Contribution of high pressure and thyme extract to control Listeria monocytogenes in fresh cheese - A hurdle approach

Innovative Food Science and Emerging Technologies 38 (2016) 7–14

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Contribution of high pressure and thyme extract to control Listeria monocytogenes in fresh cheese - A hurdle approach Iulia Bleoancă a, Klemen Saje a,b, Liliana Mihalcea a, Elena-Alexandra Oniciuc a, Sonja Smole-Mozina b, Anca Ioana Nicolau a, Daniela Borda a,⁎ a b

Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, 111 Domneasca Street, 800021 Galati, Romania Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, Ljubljana, Slovenia

a r t i c l e

i n f o

Article history: Received 28 March 2016 Received in revised form 11 August 2016 Accepted 2 September 2016 Available online 3 September 2016 Keywords: Supercritical fluid extraction Antilisterial effect Antimicrobials Inactivation Kinetics

a b s t r a c t Capability of thyme natural antimicrobials (NAs) to reduce the intensity of high pressure processing (HPP) and enhance the inactivation of Listeria monocytogenes in fresh cheese was investigated. Thyme extracts were obtained by supercritical fluid extraction at 30 MPa and 40 MPa and their concentration was estimated in units of carvacrol (190.44–609.57 μg/mL). The acceptable sensorial threshold of thyme extract in cheese was 0.2% (v/w), a value lower than the MIC for L. monocytogenes. Detailed kinetic studies of L. monocytogenes inactivation in cheese were performed following HPP (200–300 MPa) treatments. A maximum 1.68-log10 CFU/g supplementary reduction was achieved for L. monocytogenes in cheese with HPP & NAs than without NAs. The kinetic parameter zp indicated an accelerated inactivation of L. monocytogenes when HPP was combined with thyme NAs, as hurdle applied to fresh cheese (55.99 MPa for HPP and 33.29 MPa for HPP with thyme NAs). Industrial relevance: Applied as a post-processing treatment high pressure processing (HPP) is considered an effective listericidal tool that produces limited quality changes in foods compared to other preservative methods. In order to overcome the main drawback related to HPP effect on cheese structure, a combination of pressure with natural antimicrobials (NAs) is proposed for reducing the intensity of processing while improving microbiological safety of food. The efficacy of L. monocytogenes inactivation in cheese by HPP and HPP with NAs from thyme was kinetically evaluated and modeled in the present work. The current results provide support for the industrial development of more effective combined HPP with NAs treatment, able to minimize the risk of Listeria presence in cheese. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Latin-style fresh cheese, a popular dairy product in Romania (brânză proaspătă), Spain (queso fresco), Portugal (queijo branco) and in several Latin American countries (Mexico, Brazil, Peru) (queijo fresco) (Coelho, Silva, Ribeiro, Dapkevicius, & Rosa, 2014; Silva et al., 2015) is an unripened cheese produced either by acidic or combined acidic and Abbreviations: SPME GC/MS, Solid Phase Micro Extraction Gas Cromatography Mass Spectrometry; HPP, High Pressure Processing; NAs, Natural Antimicrobials; SFE, Supercritical Fluid Extraction; RI, Retention Indices; KI, Kovats Index; RH, Relative Humidity; VOC, Volatile Organic Compounds; PCA, Principal Component Analysis; PBS, Phosphate-buffered saline; BHI, Brain Heart Infusion; MIC, Minimal Inhibitory Concentration; MBC, Minimal Bactericidal Concentration; ELISA, Enzyme Linked Immunosorbent Assay; MHA, Muller Hinton Agar; RMSE, Root Mean Squared Error; RSD, Relative Standard Deviation; PCA, Principal Component Analysis. ⁎ Corresponding author. E-mail addresses: [email protected] (I. Bleoancă), [email protected] (K. Saje), [email protected] (L. Mihalcea), [email protected] (E.-A. Oniciuc), [email protected] (S. Smole-Mozina), [email protected] (A.I. Nicolau), [email protected] (D. Borda).

http://dx.doi.org/10.1016/j.ifset.2016.09.002 1466-8564/© 2016 Elsevier Ltd. All rights reserved.

enzymatic clotting of milk, with a characteristic taste of lactic fermentation. This fresh cheese has a soft texture, mild flavor and low salt content. When produced by acidic and enzymatic clotting the cheese pH value (above 4.6) and high water activity (aw ~ 0.98–0.99) could favor growth of potentially pathogenic and spoilage microorganisms, considerably reducing its shelf-life down to about two weeks (Gadotti, Nelson, & Diez-Gonzalez, 2014). Listeria monocytogenes can cause a severe human illness when contaminated food products are ingested, listeriosis being one of the most significant foodborne diseases in industrialized countries (Coelho et al., 2014). The ubiquitous presence of this foodborne pathogen in industrial premises increase the probability of a contaminated finished food product, soft cheeses being recognized as an at risk product responsible for many recurrent cases of outbreaks (Centers for Disease Control and Prevention, 2015). High pressure processing (HPP) is a cutting-edge technology and an attractive alternative to thermal processing because it has the ability to inactivate microorganisms and enzymes and, simultaneously, has a minimal impact on nutritional and sensory properties of foods

