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Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing.

Radio frequency identification (RFID) and quick response (QR) codes are an effective way to identify traceable resource units (TRUs) and are widely applied in traceability systems. TRU transformation is common for things such as beef segmentation and wheat-flour packaging. The present study describes the development and testing of equipment for barcode-RFID bidirectional transformation to improve traceability by conserving identification association and information correspondence while transforming barcodes to RFID and vice versa. The framework of traceable bidirectional identification labels is based on comparing the features of RFID and barcodes and on analyzing TRU transformation. The proposed equipment includes a tactile industrial controller, a RFID-reader module, an embedded printing module, and a barcode scanning module. Furthermore, we develop the main functions of RFID-barcode conversion, bar-code-RFID conversion, and condition monitoring. The system is tested in field experiments based on two typical scenarios: beef segmentation and wheat-flour packaging. The average conversion success rate in RFID-barcode processing is 97%, and in barcode-RFID processing is 93.48%. The proposed equipment is more rapid than the reference method: 3.2 versus 8.7 s for RFID-barcode transformation and 10.5 versus 13.3 s for barcode-RFID transformation. In addition, the proposed equipment is less expensive than the conventional equipment. Test results indicate that continuous traceability is improved because identification association and information correspondence are conserved after TRU transformation.

Background: Food safety has garnered much worldwide attention recently for reasons that are, unfortunately, not always positive. Traceability system (TS) is designed to assure safe and good quality food, while reducing the costs of food recalls. It should encompass all stakeholders, including governments, companies, and consumers, each of whom has an important role in the implementation and guardianship of such systems. The EU and China are amongst the main players implementing TS and are constantly exploring new opportunities and monitoring challenges for TS in a time of shifting consumer demands and rapid new technology innovation. Scope and approach: This article states development stages from TS 1.0 to 3.0. and reviews TS development in a number of key countries and regions. Comparisons between the EU and China are drawn in terms of government, corporate, and consumer involvement in traceability. Key findings and conclusions: A functional TS, while providing bi-directional communication between trading partners, must meet the laws and regulations where it operates. A functional system must also consider consumer value and perception, which varies with geography. There are a variety of promising technologies available on the market today to modernize TS, including artificial intelligence (AI) and blockchain. A key finding of this research is that both the EU and China have developed significant trade links in recent years which will certainly positively impact both economies. Key to underpinning the sustainability of these trade links will be the adoption of common TS to prevent negative associations., This work was funded by the National Natural Science Foundation of China (31671593) and the Agricultural Science and Technology Innovation Program Elite Youth Mission (CAAS 2060302-05-962-3). Also, this publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number 16/IFB/4439.

This study developed principal component analysis and radial basis function neural networks (PCA-RBFNNs) for predicting freshness in tilapia fillets stored at different temperatures by integrating an electronic nose and electronic tongue. Total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and K value increased at 0, 4, 7, and 10 °C, while sensory scores decreased significantly. The electronic nose and tongue acquired the volatiles and dissolved chemical compounds in the stored samples. Gas chromatography-mass spectrometry (GC-MS) verified the changes in gas species and contents in fillets stored for different periods of time at different temperatures. PCA-RBFNNs based on data fusion were developed and presented good performance for prediction of TVB-N, TAC, K value, and sensory score in tilapia fillets. The established PCA-RBFNNs based on feature variables of the electronic nose and tongue is a promising method to predict changes in the freshness of fillets stored from 0 to 10 °C in the cold chain.

Innovations in food packaging systems will help meet the evolving needs of the market, such as consumer preference for "healthy" and high-quality food products and reduction of the negative environmental impacts of food packaging. Emerging concepts of active and intelligent packaging technologies provide numerous innovative solutions for prolonging shelf-life and improving the quality and safety of food products. There are also new approaches to improving the passive characteristics of food packaging, such as mechanical strength, barrier performance, and thermal stability. The development of sustainable or green packaging has the potential to reduce the environmental impacts of food packaging through the use of edible or biodegradable materials, plant extracts, and nanomaterials. Active, intelligent, and green packaging technologies can work synergistically to yield a multipurpose food-packaging system with no negative interactions between components, and this aim can be seen as the ultimate future goal for food packaging technology. This article reviews the principles of food packaging and recent developments in different types of food packaging technologies. Global patents and future research trends are also discussed.

