Validating Accelerated Shelf Life Testing Methodology for Predicting Shelf Life in High-Pressure-Processed Meat Products

Abstract

The shelf life of meat products is a critical factor in ensuring both consumer safety and product quality. Traditional methods for determining shelf life are labor-intensive and time-consuming, making it challenging for manufacturers to adapt to market demands. The accelerated shelf life testing (ASLT) methodology offers a viable solution by exposing products to controlled elevated conditions that simulate long-term storage, allowing for faster shelf life predictions. This study evaluates the ASLT methodology as a predictive tool for determining the shelf life of high-pressure (HPP)-treated meat products. The present study includes experiments to verify the shelf life of frankfurter-type sausages under accelerated conditions. By simulating long-term storage at elevated temperatures (4–18 °C), a kinetic model was developed to monitor spoilage bacteria growth, with validation under dynamic temperature conditions. The results indicate that the main spoilage population of frankfurter-type sausages was lactic acid bacteria (LAB), which was strongly correlated with the total mesophilic microflora of the products. The findings show that elevated storage temperatures (8 and 18 °C) provide accurate shelf life predictions, offering an efficient approach to ensure product quality and meet consumer demands.

1. Introduction

The global food processing industry has seen significant transformations in recent years, driven by evolving consumer preferences and expectations. The shelf life of meat products plays a crucial role in determining food safety, quality, and economic value, making it a key focus in the food processing industry. Meat is highly perishable due to its high water activity, nutrient-rich composition, and the presence of unsaturated fats, which make it prone to microbial spoilage and oxidative degradation [1], which can negatively impact its sensory characteristics, nutritional content, and safety for consumption [2,3]. This presents significant challenges for manufacturers, particularly as consumers’ demand for minimally processed, preservative-free, and high-quality meat products continues to rise. As a result, the need for effective preservation techniques that extend shelf life without compromising product quality has become a priority. The combination of emerging technologies and consumer-driven demand for cleaner, safer, and longer-lasting meat products is shaping the future of the meat industry, pushing both manufacturers and researchers to innovate in terms of preservation techniques and sustainable processing methods [1,4,5].

Processing fresh meat offers several benefits, including reducing its perishability, extending its shelf life, lowering transportation and storage costs, and adding value to by-products such as trimmings, tendons, fat, and less desirable cuts that might not be consumed in their fresh state. The technologies used are designed to inhibit microbial growth, delay lipid oxidation, and preserve the sensory quality of meat, ensuring its safety and quality during extended storage [6]. Among these technologies, high-pressure processing (HPP) has emerged as a promising nonthermal preservation technology that addresses these challenges. HPP subjects food to high pressures (typically ranging from 100 to 800 MPa) for short processing times (usually less than 10 min), effectively inactivating spoilage microorganisms and pathogens while preserving the nutritional and sensory characteristics of the product [7,8]. The pressure is uniformly applied, which helps to maintain the product’s integrity, and can be adjusted based on specific processing requirements. This adaptability makes HPP suitable for a wide variety of food products, including meat, seafood, dairy, and fresh produce. In the context of meat products, HPP has demonstrated significant potential in extending shelf life by enhancing microbiological safety, reducing chemical spoilage, and retaining sensory attributes such as flavor and texture [9]. However, high-pressure treatments can sometimes lead to undesired changes, such as lipid oxidation, the formation of volatile compounds, and color modifications in certain meat products like sliced dry-cured ham [10,11]. These effects are highly dependent on the specific pressure levels and treatment conditions used, as well as the inherent characteristics of the meat matrix [12]. For example, in dry-cured ham, high-pressure treatments may influence sensory properties, though research on this topic remains relatively limited [7]. Understanding these impacts is critical for optimizing HPP parameters to achieve the desired shelf life extension without negatively affecting product quality.

HPP technology not only extends the shelf life but also enhances food safety, making it an attractive option for meat processors. However, despite its advantages, the need to accurately predict the shelf life of HPP-treated products remains a challenge. Accurate shelf life determination is essential for maintaining product safety and quality, ensuring consumer satisfaction, and reducing food waste. Traditionally, shelf life is determined through real-time storage studies, in which products are stored under normal conditions and periodically evaluated for microbial, chemical, and sensory changes. Although effective, this approach is time-consuming and resource-intensive, often requiring several months to produce reliable data. For manufacturers aiming to bring products to market quickly, such delays can be a significant drawback, especially in the context of rapidly changing consumer preferences and competitive market pressures.

To overcome the limitations of real-time testing, accelerated shelf life testing (ASLT) has been introduced as a more efficient alternative. ASLT involves subjecting products to controlled elevated conditions—typically higher temperatures or humidity levels—that accelerate spoilage processes, allowing for quicker predictions of shelf life. By exposing products to controlled, elevated temperature conditions, ASLT can simulate the effects of long-term storage in a fraction of the time [13,14,15,16]. This methodology allows for the rapid assessment of product stability and helps predict how products will perform under normal storage conditions. Nevertheless, the reliability and accuracy of ASLT must be validated to ensure that the accelerated conditions reliably simulate the product’s behavior during long-term storage and predictions align with actual shelf life [17,18]. This study focused on the validation of ASLT as a tool for predicting the shelf life of HPP-treated meat, specifically frankfurter-type sausages packed under vacuum conditions. These sausages are widely consumed and highly perishable, making them an ideal model for evaluating ASLT in combination with HPP. Frankfurter-type sausages, commonly known as hot dogs, are a popular processed meat product made from finely ground pork, beef, or a combination of both, often mixed with seasonings, spices, and other ingredients such as water, salt, and curing agents. These are emulsified and non-fermented products with a high protein and fat (20–30%) content submitted to moderate heat treatment that preserves the biological value of nutritionally essential compounds such as amino acids, vitamins, and minerals [19] while obtaining a smooth texture. They are typically cooked and smoked during production, giving them their distinct flavor and texture. By choosing the optimal combination of available raw materials and ingredients while still maintaining a high-quality product, production costs may be substantially reduced [20]. Due to their high protein and fat content, along with their smooth, homogeneous texture, frankfurters are widely enjoyed around the world. However, owing to their formulation and production process, they are prone to microbial spoilage, which impacts both quality and shelf life, and therefore require proper packaging and storage to maintain quality and safety. This goal is achieved with modern preservation technologies, such as HPP, extending their shelf life while retaining flavor and freshness. In the case of HPP-treated meat products, the application of ASLT presents both opportunities and challenges. The unique effects of high-pressure treatment on microbial inactivation and the stability of food components necessitate the development of specific models that account for these variables when predicting shelf life [18]. Furthermore, spoilage mechanisms in HPP-treated meats, such as the growth of spoilage microorganisms and changes in organoleptic properties, can vary depending on the product formulation, packaging, and storage conditions. Therefore, there is a need to establish a robust and validated ASLT methodology tailored specifically to HPP-treated meats.

