Inactivation Kinetics of Listeria monocytogenes Applying Mild Temperatures and Fractionated Mexican Oregano Essential Oil (Poliomintha longiflora Gray) in a Modified Simulated Meat Medium

Abstract

Meat products are highly susceptible to contamination with Listeria monocytogenes, a foodborne pathogen associated with high mortality. To mitigate this risk, this study explored the use of Poliomintha longiflora oregano essential oil, both in its pure (PEO) and fractionated (FIV, fraction IV obtained at 140 °C) forms, as part of a hurdle technology combining natural antimicrobials with mild thermal treatments. In vitro thermal inactivation experiments were conducted at 52, 54, 57.5, and 63 °C using a simulated meat medium. The FIV group, characterized by 60.23% carvacrol and 21.17% thymol, exhibited significantly enhanced bactericidal activity, achieving up to 5.5 log-reductions in L. monocytogenes at 57.5 °C within 3 min, compared to <2 log-reductions for the control group. Inactivation kinetics were well described by the Weibull–Mafart model. The δ-values, defined as the time required to achieve a 1-log reduction in bacterial population, were consistently lower for FIV compared to the control across all tested temperatures (e.g., δ₅₂°C = 0.64 min vs. 8.47 min for control). The estimated z-values, which represent the temperature increase required to achieve a tenfold change in δ-value, were 5.75 °C (control), 5.20 °C (PEO), and 5.00 °C (FIV), suggesting a consistent thermal sensitivity but enhanced inactivation efficacy with the essential oils. These findings suggest that fractionated oregano essential oil is a promising hurdle to shorten thermal treatments in meat products, thereby lowering L. monocytogenes contamination risk while preserving product quality.

1. Introduction

Meat products possess nutritional characteristics that favor the growth of a wide diversity of foodborne pathogens, making them a public health concern [1]. Listeria monocytogenes, a Gram-positive bacterium commonly found in soil, water, and mammals, is responsible for listeriosis, a serious foodborne illness with high hospitalization and mortality rates [2,3]. Gastrointestinal symptoms can progress to severe systemic complications, particularly in pregnant women, immunocompromised individuals, and the elderly [4].

In 2023, the European Food Safety Authority (EFSA) reported a 5.8% increase in listeriosis cases compared to 2022, with nearly all cases requiring hospitalization and a total of 335 deaths [4]. In North America, the Centers for Disease Control and Prevention (CDC) has reported L. monocytogenes outbreaks linked to vegetables [5], ice cream [6,7], cheeses [8], and meat [9], causing over 1500 illnesses and 200 deaths per year [10]. Its continued presence in the U.S. has potential implications for neighboring countries, such as Mexico, where similar risks are likely associated with cross-border food trade and consumption patterns [11]. The persistence of L. monocytogenes in food environments is attributed to its ability to survive under stressful conditions [12], such as low or high temperatures (<5 °C and >60 °C) [1,13], acidic pH [14], and moderate salt levels [15]. Long-term exposure to these stress conditions potentially leads to increased resistance [16,17].

To address this challenge, hurdle technologies have been explored, combining mild thermal treatments with natural antimicrobials, such as essential oils (EOs) [18,19,20,21]. These natural compounds, classified as “Generally Recognized as Safe” (GRAS), interact with bacterial membranes, altering permeability and leading to cellular damage [22]. Among them, oregano EO―particularly Origanum vulgare―is well studied for its efficacy against Gram-positive bacteria, including L. monocytogenes [23,24,25]. For instance, it has been demonstrated that sublethal concentrations (SLC) of O. vulgare (1.25 µL/mL) at 30 °C can alter the growth parameters of L. monocytogenes after 1 h of exposure [26]. This effect may improve the efficacy of antibiotics in medical applications or be combined with other inactivation treatments in the food sector [26,27].