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(Buckow, Weiss, & Knorr, 2009). HPP was successfully applied to a variety of cheese making processes in order to extend shelf-life, improve safety of many cheese varieties and/or accelerate ripening processes (Evrendilek, Koca, Harper, & Balasubramaniam, 2008; Martínez-Rodríguez et al., 2012). Generally, HPP has been recognized as a useful listericidal post-processing treatment for ready-to-eat foods (Hereu, Dalgaard, Garriga, Aymerich, & Bover-Cid, 2012). However, above certain pressure-temperature values or at long treatment time undesired side-effects were noticed in cheese, such as wheying-off and textural changes as reported by Tomasula et al., 2014 for fresh cheese treated at 400 MPa, 40 °C, with holding time longer than 15 min, or compromised crumbling properties as observed by Hnosko, Gonzalez, & Clark (2012) for queso fresco treated at 500, 550 or 600 MPa, 21 °C for 1 to 15 min. Additionally, numerous studies have shown that although bacterial inactivation by HPP exhibits first-order kinetics, a small fraction of the population remains viable even after prolonged processing (Gayán, Torres, & Paredes-Sabja, 2012). Therefore, combined HPP with other preservative methods could be employed as part of a hurdle strategy, oriented towards reducing intensity of processing without compromising food safety. Hurdle technology relies on the synergistic combination of moderate doses of bactericidal and/or bacteriostatic compounds in combination with conventional and/or novel food processing options to achieve an acceptable pathogen inactivation level (Gayán et al., 2012). The trend of combined use of HPP and natural antimicrobials (NAs) is gaining ground (Gayán et al., 2012; de Oliveira et al., 2015) and could provide viable alternative tools for improving the preservative effects of high pressure and assuring the safety and quality of processed foods. Given the high variability of NAs, the threshold sensorial acceptability in certain foods and the complexity of their composition in relation with the antimicrobial activity, detailed kinetic studies are required before applying HPP and NAs hurdles into industrial practice. The current study kinetically evaluated the possibility of combining high pressure and antimicrobial activity of thyme extracts as hurdle for controlling L. monocytogenes in fresh cheese. The specific objectives of the current study were: (i) to obtain thyme extracts by supercritical fluid extraction and characterize their antilisterial activity; (ii) to evaluate the potential of HPP and thyme NAs as hurdles for L. monocytogenes control in fresh cheese and quantify the kinetic parameters of the inactivation. 2. Materials and methods 2.1. Thyme volatile extraction and characterization 2.1.1. Raw material Fresh aerial parts of thyme were purchased at full flowering stage in August 2014 from a local producer in Galati, a city located in the Southeastern part of Romania. Voucher specimens of the plant material deposited at the Herbarium-Botanic Garden of Galati, Natural Sciences Museum Complex, were used for taxonomical identification of the samples as Thymus vulgaris L. “Smarald”. The plant material was washed in two stages, firstly using tap water to remove the epiphytes and dust and later on with deionized water, and then natural air dried at 21 ± 0.50 °C, RH 38.00% in darkness for 15 days. The dried plant material (89.23 ± 0.41% dry matter content) was stored in dark airtight containers, avoiding any direct contact with sunlight. Before extraction dried leaves were separated from the stem and the remaining dust particles were removed through sieving. 2.1.2. Supercritical fluid extraction (SFE) of thyme dried leaves Extractions were carried out in a pilot-plant supercritical carbon dioxide extractor (Natex, Prozesstechnologie GesmbH, Austria, Fabr. no. 10-023/2011) comprising a 2.0-L cylinder extraction vessel and two separators (S40 and S45) each with 1.5-L capacity. The extractor basket was filled with ~ 0.320 kg of grinded dried thyme leaves for each

extraction. During extraction, the solvent (CO2, 99.99% purity supplied by Messer S.A., Romania) was constantly chilled to remain liquid and recirculated. The solvent was brought to supercritical conditions at 7.30 MPa, and a flow rate of 21.65 ± 0.10 kg/h, as indicated by the data sheets from ABB software (ABB - Mannheim, Germany). For all extractions the temperature in the extraction vessel was set at 40 °C and each extraction was performed during 240 min. Two extraction pressures were applied, 30 MPa and 40 MPa (Bermejo et al., 2014). In order to produce fractions with different composition, the pressure in the first separator (S45) was maintained at 10 MPa, while in the second separator (S40) decompression up to recirculation pressure of 5 MPa was set (Santoyo, Jaime, García-Risco, Lopez-Hazas, & Reglero, 2014). The two steps decompression in the end of the extraction produced four extracts (S40.1 and S45.1 at 30 MPa, respectively S40.2 and S45.2 at 40 MPa) which were collected in dark bottles and kept under refrigeration conditions (2–4 °C) until further use in experiments. The extractor was fed in three batches and the extracts were collected all at once at the end of the process. 2.1.3. Gas-chromatography analysis of the essential oils 2.1.3.1. Sample preparation and extraction of volatiles. Aliquots of 10-μL sample (S40 and S45) in 9980 μL of ultrapure water (TKA, Germany) were transferred to headspace vials (15.00 mL), to which 10 μL of 2-octanol (Sigma Aldrich Chemie GmbH, Steinheim, Germany) was added as an internal standard and 2.50 g of saturated (NH4)2SO4. Each glass vial was sealed with a septum and an aluminum cap. Solid Phase Micro Extraction (SPME) with carboxen-polydimethylsiloxane (CAR/PDMS, 75 μm) fiber from Supelco (Bellefonte, PA, USA) was performed. The fiber was conditioned, according to the instructions of the manufacturer and thermally cleaned for 20 min at 220 °C in the conditioning station of the autosampler after each run. Volatiles were extracted after an incubation of the vials for 5 min at 40 °C followed by an exposure of the SPME fiber to the headspace of the sample at 20-mm depth in the vial and 40 °C for 40 min. For thermal desorption the SPME fiber was inserted into the GC port injector. A desorption time of 4 min at 250 °C was applied in splitless mode. 2.1.3.2. Gas chromatography (GC)/mass spectrometry (MS). The volatiles fingerprint of S40 and S45 thyme extracts was analyzed with Trace GC/MS Ultra equipment with ionic trap - ITQ 900 from Thermo Scientific (USA). The GC column was a TG-WAX capillary column (60 m × 0.25 mm, i.d. 0.25 μm). Helium (99.996% purity, Messer S. A., Romania) was used as a carrier gas at a flow of 1 mL/min. The oven temperature program was: 40 °C isothermal treatment for 4 min followed by an increase to 50 °C at 3 °C/min and to 120 °C with 5 °C/min, to 175 °C at 7 °C/min and finally to 230 °C at 10 °C/min, when temperature was kept constant for 8 min. Afterwards, the oven was cooled again to the initial temperature. The temperature of the transfer line in MS was set to 270 °C. Mass spectra were obtained from the full scan of the positive ions resulted with a scanning in the 35 to 450 m/z range, and operated with an electron impact mode of 200 eV. Thymol and carvacrol were identified by comparison with standard mass spectra, obtained in the same conditions and compared with the mass spectra from Wiley mass spectral library. The rest of the compounds were identified by comparison with the mass spectra from Wiley and Nist08 library database available with Xcalibur software. The identification of the volatile compounds was performed by calculation of retention indices (RI) of each compound by using n-alkane series from C8–C40 (Sigma-Aldrich Chemie GmbH, Steinheim, Germany) under the same conditions. The KI (Kovats index) values were also compared with those described in literature determined under the same conditions for matching the compounds. The individual volatile compounds (identified and unidentified peaks) were tentatively reported based on their peak areas, relative to that of the internal standards. The analysis was performed in duplicate.

