Effect of pH on the Antimicrobial Activity of Wood Vinegar (Pyroligneous Extract) From Eucalyptus

doi:10.21203/rs.3.rs-1635253/v1

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Effect of pH on the Antimicrobial Activity of Wood Vinegar (Pyroligneous Extract) From Eucalyptus

https://doi.org/10.21203/rs.3.rs-1635253/v1

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The study aimed to assess the effect of progressive neutralization on the antimicrobial properties against bacteria and yeasts of wood vinegar obtained from the pyrolysis of Eucalyptus urograndis wood. Wood samples were carbonized at a heating rate of 0.7°C min¹ until a final temperature of 450°C. The raw pyrolysis liquids were left to settle and the aqueous fraction was separated. Then, the aqueous fraction (raw wood vinegar – WV) was purified to yield the WV. WV samples were collected and neutralized in steps with a solution of 2-mol L^− 1 NaOH from pH 2.5 until 7.5 (2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, and 7.5. Through the broth microdilution method, the antimicrobial effect of the neutralized samples at each pH was assessed on Pseudomonas aeruginosa (ATCC 27853), and Salmonella enteritidis (ATCC 13076), Staphylococcus aureus (ATCC 25923), Streptococcus agalactiae (CEPA CLINICA), and Candida albicans (ATCC 10231). The minimum inhibitory concentration (MIC) and minimum bactericidal (and fungicidal) concentrations were determined. Results were submitted to regression analysis, and statistical models were fitted. A progressive decrease in the antimicrobial activity was determined as pH increased. Nevertheless, even when neutral and slightly alkaline pH values are reached, the inhibitory activity remained at a certain level. Higher pH values of the WV were associated with lower antimicrobial activity. However, its activity remained even at neutral and slightly alkaline pH values. The results demonstrated the effect of pH on the antimicrobial effect of WV, a product with applications in veterinary medicine and zootechnics.

wood vinegar and pH effect

antibacterial activity

antifungal activity

Wood vinegar, a natural antimicrobial

pH affecting the antimicrobial activity of WV

When subjected to heating until temperatures of around 400–500°C, the polymeric chemical components (cellulose, hemicelluloses, and lignin) from wood and other lignocellulosic raw materials are pyrolyzed, generating three types of products: a solid one (charcoal), raw liquid products, and gases (Protásio et al. 2014; Pimenta et al. 2018). Wood vinegar (WV), pyroligneous acid, pyroligneous extract, and water-soluble liquid smoke are expressions referring to the same product, namely the aqueous fraction separated through the settling of the raw pyrolysis liquids (Sena et al. 2014). WV has a complex chemical composition, which can reach up to 200 compounds, among them phenols, ketones, furans, aldehydes, pyrans, alcohols, and organic acids (Schnitzer et al. 2015; Araújo et al. 2017; Dias Júnior et al. 2018; Pimenta et al. 2018; Suresh et al. 2019). WV is essentially an acidic product with a pH ranging usually from 2.5 to 3.6 (Aubin and Roy, 1990; Rahmat et al. 2014; Pimenta et al. 2018), depending on the chemical composition of the pyrolyzed raw material. This acidity is due to the presence of organic acids, the most common being acetic and formic acids. The most usual organic acid in WV is acetic acid, with concentrations of 3.0 to 7.4% (Sipilä et al. 1998; Theapparat et al. 2018).

Due to its particular chemical composition, preservative, and medicinal properties, WV has been employed for several purposes since ancient times (Tiilikkala et al. 2010). There are millenary reports of its use in the treatment of diseases in China and India (Campos 2007); salt substitute in 1862 to preserve meats (Kurlansky 2002); soil disinfection (Doran, 1932), among other applications. This broad variety of applications has led to increasing interest in this wood-pyrolysis bioproduct by researchers around the world, with several studies focused on its antimicrobial action and other properties (Araújo et al. 2017; Pan et al. 2017; Souza et al. 2018; Aguirre et al. 2020; Chen et al. 2015; Maliang et al. 2020). Although the antimicrobial property of WV has been widely demonstrated, in a recent article, Medeiros and Gasparotto (2021) stated that this characteristic is due to the presence of acetic acid in the product, not to the presence of any other compounds. These two authors stated that after neutralization, WV loses all its antibacterial activity, a result that is contrary to the findings of previous and current works (Thurette et al. 1998; Lebois et al. 2004; Soares et al. 2020). The role of phenols and other compounds on the antimicrobial activity of WV has been well established in recent works, such as Suresh et al. (2019) and Suresh et al. (2020), for instance. In these works, the authors assessed WV in both acidic and neutral forms and demonstrated that even after neutralization, the product had antimicrobial activity, albeit weaker, but not absent as claimed by Medeiros and Gasparotto (2019). More than that, since after neutralization, the acetic acid is no longer present in WV, Suresh et al. (2019) highlighted that the antimicrobial effect cannot be attributed to the presence of this compound alone, and instead must be attributed to the combined action with several other compounds.