In the Americas, Lippia graveolens (Mexican oregano) has been widely used for its antimicrobial properties [28,29,30,31]. However, Poliomintha longiflora, another Mexican species with high levels of carvacrol and thymol, has been less explored [32]. Recent studies suggest that fractioning its EO enhances its bioactivity by concentrating key antimicrobial compounds [33,34]. This local and underutilized resource offers a sustainable alternative for controlling foodborne pathogens [28], especially in countries like Mexico, where the regulatory surveillance of L. monocytogenes is still developing [34], and contamination has been detected in vegetables [35], dairy [36], and meat products [11,37]. Although previous research has demonstrated the antimicrobial effects of SLC of EOs combined with physical treatments such as High-Pressure Processing (HPP), freezing, irradiation or thermal treatments [1,38,39], little is known about the inactivation kinetics of L. monocytogenes using fractionated P. longiflora EO and mild pasteurization temperatures. Predictive microbiology tools such as the Weibull and Bigelow models can help estimate microbial reductions and optimize such interventions for practical food applications [40,41,42,43].

Therefore, the aim of this study was to assess the impact of pure (PEO) and fractionated (FIV) P. longiflora EO on the thermal inactivation of L. monocytogenes at mild temperatures (52, 54, 57.5, and 63 °C), through an in vitro assay using a simulated meat medium. This work contributes to the development of sustainable antimicrobial strategies using locally sourced bioactive compounds.

2. Materials and Methods

2.1. Bacterial Culture and Growth Conditions

L. monocytogenes PM1, a clinical isolate from the listeriosis outbreak in Andalusia linked to the consumption of ready-to-eat (RTE) meats in 2019, was provided by the Food Science and Technology Department from the University of Cordoba, Spain [44]. The strain was stored at −80 °C in 20% v/v glycerol/Brain Heart Infusion broth (BHI; Difco Laboratories, Sparks, MD, USA) until use. Two successive transfers were performed in BHI broth to obtain working cultures. Initially, an aliquot of 100 μL of the frozen culture was transferred to a tube containing 10 mL of BHI and incubated at 37 °C for 24 h. After incubation, 0.1 mL of the grown culture was transferred to another 10 mL BHI tube and incubated at the same conditions described above. The inoculum level was then adjusted to an OD 600 = 0.5 (ONDA V-10 Plus spectrophotometer CE, Barcelona, Spain) to obtain an initial inoculum of ~10⁸ CFU/mL.

2.2. Essential Oil of P. longiflora

P. longiflora EO and its fraction were provided by the School of Engineering and Sciences from Tecnológico de Monterrey (Monterrey, Mexico) and obtained as follows [34]: the plant was collected during the spring-summer flowering period in northern Mexico and identified as P. longiflora by the herbarium of the Faculty of Biological Sciences of the Autonomous University of Nuevo Leon (UANL). P. longiflora EO was obtained from the leaves, flowers, and stem (ratio of 90:9:1, respectively) via steam distillation. This oil was referred to as PEO. PEO was treated by fractional distillation at 140 °C to obtain a new concentrated oil identified as FIV. PEO and FIV were characterized by Gas Chromatography (GC) coupled to Mass Spectroscopy (MS) analysis (Perkin Elmer-Clarus 690/SQ8T, PerkinElmer, Waltham, MA, USA), and characterization results were reported in a previous study performed by the authors [33]. The relative area of each compound was considered as an equivalent of the composition in percentage. The major component was carvacrol, with the highest concentration identified in FIV at 60.23%, followed by thymol at 21.17%. The concentration of carvacrol in PEO was 34.09%, followed by o-Cymene with 21.51%. However, PEO showed a greater diversity of compounds in lower concentrations, including eucalyptol, myrcene, terpinene, humulene, and caryophyllene [33].

2.3. Preparation of Oil Working Solutions

Due to the hydrophobicity of the oregano essential oil (OEO), oil-working solutions were prepared according to our previous data [33] for the antimicrobial screening tests. Briefly, a 1% (v/v) of Polysorbate 80 (Tween 80, Sigma-Aldrich, St. Louis, MO, USA) solution was prepared and sterilized. Subsequently, 400 μL of PEO and the FIV were separately mixed with 600 μL of the 1% Tween 80 solution and stirred for 2 min to ensure dilution [2]. The resulting oil-working solution was stored in the dark at refrigeration until use. PEO and FIV formulations were not subjected to filter sterilization prior to use. However, microbiological controls—including untreated broth and broth containing only the OEO formulations—were included to verify the absence of microbial contamination. No microbial growth was observed in these controls.