Microbial growth is an important factor for the shelf life and consumer acceptance of meat. The microbial flora that develops is dependent on factors affecting spoilage, such as storage temperature and packaging environment [21]. A key objective is to establish a validated kinetic model that describes the growth of spoilage bacteria—specifically total viable counts (TVCs), Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria (LAB), and Enterobacteriaceae spp.—in the temperature range of 4 to 18 °C. These bacteria are significant contributors to spoilage and can adversely affect the quality and safety of meat products. In a laboratory-simulated field test, comprehensive measurements of total viable counts and specific spoilage organisms on the sausage samples were conducted. The results were then compared to the predictions generated by the developed kinetic model. This approach not only aims to enhance the accuracy of shelf life predictions for HPP-treated meat but also provides insights into the prevailing microbial dynamics that affect product quality during storage.

This study aimed to develop a reliable ASLT methodology for predicting the shelf life of HPP-treated meat products. This validation focuses on determining whether ASLT can accurately replicate the spoilage kinetics observed under standard storage conditions, thus offering a robust and time-efficient alternative for shelf life evaluation. While the effects of HPP on product spoilage were observed in this study, they were not the central focus. Rather, they serve as an essential component of the experimental framework, providing the necessary context for applying and testing the ASLT methodology. By understanding how HPP impacts spoilage patterns, this study refined the application of ASLT, ensuring that it accounts for the unique attributes of HPP-treated products. Despite the advantages of HPP technology, predicting the shelf life of HPP-treated products remains a challenge due to the unique ways in which high pressure alters microbial and biochemical degradation processes. ASLT is a promising methodology for estimating shelf life in a shorter time, but its application to HPP-treated meat products is not yet well validated. Moreover, the application of ASLT methodology is usually related to the prediction and evaluation of chemical and biochemical degradation reactions in foodstuffs rather than microbiological spoilage phenomena. Existing studies on ASLT often focus on conventional preservation methods and fail to account for the distinct mechanisms of spoilage in HPP-processed products. This study addressed the gap by systematically evaluating and validating ASLT methodologies specifically for HPP-treated meat products. It aimed to establish a reliable framework for predicting shelf life under accelerated condition that can also mimic real-world scenarios, thereby bridging the existing gap between ASLT protocols and the unique attributes of HPP.

By creating a kinetic model that monitors spoilage indicators such as microbial growth, lipid oxidation, and sensory changes under accelerated conditions, this research seeks to provide a more efficient and accurate approach to shelf life prediction. The model will be validated against real-time data to ensure its reliability. Ultimately, the findings of this study will contribute to improving shelf life prediction methodologies for HPP-treated meats, enabling producers to optimize processing, reduce food waste, and meet consumer demands for safe, high-quality products with an extended shelf life. The successful implementation of ASLT in combination with HPP could revolutionize the meat processing industry by providing a faster, more precise method for shelf life estimation. This would allow manufacturers to respond more rapidly to market demands and regulatory requirements while ensuring that their products remain safe and are of the highest possible quality throughout their distribution and storage periods. This work not only aims to establish ASLT as a practical tool for shelf life prediction in the meat industry but also contributes to the broader understanding of how advanced preservation methods can be integrated with predictive modeling to optimize food quality.

2. Materials and Methods

2.1. Preparation of Samples and HPP Application

The frankfurter-type sausages consisted of chicken MSM (Mechanically Separated Meat), pork collagen, and pork fat, in concentrations that that are classified as industrially confidential. The sausages were pre-cooked and packed in vacuum in HPP-appropriate packaging materials, also classified as industrially confidential. The preparation of the samples was performed by the meat company NIKAS S.A. (Aghios Stefanos, Attica, Athens, Greece).

The vacuum-packed frankfurter-type sausages were cold-pasteurized in an industrial-scale HPP unit (JBT AVURE AV30, Chicago, IL, USA) at 6000 bars for 240 s. The processing temperature was set at 4 °C, recording an actual processing temperature of 7.0–7.5 °C due to adiabatic heating. The application of HPP was performed by the meat company NIKAS S.A. (Aghios Stefanos, Attica, Athens, Greece).

2.2. Experimental Design

Frankfurter-type sausages were received from the meat company NIKAS S.A. (Aghios Stefanos, Attica, Athens, Greece) and transported to the Laboratory of Food Chemistry and Technology, at the School of Chemical Engineering, National Technical University of Athens (NTUA, Athens, Attica, Athens, Greece), under cold conditions in polystyrene boxes, where they were studied in terms of their quality and spoilage to determine their shelf life and commercial durability and to provide a predictive tool to allow the estimation of the product’s shelf life under different storage conditions. For each sampling, measurements of the products’ quality parameters were conducted at least in duplicate, excluding the sensory evaluation. The quality parameters included microbial spoilage (total viable count—TVC, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria, and Enterobacteriaceae spp.) and measurement of pH value, color parameters, water activity (a_w), texture characteristics, and lipid oxidation.

The samples used for analysis were frankfurter-type sausages before HPP (CNT samples) and frankfurter-type sausages after the induction of HPP (HPP samples). The accelerated shelf life testing (ASLT) methodology was used to estimate the shelf life of the products in various conditions. A systematic study of the selected indicators was performed as a function of time and storage conditions (i.e., temperature) using appropriate measurement methods. In parallel, at selected time intervals, sensory evaluation of the selected processed meat products was carried out. All samples were stored in controlled laboratory incubators (MIR150, Sanyo, Japan) at isothermal temperatures of 4, 8, 12, and 18 °C and under dynamic conditions. The two dynamic temperature profiles selected were (a) 1 h at 12 °C, followed by 4 h at 4 °C and 7 h at 2 °C (VAR1), and (b) 2 h at 18 °C, followed by 8 h at 8 °C and 2 h at 4 °C (VAR2). The effective temperatures of the two dynamic temperature profiles were estimated according to Tsironi et al. [22] and found to be equal to T_eff = 3.8 °C (VAR1) and T_eff = 9.7 °C (VAR2) for the CNT samples and T_eff = 4.5 °C (VAR1) and T_eff = 11.1 °C (VAR2) for the HPP samples.

2.3. Physicochemical Analysis

2.3.1. pH Measurement

The pH of the frankfurter-type sausages was measured using an electronic pH meter (pH meter 338, AMEL Instruments, Milano, Italy). For the measurement, the pH meter was immersed in the homogenized solution of the sample with Ringer’s solution at a 1:10 dilution ratio, which was prepared during the microbiological analysis. The pH measurement was performed after the completion of the microbiological analysis in order to avoid contaminating the sample. The measurements were conducted in duplicate on all scheduled sampling dates.

