Electronic tongue for determining the limit of detection of human pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 237 ADMET & DMPK 11(2) (2023) 237-250; doi: https://doi.org/10.5599/admet.1650 Open Access : ISSN : 1848-7718 https://pub.iapchem.org/ojs/index.php/admet/index Original scientific paper Electronic tongue for determining the limit of detection of human pathogenic bacteria Aya Abu Rumaila1, Basima Abu Rumaila1, Wafa Masoud1, Antonio Ruiz-Canales2 and Nawaf Abu-Khalaf1* 1Department of Agricultural Biotechnology, Faculty of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK), P.O. Box 7, Jaffa Street, Tulkarm, Palestine 2Department of Engineering, School of Engineering of Orihuela (EPSO), Miguel Hernández University (UMH), Carretera de Beniel, km 3.2, 03312 Orihuela, Alicante, Spain *Corresponding Author: E-mail: n.abukhalaf@ptuk.edu.ps Received: December 23, 2022; Revised: February 13, 2023; Published: February 17, 2023 Abstract The Electronic tongue (ET) has been used as a diagnostic technique in the medical sector. It is composed of a multisensor array set with high cross-sensitivity and low selectivity characteristics. The research investigated using Astree II Alpha MOS ET to determine the limit of early detection and diagnosis of food- borne human pathogenic bacteria and to recognize unknown bacterial samples relying on pre-stored models. Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC25922) were proliferated in nutrient broth (NB) medium with original inoculum (approximately 107*105 CFU/mL). They were diluted up to 10-14 and the dilutions ranging from 10-14 to 10-4 were measured using ET. The partial least square (PLS) regression model detected the limit of detection (LOD) of the concentration that was monitored to grow the bacteria with different incubation periods (from 4 to 24 h). The measured data were analysed by principal component analysis (PCA) and followed by projecting unknown bacterial samples (at specific concentrations and time of incubation) to examine the recognition ability of the ET. Astree II ET was able to track bacterial proliferation and metabolic changes in the media at very low concentrations (between the dilutions 10 -11 and 10-10 for both bacteria). S.aureus was detected after 6 h incubation period and between 6 and 8 h for E.coli. After creating the strains’ models, ET was also able to classify unknown samples according to their foot-printing characteristics in the media (S.aureus, E.coli or neither of them). The results considered ET a powerful potentiometric tool for the early identification of food-borne microorganisms in their native state within a complex system to save patients’ lives. ©2023 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). Keywords Electronic tongue; food-borne pathogens; multivariate data analysis; principal component analysis; partial least squares. Introduction Worldwide, food-borne diseases influence public health and cause dangerous diseases. A few pathogenic bacteria are adequate to initiate infection and cause potential damage to the human host system. Patients can be treated for dangerous bacterial diseases after an accurate and early diagnosis of the infection, which https://doi.org/10.5599/admet.1650 https://doi.org/10.5599/admet.1650 https://pub.iapchem.org/ojs/index.php/admet/index mailto:n.abukhalaf@ptuk.edu.ps http://creativecommons.org/licenses/by/4.0/ A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 238 requires combining signs and symptoms with precise diagnostic tests to give suitable treatment and avoid unnecessary antibiotics [ 2,1 ]. Therefore, finding suitable detecting approaches and developing new and fast methods is important for health and safety. Colony count, enzyme-linked immunosorbent assay (ELISA), electrophoresis, polymerase chain reaction (PCR), biosensors and others