2.4. Antimicrobial Activity: Minimum Bactericidal Concentration and Sublethal Concentrations

The Minimum Bactericidal Concentration (MBC) was determined using the microdilution method from CLSI [45] with slight modifications. A 96-well microplate (Corning, Costar; Cambridge, MA, USA) was used, with each well filled with 190 μL of BHI broth previously inoculated with L. monocytogenes (1% v/v; 1 × 10⁶ CFU/mL). Subsequently, 10 μL of PEO and FIV working solution was added to the first column of wells, followed by 10 μL dilutions across the rows. This resulted in final concentrations ranging from 1.0% to 0.0078% v/v in the microplate wells. Each assay included a positive control (inoculated BHI without essential oil) to confirm bacterial growth and a negative control (uninoculated BHI) to verify sterility. Each experiment was performed in duplicate, with two replicates per treatment (PEO, FIV, and a control without OEO). After incubation at 37 °C for 24 h, 10 μL from each well was transferred to Trypticase Soy Agar (TSA; Difco Laboratories, Sparks, MD, USA) plates using the drip method. The plates were incubated under the same conditions, and the MBC was identified as the lowest concentration of PEO or FIV, at which no bacterial growth was observed. To estimate the SLC, the same microdilution method used for MBC was used but, in this case, the OEO was diluted to concentrations lower than MBC to achieve a reduction in L. monocytogenes without reaching a lethal effect, ensuring that the concentration remains compatible with minimal processing conditions, such as the use of mild temperatures [46]. SLC was considered the maximum concentration where the OEO did not cause a decrease in the bacterial population.

2.5. Thermal Inactivation Treatments

The effect of PEO or FIV in its SLC on the thermal inactivation of L. monocytogenes was evaluated at 52, 54, 57.5 and 63 °C [1,43,47,48] in a modified Simulated Meat Medium (SMM), based on BHI broth [49]. The SMM was prepared in assay tubes containing 4 mL of broth and supplemented with glucose (18 g/L) and yeast extract (3 g/L) without modifying water activity. After sterilization of the SMM, 0.5 mL of OEO (PEO and FIV) solutions were added to reach the SLC and a control with only Tween 80 was considered (each tube represented a sample). Tubes containing SMM and OEO were placed into a hot water bath, with temperature monitored using a mercury thermometer in a control tube. When the internal temperature approached the experimental temperature (±1.0 °C), 0.5 mL of the adjusted inoculum (10⁸ CFU/mL) of L. monocytogenes was added to obtain a final volume of 5 mL. When the internal temperature reached the experimental temperature, samples from the three treatments (PEO, FIV and control) were taken from the tubes at appropriate intervals, ranging from 0 to 30 min, depending on the temperature evaluated. Samples at time zero did not undergo heat treatment. Each thermal treatment was independently performed twice, and for each experiment, samples were taken in triplicate at each time point, resulting in six total observations per condition (n = 6).

Serial dilutions of the samples were carried out in saline solution (0.85% v/v), and aliquots of 100 μL were plated in Oxford agar (Difco Laboratories, Sparks, MD, USA) Petri dishes (90 × 14 mm), using a Spiral Plater (Eddy Jet 2W, IUL Instruments SA, Barcelona, Spain) and the plates were incubated at 37 °C for 48 h. The enumeration of the colonies (CFU/mL) per sampling time at different temperatures was performed using a Flash & Go automatic colony counter (Interscience^®, IUL Instruments, Barcelona, Spain).