However, the previous research works involving the antimicrobial activity of neutralized WV were carried out directly with the acidic and neutral versions, without verifying what happens to that activity during progressive neutralization. Thus, from the original acidic pH to neutrality, there is a gap in information about the antimicrobial activity of WV in the pH range from 2.5 to 7.0. In this context, the present work aimed to assess the effect of increasing pH (from the original pH of 2.5 until 7.0) on the antimicrobial activity of wood vinegar (pyroligneous extract) from eucalyptus urograndis – clone I144 on Pseudomonas aeruginosa, Salmonella enteritides, Staphylococcus aureus, Streptococcus agalactiae, and Candida albicans.

Production and purification of WV

Wood samples from Eucalyptus urograndis trees (usual denomination of hybrids of Eucalyptus urophylla and Eucalyptus grandis in Brazil), clone I144, were collected from a ten-year-old forest stand located in the Experimental Forest Area of the Graduate Program in Forest Sciences of Universidade Federal do Rio Grande do Norte (5° 51' 36'' S and 35° 20' 59'' W). Tree selection, harvesting, and wood sample collection followed the procedures described by Carneiro et al. (2013). The wood samples consisted of 3 cm thick disks, and before carbonization, they were cut into wedges and oven-dried at 103°C (± 2°C) under forced ventilation until constant weight.

The carbonization runs were performed in a laboratory muffle furnace (Labor SP-1200, SP LABOR, São Paulo, Brazil) with cry wood samples weighing about 500 g in each run. Twenty runs were conducted. In each run, the wood samples were placed inside an externally-heated steel container and submitted to a heating rate of 0.7°C min¹ from a temperature of 100°C to the final temperature of 450°C. The gases emitted from the carbonization bed were forced to pass through a water-cooled steel condenser (25°C) to yield the condensed raw liquid fraction. The raw liquids of the 20 carbonization runs were mixed to compose a single sample and stored under refrigeration (6°C) before further processing. The composite sample of the raw liquids was left to settle for two weeks until the oily fraction (wood tar) was deposited at the bottom of the decantation apparatus. Then the aqueous supernatant was separated and bi-distilled until 100–103°C to obtain the purified WV. The remaining fraction from the distillation containing oily contaminants was properly discarded. The purified WV was vacuum filtered through a 0.2 µm filter (MFMillipore, Merck, Darmstadt, Germany).

Neutralization of WV

20 mL aliquots from the purified WV resulting from the previous step were obtained with two replicates of each one. The first ones were kept at their original pH of 2.5. Then, the other aliquots, two by two, were increasingly neutralized to obtain pH values of 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, or 7.5. For neutralization, a 2 mol L¹ NaOH solution was employed and the final pH of the aliquots was monitored with a pHmeter (PG 2000, Gehaka, São Paulo, SP, Brazil). Thus, 11 types of samples of WV with different pH values were obtained.

In vitro evaluation of the antimicrobial activity

The assays were carried out by the broth microdilution method with 96-well microplates, according to the procedures of the CLSI (2012) routine. For the assessment tests, five microorganisms were employed: four bacterial strains, Pseudomonas aeruginosa (ATCC 27853), Salmonella enteritidis (ATCC 13076), Staphylococcus aureus (ATCC 25923), and Streptococcus agalactiae (CEPA CLINICA), and a strain of the yeast Candida albicans (ATCC 10231). All the microorganisms were kept in BHI (brain heart infusion, Kasvi, São José dos Pinhais-PR, Brazil) under refrigeration, and just before each assay, they were cultivated in a bacteriological oven (Fanem model 50, Guarulhos, SP, Brazil) at 37°C (± 1°C) for 24 hours with the same culture media. Then the microbial inocula were prepared. For that, after 24 h, aliquots from the colony of each microorganism were respectively obtained with an automatic pipette and transferred to new test tubes containing sterile culture media. The adjustment of the concentration of bacterial and fungal cells was achieved by employing a spectrophotometer (Biospectro SP-22, Labmais, Curitiba, PR, Brazil) at the wavelength of 530 nm with reading values from 0.08 to 0.1, corresponding to 0.5 in the MacFarland scale (density of 1.5 x 10⁸ cells mL^− 1).

Then in each well of the microplates, 100 µL of BHI culture media was added. After that, for each pH, serial dilutions were performed in the BHI medium, as follows: in the first well, 100 µL of WV was added until reaching a dilution of 50%. From that point onward, a constant volume of 100 µL was collected from the first well and added to the following one to reach 25% dilution. The procedure was repeated until reaching a concentration of 0.78123%. The 100 µL volume extracted from the last well was discarded. Therefore, seven concentrations of WV at each pH value were obtained: 50, 25, 12.5, 6.25, 3.125, 1.5625, and 0.78123% (always half of the previous concentration starting from a dilution of 50%). For each concentration and each pH, three replicates were conducted. This way, 231 wells for observation were obtained (7 concentrations x 11 pH x 3 replicates, or 77 experimental treatments x 3 replicates). After the dilutions, 0.5 µL of each microbial inoculum was added to each well. Next, the microplates were incubated in a bacteriological oven at 37°C (± 1°C) for 24 hours. After that time, the tubes were read visually and the first triplicates of each experimental treatment that were completely translucid were considered to establish the minimum inhibitory concentration (MIC). The visual reading was employed since the colorimetric method usually applied using resazurin was inefficient. This dye has a blue color and when in contact with active microbial cells it turns pink (Leong et al. 2017; Roca et al. 2019). However, when used in the present experiments, the dye reacted adversely, presenting various shades of yellow in the resulting resorufin, making it impossible to achieve an accurate reading. Most likely, some compounds present in the WV interfered with the dye, disturbing its conversion to resorufin. Hence, visual reading was the only alternative.

