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1

Modelling Nanoparticles Parameters for Antimicrobial

Activity

L.C. Razanamahandry1,2,3*, A.K.H. Bashir1,2, K. Kaviyarasu1,2, Lukhanyo Mekuto4, S.K.O Ntwampe3,5, M. Maaza1,2

1UNESCO-UNISA Africa Chair in Nanoscience’s/Nanotechnology Laboratories (U2AC2N), College of Graduate Studies, University of South Africa (UNISA), Muckleneuk Ridge, P O Box 392, Pretoria, South Africa 2Nanosciences African network (NANOAFNET), Materials Research Department (MRD), iThemba LABS-National Research Foundation (NRF), 1 Old Faure Road, 7129, P O Box722, Somerset West, Western Cape Province, South Africa

3Bioresource Engineering Research Group (BioERG), Faculty of Applied Science, Cape Peninsula University of Technology, P.O. Box 652, Cape Town 8000, South Africa

4Department of Chemical Engineering, University of Johannesburg, Johannesburg, 2028, South Africa

5 Department of Chemical Engineering, Faculty of Engineering and the Built Environment, Cape Peninsula University of Technology, PO Box 1906, Bellville, 7535, South Africa

*: Corresponding authors: clrazanamahandry@tlabs.ac.za / tantely1989@gmail.com Abstract

Current studies reveal the antimicrobial activity effectiveness of various nanoparticles (NPs) towards numerous microorganism. The present study reports on statistical models which define suitable parameters to improve the efficacy of NPs antimicrobial activity. Data on NPs antimicrobial activity were collected, focusing on parameters such as the NPs type and size (nm), bacterial strains and its initial density (O.D.600nm), inhibition zone (IZ) size (mm),

contact time (h), well and disc diffusion size (mm) and Minimum Inhibitory Concentration (MIC) (µg/mL). Correlation between these parameters were modelled by using multiple correspondence analysis (MCA) and principal component analysis (PCA) for qualitative and quantitative analyses, respectively. Results showed a significant positive correlation between the IZ size and the following parameters: MIC, well size and disc diffusion size with a Pearson ratio 95.98 %, 93.99 % and 94.82 % (α = 0.5), respectively. Antibacterial affinity between Ag, SiO2 and ZnO NPs with the IZ of strain bacteria gram-positive was obtained. In

addition, bacteria gram negative and Fungi have an affinity with the La-ZnO and Ag NPs. Antimicrobial tests with NPs could be improved by varying not only the NPs concentration but also the well or disc diffusion size. The NPs type should be chosen as a function of the bacteria gram stain type for higher efficacy.

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2 Keywords: Antimicrobial activity, Multiple correspondence analysis, Nanoparticles, Principal component analysis, Statistical modelling.

1. Introduction

Due to increasing human population growth annually, anthropogenic activity such as industrialisation has increased. These activities impact on the environment and its natural resources (Mao et al., 2019). Natural resources are the primary receptors of anthropic activity impact. Water and soil are the most affected by these activity and their quality deteriorates and subsequently results in adverse impact on human health (Razanamahandry et al., 2017). For instance, pathogenic microorganisms easily grow in modified ecosystems with degraded natural resources (Mekuto et al., 2019). Thus the remediation of these resources using various technologies is advisable. Bioremediation and phytoremediation are among the most applied technologies (Razanamahandry et al., 2016), as they are classified as environmentally being. Recently, nanosciences and nanotechnology has been developed to be used to restore both environmental and human health issues (Wang et al., 2012; Webster, 2012) . As an example, photocatalytic methods using nanomaterials in numerous studies demonstrated the removal of several pollutants affecting water and soil resources. Rhodamine B, methyl orange and methylene blue were removed by using HAp-TiO2 nanocomposite (Kaviyarasu et al., 2016b),

ZnO nanoparticles (Siripireddy and Mandal, 2017), and, SnO2 and CuO nanoparticles

