A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.

The Influence of Storage Temperature and Packaging Technology on the Durability of Ready-to-Eat Preservative-Free Meat Bars with Dried Plasma

Food business operators must include the results of shelf life testing in their HACCP plan. Ready-to-eat preservative-free meat products enriched with blood plasma are an unfathomable area of research in food safety. We tested modified atmosphere (80% N2 and 20% CO2) and vacuum packaged RTE preservative-free baked and smoked pork bars with dried blood plasma for Aerobic Plate Count, yeast and mould, lactic acid bacteria, Staphylococcus aureus, Enterobacteriaceae, Escherichia coli, and Campylobacter spp., and the presence of Listeria monocytogenes and Salmonella spp. during storage (temperatures from 4 to 34 °C) up to 35 days after production. The obtained data on the count of individual groups of microorganisms were subjected to analysis of variance (ANOVA) and statistically tested (Student's t-test with the Bonferroni correction); for temperatures at which there were statistically significant differences and high numerical variability, the trend of changes in bacterial counts were visualised using mathematical modelling. The results show that the optimal storage conditions are refrigerated temperatures (up to 8 °C) for two weeks. At higher temperatures, food spoilage occurred due to the growth of aerobic bacteria, lactic acid bacteria, yeast, and mould. The MAP packaging method was more conducive to spoilage of the bars, especially in temperatures over 8 °C.

Quality changes, potential spoilage organisms, and shelf-life prediction of brackish river prawn (Macrobrachium macrobrachion) at different storage temperatures

The brackish river prawn (Macrobrachium macrobrachion) is a species of commercial importance in West Africa. However, like other fishery products, it is prone to deterioration due mainly to microbial activities. The present study aimed at evaluating the spoilage characteristics of M. macrobrachion and predicting the growth of the main spoilage bacteria as well as the shelf-life of the product as a function of storage temperature. Freshly caught brackish river prawn samples from Lake Aheme were aerobically stored at 0, 7, 15, and 28 °C and, at pre-determined times during storage, they were taken for microbiological, chemical, and sensory analysis. At sensory rejection times, the spoilage potential of 185 isolates from specific groups of organisms enumerated was assessed in prawn of which the endogenous microbiota was heat inactivated. Isolates capable of producing strong off-odor were identified using 16S rRNA sequencing. Models predicting the maximum growth rate of Pseudomonas spp. and H2S-producing bacteria in the brackish river prawn as well as the shelf-life of the product were developed. These models were validated using an independent experiment during which prawn was stored at 0, 4, 10, and 25 °C. Results showed that Pseudomonas spp. at 0 °C, Pseudomonas spp. and H2S-producing bacteria at 7 °C, and H2S-producing bacteria at 15 °C and 28 °C were the dominant groups of microorganisms during storage. As expected, total volatile basic nitrogen, trimethylamine, and pH with initial values of 21.2 ± 3.0 mg-N/100 g, 4.1 ± 0.8 mg-N/100 g, and 7.46 ± 0.15 increased during storage reaching approximately 35 mg-N/100 g, 10 mg/ 100 g and 8, respectively at sensory rejection times which were 7 h at 28 °C, 1.2 d at 15 °C, 4.6 d at 7 °C, and 11.7 d at 0 °C. The main spoilage organisms were Citrobacter braakii at 28 °C, Citrobacter braakii, Pseudomonas kurunegalensis, and Shewanella bicestrii at 15 °C, Shewanella putrefaciens, Shewanella baltica, and Pseudomonas bubulae at 7 °C, and Pseudomonas versuta at 0 °C. The validation of the developed models showed an adequate agreement between the predicted and observed values. This study highlights the specific spoilage characteristics of the brackish river prawn and reveals that Gram-negative rod bacteria are the main spoilage organisms even at high storage temperatures, contrary to many earlier reports on the spoilage of tropical fishery products.