After the visual assessment, the microplates were read in an ELISA microplate reader (model 660, URIT Medical Electronic, Nanshan, Shenzhen, China) to determine the absorbances, a way to quantify the microbial growth according to the concentration and pH. For each experimental treatment, three replicates were read. The positive control for the readings was achieved with chlorhexidine (Vic Pharma by Shülke, Taquaritinga, SP, Brazil) at 0.2%. The minimum bactericidal concentration (MBC) and minimum fungicidal concentration (MFC) were determined from the MIC value measured for each combination of microorganism and pH levels, where the solution of the MIC well and those of higher concentration were inoculated in Petri dishes containing BHI culture medium. The Petri dishes were incubated in a bacteriological oven at 37°C (± 1°C) for 24 hours. After this period, the presence or absence of microbial colonies was assessed.

Statistical analysis

The experimental data from the readings of absorbance were subjected to regression analysis by employing the R software (version 4.1.3) at the moment of the inoculation (zero hours) and after 24 hours. For each type of microorganism, at each pH level, a regression model was fitted to describe and predict the behavior of absorbance (Y) as a function of WV concentration (X). Among several models assessed, the logistic model (Eq. 1) was the best to explain the behavior of the absorbances after 24 hours. The adjustment followed the procedures recommended by Gujarati and Porter (2009).

Equation 1:

Y = β₁ ln(X) + β₀ + ε

The data on absorbances were plotted on zero time only to demonstrate their pattern of behavior after inoculation. For the absorbance after 24 hours of incubation of the microbial cultures, a specific model was adjusted for each combination of pH level and concentration. After the adjustment of the equations, the curves of the factor pH for the same microorganism were compared to each other using the model identity test, according to the routine described by Regazzi (1993, 1996, 1999) and previously applied in a similar experiment (Araújo et al. 2017). This procedure aimed to check the identity of the regression models and the equality of the parameters in the polynomial orthogonal models, which is suitable to compare different behaviors under the same factor or external condition. In other words, by using the identity test, it was possible to know whether the action of the WV changed with the increasing neutralization; since the microorganism and WV were the same, the only variable was pH. In other words, the identity test, in this case, allowed knowing if the WV’s antimicrobial activity changed with varying pH.

Antimicrobial activity: determination of MIC, MBC, and MFC

Table 1 contains the results obtained for the MIC at each pH for the five microorganisms. The pattern observed was a decrease in the antimicrobial effect as the pH got closer to neutral. The results obtained for MBC and MFC, also displayed in Table 1 (numbers in italics), follow the same trend as the MIC. At lower pH levels, the concentrations required to inhibit the microbial growth were lower and as the pH approached neutral, the concentrations needed to achieve the same effect were higher. It is important to highlight that when the pH became neutral the antimicrobial effect did not disappear.

Table 1

Minimum inhibitory concentrations (MIC) and minimum bactericidal concentrations (MBC) values against the microorganisms according to increasing pH levels
Microorganism	WV concentration (%)
	pH
	2.5	3.0	3.5	4.0	4.5	5.0	5.5	6.0	6.5	7.0	7.5	PC
P. aeruginosa	3.12	3.12	3.12	6.25	6.25	12.5	12.5	25	25	50	25	0.003
	12.5	6.25	6.25	12.5	12.5	12.5	25	50	50	50	50	0.003
S. enteritidis	3.12	3.12	3.12	6.25	6.25	6.25	25	25	25	50	25	0.003
	3.12	6.25	6.25	25	12.5	12.5	25	50	50	> 50	50	0.003
S. aureus	6.25	6.25	6.25	6.25	12.5	12.5	25	50	50	> 50	50	0.003
	6.25	6.25	6.25	12.5	12.5	25	25	50	> 50	> 50	> 50	0.003
S. agalactiae	6.25	6.25	6.25	6.25	12.5	12.5	25	50	50	> 50	> 50	0.003
	6.25	6.25	12.5	12.5	25	25	25	50	50	> 50	> 50	0.003
C. albicans	3.12	3.12	3.12	6.25	6.25	12.5	25	25	25	25	25	0.003
	3.12	3.12	3.12	6.25	6.25	25	25	25	25	50	50	0.003
PC = positive control (chlorhexidine); In Table body, for each microorganism, the first line corresponds to the MIC and the numbers in italics are the MBC; For Candida albicans, the numbers in italics are the minimum fungicidal concentrations; **The sign > is employed to suggest the possibility that a concentration higher than 50% can result in inhibition