2.3.2. Color Measurement

The color of the samples was measured using the Minolta CR-200 device (Minolta Company, Chuo-Ku, Osaka, Japan), utilizing the CIE (Commission Internationale de l’Eclairage) lab color parameters. The colorimeter had an 8 mm diameter opening at its end. Before the measurement, calibration was performed using a white calibration plate (calibration plate CR-200, L = 97.5, a = −0.31, b = −3.83). The measurements were conducted in triplicate on all scheduled sampling dates.

2.3.3. Water Activity (a_w) Measurement

For the determination of the water activity of the frankfurter-type sausage samples, the Aqua Lab 4TEV device (Decagon Devices, Pullman, WA, USA) was used, which provides the result in relation to temperature. A small amount of sample was placed in a special plastic cup and inserted into the device. The measurements were carried out in triplicate.

2.3.4. Texture Analysis

The texture analysis was performed using a texture analyzer (Texture Analyzer, TA-XT2i, Stable Micro Systems, Godalming Surrey, UK). Briefly, a single compression test was conducted on a whole frankfurter-type sausage sample using the Warner–Bratzler blade probe (Stable Micro Systems, UK) with the following set parameters: pre-test speed 2.0 mm/s; test/post-test speed 1.5 mm/s; distance 10 mm. This was done to simulate the applied cutting force. Additionally, a double compression test was performed on a 100 mm thick sausage slice using the cylinder probe P/6 (Stable Micro Systems, UK) with the following set parameters: pre-test speed 10 mm/s; test/post-test speed 5.0 mm/s; distance 5 mm. The measurements were carried out in triplicate.

The main textural attributes (i.e., hardness, cohesiveness, adhesiveness, springiness index, gumminess, chewiness) were estimated using the appropriate software (Exponent V6.2.4, Stable Micro Systems, UK).

2.3.5. Lipid Oxidation

Throughout storage, the lipid oxidation of the frankfurter-type sausages was measured using the 2-thiobarbituric acid (TBAR) assay, performed according to the method of Lovaas [23]. The thiobarbituric acid (TBA) method is a commonly used analytical protocol to evaluate lipid oxidation in food products, particularly to detect secondary oxidation products such as malondialdehyde (MDA), which form as lipids degrade. To evaluate lipid oxidation, a 2-thiobarbituric acid reactive substance (TBAR) assay was carried out using 5 g of sausage flesh homogenized with 15 mL of distilled water. Then, 2 mL of an acid solution of TBA was added to 1 mL of the homogenized aquatic meat flesh followed by heating in a boiling water bath for 15 min to obtain maximum color development. The absorbance was measured at 532 nm with a digital spectrophotometer (Unicam Helios; Spectronic Unicam EMEA, Cambridge, UK). The concentration of TBARs was calculated using a standard curve prepared by 1,1,3,3-tetraethoxypropane and expressed as mg malonaldehyde (MDA) per kg of the sausage. The measurements were carried out in triplicate.

2.4. Microbiological Analysis

All microbiological analyses (determination of total viable count, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria, and Enterobacteriaceae spp.) were conducted based on official methods [24]. The determination of microorganisms was carried out using the spread plate method on nutrient media, specifically with the plate method. Prior to microbiological analysis, the samples were homogenized by mixing 10 g of frankfurter-type sausage sample with 90 g of sterile Ringer’s diluent (Merck, Darmstadt, Germany). Homogenization was performed using a Stomacher (BagMixer^®, Interscience, Saint Nom, France) at room temperature for 60 s. The number of microorganisms per gram of sample was expressed as colony-forming units (CFUs) per gram. The microbial load of the samples was calculated using the surface spreading and incorporation methods in plates.

Total viable count (TVC) was determined by the spread method on non-selective nutrient medium—plate count agar (PCA, Merck, Darmstadt, Germany). Incubation was carried out at 30 °C for 72 h. For the calculation of the total population of mesophilic aerobic bacteria per gram of food (CFU/g), all bacterial colonies that grew on the PCA nutrient medium were counted. Different microorganisms form colonies of varying sizes, shapes, and colors. The presence of Pseudomonas spp. was also determined by the spread method on the selective nutrient medium cetrimide agar (Merck, Darmstadt, Germany) after incubation at 30 °C for 72 h. All colonies that grew on the plates were counted for the enumeration of Pseudomonas spp. Brochothrix thermosphacta was determined by the spread method on STAA agar base (Biolife, Milano, Italy) after incubation at 30 °C for 48 h, during which, purple-colored colonies were counted. Lactic acid bacteria were determined on MRS agar (De Man–Rogosa–Sharpe agar, Merck, Darmstadt, Germany). All colonies on the plates were counted after incubation at 30 °C for 96 h, while the determination of Enterobacteriaceae spp. was carried out on VRBG agar (violet red bile glucose agar, Biolife, Monza, Italy), following incubation at 37 °C for 18–24 h. Two replicates of the appropriate dilution were carried out, while the microbiological analysis was performed in duplicate.

Even though the present study focused on predicting spoilage-related rather than safety-related bacteria, all samples were analyzed using appropriate methods to detect the presence of pathogens (e.g., Listeria spp., Salmonella spp., E. coli, Staphylococcus spp., Clostridia), as is routinely done for all HPP product batches in the collaborating meat industry. The following protocols and methods were employed: for Staphylococcus spp., detection ISO 6888-2:2021 standards were followed [25], and E. coli detection adhered to ISO 21528-2:2017, the horizontal method for the detection and enumeration of Enterobacteriaceae spp. using a colony-count technique [26]. Listeria spp. detection was performed by rapid screening methods using VIDAS^® Solutions (LDUO/LMX), an automated food pathogen detection system specifically designed for Listeria monocytogenes (VIDAS^® Solutions LDUO/LMX, bioMérieux, Rio de Janeiro, Brasil), while for the detection of Salmonella spp., the VIDAS^® UP Easy SPT, a high-performance automated solution based on ISO 16140 AFNOR BIO-12/32-10/11 was used [27]. Clostridium spp. was detected using SPS agar (sulphite–polymyxin–sulphadiazine agar, HiMedia Laboratories Pvt. Ltd., Maharashtra, India). These high-performance and standardized methods ensure the robust detection of pathogens in line with routine quality and safety monitoring in the collaborating meat industry. Results indicate that after the applied HPP conditions, no pathogens were detected, which agrees with the literature findings on the effect of HPP technology on pathogenic bacteria in similar meat products [28,29]. In fact, since HPP has been industrially applied as a cold pasteurization method, according to our meat industry partner, for the approximately 550 tn end products produced per month, there was no detection of the abovementioned pathogens.