Shelf life estimation of refrigerated vacuum packed beef accounting for uncertainty

This study estimates the shelf life of vacuum packed beef meat (three muscles: striploin (longissimus thoracis et lumborum, LTL), tenderloin (psoas major, PM) and outside chuck (trapezius thoracis, TT)) at refrigeration temperatures (0 °C-10 °C) based on modelling the growth of two relevant groups of spoilage microorganisms: lactic acid bacteria (LAB) and Enterobacteriaceae. The growth models were developed combining a two-step and a one-step approach. The primary modelling was used to identify the parameters affecting the growth kinetics, guiding the definition of secondary growth models. For LAB, the secondary model included the effect of temperature and initial pH on the specific growth rate. On the other hand, the model for Enterobacteriaceae incorporated the effect of temperature on the specific growth rate and the lag phase; as well as the effect of the initial pH on the specific growth rate, the lag phase and the initial microbial count. We did not observe any significant effect of the type of muscle on the growth kinetics. Once the equations were defined, the models were fitted to the complete dataset using a one-step approach. Model validation was carried out by cross-validation, mitigating the impact of an arbitrary division between training and validation sets. The models were used to estimate the shelf life of the product, based on the maximum admissible microbial concentration (7 log CFU/g for LAB, 5 log CFU/g for Enterobacteriaceae). Although LAB was the dominant microbiota, in several cases, both LAB and Enterobacteriaceae reached the critical concentration practically at the same time. Furthermore, in some scenarios, the end of shelf life would be determined by Enterobacteriaceae, pointing at the potential importance of non-dominant microorganisms for product spoilage. These results can aid in the implementation of effective control measures in the meat processing industry.

Ambient temperature, refrigerator food load, and door openings effect on a preservation performance indicator based on chicken temperature data and predictive microbiology

Home preservation depends on the food matrix, refrigerator design/technology, consumer actions, and ambient temperature. Storing different food matrices in product-relevant refrigerator locations generating different temperature histories can be used to develop an indicator of how refrigerator technology, consumer habits, and environment conditions impact the refrigerator food preservation performance. In this study, poultry, particularly prone to spoilage reflecting its pH, nutrient availability, and high aw, was used to evaluate refrigerator preservation performance as affected by compressor technology (single [SS] and variable speed [VS]), ambient temperature (21.1°C [LT] and 32.2°C [HT]), and refrigerator load (22.5 kg [RL] and 39 kg [HL]). Time-temperature values collected for chicken breast stored in a drawer independently controlled at 0°C in a refrigerator set 5°C, and a Pseudomonas predictive microbiology model, were used to estimate a normalized refrigerator performance indicator (RPI). Values <1, ∼1, and >1 described excellent, good, or poor performance, respectively. A first analysis revealed that up to 54% of chicken breast temperatures were above its recommended refrigerated storage value. When ignoring variability sources, SS technology yielded RPI values ranging 0.61-0.70, whereas the more energy efficient VS compressor yielded values ranging 0.86-1.14. The higher and wider VS RPI range reflects a compressor control logic optimized for energy efficiency compliance while disregarding effects on food preservation. When considering the variability of model parameters and temperature measurements through one-sided 95% confidence intervals yielded RPI reaching 1.16. Although the independently controlled drawer preservation performance was near optimal, it can improve by considering energy use and preservation impact when optimizing the compressor speed control protocol. PRACTICAL APPLICATION: Worldwide poultry meat consumption has reached 15 kg per person. Refrigeration is widely used for its safety and quality preservation. Efficiency regulations decreased the energy use of residential refrigerators by nearly tenfold even though their size increased by 50% in the last half century. In this study, we provide quantitative evidence that their preservation performance must be improved. This is particularly true for upper end units typically equipped with quieter and more energy-efficient variable speed compressors. The same methodology can be used to evaluate the preservation performance of the storage units, trucks, and display cases used for refrigerated products.

Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning

Introduction

Fruit losses in the supply chain owing to improper handling and a lack of proper control are common in the industry. As losses are caused by the inefficiency of the export method, selecting the appropriate export method is a possible solution. Several organizations employ only a single strategy, which is mainly based on a first-in-first-out approach. Such a policy is easy to manage but inefficient. Given that the batch of fruits may become overripe during transportation, frontline operators do not have the authority or immediate support to change the fruit dispatching strategy. Thus, this study aims to develop a dynamic strategy simulator to determine the sequence of delivery based on forecasting information projected from probabilistic data to reduce the amount of fruit loss.