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2.2. Test cultures

2.6. High hydrostatic pressure treatments

Antimicrobial assays were performed against L. monocytogenes EGD-e strain (Chatterjee et al., 2006), which was maintained at −80 °C in Brain Heart Infusion (BHI, Biolife Italiana, Milano, Italy) containing 25% glycerol (Sigma- Aldrich, Saint Louis, MO, USA). Broth subcultures were prepared overnight by inoculating one single colony from a Palcam (Merck, Darmstadt, Germany) plate into 10 mL BHI. The working fresh cultures (stationary phase cells) were obtained after 12 h incubation at 37 °C of 1 mL subculture into 10 mL fresh BHI broth. Stationary phase cells were preferred for the tested antimicrobial challenge given their proven higher resistance to HPP compared to the exponential phase cells (Pagán & Mackey, 2000). Fresh cultures were harvested by centrifugation at 6000 ×g at room temperature and washed three times with sterile phosphate-buffered saline (PBS) 50 mM, pH 7. Inocula were prepared at approximately 107 CFU/mL, by means of absorbance measured at 600 nm.

The pressure treatments were conducted in a multivessel (four vessels of 100 mL) high-pressure equipment (Resato, Roden, The Netherlands, 2011) at 22 ± 0.5 °C. As pressure-transmitting fluid a mixture of water and propylene glycol fluid (TR15, Resato) was used. The compression rate was 200 MPa/min, until the preset pressure was reached. Pressure was applied in the 100–300 MPa range and time zero was considered at 1 min after the come-up time. Immediately upon, one vessel was decompressed and the microbial count was considered reference value N0 at t = 0, for each pressure applied. The other vessels were decompressed as a function of time. Decompression of the vessels was almost instantaneously. Treated samples were removed from the highpressure vessels, chilled in an ice bath and transferred immediately in the fridge for 24 h. Afterwards, detection of L. monocytogenes surviving cells was performed.

2.3. Enumeration of L. monocytogenes

2.7. Data analysis

L. monocytogenes enumeration was performed following ISO 112901:1996 standard method. The two-step enrichment protocol included cultivation in Half Fraser Broth (Merck, Darmstadt, Germany) incubated for 24 h at 30 °C ± 1. For small counts, the primary enrichment (0.1 mL) was transferred to Fraser Broth (Merck, Darmstadt, Germany) and incubated for 48 h at 37 °C ± 1. Selective agar such as Palcam agar (Merck, Darmstadt, Germany) medium was inoculated with ten-fold dilutions of each Half Fraser and Fraser Broth and incubated for 48 h at 37 °C ± 1.

2.7.1. Principal component analysis A principal component analysis (PCA) that transforms the original measured variables into new variables, called principal components was performed using the Unscrambler software (Version 9.7; CAMO, Norway). PCA was performed with the peak list resulting from SPME GC/MS analysis for all the volatile compounds that resulted from supercritical fluid extraction. The data matrix was formed by n = 4 cases (independent replicates for each extract - S40.1, S40.2, S45.1 and S45.2) and 41 variables (defined as the peak area obtained for each individual component). Data were transformed by unit vector normalization prior to statistical analysis.

