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

Big data analytics in food industry: a state-of-the-art literature review

The food industry has experienced rapid growth over the past two decades, driven by technological advancements that have generated vast quantities of complex data. However, the industry's ability to effectively analyze and leverage this data remains limited due to the lack of control over diverse variables. This review addresses a critical gap by exploring how AI-ML-based approaches can be applied to solve key challenges in the food sector.

The Development of Machine Learning-Assisted Software for Predicting the Interaction Behaviours of Lactic Acid Bacteria and Listeria monocytogenes

Biopreservation technology has emerged as a promising approach to enhance food safety and extend shelf life by leveraging the antimicrobial properties of beneficial microorganisms. This study aims to develop precise predictive models to characterize the growth and interaction dynamics of lactic acid bacteria (LAB) and Listeria monocytogenes, which serve as bioprotective agents in food systems. Using both traditional and machine learning modelling approaches, we analyzed data from previously published growth curves in broth (BHI) and milk under isothermal conditions (4, 10, and 30 °C). The models evaluated mono-culture conditions for L. monocytogenes and LAB, as well as their competitive interactions in co-culture scenarios. The modified Gompertz model demonstrated the best performance for mono-culture simulations, while a combination of the modified Gompertz and Lotka-Volterra models effectively described co-culture interactions, achieving high adjusted R-squared values (adjusted R2 = 0.978 and 0.962) and low root mean square errors (RMSE = 0.324 and 0.507) for BHI and milk, respectively. Machine learning approaches further validated these findings, with improved statistical indices (adjusted R2 = 0.988 and 0.966, RMSE = 0.242 and 0.475 for BHI and milk, respectively), suggesting their potential as robust alternatives to traditional methods. The integration of machine learning-assisted software developed in this work into predictive microbiology demonstrates significant advancements by bypassing the conventional primary and secondary modelling steps, enabling a streamlined, precise characterization of microbial interactions in food products.

The Impact of Fluid Flow on Microbial Growth and Distribution in Food Processing Systems

This article examines the impact of fluid flow dynamics on microbial growth, distribution, and control within food processing systems. Fluid flows, specifically laminar and turbulent flows, significantly influence microbial behaviors, such as biofilm development and microbial adhesion. Laminar flow is highly conducive to biofilm formation and microbial attachment because the flow is smooth and steady. This smooth flow makes it much more difficult to sterilize the surface. Turbulent flow, however, due to its chaotic motion and the shear forces that are present, inhibits microbial growth because it disrupts attachment; however, it also has the potential to contaminate surfaces by dispersing microorganisms. Computational fluid dynamics (CFD) is highlighted as an essential component for food processors to predict fluid movement and enhance numerous fluid-dependent operations, including mixing, cooling, spray drying, and heat transfer. This analysis underscores the significance of fluid dynamics in controlling microbial hazards in food settings, and it discusses some interventions, such as antimicrobial surface treatments and properly designed equipment. Each process step from mixing to cooling, which influences heat transfer and microbial control by ensuring uniform heat distribution and optimizing heat removal, presents unique fluid flow requirements affecting microbial distribution, biofilm formation, and contamination control. Food processors can improve microbial management and enhance product safety by adjusting flow rates, types, and equipment configurations. This article helps provide an understanding of fluid-microbe interactions and offers actionable insights to advance food processing practices, ensuring higher standards of food safety and quality control.

Unveiling the flavor and quality variations in dried Zanthoxylum bungeanum maxim from China's diverse regions

Dried Zanthoxylum bungeanum Maxim (DZM) is one of the popular categories in spices and condiments market. The flavor and quality of DZM products determine its value and application scope. This study evaluated the flavor and quality of DZMs from various origins, considering physical, chemical, and safety attributes. Notable variations were observed, with DZMs from Maoxian and Jinyang excelling in aroma, pungency, and appearance. Chromatic analysis distinguished green and red DZMs from Meishan and Hancheng. HPLC results revealed high pungency compound levels in Maoxian and Wudu samples, while GC-MS identified 173 volatile compounds, dominated by linalool and d-limonene. Microbial contamination was minimal in DZMs from Hanyuan and Jiangjin, and the lowest heavy metal levels were in samples from Hanyuan, Hancheng, and Jingyang, indicating superior environmental conditions. The research offered insights into origin and processing influences on DZM, aiding in selection and food safety assurance.