Methods

The proposed method to accomplish asynchronous federated learning (FL) is based on blockchain technology and a serially interacting smart contract. In this method, each party in the chain updates its model parameters and uses a voting system to reach a consensus. This study employs blockchain technology with smart contracts to serially enable asynchronous FL, with each party in the chain updating its parameter model. A smart contract combines a global model with a voting system to reach a common consensus. Its artificial intelligence (AI) and Internet of Things engine further strengthen the support for implementing the Long Short-Term Memory (LSTM) forecasting model. Based on AI technology, a system was constructed using FL in a decentralized governance AI policy on a blockchain network platform.

Results

With mangoes being selected as the category of fruit in the study, the system improves the cost-effectiveness of the fruit (mango) supply chain. In the proposed approach, the simulation outcomes show fewer mangoes lost (0.035%) and operational costs reduced.

Discussion

The proposed method shows improved cost-effectiveness in the fruit supply chain through the use of AI technology and blockchain. To evaluate the effectiveness of the proposed method, an Indonesian mango supply chain business case study has been selected. The results of the Indonesian mango supply chain case study indicate the effectiveness of the proposed approach in reducing fruit loss and operational costs.

Ambient Parameter Monitoring in Fresh Fruit and Vegetable Supply Chains Using Internet of Things-Enabled Sensor and Communication Technology

Up to half of the global fruit and vegetable production is wasted or lost along the supply chain, causing wastage of resources and economic losses. Ambient parameters strongly influence quality and shelf life of fresh fruit and vegetables. Monitoring these parameters by using Internet of things (IoT)-enabled sensor and communication technology in supply chains can help to optimize product qualities and hence reduce product rejections and losses. Various corresponding technical solutions are available, but the diverse characteristics of fresh plant-based produce impede establishing valuable applications. Therefore, the aim of this review is to give an overview of IoT-enabled sensor and communication technology in relation to the specific quality and spoilage characteristics of fresh fruit and vegetables. Temperature, relative humidity (RH), O₂, CO₂ and vibration/shock are ambient parameters that provide most added value regarding product quality optimization, and can be monitored by current IoT-enabled sensor technology. Several wireless communication technologies are available for real-time data exchange and subsequent data processing and usage. Although many studies investigate the general possibility of monitoring systems using IoT-enabled technology, large-scale implementation in fresh fruit and vegetable supply chains is still hindered by unsolved challenges.

A Multi-Model Approach to Implement a Dynamic Shelf Life Criterion in Meat Supply Chains

The high perishability of fresh meat results in short sales and consumption periods, which can lead to high amounts of food waste, especially when a fixed best-before date is stated. Thus, the aim of this study was the development of a real-time dynamic shelf-life criterion (DSLC) for fresh pork filets based on a multi-model approach combining predictive microbiology and sensory modeling. Therefore, 647 samples of ma-packed pork loin were investigated in isothermal and non-isothermal storage trials. For the identification of the most suitable spoilage predictors, typical meat quality parameters (pH-value, color, texture, and sensory characteristics) as well as microbial contamination (total viable count, Pseudomonas spp., lactic acid bacteria, Brochothrix thermosphacta, Enterobacteriaceae) were analyzed at specific investigation points. Dynamic modeling was conducted using a combination of the modified Gompertz model (microbial data) or a linear approach (sensory data) and the Arrhenius model. Based on these models, a four-point scale grading system for the DSLC was developed to predict the product status and shelf-life as a function of temperature data in the supply chain. The applicability of the DSLC was validated in a pilot study under real chain conditions and showed an accurate real-time prediction of the product status.