have all been employed for the detection of these pathogens [ 4,3 ]. The bacterial normal diagnostic process includes culturing, colony counting and phenotypic characteristics. This usually requires 24 to 48 h to grow the pathogen and obtain a pure culture for further antibiotics testing. Moreover, the sensitive and available diagnostic methods (ELISA, PCR nucleic acid detection, antigen testing and surface recognition) are expensive, time-consuming and require a high sophistication level and complex sample preparation [ 6,5 ]. Consequently, ultrasensitive, advanced, new methods are required to improve the detecting capability of a few or even a single pathogenic bacterial species in the target samples (such as water, food, blood or biological tissues) [7]. Chemical and biological sensor technologies have recently become popular analytical tools for complex liquid analysis [8-10]. Human smell and taste sensing have been mimicked by the electronic nose (EN) and the electronic tongue (ET) devices (gas and liquid sensors, respectively) and their communication with the human brain [8,11-17]. Liquids and complex solutions can be analyzed using ET systems. They are based on an array of multisensor schemes having pronounced cross-sensitivity and low selectivity characteristics [18 -20]. Signals obtained from sensors and liquids are processed with multivariate data analysis (MVDA) techniques, such as principle component analysis (PCA), partial least square (PLS), soft independent model class analogy (SIMCA) and discrimination function analysis (DFA,) allowing for obtaining qualitative and quantitative information on the analyzed samples and creating models from the gathered data [15,18,21-25]. Using ET in the medical analysis is promising to have rapid bacterial detection and shortening the detecting period as much as possible for many physicians, medical laboratories, and even patients as it is an alternative, rapid, reliable and highly sensitive system [26-28]. The limit of detection (LOD) defines the lowest concentration of a variable in a sample that can be constantly detected by a particular measurement process at a specified level of confidence without the necessity of being quantitated as an exact value [29,30]. This research aims to evaluate and/or determine the limit for early detection (LOD) for both the number of colony-forming units (CFU) and incubation or growing periods of food-borne human pathogenic bacteria using ET and multivariate data analysis. Also, to identify unknown bacterial samples relying on a pre-stored bacterial model. Experimental Two bacterial isolates (Escherichia coli (ATCC25922) and Staphylococcus aureus (ATCC 25923)) obtained from the American Type Culture Collection (ATCC) were cultivated on nutrient agar (NA) medium. NA medium was prepared by dissolving 23 g of NA powder in 1 L distilled water (DW) completely with heating, sterilized at 121 °C and 15 psi for 15 min autoclaving program. The purified medium was cooled and poured into 9 cm Petri dishes under aseptic conditions on a microbiological safety cabinet (MN 120). It was then used for culturing the bacteria. The plate count was applied for the viable bacterial count. Three fresh well-isolated colonies from NA culture medium were suspended in 1 ml sterile nutrient broth (NB) medium, homogenized using a vortex, and 0.1 ml of stock was serially diluted in 0.9 ml NB tenfolds. This was followed by culturing 0.1 ml of each ADMET & DMPK 11(2) (2023) 237-250 Electronic tongue for pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 239 dilution on NA medium spread with glass hockey sticks and incubating at 37 °C for 24 h. Well-isolated colonies were counted and those within the average of 25-250 CFU were recorded for applying the following equation: CFU/mL = number of colonies  dilution factor / volume of the culture