2.6. Estimation of the L. monocytogenes Inactivation Parameters

The Weibull–Mafart and Bigelow primary inactivation models, available in the user-friendly software Bioinactivation4 (version 0.1.0, https://foodlab-upct.shinyapps.io/bioinactivation4/, accessed on 1 April 2025) [50], were fitted to the experimental data obtained (log CFU/mL) over time (s) for each treatment (PEO, FIV and control without OEO) and temperature condition (52, 54, 57.5 and 63 °C). The Mafart model describes the time required to reduce a microbial population by one Log₁₀ unit at a constant temperature, represented by the δ-value. This model is often combined with the Weibull model to interpret non-linear regression [51,52]. In contrast, the Bigelow model represents microbial inactivation at a constant temperature but follows a first-order, linear relationship [53,54]. To assess the goodness-of-fit of the primary models, several statistical indices were calculated to select the best model to fit the obtained results from the thermal inactivation of L. monocytogenes (RMSE, AIC, B_f, and A_f, as described below). The Bigelow primary model is described by Equation (1) [53].

$${Log}_{10}N\left(t\right)={Log}_{10}{N}_0-{\displaystyle \frac{1}{D_T}}t$$

(1)

where the D-value at a constant temperature T is identified as D_T; N(t) is the population at time t (CFU/mL); and N₀ is the initial population (CFU/mL).

The Weibull–Mafart model is described by Equation (2) [52]:

$${Log}_{10}N\left(t\right)={log}_{10 }{N}_0-{({\displaystyle \frac{t}{{\delta }_T}})}^p$$

(2)

where the time to reduce 90% of the microbial population at temperature T is represented by δ_T (scale parameter), and p is the shape parameter (dimensionless). When p > 1, the curve is convex; when p < 1, the curve is concave; and when p = 1, the curve follows a linear trend [55].

For the secondary models, both one-step and two-step approaches were employed to estimate the z-values, which describe the temperature dependence of the inactivation process. These secondary models were fitted to the complete experimental dataset, incorporating all temperature and treatment conditions. The time-temperature data were adjusted using 57.5 °C as the reference temperature (T_R), as recommended for similar studies on L. monocytogenes [7,43,56,57]. The z-values were calculated by fitting the secondary models to the entire dataset, ensuring that temperature variation was adequately accounted for in the inactivation curves. For the Mafart model, Equation (3) describes the secondary behavior and evaluates the sensitivity of microorganisms at a certain temperature [58]:

$${Log}_{10}{\delta }_T={log}_{10}{\delta }_R+{\displaystyle \frac{T_R-T}{z}}$$

(3)

where δ_R is the δ-value at the T_R.

In evaluating the goodness-of-fit of each model, the Root Mean Square Error (RMSE, Equation (4)) was calculated. Values lower than 0.50 indicate an acceptable fit between the model and the experimental data [59,60].

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{({\mathrm{X}}_{\mathrm{o}\mathrm{b}\mathrm{s}}-{\mathrm{X}}_{\mathrm{f}\mathrm{i}\mathrm{t}})}^2}{\mathrm{n}-\mathrm{s}}}}$$

(4)

where X_obs and X_fit represent the observed and fitted values, respectively; n is the total number of observations; and s is the number of model parameters.

In the two-step inactivation model, the Residual Standard Error (RSE) is used to measure how well the residuals fit in the regression model, accounting for the degrees of freedom (df). It is calculated using Equation (5) [59], which is closely related to RMSE in the one-step but is not identical.

$$\mathrm{R}\mathrm{S}\mathrm{E}=\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{\left({\mathrm{X}}_{\mathrm{o}\mathrm{b}\mathrm{s}}-{\mathrm{X}}_{\mathrm{f}\mathrm{i}\mathrm{t}}\right)}^2}{df}}}$$

(5)

To facilitate a direct comparison on a common scale between the RMSE and RSE in the two-step inactivation model, the RMSE is standardized (RMSE_std) by adjusting for the degrees of freedom (Equation (6)). This standardization accounts for the difference in the number of parameters between the one- and two-step models, allowing for a more meaningful comparison of their goodness-of-fit [59].

$${\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{s}\mathrm{t}\mathrm{d}}=\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}\sqrt{{\displaystyle \frac{df}{n-s}}}$$

(6)

The Akaike Information Criteria (AIC) related the maximum likelihood (loglik) to the estimated parameters and was calculated according to Equation (7) [60].

$$\mathrm{A}\mathrm{I}\mathrm{C}=\mathrm{n}\ln \left({\displaystyle \frac{\mathrm{S}\mathrm{S}\mathrm{E}}{\mathrm{n}}}\right)+2\mathrm{s}$$

(7)

where n is the total number of experiments, SSE is the sum of squares of errors, and s (or k) is the number of parameters in the model [60,61].