(Diallo et al., 2016)Ref, respectively. In addition, Nwanya et al. (2019), have reported the effectiveness of CuO nanoparticles to remediate industrial textile effluents. In terms of human health, growth of pathogenic microorganisms were inhibited by different types of nanomaterials. Bacillus cereus, Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus are among some pathogenic microorganisms responsible for various human diseases such as diarrhoea, meningitis, septicaemia and many other infections (Makvana and Krilov, 2017; Tong et al., 2015). Several nanoparticles were reported as effective against these microorganisms growth. Ahmad et al. (2018) have conducted research on the inhibition of the growth of Escherichia coli using ZnO nanoparticles; albeit, a small inhibition zone (13 mm) was obtained. Although, the same microorganism was tested by Karthik et al. (2017) using the same nanoparticle type and results showed that the obtained inhibition zone was large (30 mm). Besides, several authors have shown the effectiveness of the following nanoparticles Co(Vox), Cu, Fe3O4, Ag and CdO to inhibit the growth of Escherichia coli, Bacillus subtilis

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3 (Morones et al., 2005) and Pseudomonas fluorescens (Jiang et al., 2009), respectively. These research studies were conducted under different conditions. Webster (2012) has reported that bacteria species gram straining partite and Minimum Inhibited Concentrations (MIC) are among the main parameters which should be considered for nanoparticles antimicrobial applications. Besides, nanoparticles antimicrobial effectiveness can be evaluated by a well or disc diffusion assay (Valgas et al., 2007). In addition, the nanoparticles average size, the initial optical density of the pathogen and the contact time could influence the effectiveness of the nanoparticles for antimicrobial activity. Nevertheless, there is a lack of studies conducted to modelling these complex parameters to assess key variable parameters variation the role they play for the highest XXX inhibition effectiveness of the nanoparticle against the pathogen microorganism proliferation. These variables are complex and must be explored as combinatorial factors using mathematical and statistical modelling. Available statistical tools could therefore be used to model the identified parameters which must be considered to obtain the highest antimicrobial effectiveness of different nanoparticles. Qualitative and quantitative input variables for various research studies were resolved by using statistical tools, i.e. Multiple Correspondence Analysis (MCA) and Principal Component Analysis. Dungey et al. (2018) highlighted the application of MCA to modelling qualitative variables into relational XXX of significance. Moreover, PCA can be applied to reduce quantitative variables in order to optimise and to XXX consider variable highly adversXXX to the human health (Yacoub et al., 2013). Various applications were conducted by using MCA and PCA analyses, in hydrological studies (Stan et al. 2015) and in human health survey (Ayele et al., 2014).The lack of statistical modelling application was perceived as a limitation for nanomaterials application in antimicrobial efficacy studies.

Therefore, the aim of this research was to find nanomaterials suitable for pathogenic microorganism deactivation and to identify the suitable parameters which should be considered for high antimicrobial efficacy in future applications.

2. Materials and methods 2.1. Input Parameters

Recent data (n = 123) on the antimicrobial efficacy of nanoparticles was collected from literature review findings (Table 1 and 2). Parameters, which are related to the formation of different nanoparticle types, surface active (m2 g-1), average size (nm), maximum inhibition

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4 microorganism optical density, well and disk diffusion size (mm), contact time (h), nanoparticle concentration (µg. mL-1) and the microorganism species, were considered for the

multicriteria analyses. Statistical methods, Multiple Correspondence Analyses (MCA) and Principal Component Analysis (PCA) were used for 123 observations to follow the association and the variation of qualitative and quantitative data, respectively. These parameters were chosen based on the considered criteria for testing the effectiveness of nanomaterials to inhibit microbial growth.

2.2. MCA Analysis

Multiple correspondence analysis is a statistical approach to study the association and affinity, between categorical data (Dungey et al., 2018). This method uses a binary code matrix (0 or 1) to evaluate the standard correspondence between the rows and columns of the categorical data (Stan et al. 2015). The association between the qualitative variables, which are formed by the nanoparticles and the gram stain type of the microorganism species, was studied by choosing the MCA. A map was drawn to visualise the distances between these categories of the qualitative variables in to two dimensions with F1 and F2 axis for X and Y axis, respectively. The association between the qualitative variables was appreciated by their affinity to the axis F1 and F2 and by their distance from the axis origin. MCA considers the th

row profile ( ) and th column profile ( ) squared distance from the axis origin,

which is the th row profile (Eq. 1) and th column profile (Eq. 2) Euclidean distance and

coordinate from the origin (Eq. 1) (Costa et al., 2013):

Where, M is the dimension correspondence plot; when the distance of the th row profile and th column profile is great, the profile of the category i and j would be deviate the column and

row categories average profile. Variables that have a same direction from the origin are highly associated (Dungey et al., 2018). is the ( th element of F and the is the (

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5 element of G. F and G is the derivation of the coordinates of row and column profile respectively for singular ordered correspondence analysis using BMD (Beh, 2004). Table 1 enlists qualitative input data for MCA analysis.