2.4. Antilisterial effect of thyme extracts The extracts were studied for their MIC values according to the micro dilution method, as described by CLSI guidelines (CLSI, 2012). The extract was dissolved in a mix consisting of sterile PBS 50 mM pH 7.0, Tween 80 (1%) and ethanol 2% (all from Sigma-Aldrich, St. Louis, MO, USA) to obtain concentrations of 2.5% and 5.0% (w/v). The mixture was homogenized by ultrasonication with Bandelin sonoplus HD 3100, Germany for 210 s, amplitude 20%. A positive (100 μL of BHI broth and 10 μL L. monocytogenes inoculum) and a negative control (100 μL of sterile BHI broth) were also included in the microtiter plate. The lowest extract concentration that inhibited L. monocytogenes growth after 24 h of incubation at 37 °C ± 1 compared with positive control (broth only), was considered as MIC and measured by an ELISA (Tecan - Infinite 200 Pro) microtiter plate reader set at 600 nm. Immediately after determination of MICs, the MBCs were assayed as described by Santurio & Jesus, 2014. The lowest extract concentration that prevented growth after plating on Muller-Hinton Agar (MHA, Sigma-Aldrich, St. Louis, MO, USA) after 48 h of incubation at 37 °C ± 1 was reported as MBC. All the experiments were performed in triplicate.

2.7.2. Semi–quantitative estimation of volatile organic compounds (VOCs) A method based on the protocol presented by Bencsath et al., 2015 was adapted to evaluate the concentration of carvacrol and the relative concentration of other major volatiles present in the samples. The carvacrol calibration curve was plotted considering the relative peak areas as function of pure compound concentration in the range from 0.2 to 100 μg/mL. Matrix-matched calibration series, spiked with carvacrol, in the range from 1 to 30 μg/mL samples were analyzed for each extract. Calibration curves with regression equation were produced from the extracted ion chromatograms with very significant correlation factors (R2 N 0.99). For semi-quantitative determination of the major volatiles present in the samples, relative response factors (RR) were calculated for each of the four extract according to the following equation: X

RRS ¼

! A=Aav:IS



X ½IS= ½A

i

2.5. Preparation of the cheese sample Commercially available fresh cheese in the form of spread (Pilos, Lidl, Romania) was chosen to test the matrix effect of thyme extracts on L. monocytogenes. Prior to experiments, compatibility of the thyme essential oil and cheese was tested by sensorial analysis (Supplementary material). Proximate composition of cheese was indicated by its producer: proteins 11%, fat (saturated fatty acids) 6.5% (3.5%), carbohydrates 4%. The aw values, pH and titratable acidity were determined before the treatments (aw 0.970, pH 4.68, acidity 141.67°T). The emulsion of 5% dissolved thyme extract was diluted and added into 1 g cheese to give a final concentration of 0.03%, 0.06%, 0.12%, 0.25% and 0.5% (v/w). Cheese was inoculated with a L. monocytogenes suspension to a final concentration of approximately 107 CFU/g. For positive control, PBS was used instead of thyme extracts. After homogenization with a stomacher (Mini Mix - Interscience, France) for 2 min samples were stored at 4 °C in a refrigerator for 24 h before MIC assay.

! ð1Þ

i

where ∑ A - peak area of carvacrol in the spiked samples, Aav.IS - averi

age internal standard area; [IS] - concentration of internal standard (2octanol) and ∑ ½A - concentrations of carvacrol in the spiked samples. i

To estimate the relative VOCs concentration from the integrated individual peak areas of the VOCs in each extract, the following equation was applied: ½VOC ¼ ðAVOC =AIS Þ  ð½IS=RRs Þ

ð2Þ

2.7.3. Kinetic data analysis High pressure inactivation of L. monocytogenes has been frequently reported to follow a first-order kinetics (Dogan & Erkmen, 2004) as described by Eq. (3). However, Weibull nonlinear model (Eq. (4)) also represented an alternative to linear inactivation kinetics (Serment-Moreno,

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Barbosa-Cánovas, Torres, & Welti-Chanes, 2014) reported in many recent studies (Buzrul, Alpas, Largeteau, & Demazeau, 2008; Hereu et al., 2012). These two models were fitted to describe the inactivation of L. monocytogenes over time at different pressures in the range 200– 300 MPa and compared. log10 N



¼ −1

N0

 Dp

t

ð3Þ

  n log10 N ¼ log10 N0 − t b

ð4Þ

where N - microbial count after the HPP treatment (CFU/g) and N0 - microbial count at time zero (t = 0) after 1 min come-up time (CFU/g), Dp - decimal reduction time describing the lethal pressure effect when first-order kinetics is assumed (min); t - treatment time (min); b - constant inversely correlated with the slope steepness; n - constant indicating the shape of the survival curves. The pressure dependence of the decimal reduction time at constant temperature was expressed by the zp value, with the liniarized Bigelow equation and estimated by linear regression: log10

  D p

Dref



¼ ðP−Pref Þ

. zp

ð5Þ

abundant volatile substances present in all thyme extracts, were: γ terpinene - 65.0%, followed by p-cymene - 8.0%, carvacrol - 5.5% and bisabolene - 5.0%. The same major flavor compounds were indicated in other species of thyme such as T. algeriensis (Hazzit, Baaliouamer, Veríssimo, Faleiro, & Miguel, 2009) and T. hyemalis (Jordán, Martínez, Goodner, Baldwin, & Sotomayor, 2006). Thymol concentration represented in average only 0.03% of the total volatiles, in contrast with other researches, that indicated thymol as the most abundant volatile compound in the thyme extracts (de Carvalho et al., 2015; García-Risco, Vicente, Reglero, & Fornari, 2011). Small concentrations of thymol in T. vulgaris were reported by similar studies (Chizzola, Michitsch, & Franz, 2008). It should be also taken into consideration that both p-cymene and γ-terpinene are precursors of thymol and carvacrol and occur in variable proportions in these two chemotypes (Chizzola et al., 2008). In this study large amount of precursors were present in all the thyme extracts. Moreover, these results confirm the variability of thyme volatile substances composition influenced by factors related to the biological material nature (specie, age of plant, phenological stage, parts of plant used in extraction) and technological factors (drying, storage conditions, methods applied for extraction and extraction parameters) previously discussed by de Carvalho et al., 2015. 3.3. Semi-quantitative estimation of VOCs