This study proposed an integrated approach to evaluate the efficiency of forced-air cooling of apples stacked on pallets. Computational fluid dynamics (CFD) modeling was used to evaluate the cooling rate, uniformity, moisture loss, and energy consumption during precooling of apples. Experiments were conducted to quantify the incidence of chilling injury. A reasonable increase in cooling rate and uniformity was obtained with an increase in air-inflow rate to 2.31 L s−1 kg−1; any further increase in air-inflow rate will lead to a relatively low increase in cooling rate and uniformity, but a significant increase in energy consumption and cooling injury. An exponential relationship exists between fan power (Pw) and pressure differential (ΔP) as well as between total fan energy consumption (Ec) and air-inflow rate (Ec = hxk). Finally, the mass loss of fruit is primarily influenced by cooling time rather than air-inflow rate.

The current packaging designs and the efficiency of forced-air cooling (FAC) of fresh produce can be considerably improved by comprehensively comparing and evaluating the existing packaging designs. This study presents a market survey that studies samples of typical apple cartons used in China. Furthermore, by combining experiment and computational fluid dynamics (CFD) modelling, a novel integral approach is proposed to evaluate cooling rate and uniformity, energy efficiency, and fruit quality (including safety) as a result of FAC for different ventilated-packaging designs. The process uses CFD to simulate the three-dimensional spatio-temporal distributions of airflow and product temperatures during precooling. In addition, experiments on chilling injury and mass loss are also reported. The results show that the optimum fresh-fruit packaging design depends on the product size and the location of the product and tray inside the packaging. For all existing package designs, the optimal air-inflow velocity is found to lie in the range 0.4–1 m/s (or 3–5 L s−1 kg−1), any further increase in airflow rate simply wastes energy because it leads to a relatively low increase in cooling rate and uniformity. The level of chilling injury and mass loss per box show a different trend with increasing air-inflow velocity. The accuracy of the CFD simulations was confirmed by a good agreement with experiments. The maximum root-mean-square error and mean absolute percentage error for produce temperature are 0.727 °C and 18.69%, respectively. This research unveils the advantages and disadvantages of the various existing packaging designs and provides a reliable theoretical and experimental basis for achieving an integral evaluation of the performance of FAC.

Recent developments in the legal establishment and the market have motivated more agro-food companies to implement traceability systems (TS). TS play an important role not only for planning system implementation before development, but also for analyzing system performance after using the system. A novel agro-food TS model is presented here, based on comprehensive and quantifiable granularity concepts. A 2-layer index system was established; the first layer was mainly factors such as precision, breadth, and depth, and the second layer included seven indicator sub-factors: external trace units, internal flow units, IU conversion, information collection content, information update frequency, forward tracking distance, and backward tracing distance. An indicator’s overall score was scaled with five contributing scores that graded the assignment method. Indicator weight was confirmed with the AHP method. The weight values of the seven indicators were 0.1985, 0.1141, 0.0872, 0.1870, 0.1248, 0.1442, and 0.1442, respectively. A weighted sum model was adopted to calculate the evaluation value. A high evaluation value indicated high granularity. The granularity model was applied in two enterprises, here identified as WPF and WFPE, which were located at different stages in wheat-flour supply chain. The survey results showed that WFPE should invest more in tracing equivalent granularity than WPF should because it involves multi-stage processing, a complicated supply chain structure, it is a large enterprise, and operates in a strict regulatory environment. Furthermore, WFPE was motivated to implement a high granularity level because of benefits in supply chain management, market and customer response, and recall and risk management. In the future, an updated granularity evaluation model that could combine enterprise characteristics and uncover hidden costs and benefits will be studied further.

Optimizing the design of fresh produce packaging is vital for ensuring that future food cold chains are more energy efficient and for improving produce quality by avoiding chilling injuries due to nonuniform cooling. Computational fluid dynamics models are thus increasingly used to study the airflow patterns and heat transfer inside ventilated packaging during precooling. This review discusses detailed and comprehensive mathematical modeling procedures for simulating the airflow, heat transfer, and mass transfer that occurs during forced-air precooling of fresh produce. These models serve to optimize packaging design and cooling efficiency. We summarize the most commonly used parameters for performance, which allows us to directly compare the cooling performance of various packaging designs.

The inability to correctly implement and safeguard a product cold chain leads to premature product spoilage and increased product waste. Special care is required to both implement and monitor the cold chain for perishable goods in order to preserve them. Many technologies are available on the market today with varying levels of success. This article presents a new technique, namely thermal imaging predicts surface temperature over a pallet of apples whilst comparing packaging (plastic boxes and cardboard boxes). This temperature data was then introduced as an input in artificial neural network (ANN) software to estimate the temperature across the entire pallet. Results obtained (root mean squared error [RMSE]) indicate that the estimation with plastic boxes has an error of 0.41 °C whilst the error, taking as a reference the surface temperature, would be RMSE 2.14 °C. In the case of cardboard boxes, the estimation error is 0.086 °C whilst only taking into account the thermal image, data would be RMSE 3.56 °C. This article proves the concept of the possibility of temperature monitoring by ANN through thermal imaging technology.