Additionally, the Bias Factor (B_f, Equation (8)) and Accuracy Factor (A_f, Equation (9)) were assessed, with values closer to 1.0 indicating good predictive performance and minimal discrepancy between the predicted and observed data [62].

$${\mathrm{B}}_f={10}^{{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\log (\frac{{\mathrm{x}}_{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}}}{{\mathrm{x}}_{\mathrm{o}\mathrm{b}\mathrm{s}}})}{\mathrm{n}}}}$$

(8)

$${\mathrm{A}}_f={10}^{{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}|\log (\frac{{\mathrm{x}}_{\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{d}}}{{\mathrm{x}}_{\mathrm{o}\mathrm{b}\mathrm{s}}})|}{\mathrm{n}}}}$$

(9)

where X_pred represents the predicted values, X_obs represents the observed values, and n is the total number of experimental data points [61,63].

2.7. Statistical Analysis

The MBC values estimated for PEO and FIV were analyzed by one-way ANOVA using R (version 4.3.0) within RStudio (version 2023.06.0, Posit Software, PBC). A significance level of p < 0.05 was applied. The parameter estimates (e.g., δ and z-values) presented in this study are model-derived estimates obtained by fitting all replicate data jointly for each mild heat temperature. As they do not represent replicate-level means, no inferential statistical tests were applied. Instead, potential differences between treatments or conditions were interpreted based on the visual comparison of 95% confidence intervals around the parameter estimates.

3. Results and Discussion

Fractionation has been shown to enhance the activity of EOs by exerting bactericidal effects and interfering with microbial resistance and virulence mechanisms, providing a multifaceted approach to microbial control [64].

The MBC, defined as the lowest concentration at which no microbial growth was observed across all replicates and repetitions, was 2.00 ± 0.00% v/v for PEO. In contrast, FIV exhibited a significantly lower MBC of 0.10 ± 0.00% v/v (p < 0.05), indicating an enhanced antibacterial effect. To better simulate sublethal conditions for subsequent thermal treatments, the SLC was determined as the highest concentration at which no significant inhibition of bacterial growth occurred. The SLC values were 0.13 ± 0.00% for PEO and 0.09 ± 0.05% for FIV. However, in practice, using the full SLC values during combined treatments led to unexpectedly rapid bacterial inactivation, which hindered reliable colony enumeration. Therefore, to preserve the concept of a mild stress hurdle while still enabling measurable survival during the thermal treatments, it was selected as 0.06% for both PEO and FIV, which are concentrations below the experimentally determined SLCs. This adjustment allowed the OEOs to act as sublethal stressors rather than primary inactivating agents. The selected concentrations are consistent with previous reports on subinhibitory levels of OEO capable of enhancing microbial susceptibility to additional hurdles without achieving complete inactivation [65,66].

Figure 1 shows the inactivation curves of L. monocytogenes (log N/N₀ versus time) at 52, 54, 57.5, and 63 °C in the SMM. The treatments with PEO and FIV resulted in more pronounced initial reductions compared to the control. The inactivation data were fitted using both the Weibull–Mafart and Bigelow models (

Table 1. Goodness-of-fit indices estimated by fitting the Weibull–Mafart and Bigelow primary models to the inactivation data obtained at different temperatures for PEO (Pure Oregano Essential Oil), FIV (Fractionated Oregano Essential Oil), and Control groups.