where Dref - reference decimal reduction time (min) at the reference pressure Pref (MPa); zp - the increase in pressure that will produce a 10-fold reduction in the Dp value (MPa). Additionally, the model for pressure dependence (5) was integrated into the primary model (3) by replacing Dp parameter from Eq. (3) into Eq. (5). This allowed the combined model to be fitted to the entire set in one-step global regression approach by non-linear regression. The root mean squared error (RMSE) and the coefficient R2 were calculated as measures for goodness-of-fit. The kinetic parameters described by Eqs. (3)–(5) were estimated by linear and nonlinear regression models with SAS Windows 9.0 program (Cary, NC, SUA). 3. Results and discussions 3.1. Extraction of Thymus vulgaris leaves According to global trends of promoting green technologies SFE is increasingly considered a valuable alternative due to significant advantages over the conventional extraction procedures (hydrodistillation, Soxhlet) for its ability to produce solvent-free extracts, minimize the negative environmental impact while producing extracts with high biological functionality (Fornari, Vicente, Vázquez, García-Risco, & Reglero, 2012). Additionally, the use of CO2 as solvent provides a high compatibility with oxygen-sensitive compounds (Nisha, Udaya Sankar, & Venkateswaran, 2012) and a reduced energy consumption (Aladić et al., 2015). Extraction at mild temperature in SFE protects the thermosensitive compounds and could favor the presence of richer extracts in antimicrobials. Considering the advantages of SFE over conventionally solvent extraction this method was selected for thyme extraction in the current research. The SFE performed yielded 0.43% (w/w) thyme extract at 30 MPa and 67.44% higher at 40 MPa (0.72% (w/w)). This significant increase of yield can be explained by the 10 MPa augmentation of extraction pressure, correlated with a higher density of CO2, which consequently raised the solubility of the solutes (Bermejo et al., 2014). The obtained gravimetric yield was comparable to similar researches (Fornari et al., 2012). 3.2. GC/MS In total 42 volatile compounds, representing N94.73% of the total volatiles, were identified and listed in Table 1, excepting the trace components that represented b 0.01%. The untargeted SPME GC/MS analysis of the four supercritical fluid extracts showed that, in average, the most

The semi-quantitative estimation of the major VOCs relative to the carvacrol concentration in the four extracts (Table 2) shows that extracts S40.2 and S45.1 and S45.2 have very similar VOCs total concentration (p N 0.05). The richest extract in VOCs is S40.1. The mean relative standard deviations in all samples (RSDs) were b10% with RSDs ranging from 0.8 to 9.78% in the extracts. 3.4. PCA analysis To visualize the underlying structure of the experimental GC/MS data with the purpose of explaining the relationships between extracts and components whereas obtaining a significant decrease in noise (Lupoae et al., 2015) PCA method was applied. Two principal components were selected based on the maximum decrease of the residual variance and according to the parsimony principle. The principal components PC1 and PC2 explain 93% of the total variance and provided a projection view of the inter-variable relationships (Fig. 1). The strongest influence on PC1 that accounts for 72% of the total variation is given in the first quadrant by sesquiterpenes such as: carryophylene, muurolene, bergamotene and sesquiterpenoids such as: ledene oxide, aromandrene, calarene epoxide that associate with the extract S40.2 obtained at 400 MPa. On the second quadrant most of the variation is given by pramocaine, camphene, sabinene but also isocineole that is associated with S45.2 extract obtained at 40 MPa. S45.2 extract explain the variation given by PC2. As shown in the third quadrant, both extracts S40.1 and S45.1 obtained at 30 MPa have an important contribution to the total variation, which is brought by volatiles such as monoterpenes (p-cymene and γ-terpinene) and monoterpenoids (carvacrol). These components were reported as having strong antimicrobial effect (Burt, 2004). PCA analysis revealed that extraction at 30 MPa produces extracts rich in carvacrol, p-cymene and γ-terpinene (Fig. 1). 3.5. Antilisterial activity of thyme NAs L. monocytogenes EGD-e susceptibility to thyme NAs, performed in BHI, indicated bacterial sensitivity to all four extracts, with MICBHI ranging between 400 and 1600 μg/mL and MBCBHI between 800 and 3200 μg/mL. The MICBHI obtained in this study against L. monocytogenes were similar to other reported findings (de Carvalho et al., 2015) for all extracts. Thyme NAs were also able to inhibit the growth of L. monocytogenes EGD-e in cheese, exhibiting MICcheese of 0.06–0.50% (v/ w), the test-culture presenting the highest sensitivity to S45.1 in cheese.