plate The process was repeated three times for the average count. Bacterial DNA isolation was applied using TRIzol reagent manual (TRI reagent) (Cat. # T942) (Invitrogen, Thermo Fisher Scientific, US). Three fresh well-isolated colonies from fresh NA culture media were homogenized using vortex in 1 mL of TRI reagent in 1.5 mL microfuge tubes. After that, 200 µL of absolute cold chloroform was added to the suspension, shaken vigorously for 15 sec, and left to stand for 15 min at room temperature. The resulting mixture was centrifuged for 10 min at 11573 rpm at 4 °C to give three phases: colourless upper phase (RNA), interphase (DNA), and red organic phase (protein lower phase). At this point, 300 µL of cold 100 % ethanol was added after removing and discarding the aqueous overlying phase. Tubes were inverted a few times to be mixed and let to stand for 3 min at room temperature, then centrifuged at 4730 rpm for 5 min at 4 °C. The resulting supernatant was removed to be discarded and 1 mL of cold 0.1 M trisodium-citrate in 10 % ethanol solution was used for washing the remaining DNA pellets (twice). Tubes were allowed to stand for 30 min with occasional mixing, centrifuged at 4730 rpm for 5 min at 4 °C, and the resulting pellets were suspended with 1.5 mL of 75 % cold ethanol and allowed to stand for 20 min at room temperature. Later, tubes were centrifuged at 4730 rpm for 5 min at 4 °C discarding the resulting supernatant. In the end, under the vacuum hood, pellets were dried for 10 min, dissolved in 50 µL of TE buffer (add 10.8 g Tris and 5.5 g boric acid in 900 ml distilled water, then add 4 ml 0.5 M Na2EDTA (pH 8.0), then adjust the volume to 1 L), and stored at -20 °C for further use. PCR amplification for the templates was done using a universal 16S bacterial primer set (forward 27F (AGATTTGATCTGGCTCAG)) and reverse primers 1492R (TACGGTTACCTTGTTACGACTT)). The primers were dissolved in sterilized distilled DNase-free water to have a final concentration of 100 µM and stored at -20 °C. PCR amplification mixture was done using Go taq green 2X PCR master mix with 3 mm MgCl2 (Cat. # AF9PIM7120418M712). 25 µL PCR reaction mixture contained 12.5 µL of 2X ready mix PCR master mix (75 mM Tris-HCl, 20 mM (NH4)2SO4, 0.625 U Thermo prime taq DNA polymerase, 0.2 mM of each dNTPs, 1.5 mM MgCl2), 0.5 µL of 50 mM MgCl2, 0.125 µL of 100 µM forward primer, 0.125 µL of 100 µM reverse primer, 10.75 µL of free DNase water and 1 µL of DNA template. VertiTM 96 well thermal cycler (Cat. #: 4375786) (Applied Biosystems company, California, USA) was used to perform a PCR amplification program. The program started with an initial 94 °C cycle for 3 min, followed by 35 cycles of 45-sec denaturation cycle at 94 °C, 50 sec of 51 °C, and 1 min at 72 °C, and then 7 min of the final cycle at 72 °C. The PCR procedure was duplicated for each isolate to guarantee the reproducibility of the amplified DNA fragments. A blank negative control sample was also run. To separate the total extracted bacterial DNA, a 0.8 % agarose electrophoresis gel was used. Meanwhile, 2 % agarose electrophoresis gel was prepared to separate PCR products. The gel was prepared by dissolving 2 g of agarose powder completely in 100 mL of 1X TBE buffer with heating using the microwave. The mixture was cooled to 60 °C. After that, 4 µL of 1000X Gel Red DNA stain (Cat. #41003) (Med Chem Express, USA) was added and stirred. The suspension was then powered and allowed to solidify in a (10 x 10) tray with 13 wells comp. After submerging the gel in 1 X TBE buffer, 5 µL of PCR products were loaded and the device was run for 2 h at 70 volts. A 10000X Gel Red DNA stain and UV-illuminator were used to visualize DNA fragments and SynGene gene tool system (Synoptics Ltd., Cambridge C, UK) was used to document it using image acquisition and https://doi.org/10.5599/admet.1650 