[Enter Table 1]

Antimicrobial activity: behavior of the absorbances

Figure 1 contains the graphs based on the models fitted from the regression analysis of the absorbances, each one corresponding to a single microorganism. At zero time, for all microorganisms, the absorbances values are a result of the mixture of the culture media, the inoculum, and the WV dissolved since no microbial growth had occurred. However, the graphs at zero time were included here to give the reader a baseline to be compared with the pattern of absorbances after 24 hours when the microbial growth was complete. In all components displayed in Fig. 1, graphs identified with the letter A are representations of the behavior of the absorbances of the culture media at the 0 hours (just after the inoculation) and after 24 hours of incubation (B). Although the MIC, MBC, and MFC values could not be determined from the graphs in Fig. 1, the knowledge of the absorbances was still a valuable tool to quantify precisely the interaction between pH, WV concentration, and microbial growth, as will be discussed in the next section. In the graphs marked with the B letter, at the same pH, higher absorbances are associated with lower efficacy of the WV.

[Enter Fig. 1]

In Tables 2, 3, 4, 5, and 6, the regression models that were adjusted to each microorganism are presented. As can be observed in the figures, as the concentration decreased and the pH increased, the absorbances rose. A higher value of absorbance indicates that the culture medium became more turbid due to the higher concentration of microbial cells that developed. For the models that were adjusted to explain the effect of pH in the growth of Pseudomonas aeruginosa (Table 2), a low value of the coefficient of correlation was determined to the pH of 2.5, which implicates a relatively high dispersion of the experimental data having the regression as reference. However, a trend of behavior is reflected by the regression line and for the other pH values, this pattern is confirmed since the values of R² for the same type of model are higher with a maximum value of 0.8348 for the pH of 5.5.

Table 2

Regression models that were adjusted to explain the behavior of absorbances from cultures of *Pseudomonas aeruginosa* after 24 hours of incubation at each pH level according to WV concentration
pH	Regression Model	R²
2.5	Y = – 0.266 ln X + 0.9922	0.5539
3.0	Y = – 0.287 ln X + 1.0194	0.6047
3.5	Y = – 0.284 ln X + 1.0201	0.6416
4.0	Y = – 0.344 ln X + 1.2765	0.8074
4.5	Y = – 0.358 ln X + 1.2777	0.7514
5.0	Y = – 0.384 ln X + 1.3839	0.7885
5.5	Y = – 0.379 ln X + 1.4791	0.8348
6.0	Y = – 0.346 ln X + 1.5663	0.8170
6.5	Y = – 0.323 ln X + 1.5487	0.7152
7.0	Y = – 0.292 ln X + 1.5721	0.7252
7.5	Y = – 0,337 ln X + 1,4743	0.8003
*Y = absorbance; X = WV concentration; R² = coefficient of determination

[Enter Table 2]

Regarding the models that were adjusted to predict the growth of Salmonella enteritides (Table 3) under the effect of variable concentrations of WV in different pH, for the values of pH of 2.5 and 3.0, the coefficients of correlation were low, 0.3095 and 0.2296, respectively. From pH 3.0 ahead, the values of R² were higher reaching 0.9623 at pH 7.0. Nevertheless, the closeness of the experimental data to the regression line can be affected by other factors (genetic expression, for example) even when the parameters of the work are perfectly defined because, in the case of microorganisms most likely with different pH values, their growth is not the same varying from one to another. As commented previously, despite several other types of models being tested, the best one to explain the growth of the microorganism assessed in this work was the logistic model (Eq. 2 cited in the section Material and Methods). For Salmonella enteritides, as a general trend, the values of R² from pH of 5.5 until 7.5 were the highest among the microorganisms assessed, which represents good power of predictability of growth pattern by the logistic model.

Table 3

Regression models that were adjusted to explain the behavior of absorbances from cultures of *Salmonella enteritides* after 24 hours of incubation at each pH level according to WV concentration
pH	Regression Model	R²
2.5	Y = – 0.063 ln X + 0.4273	0.3095
3.0	Y = 0.0369 ln X – 0.0014	0.2296
3.5	Y = – 0.099 ln X + 0.5175	0.6156
4.0	Y = – 0.136 ln X + 0.6351	0.7253
4.5	Y = – 0.164 ln X + 0.6957	0.7816
5.0	Y = – 0.224 ln X + 0.8705	0.8319
5.5	Y = – 0.243 ln X + 0.9836	0.9399
6.0	Y = – 0.213 ln X + 0.9241	0.8866
6.5	Y = – 0.253 ln X + 1.1261	0.9470
7.0	Y = – 0.223 ln X + 1.1013	0.9591
7.5	Y = – 0.217 ln X + 0.9850	0.9623
*Y = absorbance; X = WV concentration; R² = coefficient of determination

[Enter Table 3]

For the growth of Staphylococcus aureus, the value of R² at the pH 2.5 was the lowest among all the microorganisms that were assessed with a value of 0.1089 (Table 4). However, from this point ahead, the values of the coefficient of correlation were higher than 0.700, except at the pH of 3.0 where the value was 0.6699. Once again, some variation in the growth of the microorganism at these values of pH (and included in the error of the adjustment) resulted in lower values of R².