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Table 1 Composition of volatiles from thyme supercritical fluid extracts (S40.1 and S45.1 - at 30 MPa, S40.2 and S45.2 - at 40 MPa), analyzed by SPME GC/MS untarget fingerprinting. No

Compounda

1 cis-10-Nonadecenoic acid 2 2-chloro-3-(p-toly) Thiophene 3 α-Pinene 4 Camphene 5 Sabinene 6 2-α-Pinene 7 2,5-(4-hydrohyphenyl) Thiophene 8 1S-α-Pinene 9 cis-Ocimene 10 α-Terpinene 11 Limonene 12 α-Phellandrene 13 γ-Terpinene 14 p-Cymene 15 4-Carene 16 α-Ocimene 17 Isocineole 18 3-Octen-2-one 19 trans-α-Bergamotene 20 3-Nitrocumene 21 Caryophyllene 22 I-Muurolene 23 Caryophyllene oxide 24 trans-Caryophyllene 25 Aromadendrene 26 α-Humulene 27 Ledene 28 cis-α-Bisabolene 29 Myrtenol 30 3-Carene 31 Limonene-6-ol 32 4-ethyl-2-formyl-3,5-dymethyl-1H-Pyrrole 33 2-(3-indenyl-3-methylbutyl) Pyridine 34 Pramocaine 35 Arachidonic acid 36 Ledene oxide 37 Morpholine, 4-(2-aminoethyl) 38 Thymol 39 Carvacrol 40 Calarene epoxide 41 Muurolane diene 10-peroxy 42 2-Allyl-4-methylphenol Identified components (%)

Classb

Precursor ionsc

KId

S40.1(%)

S45.1(%)

S40.2(%)

S45.2(%)

Ide

CA SC MT MT MT MT SC MT MT DT DT DT DT MT MT MT MT K SQT BC SQT SQT SQT SQT SQOI SQT SQOI SQT SQOI MT DTOI HA HA HA CA SQOI HA MTOI MTOI SQOI SQOI BC

55,41,44,67,81 208,209,210,192,133 91,92,93,77,79 93,91,79,77,67 91,93,77,79,41 91,77,93,39,79 79,268,269,74,270 91,93,77,79,39 91,39,41,93,77 91,93,77,212,79 67,79,93,91,39 91,77,93,77,79 91,93,77,136,112 119,91,134,117,77 91,43,93,77,79 112,91,105,79,77 43,81,71,111,55 43,45,71,69,111 91,119,77,93,41, 150,91,165,93,43 91,79,105,77,133 91,105,79,93,119 45,43,41,91,79 41,39,91,120,67 91,105,79,39,77 93,92,79,77,39 91,105,107,119,79 91,41,79,67,93 91,79,79,93,119 91,93,79,119,67 43,45,41,44,91 135,151,91,107,137 234,57,45,248,263 100,45,43,44,70 91,45,43,79,67 45,43,87,91,39 100,45,44,70,43 135,45,91,107,115 135,91,107,151,77 161,79,45,91,119 45,159,91,105,131 148,45,133,105,108

– 812 903 924 946 952 960 966 972 982 991 997 1015 1027 1032 1071 1104 1120 1127 1131 1132 1134 1135 1139 1140 1144 1148 1151 1153 1158 1171 1175 1180 1186 1195 1260 1283 1287 1303 1380 1426 1450

0.00 0.06 0.78 0.04 0.24 0.01 0.09 0.08 2.21 4.50 0.49 0.29 65.88 8.38 0.12 0.01 0.02 0.01 0.11 0.11 2.95 0.18 0.01 0.04 0.02 0.13 0.13 3.94 0.07 0.37 0.01 0.04 0.02 0.01 0.02 0.01 0.01 0.02 6.57 0.01 0.01 0.01 98.01

0.00 0.02 0.71 0.03 0.22 0.01 0.12 0.08 2.20 4.38 0.49 0.29 65.85 8.26 0.17 0.01 0.03 0.01 0.12 0.10 3.02 0.18 0.01 0.05 0.01 0.14 0.13 4.40 0.08 0.41 0.01 0.04 0.02 0.01 0.02 0.01 0.02 0.02 6.01 0.02 0.01 0.03 97.7

0.00 0.22 0.92 0.04 0.24 0.01 0.43 0.08 2.21 2.71 0.48 0.28 59.83 8.31 0.25 0.01 0.00 0.00 0.16 0.14 4.51 0.27 0.01 0.07 0.02 0.22 0.09 7.46 0.14 0.74 0.02 0.05 0.07 0.02 0.07 0.03 0.07 0.03 4.44 0.04 0.02 0.02 94.73

0.34 0.02 1.09 0.05 0.30 0.01 0.14 0.10 2.59 5.01 0.55 0.32 64.97 7.58 0.17 0.01 0.03 0.01 0.12 0.08 2.88 0.17 0.01 0.05 0.01 0.14 0.15 4.70 0.10 0.46 0.01 0.04 0.07 0.04 0.02 0.01 0.09 0.02 4.14 0.01 0.02 0.03 96.32

RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS RI, MS, IS RI, MS, IS RI, MS RI, MS RI, MS

a

Components listed in the order of their elution on the column. Class: MT - monoterperpenes, DT - diterpenes, SQT - sesquiterpenes, MTOI - monoterpenoids, DTOI - diterpenoids, SQOI - sesquiterpenoids, CA - carboxylic acids, SC - sulphur compounds, K - ketones, HA - heterocyclic compounds, BC - benzene compounds. c m/z - ions resulted from fragmentation. d KI - Kovats index relative to n-alkanes C8–C40 series. e Peak identification by comparison of spectra with commercial standards (ST) and/or tentatively identified by comparison with Wiley and Nist08 mass spectrum library available. b

Having in view the importance of consumers' acceptability of the proposed association between thyme NAs and fresh cheese, a sensorial acceptance test was performed with 10 trained panelists (Supplementary file). This test indicated that the acceptability limit is a concentration of the thyme extracts two fold lower than the one tested for MIC in cheese. The analysis showed that the same extract concentration was considered by panelists too strong-flavored in case of S40.1 and S40.2 and mildly-flavored in case of S45.1 and S45.2. Based on these sensorial results and MICcheese data which indicate L. monocytogenes

highest sensitivity to S45.1 and correlated with the GC/MS fingerprint, a hurdle approach was proposed for controlling L. monocytogenes EGD-e presence in fresh cheese, using a reduced concentration of S45.1 thyme NAs combined with HHP treatment. 3.6. L. monocytogenes inactivation kinetics in cheese The kinetics of L. monocytogenes EGD-e inactivation was studied in HPP fresh cheese, treated in the pressure range of 200 to 300 MPa, up