2.5. Data Analysis

Microbial growth was modeled using the Baranyi Growth Model [30]. For curve fitting, the program DMFit (IFR, Institute of Food Research, Reading, UK) was used (available at https://combasebrowser.errc.ars.usda.gov/DMFit.aspx). The microbial growth is described by an equation as follows:

$$\begin{gathered} y\left(t\right)=y_0+{\mu }_{max}t+{\displaystyle \frac{1}{{\mu }_{max}}}\ln \left(e^{-v·t}+e^{-h_0}-e^{-v·t-h_0}\right)- \\ -{\displaystyle \frac{1}{m}}\mathrm{l}\mathrm{n}(1+{\displaystyle \frac{e^{{\mu }_{max}t+\frac{1}{{\mu }_{max}}ln\left(e^{-v·t}+e^{-h_0}-e^{-v·t-h_0}\right)}-1}{e^{\left(y_{max}-y_0\right)}}}) \end{gathered}$$

(1)

where y_(t) is the cell concentration (log CFU/g) at any time t, y₀ is the initial cell concentration (log CFU/g), y_max is the maximum cell concentration (log CFU/g), μ_max is the maximum specific growth rate (d⁻¹), v is the rate of increase in the limiting substrate, assumed to be equal to μ_max, λ is the lag phase duration (d), and h₀ is equal to μ_max·λ. The growth rates (μ_max) and lag phase duration (λ) were estimated, and they are presented with the corresponding standard errors.

The dependence of the microbial growth rate on storage temperature was estimated according to the Arrhenius equation:

$$k=k_{ref}·exp[-{\displaystyle \frac{E_a}{R}}·\left({\displaystyle \frac{1}{T}}-{\displaystyle \frac{1}{T_{ref}}}\right)]$$

(2)

where T_ref is a reference temperature, R is the molar gas constant (8.314  J/K·mol), and E_a is the apparent activation energy (J/mol). The equation was linearized by applying the logarithm to both sides of the equation, and then coefficients k_ref and E_a were estimated using linear regression.

Sensorial deterioration, as expressed through the overall impression of the studied frankfurter-type sausages, was modeled using a zero-order kinetic equation, as follows:

$${Overall impression}_t={Overall impression}_0-k_{sensory}·t$$

(3)

where Overall impression_t is the sensory quality at any time t, Overall impression₀ is the initial sensory quality of the products (score = 9), and k_sensory is the sensorial deterioration rate constant (d⁻¹) of the samples at each storage temperature.

The shelf life of frankfurter-type sausages, based on microbiological data and with a limit of acceptance for the TVC load set to 8 log CFU/g, was estimated according to the following equation:

$$Shelf life \left({SL}_{microbial}\right)=\left({\displaystyle \frac{{\mathrm{l}\mathrm{o}\mathrm{g}(N}_{limit})-{logN}_0}{k_{TVC}}}\right)+lag phase$$

(4)

where log(N_limit) is the limit of acceptance for the microbial load (8 log CFU/g), N₀ is the initial microbial load of the TVC, and k_TVC is the growth rate constant (d⁻¹) of the TVC at each storage temperature.

The shelf life of frankfurter-type sausages, based on sensorial data and with a limit of acceptance for the overall impression set to 4.5, was estimated according to the following equation:

$$Shelf life \left({SL}_{sensory}\right)=\left({\displaystyle \frac{{Overall impression}_{initial}-{Overall impression}_{final}}{k_{sensory}}}\right)$$

(5)

where the Overall impression_(initial) represents the initial score (=9), the Overall impression_(final) represents the limit of sensorial acceptance (=4.5), and k_sensory is the deterioration rate constant of sensorial quality (d⁻¹) at each storage temperature.

2.6. Sensory Analysis

A sensory evaluation was conducted with experienced participants, including academic staff and research students, who had prior training with frankfurter-type sausage samples. The sensory panel included eight members (60% female and 40% male, 70% young (18–35 years old) and 30% middle-aged (36–55 years old)). The sensory evaluation was performed in the sensory laboratory (ISO standard 17025) of the Laboratory of Food Chemistry and Technology of the School of Chemical Engineering of the National Technical University of Athens, according to official ISO protocols [31,32,33,34]. The specific attributes assessed were color, moisture loss, flavor, hardness, stickiness, juiciness, and taste after boiling the sausage samples for 4 min, with tasters being thoroughly briefed on the definitions of each attribute. The sausage samples were given in plastic, coded (three-digit random codes) containers containing ca. 50 g of the sample. The questionnaires included a question regarding the overall acceptability of the samples, while each selected sensory characteristic was evaluated for intensity and/or preference using a 9-point scale (1; lowest intensity—9; highest intensity/1; dislike extremely—9; like extremely). The threshold for the organoleptic acceptance of the products was defined as level 5 on the 9-point preference scale. The sensory panel was instructed to cleanse their mouth with spring water (Nera Kritis, Acharnes, Greece) between different samples. It should also be noted that on the date of sample receipt, a number of product pieces were stored at 0 °C. These specific samples were used as reference samples on each organoleptic evaluation date to help the panelists assess the qualitative degradation of the products.

2.7. Statistical Analysis

Experiments were replicated twice on different occasions with different frankfurter sausages. Analysis of variance (ANOVA) at a significance level of 95% was used for the analysis of the physicochemical parameters, microbial growth kinetic parameters (k, λ), and shelf life (t_SL) of the sausage samples (STATISTICA^® 7.0, StatSoft Inc., Tulsa, OK, USA). Significant differences were calculated according to Duncan’s multiple range test (a = 0.05).

3. Results and Discussion

3.1. Physicochemical Analysis

In Figure 1a,b, the pH values of the reference samples (CNT) and the HPP-treated samples are depicted during their storage at 4, 8, 12, and 18 °C, respectively.