Table 4

Regression models that were adjusted to explain the behavior of absorbances from cultures of *Staphylococcus aureus* after 24 hours of incubation at each pH level according to WV concentration
pH	Regression Model	R²
2.5	Y = – 0.014 ln X + 0.1428	0.1089
3.0	Y = – 0.094 ln X + 0.4339	0.6699
3.5	Y = – 0.109 ln X + 0.4865	0.7822
4.0	Y = – 0.140 ln X + 0.5964	0.8711
4.5	Y = – 0.121 ln X + 0.5055	0.7285
5.0	Y = – 0.146 ln X + 0.5980	0.8722
5.5	Y = – 0.144 ln X + 0.6379	0.9417
6.0	Y = – 0.154 ln X + 0.7425	0.8401
6.5	Y = – 0.158 ln X + 0.7783	0.9051
7.0	Y = – 0.110 ln X + 0.6978	0.8046
7.5	Y = – 0.114 ln X + 0.5974	0.9420
*Y = absorbance; X = WV concentration; R² = coefficient of determination

[Enter Table 4]

As displayed in Table 5, the growth of Streptococcus agalactiae was best explained by the logistic model in the pH range of 3.5 to 5.5, which is reflected by the higher values of the coefficient of correlation in this range. As commented before in this section, the responses of a given microorganism are not necessarily the same as another one before an antimicrobial, and this variability of ranges and pH values where the WV is more or less effective possibly can be explained by the nature of the microorganism.

Table 5

Regression models that were adjusted to explain the behavior of absorbances from cultures of *Streptococcus agalactiae* after 24 hours of incubation at each pH level according to WV concentration
pH	Regression Model	R²
2.5	Y = – 0.100 ln X + 0.5120	0.5393
3.0	Y = – 0.098 ln X + 0.4993	0.6694
3.5	Y = – 0.114 ln X + 0.5509	0.7903
4.0	Y = – 0.122 ln X + 0.5932	0.8116
4.5	Y = – 0.130 ln X + 0,5996	0.8130
5.0	Y = – 0.155 ln X + 0.7264	0.8039
5.5	Y = – 0.150 ln X + 0.7993	0.7223
6.0	Y = – 0.089 ln X + 0.7560	0.3874
6.5	Y = – 0,100 ln X + 0.7058	0.6428
7.0	Y = – 0.064 ln X + 0.7066	0.5442
7.5	Y = – 0,080 ln X + 0.6972	0.5923
*Y = absorbance; X = WV concentration; R² = coefficient of determination

[Enter Table 5]

Despite presenting good coefficients of correlation in general, the logistic model was not effective to explain the growth of the yeast Candida albicans at the pH values of 2.5, 4.0, and 6.0. At these points, the R² had low values which shows a variable response of the microorganism to the action of WV as pH was varying.

Table 6

Regression models that were adjusted to explain the behavior of absorbances from cultures of *Candida albicans* after 24 hours of incubation at each pH level according to WV concentration
pH	Regression Model	R²
2.5	Y = – 0.014 ln X + 0.0513	0.2943
3.0	Y = – 0.066 ln X + 0.3515	0.5429
3.5	Y = – 0.084 ln X + 0.3891	0.7021
4.0	Y = – 0.056 ln X + 0.2616	0.2572
4.5	Y = – 0.106 ln X + 0.4449	0,5773
5.0	Y = – 0.142 ln X + 0.6274	0.7734
5.5	Y = – 0.149 ln X + 0.6668	0.7814
6.0	Y = – 0.083 ln X + 0.5174	0,3000
6.5	Y = – 0.123 ln X + 0.6511	0.5792
7.0	Y = – 0.146 ln X + 0.7763	0.7761
7.5	Y = – 0.158 ln X + 0.7951	0.7770
*Y = absorbance; X = WV concentration; R² = coefficient of determination

[Enter Table 6]

Table 7 reports the results of the identity test of the regression models. In this test, recommended by Regazzi (1993, 1996, 1999), a group of models related to similar events can be compared. In the present experiment, the constant parameters in the adjusted models are the five microorganism species and the WV.

Table 7

Results of the identity test applied to the regression models adjusted for the absorbances of the microbial cultures after 24 hours of incubation (A): *Pseudomonas aeruginosa, Salmonella enteritides, Staphylococcus aureus*, and *Streptococcus agalactiae*; (B): *Candida albicans*
A											B
Comparisons	3.0	3.5	4.0	4.5	5.0	5.5	6.0	6.5	7.0	7.5*	Comparisons	3.0	3.5	4.0	4.5	5.0	5.5	6.0	6.5	7.0	7.5*
2.5	≠	≠	≠	≠	≠	≠	≠	≠	≠	≠	2.5	≠	≠	≠	≠	≠	≠	≠	≠	≠	≠
3.0		≠	≠	≠	≠	≠	≠	≠	≠	≠	3.0		≠	=	≠	≠	≠	≠	≠	≠	≠
3.5			≠	≠	≠	≠	≠	≠	≠	≠	3.5			=	≠	≠	≠	≠	≠	≠	≠
4.0				≠	≠	≠	≠	≠	≠	≠	4.0				≠	≠	≠	≠	≠	≠	≠
4.5					≠	≠	≠	≠	≠	≠	4.5					≠	≠	≠	≠	≠	≠
5.0						≠	≠	≠	≠	≠	5.0						≠	≠	≠	≠	≠
5.5							≠	≠	≠	≠	5.5							≠	≠	≠	≠
6.0								≠	≠	≠	6.0								≠	≠	≠
6.5									≠	≠	6.5									≠	≠
7.0										≠	7.0										≠