Table 2 Comparison of the major VOCs relative concentration reported to carvacrol content, in thyme extracts (S40.1 and S45.1 - at 30 MPa, S40.2 and S45.2 - at 40 MPa) obtained by SFE. Values are mean of three replicates. Compound Carvacrol (μg/mL) Thymol (μg/mL) γ-Terpinene (μg/mL) p-Cymene (μg/mL) Total units carvacrol (μg/mL) a

S40.1 a

64.04 ± 1.21 0.17 ± 0.01 483.83 ± 10.55 61.52 ± 3.50 609.57 ± 7.82

Stdev - standard deviation of the individual components resulted from triplicates.

S45.1

S40.2

S45.2

10.87 ± 1.13 0.06 ± 0.00 175.99 ± 8.75 22.08 ± 1.72 209.00 ± 11.50

14.54 ± 1.22 0.10 ± 0.00 171.51 ± 11.15 23.83 ± 2.04 209.99 ± 17.802

16.67 ± 1.12 0.08 ± 0.00 143.90 ± 15.56 29.79 ± 1.79 190.44 ± 16.99

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Fig. 1. PCA analysis of the results obtained from the untarget SPME GC/MS analysis of four thyme extracts (S40.1, S45.1, S40.2, S45.2): scores and loadings of PC1 and PC2.

to 15 min at 22 °C, in samples with and without thyme NAs. The inactivation data were fitted to a log-linear regression model (Eq. (3)) and a Weibull model (Eq. (4)) by nonlinear regression, whereas the quality of the models was expressed by statistical parameters, R2 and RMSE. Fig. 2 shows the logarithmic survival curves of L. monocytogenes EGD-e in fresh cheese in the absence and in the presence of NAs from thyme extracts. Eq. (3) was applied to estimate the D values (Table 3). The inactivation of L. monocytogenes at 200 MPa showed no significant difference between the tested samples (with and without thyme NAs). However, an accelerated inactivation was noticed for the samples treated at 225, 250 and 300 MPa, where thyme NAs were present (p b 0.05). In both systems, the HHP treatment at 300 MPa reduced L. monocytogenes after 2 min of treatment, with N 3.5-log10 cycles for the samples without thyme NAs and with N5.0-log10 for the samples with thyme NAs. Similar results were reported by researchers (Espina, García-Gonzalo, Laglaoui, Mackey, & Pagán, 2013), with a maximum extra inactivation of 3.0-log10 cycles. However, the low pH 4.0 of the fruit juice contributed to accelerated inactivation of L. monocytogenes, while in the current study the pH value of cheese was 4.68. The D values (Table 3) reported here are in line with other studies on L.

monocytogenes inactivation in raw milk, filtered orange juice and peach juice (Dogan & Erkmen, 2004) and higher than the values reported by (Hereu et al., 2012) for inactivation in ready-to-eat cooked meat products. Nonetheless, it has been hypothesized that effectiveness of NAs largely depends on the type of food matrix and specifically on the presence of proteins and fats, which immobilize the essential oils components (Gayán et al., 2012). Cheese par excellence is a network of proteins with a high fat content which could interfere with antimicrobials and explain the limited increase in the inactivation rate compared with the system without NAs (Table 3). As alternative to linear inactivation kinetic the data were also fitted to the Weibull model, which is able to provide more information the survival curves. As shown in Table 3, all the n values estimated for the system treated by HPP were smaller than 1, which denoted an upward concavity of the inactivation curves related to an increased resistance of population upon inactivation. In the samples where HPP and NAs were applied the estimated n-values at 275 MPa and 300 MPa were higher than 1 indicating a potential accumulated damage (Serment-Moreno et al., 2014) of L. monocytogenes population. The RMSE values and R2 demonstrated goodness of fit for both linear and Weibull models, with

Fig. 2. Log-linear inactivation kinetics of L. monocytogenes in HPP fresh cheese with and without thyme NAs at pressures from 200 to 300 MPa at 22 ± 0.5 °C.

I. Bleoancă et al. / Innovative Food Science and Emerging Technologies 38 (2016) 7–14

13

Table 3 D values estimates and goodness of fit resulting from fitting log-linear and Weibull models to data obtained after HPP inactivation of L. monocytogenes inoculated in cheese with and without NAs; zp estimates from two step approach of log-linear models fitting for HPP and HPP and NAs and zp and Dref resulted from one step nonlinear approach for HPP and HPP and NAs. System

Fresh cheese HPP

Fresh cheese HPP + NAs

a b c

Log linear model

Weibull model

Pressure (MPa)

D values (min)

Stdeva

R2b

RMSEc

b

n

R2

RMSE

200 225 250 275 300 Model Log linear 1 step 200 225 250 275 300 Model Log linear 1 step

11.00 3.09 1.96 1.16 0.58 zp (MPa) 86.65 55.99 11.00 2.82 1.41 1.20 0.39 zp (MPa) 76.06 33.29

1.13 0.47 0.30 0.11 0.08 Stdev 12.90 4.27 1.13 0.45 0.07 0.06 0.05 Stdev 9.82 4.43

0.900 0.886 0.953 0.991 0.981 Dref − 1.05 0.950 0.895 0.997 0.973 0.970 Dref − 0.33