Table 1 : Input data for MCA analysis

NPs Bacteria Gram Fungi Microorganisms (References)

Negative Positive

ZnO 1a,c,m 1b,n 1d, a: Escherichia coli (Ahmad et al., 2018; Jiang et al.,

2009; Karthik et al., 2017; Kaviya et al., 2011; Kaviyarasu et al., 2017, 2016a, 2016b; Kennedy et al., 2017; Kim et al., 2007; Magdalane Maria et al., 2018; Meidanchi and Jafari, 2019; Mobeen Amanulla et al., 2018; Morones et al., 2005; Nair et al., 2009; Nwanya et al., 2019; Padmavathy and Vijayaraghavan, 2008; Pal et al., 2007; Simo et al., 2018; Simon-Deckers et al., 2009; Sondi and Salopek-Sondi, 2004; Webster, 2012; Yoon et al., 2007; Zhang et al., 2018)

b: Staphylococcus aureus (Ahmad et al., 2018; Jiang

et al., 2009; Jones et al., 2008; Karthik et al., 2017; Kaviya et al., 2011; Kaviyarasu et al., 2017; Kennedy et al., 2017; Kim et al., 2007; Magdalane Maria et al., 2018; McCarthy et al., 1992; Mobeen Amanulla et al., 2018; Nair et al., 2009; Nwanya et al., 2019; Reddy et al., 2007; Salomoni et al., 2015; Simo et al., 2018; Tran et al., 2010; Webster, 2012; Zhang et al., 2018)

c: Pseudomonas aeruginosa (Bakina et al., 2019;

Kaviya et al., 2011; Kennedy et al., 2017; Magdalane et al., 2017; McCarthy et al., 1992; Mobeen Amanulla et al., 2018; Morones et al., 2005; Nwanya et al., 2019; Webster, 2012)

d: Candida albicans (McCarthy et al., 1992; Webster,

2012; Zhang et al., 2018)

e: MRSA ATCC 43300 (Bakina et al., 2019; Simo et al., 2018)

f: Bacillus cereus (Kennedy et al., 2017)

Cu28-Fe72 1a,c, 1b,e 0

Ta2O5 1a, 0 0

Ag 1a,c,j,k,l 1b,f,g,h,i 1d,

La-ZnO 1 j,o 0 0

Co(Vox) 1a,k 1b,e 0

ZnO/TiO2 1a, q 1b, p 0 CeO2 1a,m 1b,n 0 β-CoMoO4 - Co3O4 1a,c 1b 0 CeO2/CdO 1c 0 0 HAp-TiO2 (8 dip) 1a 1r 0 Cu 1a 1h 0 Fe3O4 0 1b,s 0 Al2O3 1a,t 1h 0 TiO2 1a 0 0 SiO2 1a,t 1b 0

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6

g: Micrococcus luteus (Kennedy et al., 2017)

h: Bacillus subtilis (Jiang et al., 2009; Kennedy et

al., 2017; Webster, 2012; Yoon et al., 2007)

i: Enterococcus sp (Kennedy et al., 2017)

j: Salmonella typhi (Kennedy et al., 2017;

Manikandan et al., 2017; Morones et al., 2005; Webster, 2012)

k: Klebsiella pneumonia (Kennedy et al., 2017; Simo

et al., 2018)

l: Vibrio cholera (Morones et al., 2005; Webster,

2012)

m: Escherichia hermannii (Kaviyarasu et al., 2017;

Magdalane Maria et al., 2018)

n: Streptococcus pneumoniae (Kaviyarasu et al.,

2017) (Magdalane Maria et al., 2018)

o: Proteus mirabilis (Manikandan et al., 2017)

p: Streptococcus mut (Kaviyarasu et al., 2016a)

q: Salmonella sps (Kaviyarasu et al., 2016a)

r: Bacillus spp (Kaviyarasu et al., 2016b)

s: Staphylococcus epidermidis (Taylor and Webster,

2009; Webster, 2012)

t: Pseudomonas fluorescens (Jiang et al., 2009;

Webster, 2012)y: Bacillus licheniformis (Nwanya et al., 2019)