Propidium monoazide (PMA) qPCR assay compared to the plate count method for quantifying the growth of Salmonella enterica serotypes in vacuum-packaged turkey breast combined with a mathematical modeling approach

This study compares the plate count (PC) and the Propidium Monoazide-quantitative Polymerase Chain Reaction (PMA-qPCR) methods to assess the growth of a cocktail of three serotypes of Salmonella enterica (Heidelberg, Typhimurium, and Enteritidis) in cooked, sliced, and vacuum-packaged turkey breast (STB) under isothermal storage temperatures (8 °C-20 °C), using predictive models. Standard curves were developed for PMA-qPCR, demonstrating high efficiency (101%) and sensitivity, with quantification limits ranging from 1 to 2 log10 CFU/g for all temperatures studied. Comparative analysis revealed a significant correlation (R2 = 0.99; 95% CI) between the PC and PMA-qPCR methods; however, the agreement analysis indicated a mean difference (Bias) of -0.11 log10 CFU/g (p < 0.05), suggesting underestimation by the PC method. This indicates the presence of stressed or viable but nonculturable (VBNC) cells, detectable by PMA-qPCR but not by PC. The Baranyi and Roberts model showed a good ability to describe the behavior of S. enterica cocktail in STB for PC and PMA-qPCR data under all isothermal conditions. The exponential secondary model more accurately represented the temperature dependence of the maximum specific growth rate compared to the Ratkowsky square root model, with R2 values ≥ 0.984 and RMSE values ≤ 0.011 for both methods. These results suggest that combining PMA-qPCR with predictive modeling allows for a more accurate prediction of S. enterica growth, compared to PC method. In the event of cold chain disruptions of meat products, the use of PMA-qPCR method allow the quantification of VBNC cells, that can still pose a health risk to consumers, especially in ready-to-eat products.

Comprehensive review of dysphagia and technological advances in dysphagia food

As the global population ages, dysphagia is becoming increasingly common among the elderly, posing serious risks such as choking, aspiration pneumonia, and even death. Leveraging advanced technologies to develop specialized food products for those with dysphagia not only serves the economic interests of the elderly food market but also significantly enhances the nutritional health and social satisfaction of this demographic. This review examines the causes and early symptoms of dysphagia, the development of texture-modified foods (TMFs), and the relevant regulations and standards. It also addresses the key factors influencing the swallowing of foods, focusing on rheology and tribology. Most texture-modified foods currently available do not provide an enjoyable eating experience for the elderly or those with dysphagia. The integration of artificial intelligence (AI) and mathematical modeling with food additive manufacturing technology appears promising for improving foods designed for the elderly and those with dysphagia. This paper highlights the critical benefits and potential applications of AI, mathematical modeling, and food additive manufacturing in creating dysphagia-friendly foods and provides a conceptual system for designing diets based on AI for dysphagic foods. AI and mathematical model-based food processing technology enable the food industry to achieve digitalization and large-scale customization, potentially revolutionizing the approach to dietary management in dysphagia.

Applying advanced predictive microbiology techniques to static and dynamic growth studies of Listeria monocytogenes

This project, titled 'Listeria Control,' aimed to advance expertise across Europe in applying predictive microbiology to shelf-life studies of Listeria monocytogenes in ready-to-eat (RTE) products. By increasing the capacity of the two participating organisations in predictive microbiology, this initiative strengthens Europe's overall ability to manage and mitigate the risk of L. monocytogenes in RTE foods. The project's first experimental phase involved experimental trials that examined the growth of L. monocytogenes under both constant and dynamic temperature conditions. Subsequent analysis fitted existing primary growth models to the constant temperature growth data. The resulting models were then employed to predict L. monocytogenes growth under fluctuating temperature scenarios. Given the limited reported research on modelling L. monocytogenes growth in dynamic environments, this work represents a significant contribution to this emerging field. Furthermore, this fellowship facilitated collaboration between IZS-Teramo and UCD, leading to enhanced and harmonised expertise in experimental and predictive techniques for L. monocytogenes shelf-life studies - a partnership that both organisations are committed to continuing beyond the fellowship's duration.

Synergistic effects of gamma irradiation/salmide®, a sodium chlorite-based oxy-halogen, on microbiological control and the shelf life of chicken breasts