The association between total volatile basic nitrogen (TVB-N) concentration and other biomarkers of quality and spoilage for vacuum packaged beef

Beef longissimus lumborum muscles (24) were each portioned into 6 steaks, and these were held at ~0.5 °C for up to 14 weeks. Total volatile basic nitrogen (TVB-N) data was compared against other measures of quality and freshness: total viable microbial count (TVC), ultimate pH, drip loss, purge, moisture content, cook loss, shear force, particle size, vitamin E, intramuscular fat content (IMF) and colour parameters. There was an association between TVB-N and colour parameters; and between TVB-N and several parameters for microbial load, tenderness and moisture. IMF and vitamin E were not correlated to TVB-N. TVB-N of 5.1 mg/100 g (95% confidence intervals: 4.6-5.5 mg/100 g) was estimated as equivalent to a TVC of log 7 cfu/g. When beef samples were categorised as either spoilt or fresh using this TVC recommendation, TVB-N was correct in 77.9% of its assignments. Additional investigation is necessary to confirm this result against a larger sample population with a broader range of TVB-N values.

A Multi-Objective Model for Sustainable Perishable Food Distribution Considering the Impact of Temperature on Vehicle Emissions and Product Shelf Life

The food distribution process is responsible for significant quality loss in perishable products. However, preserving quality is costly and consumes a tremendous amount of energy. To tackle the challenge of minimizing transportation costs and CO2 emissions while also maximizing product freshness, a novel multi-objective model is proposed. The model integrates a vehicle routing problem with temperature, shelf life, and energy consumption prediction models, thereby enhancing its accuracy. Non-dominated sorting genetic algorithm II is adapted to solve the proposed model for the set of Solomon test data. The conflicting nature of these objectives and the sensitivity of the model to shelf life and shipping container temperature settings are analyzed. The results show that optimizing freshness objective degrade the cost and the emission objectives, and the distribution of perishable foods are sensible to the shelf life of the perishable foods and temperature settings inside the container.

Predictive modelling for the growth kinetics of Pseudomonas spp. on button mushroom (Agaricus bisporus) under isothermal and non-isothermal conditions

Baranyi model was fitted to experimental growth data of Pseudomonas spp. on the button mushrooms (Agaricus bisporus) stored at different isothermal conditions (4, 12, 20 and 28 °C), and the kinetic growth parameters of Pseudomonas spp. on the button mushrooms were obtained. The goodness of fit of the Baranyi model was evaluated by considering the root mean squared error (RMSE) and the adjusted coefficient of determination (adjusted-R²). The Baranyi model gave RMSE values lower than 0.193 and adjusted-R² values higher than 0.975 for all isothermal storage temperatures. The maximum specific growth rate (µ_max) was described as a function of temperature using secondary models namely, Ratkowsky and Arrhenius models. The Ratkowsky model described the temperature dependence of µ_max better than the Arrhenius model. Therefore, the differential form of the Baranyi model was merged with the Ratkowsky model, and solved numerically using the fourth-order Runge-Kutta method to predict the concentration of Pseudomonas spp. populations on button mushrooms under non-isothermal conditions in which they are frequently subjected to during storage, delivery and retail marketing. The validation performance of the dynamic model used was assessed by considering bias (B_f) and accuracy (A_f) factors which were found to be 0.998 and 1.016, respectively. The dynamic model developed also exhibited quite small mean deviation (MD) and mean absolute deviation (MAD) values being -0.013 and 0.126 log CFU/g, respectively. The modelling approach used in this work could be an alternative to traditional enumeration techniques to determine the number of Pseudomonas spp. on mushrooms as a function of temperature and time.

Implementation of Time Temperature Indicators to Improve Temperature Monitoring and Support Dynamic Shelf Life in Meat Supply Chains

Based on the well-investigated OnVu™ TTI kinetics, models were developed to adjust the label to different food products and predict the discolouration process under dynamic temperature conditions. After the successful validation under laboratory conditions, the applicability of the time temperature indicator (TTI) as shelf life indicator was tested in a national poultry chain. The TTI accurately reflected the temperature fluctuations occurring under real chain conditions. Shelf life predictions based on the discolouration of the TTIs were in accordance with the microbial shelf life of the product. The models were integrated in an online software tool to check for the compliance of the cold chain and predict the remaining shelf life of the product. The implementation of TTI and the software result in a valuable tool to support the decision-making process in the cold chain. The application of flexible shelf life enables the reduction of food waste in the meat supply chain.