		Weibull–Mafart				Bigelow
Group	T (°C)	RMSE *	Loglik	AIC	Af/Bf	RMSE	Loglik	AIC	Af/Bf
Control	52	0.10	42.70	−81.40	1.01/0.99	0.20	22.36	−42.72	1.02/1.00
	54	0.16	24.40	−44.80	1.02/1.00	0.29	13.30	−24.60	1.04/1.00
	57.5	0.08	47.44	−90.88	1.01/1.00	0.37	11.92	−21.84	1.04/1.00
	63	0.06	46.21	−88.42	1.01/0.99	0.48	5.35	−8.70	1.07/1.00
PEO	52	0.42	10.36	−16.72	1.07/1.00	0.55	7.57	−13.14	1.09/1.00
	54	0.18	22.73	−41.46	1.02/1.00	0.30	14.33	−26.66	1.05/1.01
	57.5	0.33	10.96	−17.92	1.07/1.00	0.62	6.47	−10.94	1.45/1.42
FIV	63	0.34	11.06	−18.12	1.06/1.00	1.00	2.52	−3.04	1.26/0.99
	52	0.20	19.14	−34.28	1.06/1.03	0.61	6.22	−10.44	1.12/1.02
	54	0.33	11.55	−19.10	1.05/1.01	0.51	8.35	−14.70	1.06/1.00
	57.5	0.20	19.50	−35.00	1.10/0.98	1.04	4.14	−6.28	1.30/0.94
	63	0.24	11.94	−19.88	0.99/1.07	1.11	2.28	−2.56	1.39/0.95

* RMSE—Root Mean Square Error, Loglik—Log-likelihood, AIC—Akaike Information Criteria, A_f—Accuracy factor, and B_f—Bias factor.

). Although both models showed low RMSE values, the Weibull–Mafart model provided a better fit, with lower RMSE and AIC and A_f/B_f values closer to one. Therefore, this model was selected to estimate thermal inactivation parameters.

The Weibull–Mafart model estimates the δ-value, representing the time to achieve one logarithm reduction in the microbial population under a specific temperature, which is equivalent to the D-value (min) [52].

Table 2. Weibull–Mafart model parameters estimated by fitting the inactivation data obtained at 52, 54, 57.5, and 63 °C for the PEO (Pure Oregano Essential Oil), FIV (Fractionated Oregano Essential Oil), and Control groups.

	Control		PEO		FIV
Temp (°C)	δ-Value (min)	p	δ-Value (min)	p	δ-Value (min)	p
52	8.470 ± 1.510	0.51 ± 0.05	1.750 ± 1.050	0.52 ± 0.10	0.640 ± 0.240	0.40 ± 0.04
54	1.800 ± 0.500	0.53 ± 0.07	1.350 ± 0.350	0.61 ± 0.07	0.540 ± 0.350	0.44 ± 0.09
57.5	0.330 ± 0.170	0.17 ± 0.03	0.170 ± 0.090	0.47 ± 0.07	0.020 ± 0.010	0.35 ± 0.02
63	0.007 ± 0.004	0.15 ± 0.02	0.002 ± 0.001	0.32 ± 0.05	0.002 ± 0.002	0.33 ± 0.04

summarizes the estimated δ-values (min) of L. monocytogenes for each group at the four temperatures evaluated. As expected, δ-values decreased with increasing temperature. Across all temperatures, FIV exhibited consistently lower δ-values than the control, with non-overlapping confidence intervals, suggesting a meaningful difference. PEO also showed lower δ-values compared to the control at 52 and 63 °C. At 54 and 57.5 °C, the δ-values estimates for PEO and FIV were closer in magnitude, but some separation between their confidence intervals was still observed. The higher inactivation observed with FIV may be attributed to its higher concentrations of carvacrol and thymol, which are known to exert strong antimicrobial effects by disrupting bacterial membranes, increasing their permeability and interfering with essential cellular processes [42,67].

The p parameter of the Weibull–Mafart model was <1 in all cases (Table 2), indicating a concave survival curve shape (Figure 1). The presence of a tail in the curves as time progressed suggests either microbial adaptation or the existence of a more resistant subpopulation (Figure 1) [68,69]. Such tailing effects are often associated with the induction of stress response mechanisms or the selection of subpopulations with increased resistance to sequential hurdles. Fang et al. [68] demonstrated that L. monocytogenes exposed to mild bactericidal treatments can develop tolerance, leading to slower inactivation rates and persistent survival. Similarly, Arioli et al. [69] reported tailing in inactivation curves when L. monocytogenes was subjected to mild heat combined with thymol and carvacrol, which is consistent with the primary components of FIV, suggesting that sublethal damage and cellular repair processes may contribute to the observed kinetic behavior.