A novel portable chamber was developed to extend the shelf life of chicken breasts through a synergistic treatment of gamma irradiation and Salmide®, a sodium chlorite-based oxy-halogen. This combination successfully enhanced the shelf life by utilizing a low dosage of gamma irradiation alongside low concentrations of Salmide (200 ppm sodium chlorite). Fresh chicken breast samples were treated with gamma irradiation, then packed in ice containing Salmide within the portable chamber, and subsequently stored for 20 days in a refrigerator at 4 °C ± 1. The study investigated aerobic bacterial counts, sensory analysis, and Thiobarbituric acid (TBA) levels. Results showed that Salmide alone significantly reduced microbial counts and extended shelf life by 8 days. Gamma irradiation at 1 kGy, either alone or combined with Salmide, caused a sequential reduction in total aerobic bacterial counts by 2,3 logarithmic cycles, respectively, extending the storage period to 12 days. Furthermore, a 16 day shelf life extension was achieved with gamma irradiation at 3 kGy, either alone or in combination with Salmide, resulting in a reduction of total aerobic bacteria by 5 logarithmic cycles. This study is the first to employ Salmide in conjunction with gamma irradiation as an innovative technology in a portable chamber to enhance the safety and shelf life of chicken breasts during storage in the designed portable chamber.

Machine Learning-Based Software for Predicting Pseudomonas spp. Growth Dynamics in Culture Media

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors-temperature, water activity, and pH-served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.

Evaluation of the Sensory Quality and Shelf Life of a Bioactive Essence Rich in Monounsaturated Fatty Acids and Antioxidants, Obtained from Eco-Sustainable Iberian Ham

Food sustainability through traditional food production and the reuse of food by-products is one of the characteristics most valued by consumers. The production of Iberian ham is linked to the vaporization and sustainability of the dehesa and the conservation and maintenance of the rural environment, but there are some by-products that are not destined for direct consumption. In this context, previous studies have used trimmed fat to obtain a bioactive essence rich in antioxidants and monounsaturated fatty acids. Furthermore, it is important to keep in mind that the consumer's decision is influenced by the nutritional/health and sensory characteristics of the product and its shelf life. The objective of the present study was to evaluate consumer acceptance and/or preference of different essences obtained from the trimmed fat of sliced Iberian ham and to determine the microbiological and physicochemical stability of the selected sustainable essence over time. The results showed that this essence is generally accepted by consumers and is microbiologically stable over time.

The influence of storage conditions on hygiene condition and eicosapentaenoic acid oxidation in Nannochloropsis algae for sustainable food supply

BACKGROUND

Nannochloropsis algae contain approximately 20% polyunsaturated fatty acids (PUFA) and hold significant potential for high-quality eicosapentaenoic acid (EPA) food industrialization. However, EPA in Nannochloropsis sp. is prone to oxidation, and microbial growth is a critical factor affecting the shelf life of fresh food. Storage composition and temperature are primary factors influencing microbial growth, yet these aspects are not fully understood. This study investigates the effects of temperature and encapsulation on EPA content in nano-products over time. Nano-powder and nanobeads derived from Nannochloropsis sp. served as raw materials. Additionally, changes in aerobic plate counts and coliform groups were monitored.

RESULTS

The results indicated that nanobeads, due to their more complex processing and less mature packaging, were more susceptible to coliform contamination compared to nano-powder. In terms of EPA stability, nanobeads exhibited a longer storage life than nano-powder. The oxidation rate of both nano-powder and nanobeads was faster at 37 °C than at 25 °C.

CONCLUSION

These findings can inform general shelf life estimation, rapid detection of total lipid content in nano-products and macro extraction of nano-oil. Moreover, they have significant implications for delaying EPA oxidation in nano-products and improving hygienic quality control for microbial detection. © 2024 Society of Chemical Industry.

Evaluating the Impact of Green Coffee Bean Powder on the Quality of Whole Wheat Bread: A Comprehensive Analysis

The current investigation focuses on the effect of different concentrations of green coffee bean powder (GCBp) on the physicochemical, microbiological, and sensory characteristics of whole wheat bread (WWB). C1 bread formulation (containing 1% GCBp) exhibited the highest loaf volume, suggesting optimal fermentation. Moisture analysis revealed minor alterations in the moisture retention attributes of the bread formulations. Impedance analysis suggested that C1 exhibited the highest impedance with a high degree of material homogeneity. Swelling studies suggested similar swelling properties, except C5 (containing 5% GCBp), which showed the lowest swelling percentage. Furthermore, color and microcolor analysis revealed the highest L* and WI in C1. Conversely, higher concentrations of GCBp reduced the color attributes in other GCBp-containing formulations. FTIR study demonstrated an improved intermolecular interaction in C1 and C2 (containing 2% GCBp) among all. No significant variation in the overall textural parameters was observed in GCBp-introduced formulations, except C2, which showed an improved gumminess. Moreover, the TPC (total phenolic content) and microbial analysis revealed enhanced antioxidant and antimicrobial properties in GCBp-incorporated formulations compared to Control (C0, without GCBp). The sensory evaluation showed an enhanced appearance and aroma in C1 compared to others. In short, C1 showed better physicochemical, biological, and sensory properties than the other formulations.