The pH value is an important parameter in monitoring meat quality. The pH values of the samples under various isothermal storage conditions (4, 8, 12, and 18 °C) ranged from 6.3 (day 0) to 4.8 for the CNT samples and 5.3 for the HPP samples at the end of the storage time. Over the storage time, the pH of the frankfurters decreased significantly (p < 0.05) in all treatments. This significant decrease in pH began on day 20 for the CNT samples and on day 96 for the HPP samples stored at 4 °C. The pH values of all samples gradually decreased with increasing storage time. In general, when the main spoilage microorganism LAB reached high levels (6.5–8 log CFU/g) in all frankfurter sausages, the pH began to decrease (p < 0.05), which may be attributed to the organic acid accumulation produced by spoilage microorganisms [35,36]. LAB are known to produce organic acids such as lactic acid, acetic acid, and formic acid, which contribute to a decrease in the pH values of meat products. The extent of acid production depends on factors such as genus, species, and the specific growth conditions [37]. The reduction in pH within meat products is also largely influenced by the availability of fermentable carbohydrates. For instance, Pexara et al. [38] observed a decline in the pH of turkey fillets during storage, from an initial value of 6.2 to 5.5. However, in piroshki sausages, which contain lower levels of carbohydrates, the rate of pH decrease was slower compared to turkey fillets. Similarly, Han et al. [39] reported a significant decrease in the pH of vacuum-packed, untreated, and HPP-treated cooked ham at 400 or 500 MPa over the course of storage. The results of the present study also agree with the results of Xiao et al. [40]. Considering that all samples showed a decrease in pH value and increase in the growth of lactic acid bacteria, yet the same drop in pH value was not observed, it can be concluded that at higher storage temperatures, additional chemical and biochemical phenomena are likely to occur and accelerate, affecting the pH value of the overall system. These reactions seemed to slow down in the case of HPP samples, as in this case, the decrease in pH value was lower. The samples stored under dynamic conditions with T_eff = 4.5 °C exhibited similar behavior to those stored under isothermal conditions of 4 °C, with the HPP samples showing an overall slight, yet significant (p < 0.05), pH drop of approximately 0.3 units (from an initial mean value of 6.31 ± 0.01 to a final mean value of 6.19 ± 0.04).

Figure 2a,b show the evolution of the color parameters L*, a, and b of the reference samples (CNT) and the HPP-treated samples during their storage at 4, 8, 12, and 18 °C and dynamic storage conditions (VAR1, VAR2).

The results obtained indicate that neither the application of HPP on the meat products nor the storage time affected the values of the tested color parameters. These findings are in accordance with previous studies where the application of pressures up to 600 MPa for 5 min did not lead to color changes during the storage of either sliced cooked ham and turkey or bratwurst sausages [9].

Regarding the cutting force and the textural attributes of the studied frankfurter-type sausages, the results show that the application of HPP did not significantly (p > 0.05) affect either the cutting force or the hardness, index of elasticity, adhesiveness, gumminess, and chewiness of the samples.

The initial cutting force for the reference (CNT) samples was 5.16 ± 0.17 N, while for the HPP samples, it was 4.92 ± 0.26 N. These values did not change during storage under either isothermal or dynamic storage conditions for all samples, exhibiting slightly, though insignificantly, decreased final means (p > 0.05) of 4.95 ± 0.28 N and 4.87 ± 0.72 N for the CNT and the HPP samples, respectively.

The initial values of the textural attributes obtained after the double compression test on the slice of CNT frankfurter-type sausages were calculated as 4.65 ± 0.26 N, 0.74 ± 0.01, 0.069 ± 0.007 N·s, 3.44 ± 0.19 N·s, and 3.54 ± 0.11 N·s for hardness, cohesiveness, adhesiveness, gumminess, and chewiness, respectively, whereas the index of elasticity was found equal to 1.00 ± 0.02 for all studied cases. At the end of the storage period, a slight, yet significant (p < 0.05), change in the main textural attributes was detected, exhibiting mean values of 5.05 ± 0.24 N, 0.84 ± 0.06, 0.029 ± 0.002 N·s, 4.25 ± 0.41 N·s, and 4.37 ± 0.14 N·s for hardness, cohesiveness, adhesiveness, gumminess, and chewiness, respectively, without presenting significant variations among samples stored at different temperature conditions.

Similarly, the corresponding values for the main textural attributes of HPP frankfurter-type sausages were calculated as 4.60 ± 0.17 N, 0.83 ± 0.04, 0.064 ± 0.004 N·s, 3.83 ± 0.35 N·s, and 3.94 ± 0.36 N·s for hardness, cohesiveness, adhesiveness, gumminess, and chewiness, respectively. Based on the results of the texture profile analysis, it is evident that the application of HPP affected only the cohesiveness of the frankfurter-type sausages, leading to a significant (p < 0.05) increase of approximately 0.1 units, indicating that more coherent products are formed after HPP treatment. Similar results have been previously reported, where the application of pressures up to 600 MPa at 25 °C for 5 min also led to a comparable increase in the cohesiveness values of bratwurst sausages [9,41]. At the end of the storage period, no significant (p > 0.05) changes in the main textural attributes’ values of HPP sausages were observed, regardless of the storage temperature.

The water activity (a_w) of the frankfurter sausages with storage time was also monitored in the present study. The results of the a_w measurement of the frankfurter-type sausage samples in relation to storage time, under isothermal conditions of 4, 8, 12, and 18 °C, are presented in Figure 3a–d and Figure 4a–d for the CNT and HPP samples, respectively.

The sausages had a_w values ranging from 0.978 to 0.994 (Figure 3 and Figure 4). Based on the results of the measurement of a_w, the greatest fluctuations were noted for the samples exhibiting greater and more evident spoilage, with liquid exudation in the packaging, mainly for the reference samples. Regarding the HPP samples, the smallest and statistically insignificant differences (p > 0.05) were observed throughout the storage period and for all the studied temperature conditions (constant and dynamic). As for the samples stored under dynamic storage conditions, it was observed that the samples stored under conditions with T_eff = 4.5 °C exhibited similar behavior to those stored under isothermal conditions at 4 °C, and the samples stored under conditions with T_eff = 11.1 °C exhibited similar behavior to those stored under isothermal conditions at 12 °C (Figure 5).

Apart from microbial spoilage, lipid oxidation is one of the main factors affecting the quality of meats and poultry. From a sensory perspective, lipid oxidation leads to rancidity, which is undesirable to consumers [42]. Additionally, lipid oxidation has been associated with an increase in protein oxidation [43], a negative impact on texture [44], and discoloration of meat products [45]. For this reason, it was considered necessary to determine the degree of oxidation. The results of the lipid oxidation show that the MDA values ranged from 0.82 ± 0.11 to 1.52 ± 0.03 mg MDA/kg of frankfurter-type sausage. Independent of the formulation used to manufacture frankfurters, throughout storage, the TBARS values remained below the maximum acceptable limit, which is reported as the limit beyond which processed meat products will normally develop objectionable odors/tastes. For processed meats (like sausages, bacon), the limits may be a bit higher, around 2–4 mg MDA/kg, due to added ingredients and processing steps that can influence lipid oxidation [46,47]. Similar results were reported by Parra et al. [48] and Ospina-E et al. [49], where the TBARS values of dry-cured Iberian ham and frankfurters, respectively, increased during chilled storage. Therefore, lipid oxidation did not serve as a significant indicator for the spoilage of these products. The results of the present study are not in agreement with other researchers who have shown that HPP can accelerate lipid oxidation on HPP meat products [50] by triggering intrinsic pro-oxidants such as myoglobin [51].