This study develops a reliable radial basis function neural networks (RBFNNs) to estimate freshness for tilapia fillets stored under non-isothermal conditions by using optimal wavelengths from hyperspectral imaging (HSI). The results show that, for tilapia fillet stored at −3, 0, 4, 10, and 15 °C and non-isothermal conditions, total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and the K value increase whereas sensory scores decrease with increasing storage time. To simplify the models, nine optimal wavelengths were selected by using the successive projections algorithm (SPA), following which SPA–RBFNN models were built based on the selected wavelengths and the values of TVB-N, TAC, K, and sensory evaluations for tilapia fillets store isothermally. The ability of the models based on HSI to predict the freshness indicators were verified for tilapia fillets stored under non-isothermal conditions. HSI thus has an excellent potential for nondestructive determination of freshness in tilapia fillets.

In this work, to improve the rate at which cool air ventilates the tail of a refrigerated compartment and thereby reduces the temperature difference between the front and rear of the cargo area, we increase the length of an air conduit in the direction of the air outlet of the refrigerating unit. In addition, minimizing the cooling time during transportation reduces energy consumption and improves the economics of cold-chain transport. To address this problem, we created a three-dimensional computational fluid dynamics model of a transport compartment and used potatoes as test cargo. The unsteady shear stress transport κ-ω model was adopted to simulate air flow and heat transfer inside the compartment. The results show that the additional air conduit inside the compartment should significantly improve the spatial uniformity of the temperature inside the cargo area. The results of the simulation are consistent with experiments. Practical Applications Although cold-chain transport must ensure a constant low-temperature environment for perishable foods, more important is the requirement to maintain temperature uniformity in the cargo area. This is the basis by which the quality, safety and shelf life of perishable food are ensured. In this study, we have investigated how to minimize the cooling time during transportation in order to reduce energy consumption and improve the rate at which cool air ventilates the tail of a refrigerated compartment. This research provides reliable theoretical arguments for enhancing temperature uniformity in the cargo enclosure of refrigeration trucks and for reducing unnecessary energy consumption during transportation.

The study assessed the feasibility of developing a machine vision system based on pupil and gill color changes in tilapia for simultaneous prediction of total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA) and total viable counts (TVC) during storage at 4 °C. The pupils and gills were chosen and color space conversion among RGB, HSI and L∗a∗b∗ color spaces was performed automatically by an image processing algorithm. Multiple regression models were established by correlating pupil and gill color parameters with TVB-N, TVC and TBA (R2 = 0.989–0.999). However, assessment of freshness based on gill color is destructive and time-consuming because gill cover must be removed before images are captured. Finally, visualization maps of spoilage based on pupil color were achieved using image algorithms. The results show that assessment of tilapia pupil color parameters using machine vision can be used as a low-cost, on-line method for predicting freshness during 4 °C storage.

In this work, we study how different air-inflow velocities affect the apple-temperature distribution during forced-convection cooling of individual apples by simultaneously modeling airflow and heat transfer. In general, an increase in airflow velocity increases the cooling rate and heat transfer fluxes across the apple surface and decreases cooling time. The results show that a reasonable increase in cooling rate is obtained with an increase in airflow velocity to 2.5 m/s; any further increase in airflow velocity simply wastes energy because it leads to a relatively low increase in cooling and heat transfer fluxes across the apple surface. By comparing the temperature simulated with and without accounting for respiratory heat, the maximum temperature difference is ∼0.033 K during cooling. Therefore, respiratory heat has a negligible effect on the temperature variations of fruit. The model was verified by comparing its results with those of experiments. The predicted results are consistent with the measured results. Practical Applications To ensure the quality and safety of horticultural products and extend their storage and shelf life across the entire cold chain, a critical step in the postharvest cold chain is the rapid precooling after harvest to remove field heat. In this work, we investigate how to improve the cooling rate and minimize the cooling time during forced-convection cooling of apples. In addition, the effect of respiratory heat on temperature variations of fruit is also studied. This research provides not only a more detailed understanding of flow distribution and temperature variations of fruit during cooling, but also a reliable theoretical basis for minimizing unnecessary energy consumption during forced-convection cooling of produce.