1: data available 0: data not available

2.3. PCA Analysis

Principal component analysis is a statistical approach to study the correlation and the variance between continuous variables (Ayele et al. 2014). PCA was used to study the variance between the quantitative variables which are: the nanoparticle size, the inhibition zone size, the ratio of the eliminated microorganisms, the well size, the disc size, the contact time, the microorganism initial density and the initial concentration of the nanoparticles. The PCA method uses an orthogonalisation technique than distance measure which is used in the MCA technique (Dungey et al. 2018). Two hypotheses, which are the null (H0) and alternative hypothesis (Ha), were considered in the PCA analysis. H0 defines the absence of significant

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7 correlation between the variables. Ha states at least there is some correlations between the strived variables. Bartlett's sphericity test was run to evaluate the validity of each hypothese. Pearson (n) matrix was chosen to show the correlation between these quantitative variables. Pearson ratio for each (i, j)th cell entry as described by Goodman (1985) was used to

measure the departure from row i and column j independence, according to Eq.3:

The (i, j)th cell value is the Pearson ration . is defined by Eq. 4:

Where, : is the × diagonal of with (i, i)th cell entry named ; and is the ×

diagonal of with (j, j)th cell entry named

If × = 1, columns and rows are independent. A Pearson ratio value with is significantly different from 1 defines the dependence between rows and columns. Table 2 enlists quantitative input data for PCA analysis.

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8 Table 2 : Input data for PCA analysis

NPs Average Size (nm) Inhibition Zone(mm) Inhibition Ratio (%) Well size (mm) Disc size (mm) Contact time (h) Optical Density initial (CFU) MIC (µg mL-1) Microorganisms (References) ZnO 20 20 90 90 9 9 9 9 13 60 40 40 12 - - - 25.13 ± 0.05a 30.17 ± 0.03b 13 a 16 b 16m 15 b 5n 4.5 a - - - - - - - - - - - - - - - - 95 b 50 b 99 b 99 a 90 a 100c 100 b 100d 7 7 - - - - - - - - - - - - - - 10 10 - - - - - - - - - - - - - - 24 24 13 13 24 - - - - - - - - - - - - - 5×106 5×106 - - - - - - - - - - - - 105 105 -- 400 200 200 200 80 400 400 400 400 1917 9 39

a: Escherichia coli (Ahmad et al., 2018; Jiang et al., 2009; Karthik et al., 2017; Kaviya et al., 2011; Kaviyarasu et al., 2017, 2016a, 2016b; Kennedy et al., 2017; Kim et al., 2007; Magdalane Maria et al., 2018; Meidanchi and Jafari, 2019; Mobeen Amanulla et al., 2018; Morones et al., 2005; Nair et al., 2009; Nwanya et al., 2019; Padmavathy and Vijayaraghavan, 2008; Pal et al., 2007; Simo et al., 2018; Simon-Deckers et al., 2009; Sondi and Salopek-Sondi, 2004; Webster, 2012; Yoon et al., 2007; Zhang et al., 2018) b: Staphylococcus aureus (Ahmad et al., 2018; Jiang et al., 2009; Jones et al., 2008; Karthik et al., 2017; Kaviya et al., 2011; Kaviyarasu et al., 2017; Kennedy et al., 2017; Kim et al., 2007; Magdalane Maria et al., 2018; McCarthy et al., 1992; Mobeen Amanulla et al., 2018; Nair et al., 2009; Nwanya et al., 2019; Reddy et al., 2007; Salomoni et al., 2015; Simo et al., 2018; Tran et al., 2010; Webster, 2012; Zhang et al., 2018)

c: Pseudomonas aeruginosa (Bakina et al., 2019; Kaviya et al., 2011; Kennedy et al., 2017; Magdalane et al., 2017; McCarthy et al., 1992; Mobeen Amanulla et al., 2018; Morones et al., 2005; Nwanya et al., 2019; Webster, 2012)

d: Candida albicans (McCarthy et al., 1992; Webster, 2012; Zhang Cu28-Fe72 70 70 - - - - - - - - 16 16 - - 125a 125b