3.2. Estimation of Microbial Growth Parameters and Shelf Life

3.2.1. Microbial Growth Curves of Reference (CNT) Samples

The presence of microorganisms in frankfurter sausages is the most critical indicator of their quality and safety. While it is common for food products to contain some level of microorganisms, it is essential to ensure that microbial counts remain within safe limits to protect human health [52]. Monitoring these levels helps to ensure the high quality of the sausages in order to simultaneously meet food safety standards and consumers’ demands.

In Figure 6a–d and Figure 7a–d, the growth of the TVC and lactic acid bacteria in the CNT samples during storage at 4, 8, 12, and 18 °C, respectively, is illustrated.

The results of the microbiological analysis indicate that both the TVC and lactic acid bacteria increased with an increase in the storage temperature and time. The initial TVC for the CNT sausage samples was ca. 3.56 log CFU/g (Figure 6), indicating the initial good microbiological quality of all frankfurter-type sausages. It was observed that counts increased with an increase in both storage temperature and storage time, reaching 8.20–8.70 log CFU/g on the last day of storage at 4, 8, 12, and 18 °C. The growth of microorganisms increased with longer storage times because this provided sufficient time for microorganisms to multiply. For frankfurters, the limit of acceptability in terms of the TVC (8 log CFU/g) for the CNT samples was reached after 18, 11, 8, and 3 days of storage at 4, 8, 12, and 18 °C, respectively. Regarding the other spoilage microorganisms analyzed in this study, it was observed that the populations of Pseudomonas spp. and Enterobacteriaceae spp. remained below the detection threshold (<2 log CFU/g and <1 log CFU/g, respectively) throughout the storage of CNT samples at 4, 8, 12, and 18 °C. These findings are consistent with those reported by other researchers [53,54].

It is well established that under aerobic storage conditions, Pseudomonas spp. dominate at refrigeration temperatures and are a primary cause of spoilage in such products [54]. Remenant et al. [55] have also discussed the role of Pseudomonas spp. in the aerobic spoilage of meat and how different packaging methods impact bacterial growth. Additionally, Grau et al. [56] explained the way that vacuum packaging inhibits the growth of spoilage bacteria, including Pseudomonas spp., in meat products. It is evident that the use of vacuum packaging in this study effectively inhibited the growth of these spoilage microorganisms.

Spoilage signs became apparent once the microbial load reached approximately 10⁷ CFU/g, with significant sensory degradation occurring when TVCs reached 10⁸ CFU/g. At this point, slime formation and acidification of the samples were evident. In addition, the population of lactic acid bacteria consistently reached 8 log CFU/g across all storage temperatures, following a growth profile similar to that of the TVC. The present study reaffirms the importance of storage conditions, packaging methods, and novel preservation technologies like HPP in controlling microbial growth and spoilage, findings that are in line with the broader body of research.

To determine the kinetic parameters of TVC growth, the Baranyi and Roberts [30] model was applied. The kinetic data for TVC growth in frankfurter-type sausages before HPP, the reference samples (CNT), are presented in

Table 1. Kinetic parameters of the TVC growth in frankfurter-type sausages before HPP (reference samples (CNT)) at different isothermal and dynamic storage conditions.

Storage Temperature (°C)	Growth Rate k (d−1)	Lag Phase (d)	R2	Doubling Time Td (d)
4	0.384 ± 0.041 b	8.27 ± 0.87 a	0.984	1.8
8	0.465 ± 0.063 c	2.15 ± 0.42 c	0.971	1.4
12	0.521 ± 0.081 d	N.D.	0.924	1.3
18	1.132 ± 0.301 e	N.D.	0.947	0.6
VAR1 (Teff = 3.8 °C)	0.267 ± 0.032 a	5.34 ± 1.57 b	0.979	2.6
VAR2 (Teff = 9.7 °C)	0.853 ± 0.147 e	2.05 ± 0.84 c	0.981	0.8

N.D.: not detected. Different letters among lines indicate significant differences between means (±standard deviation) according to the mean values of Duncan’s post hoc comparison test for a significance level of p = 0.05.

.

3.2.2. Microbial Growth Curves of HPP Samples

In Figure 8a–d and Figure 9a–d, the growth of TVCs and lactic acid bacteria in the HPP samples during storage at 4, 8, 12, and 18 °C is depicted, respectively.

The results show that the predominant spoilage microorganisms were lactic acid bacteria for the HPP samples stored at all temperatures studied. Regarding Brochothrix thermosphacta, its population remained below the detection limit (<2 log CFU/g) in the HPP frankfurter-type sausages during storage at all temperatures examined in this study. This growth can be attributed to the low redox potential (anaerobic environment) created due to vacuum and skin packaging, which inhibits aerobic microorganisms and favors the growth of lactic acid bacteria.

The results of the microbiological analysis indicate that growth of the total viable count (TVC) and lactic acid bacteria increased with an increase in the storage temperature and time, following a similar profile to the CNT sausages. The initial TVC for the HPP sausage samples was below the detection limit <2.00 log CFU/g (Figure 8), showing that the initial microbiological quality of all frankfurter-type sausages was superior. It was observed that the numbers increased with an increase in both storage temperature and storage time, reaching 8.40–8.80 log CFU/g on the last day of storage at 4, 8, 12, and 18 °C. The longer frankfurter sausages were stored, the more the growth of microorganisms increased due to there being sufficient time for microbial multiplication. For frankfurters, the limit of acceptability in terms of the TVC (8 log CFU/g) for the HPP samples was reached after 89, 41, 11 and 7 days of storage at 4, 8, 12, and 18 °C, respectively. High-pressure processing (HPP)–cold pasteurization was observed to disrupt the microbial outer membrane, impacting both its structure and function [19]. The details of the mechanism by which HPP disrupts microbial cell membranes, leading to reduced microbial viability in meat products, have also been described by Campus et al. [57]. Previous research has documented HPP as a highly effective “cold” sterilization technology that is widely used to improve microbiological safety and extend the shelf life of both pre- and post-packaged meat products [58]. The LAB behavior in response to HPP during storage at 4, 8, 12, and 18 °C has rarely been investigated. The initial LAB counts in all the groups were not detected (<1 log CFU/g; Figure 9). Similar growth to that of the TVC was also observed for the LAB on the HPP samples. As the storage time increased, the LAB counts in the HPP sausages became significantly lower (p < 0.05) than those in the CNT group. High-pressure treatment may have caused sublethal damage for some of the LAB at the beginning of storage. The bacteria could recover and outgrow during the late period of storage at all storage temperatures examined.