[Enter Table 7]

As a general trend, the identity tests demonstrated that as pH increased, the quality of the WV as an antimicrobial changed, most likely due to the differential neutralization of its phenolic components. This difference in the quality of WV in each pH was positively determined by the statistical dissimilarity among the models compared through the identity test. In other words, for each microorganism, the variation of pH could result in a different effect of WV and concomitantly a specific type of interaction, because, as commented above, the quality of the WV as an antimicrobial is different at each value of pH.

Antimicrobial activity: determination of MIC, MBC, and MFC

The data displayed in Table 1 corroborate the results of the positive antimicrobial effects achieved by several researchers listed in the review published by Tiilikkala et al. (2010), and more recently by Souza et al. (2018). Other studies have demonstrated the antibacterial and antifungal properties of WV from varied lignocellulosic sources (Ibrahim et al. 2013; Araújo et al. 2013; Abas et al. 2018, Souza et al. 2018). Besides bacteria, some of the cited works demonstrate the biological effect on fungi, viruses, and protozoans. The experimental results presented here corroborate the results reported by Velmurugan et al. (2009) and Suresh et al. (2019), who assessed the antimicrobial effect of different types of WV both in their original acid form and after neutralization. Both those studies described the antimicrobial effect of WV before and after neutralization and found a decrease in the activity but not the disappearance of the biological effect when pH became neutral.

As can be observed in Table 1, our results demonstrated that as the neutralization increased and the pH approached 7.0, the concentration of WV required for microbial inhibition increased. Even at neutral pH, the WV still had antimicrobial activity, although needing higher concentrations for this purpose. For Pseudomonas aeruginosa, the MIC of WV at pH 2.5 was 3.12% and increased to 50% at neutral pH. When the pH reached 7.5, the MIC returned to the same value of 25% observed for pH 6.5. The same relationship between MIC and pH was determined for Salmonella enteritides. For Candida albicans, the MIC at pH 2.5 was 3.12% and increased to 25% at pH 5.5, after which it remained constant until the pH reached 7.5. The pattern of inhibition as a function of the concentration and pH of the WV is different for each microorganism. As commented by Suresh et al. (2019), the activity of neutralized WV indicates that the antibacterial property of the product is due to its complex chemical composition, and not the presence of acetic acid. According to the same authors, the antibacterial efficiency of the neutralized WV could also be attributed to the antimicrobial effect of ketones, such as furfural and vanillin. Citing other authors, Suresh et al. (2019) highlighted that the inhibition of the WV against microorganisms, especially fungi, is caused by the antioxidative property of the phenolic compounds. In this respect, previous studies have reported that the inhibition of lipid oxidation caused by phenolics is enhanced at acidic pH.

However, for Staphylococcus aureus and Streptococcus agalactiae, a different pattern of inhibition was observed. For the first microorganism, when the pH was equal to 7.0 (neutral), no inhibition in the growth of the culture was observed. When the pH became slightly alkaline (7.5), the MIC was 50%, which is the same concentration required to inhibit completely the culture growth at pH 6.5. In the case of Streptococcus agalactiae, for both pH levels, 7.0 and 7.5, a WV concentration of 50% was not enough to inhibit the microbial growth. Is important to highlight that both microorganisms required higher WV concentration to inhibit growth starting with the original pH, which was 2.5. For the other three microorganisms, the initial MIC value was 3.12%. There are several compounds (phenolics and ketones) in WV’s chemical composition that have antimicrobial activity, so most likely they interact differently with one or another microorganism due to differences in cell wall structure and composition. These differences among microorganisms combined with the degree of response to the action of one or another compound in the chemical composition of WV probably explain why the product does not inhibit the microbial growth with the same inhibition results at the same concentration. In the present study, Staphylococcus aureus and Streptococcus agalactiae were the most resistant species to the inhibitory effect of WV, while Candida albicans was the most sensitive one (Table 1).