A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 240 documentation. For estimating DNA fragments size, a DNA ready-to-use (RTU) ladder (Cat. # DM001-R500) of 100 bp was used as a molecular marker. Finally, PCR products were stored and sent for sequencing. The obtained bacterial sequences were aligned using the universal BLAST program (National Center for Biotechnology Information, Maryland, USA). Meanwhile, for ET measurements, a liquid taste analyzer Astree II ET (Alpha MOS Company, Toulouse, France) was used. That is composed of seven sensor arrays (CA, JB, HA, ZZ, BB, JE and GA) with an Ag/AgCl reference electrode. Five testing rounds of bacterial samples were measured on ET. The first round was for determining the limit of detection (LOD) (limited CFU) for E.coli samples that ET can detect after 24 h incubation period. The second was for determining the least incubation time for E.coli that ET can detect after cultivating the detected least CFU (the same two rounds were applied for S.aureus). The final fifth round was done to test ET capability to recognize unknown bacterial samples of E.coli, S.aureus, and others (S.agalactiae and P.aeruginosa) that were grown at the least incubation time and CFU. In each round, 11 bacterial samples with a Nutrient broth (NB) media sample (control) were tested in triplicate. NB was prepared by dissolving a complete weight of 13 g NB powder in 1 L DW by heating, suspended in 250 mL Erlenmeyer flasks, each containing 100 mL of the suspension that was labelled and sealed with aluminium foil for autoclaving at 121 °C and 15 psi for 15 min, and left to cool. The overall action was also done at aseptic conditions. Bacterial proliferation was done by cultivating three fresh colonies (approx. 107105 CFU/mL) of pure cultured bacteria in 100 ml NB media. The dilution test was applied by serially diluting 1 ml of stock in 99 ml of sterilized NB media up to 14 folds. Flasks were then incubated at 37 °C with shaking at 150 rpm for 24 h (the samples with dilutions 10-14 to 10-4 were analyzed using ET). Meanwhile, the growth period test was applied by inoculating the media with the determined least concentration CFU (approx. 8810-9 CFU/mL) of each bacterial type that was then incubated at 37 °C with shaking at 150 rpm for different periods (4, 6, 8, 10, 12, 14, 16, 18, 20, 22 and 24 h). In the fifth final round, bacterial samples of E.coli, S.aureus, S.agalactiae and P.aeruginosa with NB as a control sample were tested. Those samples were measured at 10-9 CFU concentration after 10, 12 and 14 h of inoculation to identify unknown bacterial samples relying on pre- stored bacterial data and if it can recognize them from other types of bacteria. To create the sequence on ET a two parts labelling was applied, where the first part has the bacterial name (i.e. E.coli, Staph, UnEc, UnSa, UnPs, UnSr and UnNB), the other for the concentration (i.e., -04 to -14 or NB) and/or incubation period (i.e., 04h to 24h or NB) (Table 1). Before ET testing, bacterial samples were filtered using a white cheesecloth to obtain approximately 80 mL of each broth to be placed on ET’s 16-position autosampler, with an automatic stirrer, after creating the sequence. Samples were separated by four water-cleaning samples for cleaning ET sensors after each test. After each measurement, the data from each sensor was collected in a folder categorized by bacterial sequence for each round after creating a library of the experiment. The collected raw data from analyzed sensors were exported to Unscrambler X (version 10.3, Camo Software AS, Oslo, Norway), where the signals of each sensor were numerically analyzed and normalized to values to be categorized using PLS and PCA. ADMET & DMPK 11(2) (2023) 237-250 Electronic tongue for pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 241 