The results of the analyses conducted confirm the positive effect of high-pressure processing (HPP) on slowing down microbial growth in the studied sausages, and consequently on their shelf life. Overall, these results indicate the effectiveness of the combined effect of HPP and low storage temperatures in enhancing the quality and shelf life of processed meat products. For instance, the shelf life of the HPP sausages was extended by approximately 70 days compared to the CNT samples during their storage at 4 °C. This significant extension in shelf life was observed in the HPP-treated samples compared to the corresponding samples that had not undergone HPP (CNT). Previous studies conducted on cooked ready-to-eat meat products indicated that HPP can significantly extend the shelf life of vacuum-packed meat products such as wieners, turkey breast ham, cooked pork ham, dry-cured ham and marinated beef loin [9,39,59,60,61,62]. HPP is known to effectively inactivate a wide range of microorganisms, including spoilage bacteria and pathogens, by applying high pressure (typically between 400 and 600 MPa) for a few minutes at room temperature [63]. This process disrupts the cellular functions of microorganisms, leading to their inactivation without significantly affecting the sensory and nutritional qualities of the food. Studies have shown that HPP can significantly reduce the populations of spoilage microorganisms such as Pseudomonas spp., Lactobacillus spp., and Enterobacteriaceae spp., thereby enhancing the safety and extending the shelf life of meat products. For example, research has demonstrated that combining HPP with natural antimicrobials like spice extracts can further inhibit microbial growth and improve the storage quality of low-salt sausages [40]. Moreover, the application of HPP has been found to delay the onset of spoilage by extending the lag phase and reducing the growth rate of microorganisms during storage [63]. This is particularly beneficial for products stored under refrigeration, as it helps products maintain their quality and safety over extended periods. In conclusion, the use of HPP in the processing of frankfurter-type sausages has been validated as an effective method to prolong shelf life and ensure microbial safety, making it a valuable processing tool in the food industry.

To determine the kinetic parameters of TVC growth, the Baranyi and Roberts [30] model was applied, and the data for TVC growth in the HPP-treated frankfurter-type sausages are presented in

Table 2. Kinetic parameters of the TVC growth in frankfurter-type sausages after HPP at different isothermal and dynamic storage conditions.

Storage Temperature (°C)	Growth Rate k (d−1)	Lag Phase (d)	R2	Doubling Time Td (d)
4	0.084 ± 0.007 a	18.9 ± 5.43 a	0.988	8.2
8	0.183 ± 0.018 b	8.51 ± 0.89 b	0.990	3.7
12	0.595 ± 0.037 c	4.27 ± 0.70 c	0.994	1.1
18	1.058 ± 0.102 d	2.28 ± 0.48 d	0.992	0.6
VAR1 (Teff = 4.5 °C)	0.084 ± 0.005 a	18.1 ± 4.11 a	0.994	8.3
VAR2 (Teff = 11.1 °C)	0.701 ± 0.110 c	7.11 ± 1.21 b	0.974	1.0

Different letters among lines indicate significant differences between means (±standard deviation) according to the mean values of Duncan’s post hoc comparison test for a significance level of p = 0.05.

.

3.2.3. Shelf Life Estimation

The shelf life of all samples examined in the present study was calculated based on Equation (4) and the limit of TVC load of 8 log CFU/g. The estimated values of shelf life for each sample and storage temperature are presented in

Table 3. Shelf life based on the TVC growth in CNT and HPP frankfurter-type sausages stored at different isothermal and dynamic storage conditions.

Sample	Storage Temperature (°C)	Shelf Life (d)
CNT	4	18
	8	11
	12	8
	18	3
	VAR1	20
	VAR2	6
HPP	4	89
	8	41
	12	11
	18	7
	VAR1	89
	VAR2	15

.

3.3. Validation of the ASLT Methodology in Predicting the Shelf Life of Frankfurter-Type Sausages

In order to determine the dependence of the TVC (total viable count) growth rate on the temperature, the Arrhenius equation, as described in Equation (1), was applied. The Arrhenius equation was applied to (i) the whole set of kinetic data, as well as to the cases where (ii) the data set of the three most severe temperatures (above 8 °C) and (iii) only a two-point data set (8 and 18° C) were taken into account, to validate whether the ASLT methodology could be effectively applied for the estimation of shelf life of the tested meat products. The results are presented in Figure 10a–c and

Table 4. Parameters of the Arrhenius equation for each studied case.

Estimated Parameters	Kinetic Data Whole Data Set	Kinetic Data 3 Temperatures	Kinetic Data 2 Temperatures
Ea (kJ /mol)	125.6 ± 18.4 a	116.2 ± 35.9 a	119.3 a
kref (d−1)	0.199 ± 0.031 a	0.219 ± 0.074 ab	0.183 a
R2	0.959	0.912	-

Different letters among columns indicate significant differences between means (±standard deviation) according to the mean values of Duncan’s post hoc comparison test for a significance level of p = 0.05.

.

As indicated from the obtained results, the Arrhenius equation can adequately describe the temperature dependence of microbial growth rate, even when applied for a small range of data sets (R²: 0.912–0.959), without statistically significant differences among predictions.

Similarly, the dependence of the lag phase duration (λ) on temperature was determined with a modified Arrhenius equation, as follows:

$$\lambda ={\lambda }_{ref}·\mathrm{e}\mathrm{x}\mathrm{p}[-{\displaystyle \frac{Ea}{R}}·\left({\displaystyle \frac{1}{T}}-{\displaystyle \frac{1}{T_{ref}}}\right)]$$

(6)

where λ is the lag phase duration at temperature T (d), λ_ref is the lag phase duration at temperature T_ref (d), T_ref is a reference temperature (K), R is the molar gas constant (8.314  J/K·mol) and E_a is the apparent activation energy (J/mol).

The equation was applied for the above mentioned case studies (i, ii and iii), linearized by applying the logarithm to both sides of the equation, then coefficients λ_ref and E_a were estimated using linear regression, and the results are presented in Figure 11 and

Table 5. Parameters of the Arrhenius equation for each studied case regarding the dependence of the lag phase duration on temperature.

Estimated Parameters	Kinetic Data Whole Data Set	Kinetic Data 3 Temperatures	Kinetic Data 2 Temperatures
Ea (kJ/mol)	105.6 ± 3.9 a	101.6 ± 6.1 a	102.3 a
λref (d)	9.07 ± 0.30 a	8.73 ± 0.49 a	9.00 a
R2	0.997	0.996	-

Different letters among columns indicate significant differences between means (±standard deviation) according to the mean values of Duncan’s post hoc comparison test for a significance level of p = 0.05.

.

As indicated from the obtained results, the Arrhenius equation can adequately describe the temperature dependence of lag phase duration, even when applied for a small range of data sets (R²: 0.996–0.997), without statistically significant differences among predictions.

Considering the above predicted parameters, the microbial growth rate of the TVC and the shelf life of the sausage samples were predicted for a wide range of storage temperatures, and the predicted values were plotted along with the experimental ones for all the isothermal and the dynamic temperature conditions, as shown in Figure 12 and Figure 13.