Freshness-based real-time shelf-life estimation of packaged chicken meat under dynamic storage conditions

The current study was performed to develop dynamic quality and shelf-life prediction models using selected index for packaged chicken meat during storage. Generally, the results showed that meat deterioration, with respect to the different quality indices considered in the investigation, proceeds with increasing temperature and storage time. Highly significant (P ≤ 0.01) correlations were obtained between TPC (total plate count) and SI (sensory index) (r = −0.94 to −0.97), coliforms and SI (r = −0.89 to −0.95), and LAB (lactic acid bacteria) and SI (r = −0.93 to −0.98). However, only the microbiological spoilage regarding TPC, whose values ranged from 7.0 to 8.0 log CFU/g under all investigated temperature conditions, were in compliance with the end of sensory shelf-life defined at SI = 5. To develop dynamic quality prediction model, 4 isothermal (0, 4, 10, and 15°C) experiments in 2 batches were performed for TPC evaluation. Growth data were fitted in the Baranyi and Roberts and quadratic polynomial model as the primary and secondary models, respectively. The model was validated under dynamic conditions (0–8°C scenario with periodic 12-h changes). The accuracy and bias factors were estimated to be 1.045 and 0.991 for fluctuating conditions and 1.016 and 1.015 for real-time conditions, respectively, suggesting good applicability of the model. The remaining shelf-life estimation model developed based on mean kinetic temperature showed an even decrease of shelf-life under dynamic conditions in time. The developed model scan can be used for effective monitoring of packaged chicken meat freshness and shelf-life during distribution with temperature fluctuation.

Shelf life prediction and food safety risk assessment of an innovative whole soybean curd based on predictive models

The aim of the present study is to predict the shelf life and evaluate the risk profile of an innovative whole soybean curd (WSC). Two main spoilage strains were isolated from spoiled WSC and identified as B. subtilis and B. cereus. The origin analysis confirmed that B. subtilis and B. cereus originated from soybean materials and survived in soybean curd. For microbial contamination analysis, thermotolerant coliforms, E. coli and S. aureus were not detected in soybean curd. The predicted shelf life of WSC and okara-filtered curd that was stored at 10 °C were 141.95 h (5.91 d) and 206.25 h (8.59 d), respectively. Moreover, the models applied in this study exhibited great fitting goodness and the predicted growth parameters were fail-safe. To conclude, introduction of okara into soybean curd reinforced the initial contamination level but didn't significantly increase the risk profile of WSC.

Optimal consolidation of fresh agricultural products in a multi-temperature joint distribution system

Purpose

The purpose of this paper is to deal with an optimal consolidation problem for fresh agricultural products (e.g. fruits and vegetables) in a multi-temperature joint distribution (MTJD) system that is developed to resolve the challenge of timely delivery of small and diverse shipments in food cold chains.

Design/methodology/approach

An integer programming optimization model is developed to consolidate a set of agricultural shipments with different storage requirements into a number of distinct containers according to the classification criteria. The formulated model for consolidating fresh agricultural products is evaluated using numerical examples.

Findings

Critical factors that affect the quality or shelf life of fresh agricultural products are examined to form the criteria for classifying the storage requirements of these products. The formulated model can minimize the consolidation cost and the loss of product value due to a reduction in shelf life after consolidation.

Research limitations/implications

Although the decision model for product consolidation developed in this paper takes into account practical concerns as much as possible, some additional conditions in the cold chain of fresh fruits and vegetables can be included to further enhance the application of the proposed consolidation model.

Practical implications

Provided that the container environment is appropriately controlled, the shelf life of fresh fruits and vegetables can be maintained during the logistics process. As a result, product quality can be managed to reduce product loss.

Originality/value

This paper adopts temperature, relative humidity and ethylene production, which generally affect the quality and shelf life of fresh agricultural products, as the main factors for determining the product consolidation. It is among the first to deal with the optimal consolidation of fresh agricultural products in the MTJD system with the consideration of product shelf life.