0.020 0.135 0.056 0.023 0.028 Stdev − 0.14 0.011 0.198 0.013 0.007 0.099 Stdev − 0.05

2.81 0.70 0.31 0.39 0.19 R2 0.912 0.997 4.59 0.40 0.44 0.84 0.32 R2 0.937 0.828

0.67 0.76 0.66 0.86 0.85 RMSE 0.063 0.004 0.96 0.66 0.87 1.24 1.34 RMSE 0.013 0.026

0.952 0.936 0.999 0.997 0.987

0.063 0.542 0.000 0.014 0.427

0.956 0.951 0.983 0.988 0.999

0.057 0.518 0.000 0.077 0.000

Stdev - standard deviation of the estimated parameters. RMSE - root mean square error. R2 - correlation coefficients for the models.

the smallest RMSE values for the Weibull model and the highest R2 values for log-linear model. A linear relation was observed between log10-transformed decimal reduction time (log10 D) and experimental pressure levels. The zp estimates (Table 3) show that a smaller increase (76.06 ± 9.82 MPa) in pressure is required to obtain a 10-fold reduction of D values for the samples where NAs and HPP are employed, compared with the samples where HPP (86.65 ± 12.90 MPa) was applied. However, the large standard deviation values suggest that the difference between the zp values obtained for the inactivation of L. monocytogenes in cheese treated with HPP and with HPP and NAs is not significant (p N 0.05). To acquire a more accurate estimation of the zp values a single step equation resulted from combining Eqs. (3) and (5), was applied. The zp and Dref values were obtained by nonlinear regression, considering pref as the median value of the experimental domain (250 MPa). In this case the estimated zp values showed a higher difference (22 MPa) between the inactivation of L. monocytogenes by the HPP and NAs system compared to the HPP and smaller standard deviation values. The zp value obtained for fresh cheese (55.99 MPa) is lower both than the value of 229 MPa reported in a meta-analysis of L. monocytogenes inactivation in different foods (Santillana Farakos & Zwietering, 2011) and the value of 159 MPa obtained by Hereu et al., 2012 in RTE meat products. In addition, the even lower value obtained for zp in fresh cheese with NAs (33.29 MPa) demonstrates a very high sensitivity of L. monocytogenes to pressure change; this difference could be attributed mainly to the cheese matrix influence. The multiple mechanisms of L. monocytogenes inactivation involved when HPP and NAs are applied as hurdles are related to the ability of NAs to translocate thorough the cell wall and interact either with cell membrane phospholipid bilayers and thus affecting its integrity and functionality, with cytoplasm membrane or with specific enzymes, inhibiting them and causing coagulation and precipitation of cytoplasmic constituents (Feyaerts, Rogiers, Corthouts, & Michiels, 2015; Siroli, Patrignani, Gardini, & Lanciotti, 2015). As it was proven by recent researches (Feyaerts et al., 2015) carvacrol and monoterpenoids, in general, did not display a strong synergy with HPP due lack of thiol reactivity. The NAs identified in the studied SCF thyme extracts have high concentration of terpenes and terpenoids but only small amounts of thiophene derivates and other sulphur compounds (Table 1). This could explain the relatively reduced augmentation of the L. monocytogenes EGD-e inactivation rate in cheese treated with HPP and thyme NAs compared to the cheese treated only with HPP (Table 3).

These findings suggest that NAs and HPP could be an addition to the traditional preservation methods employed by industry, as part of hurdle strategy to control L. monocytogenes in dairy products. However, detailed kinetic profile of pathogen inactivation, are required to quantify the effect of HPP and NAs together with qualitative and quantitative information on VOCs composition in the plant extracts. 4. Conclusions In the present study thyme extracts obtained by supercritical fluid extraction at 40 °C and pressures of 30 and 40 MPa demonstrated antilisterial activity. The sensorial analysis showed that consumers liked the combination of fresh cheese with thyme extracts having low concentrations of thymol and carvacrol. The threshold value accepted for fresh cheese was twofold lower than MIC concentration in cheese and it was further applied as hurdle for the kinetic HPP and NAs studies of L. monocytogenes EGD-e inactivation. The kinetic parameters were estimated by two mathematical models; an acceleration of the inactivation of L. monocytogenes EGD-e in cheese was present for the system where two hurdles (HPP and NAs from thyme) were applied in comparison with the system where only one (HPP) was employed. Application of hurdles such as HPP and NAs based on kinetic models, provide for food producers the parameters to control L. monocytogenes presence in cheese. However, a follow-up study is necessary to evaluate how stress affects the survival of remaining pathogenic cells during storage and if NAs could play a role in the inhibition of L. monocytogenes recovery. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ifset.2016.09.002. Acknowledgements This work has benefited of technical support provided by the project RE-SPIA (695/09.04.2010, SMIS code 11377). Klemen Saje has benefited from Erasmus Student Mobility Program 2015. References Aladić, K., Jarni, K., Barbir, T., Vidović, S., Vladić, J., Bilić, M., & Jokić, S. (2015). Supercritical CO2 extraction of hemp (Cannabis sativa L.) seed oil. Industrial Crops and Products, 76, 472–478. http://dx.doi.org/10.1016/j.indcrop.2015.07.016. Bencsath, F. A., Benner, R. A., Abraham, A., Wang, Y., El Said, K. R., Jester, E. L. E., & Plakas, S. M. (2015). Screening for petrochemical contamination in seafood by headspace solidphase microextraction gas chromatography–mass spectrometry. Analytical and

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