Explore the vast range of titles available.

Recently, due to the enhancement in consumer awareness of food safety, considerable attention has been paid to intelligent packaging that displays the quality status of food through color changes. Natural food colorants show useful functionalities (antibacterial and antioxidant activities) and obvious color changes due to their structural changes in different acid and alkali environments, which could be applied to detect these acid and alkali environments, especially in the preparation of intelligent packaging. This review introduces the latest research on the progress of pH-responsive freshness indicators based on natural food colorants and biodegradable polymers for monitoring packaged food quality. Additionally, the current methods of detecting food freshness, the preparation methods for pH-responsive freshness indicators, and their applications for detecting the freshness of perishable food are highlighted. Subsequently, this review addresses the challenges and prospects of pH-responsive freshness indicators in food packaging, to assist in promoting their commercial application.

Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. If fish is stored for an extended period, its freshness deteriorates. Determining the freshness of fish can be done by examining its eyes, smell, skin, and gills. In this study, artificial intelligence techniques are employed to assess fish freshness. The author’s objective is to evaluate the freshness of fish by analyzing its eye characteristics. To achieve this, we have developed a combination of deep and machine learning models that accurately classify the freshness of fish. Furthermore, an application that utilizes both deep learning and machine learning, to instantly detect the freshness of any given fish sample was created. Two deep learning algorithms (SqueezeNet, and VGG19) were implemented to extract features from image data. Additionally, five machine learning models to classify the freshness levels of fish samples were applied. Machine learning models include (k-NN, RF, SVM, LR, and ANN). Based on the results, it can be inferred that employing the VGG19 model for feature selection in conjunction with an Artificial Neural Network (ANN) for classification yields the most favorable success rate of 77.3% for the FFE dataset.

The increase in new technology and consumer demand for healthy and safe food has led to the development of smart packaging to help consumers understand food conditions in real time. The incorporation of red dragon fruit peel anthocyanin into cassava starch and chitosan films was used in this study as a color indicator to monitor food conditions. This indicator film was generated using the solvent-casting method. The mechanical, morphological, and physicochemical characterizations of the film were studied, and food freshness monitoring was carried out. The results showed that adding red dragon fruit peel anthocyanin increased up to 94.44% of the antioxidant activity. It also improved its flexibility, indicated by the lowest tensile strength (3.89 ± 0.15 MPa) and Young’s modulus (0.14 ± 0.01 MPa) and the highest elongation at break (27.62 ± 0.57%). The indicator film was sensitive to pH, which was indicated by its color change from red to yellow as pH increased. The color of the film also changed when it was used to test the freshness of packaged shrimp at both room and chiller temperatures. According to the results, the indicator film based on cassava starch–chitosan incorporated with red dragon fruit peel anthocyanin showed its potential as a smart packaging material.

The colorimetric food freshness indicator (CFFI) is a promising technology in intelligent food packaging, offering the capability for real-time monitoring of food freshness through colorimetric changes. This technology holds significant promise in mitigating food waste and enhancing transparency across the supply chain. This paper provides a comprehensive review of the classification system for the CFFI, encompassing colorimetric films and sensor arrays. It explores their applications across key perishable food categories, including meats, seafoods, fruits, and vegetables. Furthermore, this paper offers an in-depth analysis of three critical challenges currently hindering technological advancement: safety concerns, stability issues, and limitations in sensitivity and selectivity. In addressing these challenges, this paper proposes forward-looking solutions and outlines potential research directions aimed at overcoming these bottlenecks, thereby fostering substantial progress in the development of this field.

We consider a supply chain in which a distributor procures from a producer a quantity of a fresh product, which has to undergo a long‐distance transportation to reach the target market. During the transportation process, the distributor has to make an appropriate effort to preserve the freshness of the product, and his success in this respect impacts on both the quality and quantity of the product delivered to the market. The distributor has to determine his order quantity, level of freshness‐keeping effort, and selling price, by taking into account the wholesale price of the producer, the cost of the freshness‐keeping effort, the likely spoilage of the product during transportation, and the possible demand for the product in the market. The producer, on the other hand, has to determine the wholesale price based on its effect on the order quantity of the distributor. We develop a model to study this problem, and characterize each party's optimal decisions in both decentralized and centralized systems. We further develop an incentive scheme to facilitate coordination between the two parties. Computational results are reported to show the effects of freshness‐keeping efforts.