Last-mile distribution planning for fruit-and-vegetable cold chains

Purpose

The purpose of this paper is to formulate and solve a last-mile distribution plan problem with concern for the quality of fruits and vegetables in cold chains.

Design/methodology/approach

The vehicle routing problem with time windows (VRPTW) is extended based on the characteristics of fruit-and-vegetable cold chains. The properties of multiple perishable foods, continuing decline in quality, various requirements for quality levels and optimal temperature settings during vehicle transportation are considered in the VRPTW. The product quality level is defined by the estimation of residual shelf life, which changes with temperature, and is characterized by a stepped decrease during the transportation process as time goes on. A genetic algorithm (GA) is adapted to solve the problem because of its convincing ability to solve VRPTW-related problems. For this purpose, solution encoding, a fitness function and evolution operators are designed to deal with the complicated problem herein.

Findings

A distribution plan including required fleet size, vehicle routing sequence and what quality level should be shipped out to account for the quality degradation during vehicle transportation is generated. The results indicate that the fulfillment of various requirements of different customers for various fruits and vegetables and quality levels can be ensured with cost considerations.

Originality/value

This study presents a problem for last-mile delivery of fresh fruits and vegetables which considers multiple practical scenarios not studied previously. A solution algorithm based on a GA is developed to address this problem. The proposed model is easily applied to other types of perishable products.

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Mathematical models are often used to predict microbial growth in food products. An important class of these models involves the adaptation of classical sigmoid functions, such as the Gompertz and logistic functions. This study aimed to validate the use of the modified Richards model in various situations, which have not previously been tested. The model was obtained through solving a system of two differential equations and could be applied to both isothermal and non-isothermal environments. To test and validate this model, we used published datasets containing data for the growth of Pseudomonas spp. in fish products. The results obtained after fitting the model showed that it could be effectively used to describe and predict the Pseudomonas growth curves under various temperature regimens. However, the influence of the shape parameter on the growth curve is an issue that needs further evaluation.

Application of a Predictive Growth Model of Pseudomonas spp. for Estimating Shelf Life of Fresh Agaricus bisporus

For prediction of the shelf life of the mushroom Agaricus bisporus, the growth curve of the main spoilage microorganisms was studied under isothermal conditions at 2 to 22°C with a modified Gompertz model. The effect of temperature on the growth parameters for the main spoilage microorganisms was quantified and modeled using the square root model. Pseudomonas spp. were the main microorganisms causing A. bisporus decay, and the modified Gompertz model was useful for modelling the growth curve of Pseudomonas spp. All the bias factors values of the model were close to 1. By combining the modified Gompertz model with the square root model, a prediction model to estimate the shelf life of A. bisporus as a function of storage temperature was developed. The model was validated for A. bisporus stored at 6, 12, and 18°C, and adequate agreement was found between the experimental and predicted data.

Kinetics of Quality Changes ofPangasiusFillets at Stable and Dynamic Temperatures, Simulating Downstream Cold Chain Conditions

This study was about the quality changes ofPangasiusfillets during storage under simulated temperature conditions of downstream cold chain. Sensory, chemical, and microbiological analyses were conducted over storage time and bacterial growth was modelled. Sensory quality index (QI), at five stable (1, 4, 9, 15, and 19 ± 1°C) and three dynamic temperatures, progressed faster at higher temperatures, especially with sooner temperature abuses. Total volatile basic nitrogen remained under the acceptable limit throughout all the storage conditions. Total viable psychrotrophic counts (TVC) were around 5.68 ± 0.24 log CFU g−1at the beginning and exceeded the limit of 6 log CFU g−1after 216, 96, 36, 16, and 7 h at 1, 4, 9, 15, and 19 ± 1°C, respectively. Meanwhile,Pseudomonascounts started at 3.81 ± 0.53 log CFU g−1and reached 4.60–6.36 log CFU g−1by the time of TVC rejection. Since lower shelf lives were given by TVC rather than QI, it should be appropriate to base the product shelf life on the TVC acceptable limit. Kinetics models based on the Baranyi and Roberts and square root models, developed for TVC andPseudomonasspp., gave acceptable bacterial estimations at dynamic temperatures, with over 80% of observed counts within the acceptable simulation zone, revealing promising model applicability as a supporting tool for cold chain management. However, further improvement and validation of the models are needed.