Process Standardization of Functionally Enriched Millet-Based Nutri-Cereal Mix Using D-Optimal Design Approach for Enhancing Food and Nutritional Security

Millets are currently employed in a variety of ways, including direct consumption and usage in the manufacture of certain cuisines or snacks. The present investigation was aimed at optimizing functionally enriched millet-based nutri-cereal mix comprising chicken and vegetable for a nutrition-deficient population. A total of 16 experiments were carried out by using optimal (custom) design model of mixture design with 60% major ingredients, including malted sorghum flour (20-30%), malted green gram flour (15-25%), and boiled chicken powder (5-15%). To make 100% of the total nutri-cereal mixture, other ingredients such as malted pearl millet (10%), finger millet flour (10%), beetroot powder (2.5%), pumpkin powder (7.5%), skimmed milk powder (9.5%), and stevia powder (0.5%) were added. Numerical optimization was done using Design Expert software, version 13. The optimized ratio was 30% malted sorghum flour, 15% malted green gram flour, and 15% chicken powder. The predicted values of responses 5.101%, 3.616%, 1.963%, 11.165%, 28.005%, 50.149%, 330.282 kcal, and 0.373 were in accordance with experimental values 6.426%, 3.455%, 1.714%, 11.432%, 29.12%, 47.853%, 323.318 kcal, and 0.385 for moisture, ash, fat, fiber, protein, carbohydrates, energy, and water activity, respectively, with a small error percentage. The results of mineral content, phenolic content, and amino acid profiling revealed that the optimized Nutri-cereal mix have higher amounts of these components. The results also suggested that the optimized Nutri-cereal mix of these malted millet flours can potentially enhance the nutritional deficiency as well as improve food and nutritional security.

Modeling the Thermal Inactivation of Monascus ruber Ascospores Isolated from Green Olive (Arauco Cultivar) Storage Brine: An Alternative Strategy to Reduce Antifungal Chemical Agents

Monascus ruber is an important fungus that causes spoilage in table olives, resulting in the darkening of the brine, the softening of the fruit, increased pH, and apparent mycelial growth. This study aimed to evaluate this resistance, providing a model to determine the optimal processing conditions for mitigating fungal contamination and prolonging shelf life without antifungal agents while optimizing pasteurization to reduce energy consumption. The resistance in brine (3.5% NaCl; pH 3.5) from Arauco cultivar green olives imported from Argentina was assessed. Four predictive models (log linear, log linear + shoulder, log linear + tail, log linear + shoulder + tail) estimated kinetic parameters for each survival curve. Log linear + shoulder + tail provided the best fit for 70 °C and 75 °C, with low RMSE (0.171 and 0.112) and high R2 values (0.98 and 0.99), respectively, while the log linear model was used for 80 °C. Decimal reduction times at 70, 75, and 80 °C were 24.8, 5.4, and 1.6 min, respectively, with a z-value of 8.2 °C. The current regulatory processes are insufficient to eliminate M. ruber at requisite levels, considering reduced antifungal agents.

Exploring Chitosan Lactate as a Multifunctional Additive: Enhancing Quality and Extending Shelf Life of Whole Wheat Bread

The shelf life of whole wheat bread (WWB) significantly impacts its freshness and overall quality. This research investigated the impact of chitosan lactate (CL) on various characteristics influencing the shelf life of WWB, including its physical, chemical, textural, antimicrobial, and sensory attributes. These characteristics were evaluated by conducting various experiments such as physical inspection, moisture, impedance, swelling, color, texture, FTIR, microbiological, and sensory analysis. CL with different concentrations was incorporated into WWB formulations: P0.0 (0.0% w/w CL, control), P0.5 (0.5% w/w CL), P1.0 (1.0% w/w CL), P2.0 (2.0% w/w CL), and P3.0 (3.0% w/w CL). The inclusion of CL promoted the Maillard reaction (MR) compared to P0.0. The promotion of MR resulted in the formation of a shinier crust, which increased as the CL content was increased. P0.5 comprised large-sized pores and exhibited increased loaf height. CL-containing WWB formulations showed an increased moisture content and decreased impedance values compared to the control. FTIR analysis of P0.5 demonstrated the enhanced interaction and bonding of water molecules. P0.5 demonstrated optimal textural, colorimetric, and antimicrobial properties compared to other formulations. The sensory attributes of WWBs remain unchanged despite CL addition. In conclusion, P0.5 exhibited optimal characteristics associated with better quality and prolonged shelf life.