In the research of Medeiros and Gasparotto (2021), they found zero effect of neutralized WV on the growth of Escherichia coli and Staphylococcus aureus and attributed the loss of antimicrobial activity to the neutralization of the acetic acid. The authors went further and stated that this organic acid is the only one responsible for the antimicrobial effect of the WV. However, their results were not corroborated by the results of other authors, since, for example, when assessing the antimicrobial activity of neutralized WV on E. coli and S. aureus, Suresh et al. (2019) observed a decrease in the effect of the product after neutralization but not complete disappearance. This trend reported by Suresh et al. (2019) was also identified by us. Still, based on their experimental data, those authors stated categorically that the antimicrobial effect of the WV even after neutralization indicated that the antibacterial property of the product was due to its complex chemical composition rather than the large presence of acetic acid. In their experiments assessing the antimicrobial action on Escherichia coli, Enterobacter aerogenes, Pseudomonas aeruginosa, Listeria monocytogenes, and Enterococcus faecalis, the authors found a loss of activity ranging from 10 to 24%, with the degree of loss varying according to the species.

In Table 1, since the antimicrobial activity occurred even at neutral pH for three of the five microorganisms assessed, the sign “ > ” is placed after some MIC values of Staphylococcus aureus and Streptococcus agalactiae to suggest that perhaps with concentrations higher than 50%, some inhibition might occur. However, another study should be carried out to confirm that hypothesis, which is not unlikely since other authors have found inhibition at concentrations of such order. Also in Table 1, the sign “ > ” is placed after some MBC and MFC values (numbers in italics) to suggest that maybe with WV concentrations higher than 50%, some inhibition might occur. But in Table 1, the sign is placed after the MIC found for Salmonella enteritides (pH value of 7.0), Staphylococcus aureus (6.5, 7.0, and 7.5), and Streptococcus agalactiae (7.0 and 7.5).

Antimicrobial activity: behavior of the absorbances

The pattern of increasing values of absorbance in the graphs in Fig. 1 reflects the intensity of the microbial growth, which means more cells could develop in the medium, making it more turbid and preventing light from passing through (Cunha and Vieira 2014). On the right side of the figures, the different pH levels are identified by colors that are the same as the plotted curves. In Tables 2, 3, 4, 5, and 6, the regression models fitted to explain the effect of the pH of WV on each microorganism according to the concentration are displayed. The models can predict the behavior of the absorbance (Y) as a function of WV concentration (X).

From the curves displayed in Fig. 1, it is possible to verify that at lower pH levels, the concentrations of WV required to achieve the antimicrobial effect were lower, corroborating the experimental data on MIC described previously. As the pH of WV increased, the requirement for higher concentrations of the product to inhibit microbial growth also increased, reflected by higher absorbance values when WV low concentrations were not effective. Another trend that corroborates the behavior of the data displayed in Table 1 is that the absorbances at pH of 7.0 were always higher than those at pH 7.5. That fact indicates that the statement that the antimicrobial properties are not solely related to the effect of acetic acid is correct (Suresh et al. 2019; Pimenta 2021). The acetic acid present in a given aqueous solution is completely converted to sodium acetate at pH 7.0 and remains that way at alkaline pH levels. Consequently, there is no possibility of its being responsible for the antimicrobial effect of WV found in this work, at both pHs of 7.0 and 7.5. The same pattern was found by other authors cited previously.

Therefore, the pH of the WV undoubtedly influences its antimicrobial activity but does not cause it alone. The chemical composition of the WV reported in the literature shows a product composed of around 200 compounds (Schnitzer et al. 2015; Araújo et al. 2017; Pimenta et al. 2018). Among these components, alcohols, furans, ketones, organic acids, phenols, and pyrans are the most representative and abundant (Theapparat et al. 2018; Medeiros et al. 2020). The phenolic compounds are the main group with which antimicrobial properties are closely associated, a fact is long proven by scientific reports (Abas et al. 2018). Thus, the results presented here reinforce the statement that the antimicrobial activities of WV on both bacteria and fungi cannot be associated with the action of a single compound, but rather by the synergistic action of different compounds, as mentioned, for instance, by Yang et al. (2016). Also, Suresh et al. (2019) highlighted that fact and also mentioned that each component has a different mode of action. Because of this, according to the authors, WV is even more interesting for use as an antimicrobial agent, since it is unlikely the microorganisms will develop any mechanism of resistance against all the components of the product at the same time.

In the present work, what varies regarding a particular microorganism are the pH of the WV and the concentration of this product. Thus, for each microorganism, regression models were fitted, one model for each pH level, where the independent variable is the absorbance and the dependent one is the WV concentration. This way, for each microorganism subjected to the action of WV, the models fitted for each pH could be compared to detect differences in the antimicrobial action depending on pH. The importance of this comparison is not only to determine the effectiveness of the WV itself at each pH level but also to provide information about the interaction between the microorganism and the WV at a given pH when the concentration is varied.

Then, as displayed in Table 7 (A), the quality of WV changed as pH increased, since the comparison among the models was different from one pH to another. In other words, for each microorganism, the variation of pH could result in a different effect of WV and concomitantly a specific type of interaction. Something in the WV was decreasing and making it weaker, so as the pH of the WV increased, higher concentrations of the product were required to continue inhibiting the growth of the five microorganisms in the culture medium. This pattern corroborates the results of Setiawati et al. (2019), cited previously, who observed that changes in the composition occur with different pH levels. A slightly different pattern was observed in Table 8 (B), where the identity test, when applied to the models fitted for Candida albicans, determined that the effect of WV at pH of 3.0 and 3.5 was equal to that at pH 4.0, probably meaning the presence of an interaction of the inhibitory product with this microorganism. Nevertheless, for all other pH levels, the regression models were different from each other.