Predicting bacterial growth in raw, salted, and cooked chicken breast fillets during storage

Growth curves were evaluated for aerobic mesophilic and psychrotrophic bacteria, Pseudomonas spp. and Staphylococcus spp., grown in raw, salted, and cooked chicken breast at 2, 4, 7, 10, 15, and 20 ℃, respectively, using the modified Gompertz and modified logistic models. Shelf life was determined based on microbiological counts and sensory analysis. Temperature increase reduced the shelf life, which varied from 10 to 26 days at 2 ℃, from nine to 21 days at 4 ℃, from six to 12 days at 7 ℃, from four to eight days at 10 ℃, from two to four days at 15 ℃, and from one to two days at 20 ℃. In most cases, cooked chicken breast showed the highest microbial count, followed by raw breast and lastly salted breast. The data obtained here were useful for the generation of mathematical models and parameters. The models presented high correlation and can be used for predictive purposes in the poultry meat supply chain.

Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese

Mathematical models were developed and evaluated for growth of psychrotolerant pseudomonads in chilled milk and in cottage cheese with cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic acid and sorbic acid. A simplified cardinal parameter growth rate model was developed based on growth in broth. Subsequently, the reference growth rate parameter μref25°C-broth of 1.031/h was calibrated by fitting the model to a total of 35 growth rates from cottage cheese with cultured cream dressing. This resulted in a μref25°C-cottage cheese value of 0.621/h. Predictions from both growth rate models were evaluated by comparison with literature and experimental data. Growth of psychrotolerant pseudomonads in heat-treated milk (n=33) resulted in a bias factor (Bf) of 1.08 and an accuracy factor (Af) of 1.32 (μref25°C-broth), whereas growth in cottage cheese with cultured cream dressing and in non-heated milk (n=26) resulted in Bf of 1.08 and Af of 1.43 (μref25°C-cottage cheese). Lag phase models were developed by using relative lag times and data from both the present study and from literature. The acceptable simulation zone method showed the developed models to successfully predict growth of psychrotolerant pseudomonads in milk and cottage cheese at both constant and dynamic temperature storage conditions. The developed models can be used to predict growth of psychrotolerant pseudomonads and shelf life of chilled cottage cheese and milk at constant and dynamic storage temperatures. The applied methodology and the developed models seem likely to be applicable for shelf life assessment of other types of products where psychrotolerant pseudomonads are important for spoilage.

Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions

The aim of this study was the development of a model to describe the growth kinetics of aerobic microbial population of chicken breast fillets marinated in pomegranate juice under isothermal and dynamic temperature conditions. Moreover, the effect of pomegranate juice on the extension of the shelf life of the product was investigated. Samples (10 g) of chicken breast fillets were immersed in marinades containing pomegranate juice for 3 h at 4 °C following storage under aerobic conditions at 4, 10, and 15 °C for 10 days. Total Viable Counts (TVC), Pseudomonas spp and lactic acid bacteria (LAB) were enumerated, in parallel with sensory assessment (odor and overall appearance) of marinated and non-marinated samples. The Baranyi model was fitted to the growth data of TVC to calculate the maximum specific growth rate (μmax) that was further modeled as a function of temperature using a square root-type model. The validation of the model was conducted under dynamic temperature conditions based on two fluctuating temperature scenarios with periodic changes from 6 to 13 °C. The shelf life was determined both mathematically and with sensory assessment and its temperature dependence was modeled by an Arrhenius type equation. Results showed that the μmax of TVC of marinated samples was significantly lower compared to control samples regardless temperature, while under dynamic temperature conditions the model satisfactorily predicted the growth of TVC in both control and marinated samples. The shelf-life of marinated samples was significantly extended compared to the control (5 days extension at 4 °C). The calculated activation energies (Ea), 82 and 52 kJ/mol for control and marinated samples, respectively, indicated higher temperature dependence of the shelf life of control samples compared to marinated ones. The present results indicated that pomegranate juice could be used as an alternative ingredient in marinades to prolong the shelf life of chicken.