Fish is commonly acknowledged as a highly nutritious food in many regions worldwide, and humans have been consuming fish for centuries to meet their protein and nutritional requirements. The consumption of fresh fish offers numerous benefits, as they contain essential proteins and materials that may be challenging to obtain from alternative sources. However, the freshness of fish decreases after a few days. Humans can determine the freshness of fish by looking at its eyes, smelling it, and checking its gills. But, can machines do the same? This study proposes a novel approach to evaluate the freshness of fish using deep learning techniques. Despite the long-standing tradition of humans determining fish freshness by sensory analysis, the objective evaluation of fish freshness has been challenging. By employing deep learning algorithms (SqueezeNet and InceptionV3) to classify fish based on their freshness using a dataset of 4476 images of fish bodies categorized as fresh and stale, this study provides a new method to address this challenge. Analyzing the results of the study revealed that the SVM, ANN, and LR models result in an accuracy rate of 100% for each deep learning method. This outcome indicates a greater percentage than the previous research, which was 98.0%. This research's novelty lies in its application of deep learning techniques to determine fish freshness objectively, providing a reliable and cost-effective method to evaluate fish freshness. The significance of this study lies in its potential applications in the food industry, offering a reliable method for quality control and food safety.

The growing interest in food quality and safety requires the development of sensitive and reliable methods of analysis as well as technology for freshness preservation and food quality. This review describes the status of chemical and biological sensors for food monitoring and smart packaging. Sensing designs and their analytical features for measuring freshness markers, allergens, pathogens, adulterants and toxicants are discussed with example of applications. Their potential implementation in smart packaging could facilitate food-status monitoring, reduce food waste, extend shelf-life, and improve overall food quality. However, most sensors are still in the development stage and need significant work before implementation in real-world applications. Issues like sensitivity, selectivity, robustness, and safety of the sensing materials due to potential contact or migration in food need to be established. The current development status of these technologies, along with a discussion of the challenges and opportunities for future research, are discussed.

Food packaging is an essential line to defend against the intrusion of undesirable external factors to protect food. The antibacterial edible films are considered as promising food packaging due to their biodegradability, environmental friendliness, and safety. In this paper, recent research progress on antimicrobial edible films based on polysaccharides, proteins, and lipids is reviewed. It is worth noting that polysaccharides and proteins are the main substrates of antimicrobial edible films, while lipids are mainly plasticizers and carriers of active substances in composite films. For example, second-generation liposomes have been investigated as promising carriers for antimicrobial substances or other bioactive substances due to their excellent stability. Moreover, recent advances and future trends of antimicrobial edible films are then analyzed. Linking antimicrobial edible film materials to delivery systems for stable loading of bioactive substances is a promising future direction, such as nanoemulsion and microencapsulation technologies. In addition, a smart and active packaging that allows consumers to directly determine the freshness of food products without unwrapping the package has come into the limelight. Currently, pH-sensitive films and smart fluorescent “on–off” sensors for humidity have been investigated as smart and active packaging materials for monitoring the freshness of food products, which can be vigorously explored in the future.

Food quality control is essential in industry and daily life. In this work, we developed a novel colorimetric sensor array composed of several pH-sensitive dyes for monitoring meat freshness. A color change in the sensor array was seen after exposure to volatile organic compounds (VOCs), and the images were captured for precise quantification of the VOCs. In conjunction with pattern recognition, meat freshness at different storage periods was readily discerned, revealing that the as-fabricated colorimetric sensor array possessed excellent discrimination ability. The linear range for quantitative analysis of volatiles related to meat spoilage was from 5 ppm to 100 ppm, with a limit of detection at the ppb level (S/N = 3). Furthermore, the testing results obtained by the sensor in assessing meat freshness were validated by a standard method for measuring the total volatile basic nitrogen (TVB-N). The sensing signals showed good agreement with the results obtained in TVB-N when measuring real food samples. The sensor also displayed good reproducibility (RSD < 5%) and long-term stability. The sensor was successfully used for on-site and real-time determination of volatiles emitted from rotting meat, demonstrating its potential application in monitoring the quality and safety of meat products.

Application of freshness preservation technologies in case of perishable inventory management can play a vital role in mitigating the losses that occur due to deterioration of perishable inventory, typical perishable inventory includes Agri-fresh products like fresh fruits, fresh meat, fresh vegetables, seafood and packed foods. In case of perishable inventory price as well as freshness both decide the demand, so supplying a fresh product at a competitive price creates more customer satisfaction and makes a firm profitable. This paper develops two warehouse dispatching policies, first in, first out (FIFO) and last in, first out (LIFO) for perishable items taking deterioration into account. Demand varies as a function of price and freshness keeping effort. As the price increases and freshness keeping efforts decreases, demand decreases, and vice versa. Stockout is allowed, and during the stockout period as the waiting time increases backlogging rate decreases exponentially. Inflation is considered while calculating different costs. The objective of this paper is to determine the optimum price per unit item and lot size to maximize the profit. The sensitivity of the models is examined through the design of experiment. The present models are applicable for perishable items that start losing their quality immediately after arriving into the system.