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9 70 70 - - - - - - - - 16 16 - - 250c 250e et al., 2018)

e: MRSA ATCC 43300 (Bakina et al., 2019; Simo et al., 2018) f: Bacillus cereus (Kennedy et al., 2017)

g: Micrococcus luteus (Kennedy et al., 2017)

h: Bacillus subtilis (Jiang et al., 2009; Kennedy et al., 2017; Webster, 2012; Yoon et al., 2007)

i: Enterococcus sp (Kennedy et al., 2017)

j: Salmonella typhi (Kennedy et al., 2017; Manikandan et al., 2017; Morones et al., 2005; Webster, 2012)

k: Klebsiella pneumonia (Kennedy et al., 2017; Simo et al., 2018) l: Vibrio cholera (Morones et al., 2005; Webster, 2012)

m: Escherichia hermannii (Kaviyarasu et al., 2017; Magdalane Maria et al., 2018)

n: Streptococcus pneumoniae (Kaviyarasu et al., 2017; Magdalane Maria et al., 2018)

o: Proteus mirabilis (Manikandan et al., 2017) p: Streptococcus mut (Kaviyarasu et al., 2016a) q: Salmonella sps (Kaviyarasu et al., 2016a)

Ta2O5 12.5 - - - - 24 7.05 ± 0.05a - Ag - - - - - - - - - - - - - - - - - - - - - 35 35 - - - 18f 16b 16g 20h 18i 21c 17j 22a 17k 21a 21l 21j 21c 50 a 12 a 13.5a 13.5b 12.5 a 11.7 c 90 80 70 - - - - - - - - - 100 100 100 100 99 70 100 100 100b - - - - - - - - - - - - - - - - - - - - - - - 10 10 6 6 6 - - - - - - - - - - - - - - - - - - - - - - - 24 24 24 24 24 24 24 24 24 - - - - - - - - 12 24 24 107 107 107 - - - - - - - - - - - - - - - - - - - - 15.84a 31.68c 31.68d - - - - - - - - - 75 75 75 75 0.1 0.356 3.56 5 50 50

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10 35 10 10 10 7.8 b 16 a 13.4 c 9.2 b - - - - 10 10 10 10 - - - - 24 24 24 24 - - - - 50 50 50 50

r: Bacillus spp (Kaviyarasu et al., 2016b)

s: Staphylococcus epidermidis (Taylor and Webster, 2009; Webster, 2012)

t: Pseudomonas fluorescens (Jiang et al., 2009; Webster, 2012) y: Bacillus licheniformis (Nwanya et al., 2019)

La-ZnO 12.91 12.91 22o 7j - - 3 3 - - 24 24 - - 86.53×106 86.53×106 Co(Vox) 150 150 150 150 - - - - 50 50 50 50 - - - - - - - - 24 24 24 24 - - - - 1.8×103 a 1.8×103 e 1.2×103 b 1.7×103 k ZnO/TiO2 24.6 24.6 24.6 24.6 16 17 20 15.5 - - - - - - - - - - - - 24 24 24 24 - - - - 15×103 a 15×103 c 15×103 p 15×103 q CeO2 20 20 20 20 10 15 4 4 - - - - - - - - - - - - 24 24 24 24 - - - - 200 n 200 m 200 b 200 a β -CoMoO4 - Co3O4 - - - 20a 19c 18b - - - 6 6 6 - - - 24 24 24 - - - 50 ×103 50 ×103 50 ×103 CeO2/CdO 10 33 c - - - - - 200 HAp-TiO2 (8 dip) 10 10 15 a 15 r - - - - - - 24 24 - - - - Cu - - 100 a 100 h 90 90 - - - - - - - - 33.40 28.20

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11 CuO 54.5 18 60 15a,b,c,y 10 a,b,c,y 7 a,b,c,y - - - - - - - - - 24 24 24 - - - - - - Fe3O4 - - 9b 8s 100 65 - - - - 24 24 - - 3×103 2×103 Al2O3 - - - - - - 11 11 11 60 60 60 35a 70a 68a 36a 57h 70t - - - - - - - - - - - - 24 24 24 24 24 24 - - - - - - 10 100 500 20 20 20 TiO2 - - - 17 17 17 0 a 35 a 80 a - - - - - - 24 24 24 10 100 500 SiO2 - - - 20 20 20 58 a 40h 70t - - - - - - 24 24 24 20 20 20

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12 Software XLSTAT V.15.1 was used to treat the data for PCA and MCA analysis with 95 % as confidence interval.

