are predicted using the random forest and LSTM neural network prediction models. The findings indicate that the prediction model in this paper can have a good predictive effect and that the decisions made by fresh supermarkets regarding pricing and replenishment are related to the historical time series data. This paper's study can assist in resolving price and replenishment issues in fresh food stores, which will have some positive economic effects.

Optimization Of Automatic Pricing and Replenishment Decision Based on Time Series and BP Neural Network Model

In major fresh food supermarkets, vegetable products often have a short shelf life and poor product quality, which affects their sales. Therefore, it is particularly important to make replenishment decisions for each vegetable category on the same day without knowing the specific individual product and purchase price. This article focuses on the problem of automatic pricing and replenishment decision-making for vegetable products. Based on the fact that vegetable products have a relatively short shelf life, the automatic pricing and replenishment strategies for vegetable products are optimized, indirectly proving the correlation between certain individual products. A mathematical model is constructed using time series and BP neural network models to obtain corresponding replenishment strategies, Finally, provide recommendations on automatic pricing and replenishment decisions for vegetable products. The "cost plus pricing" method involved in this question plays a crucial role in the pricing of vegetables. At the same time, from the perspectives of the demand side and supply side, the limitations of sales time and sales space are also particularly important.

Research On Time Series Forecasting-Based Pricing Strategy for Supermarket

In fresh food supermarkets, vegetables have a short shelf life. It is of great practical significance to price different products based on market research in order to maximize the benefits of supermarkets. The aim of this paper is to investigate and address the pricing decision question of vegetable products in fresh food supermarkets. This article investigates the correlation between cost-based pricing and sales revenue by establishing linear regression models and time series models.In addition, we have also made price predictions for various categories of products in the supermarket. Finally, this article provides pricing decisions for various categories in the first week of July 2023 for the supermarket.

Pricing And Replenishment Strategies for Vegetable Products Based on Planning Models

Reasonable pricing and replenishment strategies for vegetable products are crucial for maximizing profits for fresh food supermarkets. This article mainly studies the relationship between the sales volume and sales unit price of vegetable products and time, establishes a pricing and replenishment decision model based on BP neural network and mathematical programming, and provides strategies to meet the requirements of maximizing the interests of supermarkets.