Dissolved carbon dioxide and oxygen concentrations in purge of vacuum-packaged pork chops and the relationship to shelf life and models for estimating microbial populations

The objectives of this study were to determine the dissolved CO2 and O2 concentrations in the purge of vacuum-packaged pork chops over a 60 day storage period, and to elucidate the relationship of dissolved CO2 and O2 to the microbial populations and shelf life. As the populations of spoilage bacteria increased, the dissolved CO2 increased and the dissolved O2 decreased in the purge. Lactic acid bacteria dominated the spoilage microflora, followed by Enterobacteriaceae and Brochothrix thermosphacta. The surface pH decreased to 5.4 due to carbonic acid and lactic acid production before rising to 5.7 due to ammonia production. A mathematical model was developed which estimated microbial populations based on dissolved CO2 concentrations. Scanning electron microscope images were also taken of the packaging film to observe the biofilm development. The SEM images revealed a two-layer biofilm on the packaging film that was the result of the tri-phase growth environment.

Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes

The spoilage activity of Pseudomonas psychrophila and Carnobacterium maltaromaticum, two tropical shrimp (Penaeus notialis) spoilage organisms, was assessed in cooked shrimps stored at 0 to 28 °C. Microbiological, chemical and sensory analyses were performed during storage. P. psychrophila had a higher growth rate and showed a higher spoilage activity at temperatures from 0 to 15 °C, while at 28 °C, C. maltaromaticum had a higher growth rate. The spoilage activity of P. psychrophila was found to be higher in cooked shrimp than in fresh shrimp. Observed shelf-life data of shrimps stored at constant temperatures were used to validate a previously developed model that predicts tropical shrimp shelf-life at constant storage temperatures. Models predicting the growth of the spoilage organisms as a function of temperature were constructed. The validation of these models under dynamic storage temperatures simulating temperature fluctuation in the shrimp supply chain showed that they can be used to predict the shelf-life of cooked and fresh tropical shrimps.

Reducing food losses by intelligent food logistics

The need to feed an ever-increasing world population makes it obligatory to reduce the millions of tons of avoidable perishable waste along the food supply chain. A considerable share of these losses is caused by non-optimal cold chain processes and management. This Theme Issue focuses on technologies, models and applications to monitor changes in the product shelf life, defined as the time remaining until the quality of a food product drops below an acceptance limit, and to plan successive chain processes and logistics accordingly to uncover and prevent invisible or latent losses in product quality, especially following the first-expired-first-out strategy for optimized matching between the remaining shelf life and the expected transport duration. This introductory article summarizes the key findings of this Theme Issue, which brings together research study results from around the world to promote intelligent food logistics. The articles include three case studies on the cold chain for berries, bananas and meat and an overview of different post-harvest treatments. Further contributions focus on the required technical solutions, such as the wireless sensor and communication system for remote quality supervision, gas sensors to detect ethylene as an indicator of unwanted ripening and volatile components to indicate mould infections. The final section of this introduction discusses how improvements in food quality can be targeted by strategic changes in the food chain.

Quality tracing in meat supply chains

The aim of this study was the development of a quality tracing model for vacuum-packed lamb that is applicable in different meat supply chains. Based on the development of relevant sensory parameters, the predictive model was developed by combining a linear primary model and the Arrhenius model as the secondary model. Then a process analysis was conducted to define general requirements for the implementation of the temperature-based model into a meat supply chain. The required hardware and software for continuous temperature monitoring were developed in order to use the model under practical conditions. Further on a decision support tool was elaborated in order to use the model as an effective tool in combination with the temperature monitoring equipment for the improvement of quality and storage management within the meat logistics network. Over the long term, this overall procedure will support the reduction of food waste and will improve the resources efficiency of food production.