Abstract The issue of preserving fresh food during the transportation is one of the long-standing issues for food distributors. On the other hand, the issue of vehicle routing is one of the most important issues in distribution management. Affecting the quality of food is the distribution process, which reduces the distribution time through vehicle routing plays an important role in reducing food spoilage. Therefore, in this study, the problem of vehicle routing in the distribution of perishable food materials is investigated. The time is different so that the distance is minimized and the capacities and time windows are not violated. This research aims to find facilities, identify the size of each cargo, and establish the most efficient vehicle routes to reduce overall costs at three levels in the event of a system failure. Undoubtedly, the traveling salesman issue is one of the most famous issues in the field of artificial intelligence.

Abstract The main target of the current study is to review the latest developments in accurate, reliable, and low-cost non-contact or remote techniques, including the usage of artificial intelligence (AI)-based methods, image processing (IP) system, and sensor technology for quality assessment in the food industry (FI). The IP systems and AI can be used for various purposes, such as classifying products based on size and shape, detecting product defects, the presence of microbes, and grading food quality. The sensor technology is now highly developed to assess food products' quality and safety due to the dramatic growth of nanotechnology and biotechnology. Also, in this paper, it was tried to examine the role of robots in the FI and discusses the advantages and disadvantages of using them in the FI.

Abstract Aiming at the problem that it is difficult to achieve rapid and accurate detection of pesticide residues, the artificial neural network method is used to separate the mixed fluorescence spectra in the measurement of acetamiprid pesticide residues, and a fluorescence spectrum that can quickly detect the pesticide residues of acetamiprid on solid surfaces is designed. According to the back-propagation algorithm, the three-layer artificial neural network principle is used to detect the acetamiprid residue in the mixed system of acetamiprid and filter paper with severely overlapping fluorescence spectra. In the range of 340nm~400nm, using the fluorescence intensity values at 20 characteristic wavelengths as the characteristic network parameters, after network training and testing, the recovery rates of acetamiprid concentrations of 40mg/kg and 90mg/kg are 102% and 97%, respectively. The relative standard deviations of the determination results were 1.4% and 1.9%, respectively. The experimental results show that the BP neural network-assisted fluorescence spectroscopy method for the determination of acetamiprid pesticide residues on filter paper has the characteristics of fast network training, short detection period, and high measurement accuracy.

Abstract Vegetable oils (VOs) come in a wide range of flavors and trademarks. VOs are very similar in appearance, flavor, and taste, and it's frequently difficult to tell them from just by looking at them. Approaches for classifying these oils are sometimes expensive and time-intensive, and they frequently include analytical chemical techniques as well as mathematical algorithms like as Artificial Neural Networks (ANNs), Properties of Partial Least Squares (PLS), Principal Components Regression (PCR), and Principal Component Analysis (PCA) to enhance their effectiveness. Because of the large range of goods available, more productive techniques for qualifying, characterizing, and classifying these substances are required, as the ultimate cost should indicate the quality of the commodity that reaches the user. This study provides a technique for classifying VOs such as different manufacturers' soybean, corn, sunflower, and canola. This method utilized a Charge-Coupled Device (CCD) array sensor, a light emission diode, and a straightforward mathematical approach to capture the generated fluorescence spectrum (FS) in diluted oil. The spectrum classifications are performed using an ANN with three layers, each having four neurons. The approach can categorize VO and enables rapid network training with a 72% success rate utilizing only a few mathematical changes in the spectra data.

Abstract The food industry (FI) is one of the most important parts of any country's industry when it comes to food safety and security. A new approach that can be considered is the use of new technologies in this area. Given the rise in population and per capita food consumption, this sector of the industry should pursue sustainable development through the use of nanotechnologies and increased productivity. In the FI, a number of nanostructured materials (NSMs) have been used, ranging from inorganic metals, metal oxides, and their nanocomposites to nano-organic materials comprising bioactive agents. Given the various advantages of nanotechnology, there are increasing questions concerning its application, as the aggregation of nanoscale materials (NSMs) in human bodies and the atmosphere may pose a variety of health and safety risks. As a result, when producing, handling, intelligently and actively packaging, and using nano-processed food items, safety, and health risks, as well as regulatory policies, must be taken into account. The aim of this analysis is to provide a basic understanding of nanotechnology applications in the food packaging (FP) and processing industries, as well as to recognize possible opportunities and potential risks associated with the usage of NSMs.

Abstract International food trade necessitates the existence of effective anti-tampering systems to protect against food toxins, thus making this kind of contamination difficult to get around. Due to a short growing season, fresh fruits and vegetables are easily damaged by spoilage and can lose much of their value. Being transported and stored is dangerous. Due to these risks, food prices will continue to rise for fresh produce. They are in such short supply that the fresh products will have a dramatic and urgent need for the supply chain. The journey to food and nutrition protection starts with a thorough examination of the problems that must be addressed in order to establish appropriate solutions. As a result, decision-making is defined as the process of selecting the best option from a set of options. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work well among various multi-criteria decision-making models. Based on the TOPSIS method, this study focuses on the risk level of a food production enterprise.

Abstract With numerous promising cases in image processing, voice recognition, target detection, and other fields, deep learning (DL) have proven to be an advanced tool for big data analysis. It's been used in food science and engineering recently as well. This is the first food-related study that we are aware of. We gave a brief overview of DL in this paper, as well as comprehensive descriptions of the structure of some common deep neural network (DNN) architectures and training approaches. We looked at hundreds of publications that used DL as a data processing method to address problems and issues in the food domain, such as food identification, calorie estimating, fruit, potato, meat, and aquatic commodity quality detection, food supply chain, and food pollution. Each study looked at the particular challenges, datasets, preprocessing techniques, networks and systems used, the efficiency achieved, and comparisons with other common solutions. We examined the degree to which big data is being used in the food safety domain and found some positive developments in this article. According to our study results, DL outperforms other approaches such as manual attribute extractors, traditional machine learning algorithms, and DL as a promising technique in food quality and safety inspection.

Abstract The characterization of strain rate effects near glass transition temperature (Tg) gives important information on flow behavior of metallic glasses (MGs). For this purpose, the tensile strain rate jump test was carried out near glass transition temperature to evaluate the flow behaviors of ZrCoAl(Si) MG ribbons. The primary thermal analyses showed that the Si minor addition into the ZrCoAl alloy leads to the betterment of thermal stability and the glass forming ability (GFA), correlated with the small size of Si atoms and subsequent formation of denser atomic clusters and thermodynamically liquid stabilization in the system. The tensile strain rate jump test indicated that minor adding of Si element decreases the sensitivity of glassy alloy to the flow stress and improves the stability of amorphous structure under increase in the strain rate and applied temperature. It was also observed that the viscosity behavior is more stable with the increase of the strain rate and temperature in the Si-added sample, implying the high capability of viscoplastic response in this state.