The one-step procedure was applied to estimate the z-values from the inactivation data, calculated as the negative inverse of the slope obtained from the linear regression of Log δ-value versus temperature (

Table 3. z-values (°C) estimated by the one-step and two-step procedures and the Root Mean Square Error (RMSE), Residual Standard Error (RSE) and Standardized RMSE (RMSE_std) indices, estimated from the thermal inactivation of L. monocytogenes using PEO (Pure Oregano Essential Oil), FIV (Fractionated Oregano Essential Oil), and the Control group.

	One-Step			Two-Step
Group	RMSE	RMSEstd	z-Value (°C)	z-Value (°C)	RSE	t Value	Pr (>|t|)
Control	0.31	0.06	5.75 ± 0.28	3.63 ± 0.19	0.12	19.29	0.003
PEO	0.43	0.08	5.20 ± 0.14	3.69 ± 0.46	0.28	8.00	0.02
FIV	0.46	0.09	5.00 ± 0.13	4.03 ± 0.55	0.28	7.27	0.02

, Figure 2). The resulting z-values were 5.75 °C for the control, 5.20 °C for PEO, and 5.00 °C for FIV. These results are consistent with other studies reporting z-values around 5 °C when using OEO against L. monocytogenes in food matrices [43,70,71]. Although no formal statistical tests were applied, the confidence intervals for the Control and FIV groups did not overlap, suggesting a potentially meaningful reduction in thermal resistance in the presence of the fractionated essential oil.

Table 4. D- or δ- and z-values of L. monocytogenes using mild temperatures and essential oil reported in previous studies.

Matrix	Treatment	D- or δ-Values (min)							z-Values (°C)	Reference
		D52	D54	D55	D57.5	D60	D63	D65
BHI broth supplemented with glucose and yeast extract	P. longiflora PEO 0.06%:	1.75	1.35	-	0.17	-	2.00 × 10−3	-	5.20	Present study
	P. longiflora FIV 0.06%:	0.64	0.54	-	0.02	-	2.00 × 10−3	-	5.00
Sous-vide salmon	Origanum vulgare EO 1%	-	-	10.03	4.88	1.81	-	-	5.62	[43]
Sous-vide beef	Salvia officinalis EO 0.6%	-	-	21.17	-	-	-	-	-	[72]
Ground pork	Cinnamaldehyde 0.5%	-	-	3.61	-	0.63	-	0.52	-	[73]
BHI broth	Carvacrol 30 µg/mL	-	-	8.17	-	0.67	0.17	0.07	-	[74]
	2-Hexenal 65 µg/mL	-	-	8.03	-	0.60	0.12	0.08	-
	Citral 50 µg/mL	-	-	8.42	-	0.66	0.16	0.08	-
TSBYE	Thymol	-	-	0.25	-	-	-	-	-	[75]
	Carvacrol	-	-	0.25	-	-	-	-	-
	Thymol + Carvacrol	-	-	0.18	-	-	-	-	-
PBS	Thymol	-	-	1.47	-	-	-	-	-	[69]
	Carvacrol	-	-	1.48	-	-	-	-	-
	Thymol + Carvacrol	-	-	0.38	-	-	-	-	-
Pineapple juice	Mexican coriander 15 µg/mL	-	-	5.61	-	0.53	-	0.28	-	[76]
	Mexican coriander 60 µg/mL	-	-	5.47	-	0.44	-	0.16
Beef marinated	Grapefruit seed extract 200 ppm	-	-	22.17	6.11	3.69	-	-	7.98	[56]
Ground Turkey	Sodiumchloride (1%) and green tea polyphenol extract (1.5%)	-	-	30.40	-	5.50	-	0.90	-	[77]

summarizes the findings from previous studies on thermal inactivation of L. monocytogenes at temperatures ranging from 52 to 65 °C, using EOs, phenolic compounds and plant extracts as natural antimicrobial agents. Notably, at 55 °C, the D-value varied depending on the food matrix, highlighting the influence of the matrix on the effectiveness of thermal treatments. The lowest D-values were observed with treatments containing isolated EO compounds, such as carvacrol, thymol, and cinnamaldehyde, indicating their strong antimicrobial properties. These results aligned with those obtained in our study using FIV, where the higher concentration of active compounds and reduced interference from OEO components with lower antimicrobial activity contributed to more effective bacterial inactivation.