Considering the shelf life stated by the manufacturers for similar commercial meat products, which is 75 days, and in conjunction with the models derived from this research, it is determined that this duration corresponds to storage temperatures of 4.2, 3.2, and 3.8 °C. These temperatures were calculated using the produced models, including: (i) data from all four studied storage temperatures, (ii) data from three studied storage temperatures (8, 12, and 18 °C), and (iii) data from two studied storage temperatures (8 and 18 °C). The results indicate that the models developed through the ASLT methodology could provide highly accurate and reliable predictions of the shelf life of the studied products. Consequently, these models can be utilized as valuable tools for estimating product shelf life by performing tests exclusively at higher storage temperatures, which can lead to significant time savings in the testing process. This approach not only optimizes resource use but also improves the understanding of the factors affecting product stability under various conditions, thereby facilitating more efficient product development and quality assurance practices.

This study demonstrated the successful validation of the ASLT methodology for HPP meat products, specifically frankfurter-type sausages. The findings confirm ASLT as a reliable and efficient tool that replicates spoilage patterns under accelerated conditions, reducing the time and cost of traditional shelf life studies. The results show that the study also provides insights into the spoilage dynamics unique to HPP-treated products, such as the role of specific spoilage organisms like lactic acid bacteria, which inform and enhance the ASLT framework. These dual contributions—validating ASLT and refining its application for HPP products—highlight its value for the meat industry. ASLT enables faster product development, optimized resource allocation, and better management of storage and distribution conditions. This work bridges the gap between HPP technology and predictive modeling, advancing the efficiency and reliability of shelf life assessment.

3.4. Sensory Analysis

The results of the sensory evaluation are shown in Figure 14a,b, where the scores for the overall impression of samples stored under isothermal conditions are presented.

The results of the sensory evaluation agree with the corresponding results of the microbiological analysis. The shelf life of the samples, based on the findings of the aforementioned analyses, matched. During the initial days of storage, all sausages showed similar scores for all the sensory characteristics studied, except for taste, which started to become unpleasant at higher temperatures from the first days of storage and analysis for both CNT and HPP samples. Higher scores were recorded for the HPP samples compared to the corresponding CNT samples. Similar scores were noted for the moisture loss and taste of the samples. Regarding the remaining characteristics studied, no statistically significant differences were observed in the scores given by the testers, and the changes in these characteristics were not distinct or perceptible to the evaluators. When the microbial load reached approximately 10⁷ CFU/g, the first signs of spoilage began to appear. The spoilage of the samples became evident when the population of both the total viable counts (TVCs) and the lactic acid bacteria reached 10⁸ CFU/g, at which point sensory deterioration of the samples was also recorded.

During the sensory evaluation of the samples, it was observed that, for the samples treated with HP and stored at temperatures < 8 °C, the samples developed white spots on their surface, which were not associated with microbiological spoilage of the products and thus did not affect the samples’ acceptance (Figure 14b). The most significant sensorial deterioration observed during storage was the appearance of ropy slime and milky fluid. Moisture loss was also noted, mainly at higher storage temperatures, along with the acidification of the samples. Towards the end of their shelf life, the taste of the samples was characterized as sour, and when the TVCs and lactic acid bacteria populations reached 8 log CFU/g, the products were deemed unsuitable for consumption. Towards the end of their shelf life, packaging swelling was observed due to gas formation within the packaging, which was attributed to the growth of lactic acid bacteria and evolution of metabolic gases from microorganisms, followed by a decrease in the pH of the products, in both the CNT and HPP, at all storage temperatures.

The deterioration of sensorial quality (in terms of overall acceptability) of the frankfurters sausages followed zero-order kinetics, and the rates of sensorial degradation were estimated and are presented in

Table 6. Shelf life estimation based on the overall acceptance of CNT and HPP frankfurter-type sausages stored at 4, 8, 12, and 18 °C (limit of acceptance set to 4.5).

Sample	Storage Temperature (°C)	Sensorial Deterioration Rate (d−1)	R2	Shelf Life (d)
CNT	4	0.211 ± 0.016 c	0.970	21
	8	0.328 ± 0.013 d	0.988	13
	12	0.608 ± 0.082 f	0.906	8
	18	0.709 ± 0.112 f	0.861	6
HPP	4	0.048 ± 0.003 a	0.974	93
	8	0.085 ± 0.007 b	0.937	52
	12	0.245 ± 0.030 c	0.922	18
	18	0.475 ± 0.052 e	0.949	9

Different letters among columns indicate significant differences between means (±standard deviation) according to the mean values of Duncan’s post hoc comparison test for a significance level of p = 0.05.

, both for CNT and HPP samples. Considering that the limit of acceptance based on the scoring of overall impression was set to 4.5, the shelf life values based on the sensorial quality of the studied products were calculated according to Equation (5). The obtained results were correlated to the ones based on microbiological quality, and it was found that the evaluation of the sensorial quality of the meat products led to a slight overestimation of the corresponding shelf life, and that the rejection of overall quality should be mainly based on the microbiological quality of the products.

4. Conclusions

The models developed in the present study through accelerated shelf life testing (ASLT) methodology demonstrated a high degree of accuracy in predicting the shelf life of HPP frankfurter-type sausages. These models not only provide valuable insights into the microbial stability and quality of the products over time but also allow for the identification of critical storage conditions that may affect their longevity. By leveraging these models, future assessments of product shelf life can be conducted more efficiently, as testing can be limited to high storage temperatures, which simulate extreme conditions. This approach not only reduces the time required for extensive shelf life studies but also optimizes resource allocation, making the evaluation process more cost-effective. Ultimately, the application of ASLT methodologies can facilitate better decision-making in product development, quality assurance, and marketing strategies, enhancing the overall sustainability and safety of food products in the marketplace. Although investigated and established for a specific food (frankfurter-type sausages), the proposed methodology could be generalized after respective validation and utilized as a practical protocol at 8 and 18 °C for estimating the product shelf life of other HPP products in the meat industry, with significant time savings, balancing efficiency and reliability. In conclusion, the application of ASLT methodologies offers the meat industry a powerful tool for balancing efficiency, reliability, and sustainability. By integrating these methods into their operations, manufacturers can enhance quality assurance, reduce costs, and strengthen consumer trust in their products. In the broader context, this research is significant because it addresses a critical bottleneck in the adoption of HPP technology. By fine-tuning shelf life prediction models, it paves the way for wider industrial implementation, reducing food waste and meeting consumer demand for minimally processed, long-lasting meat products. The findings of this work not only advance the scientific understanding of shelf life prediction for HPP products but also provide actionable solutions that can drive innovation and competitiveness in the meat industry.