3. Results and Discussion

3.1. Microorganism Affinity with Nanoparticles

MCA analysis showed the affinity between the microorganisms and the nanoparticles, which has variables matrix coefficient belonging to each axis F1 and F2 as shown in Table 3. Bold values are significant at the level α = 0.05. Axis F1 has significant values, which regroups bacteria which is gram negative and gram positive with nanoparticles Ag, La-ZnO, SiO2.

However, bacteria which are gram negative and La-ZnO NPs have a similar affinity. Then, gram positive bacteria and Ag-NPs and SiO2-NPs have shown the same trend. Axis F2

defined by the following significant parameters: Fungi, gram positive bacteria, Ag-NPs, ZnO-NPs. Nevertheless, a similar trend was observed for Fungi and Ag-NPs and for gram positive bacteria and the ZnO-NPs. A correlation was possible for the parameters which have the same trend (Costa et al., 2013).

Table 3 : Variables matrix coefficient for Multiple Correspondence Analysis of each qualitative parameter Qualitative variables F1 F2 Fungi 1.5427 -5.3448 Gram Bacteria-negative -5.9618 -0.5980 Gram Bacteria-positive 5.5575 2.0782 NPs-Ag 2.8783 -4.8522 NPs-Ag NPs Nanocellulose -1.8836 -0.3883 NPs-CeO2 0.2536 1.2868 NPs-CeO2/CdO -1.8836 -0.3883 NPs-Co (Vox) -1.8148 0.2431 NPs-Cu28-Fe72 -0.9646 0.5066 NPs-HAp-TiO2 (8 dip) 0.1757 0.8915 NPs-La-ZnO -2.6903 -0.5546 NPs-SiO2 2.1296 1.6367 NPs-Ta2O5 -1.8836 -0.3883 NPs-ZnO 1.7893 3.1015 NPs-ZnO/TiO2 -0.9646 0.5066 NPs-β-CoMoO4 - Co3O4 -0.9646 0.5066

Figure 1 presents the MCA plot for the qualitative variable for MCA analysis. A majority of the variables belonging to the axis F1. F1 explains 80.86 % of the data and only 19.14 % of the data were explained by the axis F2. The data spread was homogenous around the axis

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13 centre. Only Fungi are far from the axis centre, which meant that this parameter behave differently to the mean of the observable data (Dungey et al., 2018). However, Fungi and Ag-NPs have the same trend and form the first group encircled (pink colour). In fact, Figure 1 confirmed the affinities between the microorganisms and the nanoparticles by regrouping the parameters which have the same trend (Ayele et al., 2014). Three groups were observed in Figure 1. The first group was previously described, is XXXX shows a regrouping between the Fungi and NPs Ag. The second group is encircled in orange colour, which regroups the gram positive bacteria with the following NPs: Ag, SiO2, ZnO, CeO2 and TiO2. The last group,

encircled by a blue colour, is formed by the gram negative bacteria with NPs: La-ZnO, CeO2CdO, TaO5, Co (Vox), Cu, CuO, TiO2. Each type of microorganism was XXX limited

with several associated NPs, but only the matrix coefficient of each qualitative variable in Table 3 confirms the NPs highly correlated with specific microorganism. NPs that have bold values have significant antimicrobial properties towards gam positive bacteria, which are the NPs: Ag, SiO2 and ZnO with NPs of La-ZnO have an affinity for gram-negative bacteria with

only the Ag-NPs having antimicrobial activity against Fungi.

Figure 1 : Principal Multiple Correspondence Analysis (MCA) plot for the variable qualitative of the nanoparticle’s antimicrobial activities

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14 3.2. Antimicrobial Test Conditions

The scree plot of the PCA displayed as a 2-dimensional (2D) XXXX for the comparative location of the quantitative variables to each other is presented in Figure 2. Data variability was computed in 8 dimensions as illustrated by the axis from F1 to F8 in comparison for the cumulative variability and the Eigen values as shown in Figure 2. Based on the scree plot, PCA analysis revealed that the treated data was reliable in a 2D spacing when comparing F1 against F 2 = 45.50 %.