As commented in previous work (Pimenta 2021), the partial loss of the antimicrobial activity can probably be attributed to the reaction of the sodium hydroxide with the phenolic compounds, turning them into salts, a type of chemical change that deactivates their hydroxyl groups, which are responsible for the antiseptic properties presented by most of them in the pristine form (Aldred et al. 2009). Phenols are acids with pKa around 10.0, so they are weak acids. There are at least 20 types of phenols in WV’s chemical composition (Araújo et al. 2017; Pimenta et al., 2018), and since each compound is different, as the pH increases, their hydroxyl groups are not equally neutralized because of the substituent groups that are present in the aromatic ring. This way as the pH increases, most likely different chemical species are generated in aqueous media, ones more available than others to exert an antimicrobial effect.

The results of Setiawati et al. (2019) and the results of the present work corroborate the points raised in the previous paragraph. The cited authors, when evaluating neutralized WV, found some change in the percentage of phenolic compounds in the chemical composition of the product obtained from durian wood. According to them, in the acidic version, the main compound was guaiacol, while in the neutralized product, pyrocatechol was the prevailing substance. The explanation presented by the authors was that in the neutralized form, alkyl groups in the para position (carbon 4) of phenolics accept electrons and that behavior decreases the ionization of the compounds due to the addition of NaOH in WV. The changes in the proportion of phenolic compounds in the neutral version of WV explain why the product becomes less effective when compared to the acidic versions in terms of the power to inhibit the growth of microorganisms. These results were expected to a certain extent because according to Brown et al. (1997), acid and basic solutions can differ greatly from each other in their chemical properties so products obtained from the reaction after neutralization do not have the same characteristics as the original solution. However, since WV is a solution containing many kinds of compounds, even with its acid fraction completely neutralized, there are still other compounds preserving the bioactive characteristic of the product.

Further research should be performed to understand the specific chemical species of WV that prevail as inhibitors at each pH level when increasing neutralization as carried out. This could enable predicting the behavior of the product when employed in varied applications as a natural antibiotic or parasiticide. For example, if the product is employed as an additive for animal feed, the WV after being swallowed will find strong acidic conditions in the digestive tract of poultry and swine. In this condition, the inhibitory power of WV on microorganisms is maximized. However, if the intention is to use the product to compose drug formulations for external uses such as ointments and creams, or to deter parasites like ticks, the importance of the pH in the final use may be important to maximize the action of the product.

The eucalyptus WV maintained its antimicrobial effect even at neutral and slightly alkaline pH, which runs counter to the claims in other studies that the inhibitory action of the product versus microorganisms is related just to the presence of acetic acid in its chemical composition. Our experimental data indicate the potential of the possible antibacterial and antifungal use of WV. However, it is important to highlight that all the experiments reported here were conducted in vitro so other research works should investigate the behavior of WV under in vivo conditions. Another important aspect is the degree of purification of the AP for such applications. The model identity test was a valuable tool to detect different responses of microorganisms at each pH level, validating the attribution of the bioactive action of WV to the set of components as a whole and not only to a single compound.

ETHICAL STATEMENT

The present research work complies with ethical standards.

AUTHORS’ CONTRIBUTIONS

GSP Gama conducted the main experiments and elaborated the manuscript draft in Portuguese; AS Pimenta raised funds, supervised all the research steps, interpreted the statistical analyses, revised and translated the manuscript to English; FMC Feijó provided the reagents and all the infrastructure to conduct the microbiological assays, revised the manuscript draft; CS dos Santos assisted to the microbiological assays; RVO Castro performed the statistical analyses; TKB Azevedo assisted to the fundraising and revised the manuscript draft; LCD de Medeiros established the routine to adjust the pH of wood vinegar samples.

FUNDING

This study was financed in part by the Office to Coordinate Improvement of Higher Education Personnel (CAPES), Finance Code 001, and the National Research Council (CNPq).

CONFLICT OF INTEREST:

All authors declare that have no conflict of interest.

ETHICAL APPROVAL

This article does not contain any studies with human participants or animals performed by any of the authors.

DATA AVAILABILITY STATEMENT:

All data generated or analyzed during this study are included in this published article.

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Effect of pH on the Antimicrobial Activity of Wood Vinegar (Pyroligneous Extract) From Eucalyptus

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Abstract

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Highlights

Introduction

Materials And Methods

Production and purification of WV

Neutralization of WV

In vitro evaluation of the antimicrobial activity

Statistical analysis

Results

Antimicrobial activity: determination of MIC, MBC, and MFC

Antimicrobial activity: behavior of the absorbances

Discussion

Antimicrobial activity: determination of MIC, MBC, and MFC

Antimicrobial activity: behavior of the absorbances

Conclusions

Declarations

References

Additional Declarations

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