The combination of lower δ-values for the OEO treatments and similar z-values across groups suggests that L. monocytogenes maintains consistent thermal sensitivity, but its susceptibility varies depending on the antimicrobial agent applied [29]. These findings support the use of OEO, especially its fractionated form, as an effective component in hurdle technology to control this pathogen. It is important to highlight that in our study, we preferred using a single L. monocytogenes strain rather than a multi-strain cocktail. While this choice provides real-world relevance and ensures the strain’s virulence and environmental resilience, it may not fully capture the strain-to-strain variability typically observed in foodborne pathogens. The use of cocktails comprising multiple strains is common in challenge studies to account for this diversity. However, for mechanistic investigations and thermal inactivation modeling, the use of a single, well-characterized strain allows for more controlled comparisons and reproducible kinetic analyses.

The integration of FIV and PEO into food processing offers a promising strategy to control L. monocytogenes, particularly in RTE foods where the pathogen’s ability to proliferate at refrigeration temperatures presents a major food safety concern [78]. Applying predictive microbiology models allows for the fine-tuning of processing parameters to optimize safety while minimizing thermal damage. This is particularly relevant in regions with limited regulatory oversight of listeriosis, where evidence-based decision-making is essential [79].

Moreover, integrating these predictive tools into user-friendly software could facilitate broader adoption by food processors and regulators, enhancing both safety and operational efficiency. For instance, results of previous research have shown that combining nanoemulsions of D-limonene with heat treatments has shown a decrease in L. monocytogenes‘ heat resistance by about one hundred times [80]. The inactivation parameters estimated by these authors were further integrated into the D database (https://foodmicrowur.shinyapps.io/Ddatabase/, accessed on 1 April 2025), a resource that supports benchmarking, model development and the sharing of validated data. Similarly, the inactivation parameters estimated in our study will also be submitted to the D database to encourage their practical application in food safety interventions.

FIV enhances antimicrobial efficacy through its synergistic thermal effects and could reduce the sensory impact commonly associated with essential oils, which is critical for consumer acceptance [27]. At the applied concentration of 0.06% v/v, the levels of carvacrol and thymol were below thresholds reported to impact sensory attributes in food. Specifically, the resulting concentrations were approximately 319 µg/mL carvacrol and 112 µg/mL thymol for FIV and 159 µg/mL carvacrol and 63 µg/mL thymol for PEO. These values correspond to less than 100 mg/kg when extrapolated to food matrices, which is within the range of natural occurrence in herbs and spices. According to U.S. toxicological data, thymol has been detected in foods at up to 100 mg/kg, with the lowest level at which adverse effects were observed being 640 mg/kg [81]. Additionally, the Official Journal of the European Union has set a safe use level for carvacrol and thymol in animal feed at 125 mg/kg [82], suggesting a broad safety margin. Importantly, a previous study reported that the addition of 0.5% OEO in meatballs was considered sensory acceptable, further supporting the potential of lower concentrations, such as 0.06%, for use in RTE foods without compromising sensory quality [83]. Therefore, further studies in real food systems are needed to confirm sensory acceptability and regulatory compliance.

4. Conclusions

P. longiflora fractionated oregano essential oil consistently reduced the time required to achieve L. monocytogenes logarithmic reductions under mild heat treatments at all tested temperatures. This effect is attributed to the high concentration of carvacrol and thymol obtained through fractional distillation at 140 °C. The estimated inactivation parameters can inform the design of optimized hurdle strategies using natural antimicrobials and reduced heat exposure. Preliminary trials in ground meat are currently underway and have shown results consistent with those obtained in the meat-simulated medium, suggesting strong potential for application in this food matrix. Further studies are needed to evaluate sensory acceptability and validate efficacy across diverse product types and processing environments.