Figure 2 : Principal component analysis (PCA) scree plot depicting the variability of the quantitative data in the eight dimensions against the cumulative variability comparative to the

eigenvalues

The Pearson correlation matrix table (Table 4) shows the correlation coefficient between each considered parameter to NPs test conditions. The inhibition zone size was demonstrably correlated to the MIC, the well size and the disc size with correlation coefficient (r2) of

0.9598, 0.9399 and 0.9482, respectively. Besides, the well size was positively correlated to disc sizes (r2 = 0.9787, α = 0.05) and to MIC (r2 = 0.2783, α = 0.05). The disc size as well was

shown to have a positive correlation with the MIC with a correlation coefficient (r2)of 0.4814

and α of 0.05. The values of the correlation coefficient have a confidence interval of 95 % to accept that the hypothesis alternative Ha, which states at least one of the correlations between the variables is significantly different from 0, was verified.

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15 Table 4 : Correlation matrix (Pearson (n)) of quantitative variables for nanoparticles

antimicrobial activity

Variables

Average Size

(nm) IZ (mm) Ratio (%) size(mm) well Disc size (mm) Contact time (h) Od(initial) (µg/ml) MIC

Average Size (nm) 1 0.0321 -0.1438 -0.0641 0.0200 -0.9893 -0.1410 -0.1448 IZ (mm) 0.0321 1 0.0000 0.9399 0.9482 0.1550 0.0232 0.9598 Ratio (%) -0.1438 0.0000 1 0.0000 -0.0032 -0.1267 0.0321 -0.0039 well size(mm) -0.0641 0.9399 0.0000 1 0.9787 0.0000 0.0000 0.2783 Disc size (mm) 0.0200 0.9482 -0.0032 0.9787 1 0.0116 -0.0497 0.4814 Contact time (h) -0.9893 0.1550 -0.1267 0.0000 0.0116 1 0.2706 0.2034 Od(initial) -0.1410 0.0232 0.0321 0.0000 -0.0497 0.2706 1 -0.0725 MIC (µg/ml) -0.1448 0.9598 -0.0039 0.2783 0.4814 0.2034 -0.0725 1

Correlation of the PCA is displayed in Figure 3, which depicts the projection of the NPs antimicrobial activities and quantitative variables in the 2D space. A positive correlation was observed between the IZ of the microorganism growth and the MIC of the NPs. Besides, the PCA revealed a positive correlation between the well size and disc diffusion size created on the agar plates during the NPs antimicrobial activity tests. A negative correlation was observed between the average size of the NPs and the contact time with the test microorganism. These correlations were similar to the correlation matrix of Pearson (Table 4). The IZ size is defined not only by the MIC, but also the well and disc size containing the NPs on the agar plates. Therefore, the necessity to vary the NPs concentration to improve the antimicrobial test is retained as reported in Webster (2012). Effectively, when the MIC is high, the IZ size would be large as reported in Simon-Deckers et al. (2009). Besides, the NPs average size plays a major role on the contact time with the microorganism. Similarly, when the NPs average size is large, the contact time is reduced. The NPs with a large average size effectively inhibited a few microorganisms growth.

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16 Figure 3 : Principal component analysis (PCA) correlation circle depicting the projection of

the quantitative variables for nanoparticles antimicrobialactivities 4. Conclusion

Briefly, MCA and PCA analyses were investigated to study the correlation between the qualitative and quantitative variables, respectively, on the antimicrobial activity effectiveness of numerous NPs. Qualitative variables in the MCA analysis showed the affinity between the gram-positive bacteria and both the Ag-NPs, SiO2-NPsand ZnO, while for the gram-negative

bacteria it was the NPs of ZnO. For the Fungi, this was observed Ag-NPs. The PCA analysis further highlighted a positive correlation of the inhibition zone (IZ) size with the well size, disc size and the Minimum Inhibitory Concentration (MIC) with r2 > 0.90, α = 0.05. Besides,

a negative correlation was observed between the NPs average size and the contact time. Therefore, the choice of NP types for antimicrobial activity effectiveness depends on the microorganism type and the MIC, with some XXX associated to the well size and the disc size. The necessity to vary the concentration of the NPs as well as the well size and disc size, will improve the effectiveness of the NPs against microbial growth. Also, research on the NPs antimicrobial application on Fungi should be investigated in future studies to ascertain of the effectiveness NPs for antifungal studies.

Acknowledgement

We are grateful to the National Research Foundation (NRF) and The World Academy of Sciences (TWAS) for their financial support under grant unique number 110793 and the UNESCO-UNISA Africa Chair in Nanosciences/Nanotechnology Laboratories. College of

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17 Graduate Studies. University of South Africa (UNISA). Muckleneuk Ridge. Pretoria. South Africa as host institution.

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