Table 1. The ET five experiments rounds and the labelling for each tested sample Round No. Sample No. Bacterial type Dilution factor The incubation period, h ET code Goal of the experiment Round 1 1 E.coli 10-4 24 E.coli_-04 E.coli concentration LOD test 2 E.coli 10-5 24 E.coli_-05 3 E.coli 10-6 24 E.coli_-06 4 E.coli 10-7 24 E.coli_-07 5 E.coli 10-8 24 E.coli_-08 6 E.coli 10-9 24 E.coli_-09 7 E.coli 10-10 24 E.coli_-10 8 E.coli 10-11 24 E.coli_-11 9 E.coli 10-12 24 E.coli_-12 10 E.coli 10-13 24 E.coli_-13 11 E.coli 10-14 24 E.coli_-14 12 -------- ------- 24 E.coli_NB Round 2 1 E.coli 10-9 4 E.coli_04h E.coli LOD for incubation periods test 2 E.coli 10-9 6 E.coli_06h 3 E.coli 10-9 8 E.coli_08h 4 E.coli 10-9 10 E.coli_10h 5 E.coli 10-9 12 E.coli_12h 6 E.coli 10-9 14 E.coli_14h 7 E.coli 10-9 16 E.coli_16h 8 E.coli 10-9 18 E.coli_18h 9 E.coli 10-9 20 E.coli_20h 10 E.coli 10-9 22 E.coli_22h 11 E.coli 10-9 24 E.coli_24h 12 -------- ------- 24 E.coli_NB Round 3 1 S.aureus 10-4 24 Staph_-04 S.aureus concentration LOD test 2 S.aureus 10-5 24 Staph_-05 3 S.aureus 10-6 24 Staph_-06 4 S.aureus 10-7 24 Staph_-07 5 S.aureus 10-8 24 Staph_-08 6 S.aureus 10-9 24 Staph_-09 7 S.aureus 10-10 24 Staph_-10 8 S.aureus 10-11 24 Staph_-11 9 S.aureus 10-12 24 Staph_-12 10 S.aureus 10-13 24 Staph_-13 11 S.aureus 10-14 24 Staph_-14 12 -------- ------- 24 Staph_NB Round 4 1 S.aureus 10-9 4 Staph_04h S.aureus LOD for incubation periods test 2 S.aureus 10-9 6 Staph_06h 3 S.aureus 10-9 8 Staph_08h 4 S.aureus 10-9 10 Staph_10h 5 S.aureus 10-9 12 Staph_12h 6 S.aureus 10-9 14 Staph_14h 7 S.aureus 10-9 16 Staph_16h 8 S.aureus 10-9 18 Staph_18h 9 S.aureus 10-9 20 Staph_20h 10 S.aureus 10-9 22 Staph_22h 11 S.aureus 10-9 24 Staph_24h 12 -------- ------- 24 Staph_NB Round 5 1 E.coli 10-9 10 UnEc_10 Identify unknown bacterial samples relaying on pre-stored bacterial model 2 E.coli 10-9 12 UnEc_12 3 E.coli 10-9 14 UnEc_14 4 S.aureus 10-9 10 UnSa_10 5 S.aureus 10-9 12 UnSa_12 6 S.aureus 10-9 14 UnSa_14 8 P.aeruginosa 10-9 14 UnPs_14 10 S.agalactiae 10-9 14 UnSr_14 11 -------- -------- 12 UnNB_01 12 -------- -------- 14 UnNB_01 https://doi.org/10.5599/admet.1650 A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 242 Results and Discussion Bacterial experiment Bacterial colony forming unit (CFU) counting Figure 1 represents the plated bacterial dilution (approximately 88*10-9 CFU/mL) with well-separated and countable colonies of 25-250 CFU, considered for the ET LOD testing procedure. Figure 1. Plated bacterial dilution of 88*10-9 CFU/mL with well-separated and countable colonies. A: plate with E.coli, B: plate with S.aureus. Bacterial DNA isolation and PCR The total DNA extracted from four bacterial samples using TRI reagent method is shown in Figure 2. PCR amplification of DNA templates using a universal 16S bacterial primer set (27F and 1492R) resulted in 1500 pb bands used for the sequencing process (Figure 3). Figure 2. Gel electrophoresis documentation of bacterial total DNA isolation using TRI reagent method. Where lanes 1 and 2 represent E.coli samples, 3 and 4 represent S.aureus samples, 5 is a negative control. M=100 bp ladder as a molecular size marker. ADMET & DMPK 11(2) (2023) 237-250 Electronic tongue for pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 243 Figure 3. Gel electrophoresis documentation of bacterial 16S rRNA amplification in eight bacterial isolates using primer 27F and 1492R. 1-4 represents E.coli samples, 4-8 represents S.aureus samples and 9 is a negative control M=100 bp ladder as a molecular size marker. Sequence identification BLASTn alignment of the 16S rRNA gene sequences of E.coli and S.aureus bacterial samples are shown in Figure 4 and Figure 5, respectively. It shows obtained sequence homology of 99 % for E.coli to strain NBRC 102203 and 100 % for S.aureus to strain ATCC 12600. Figure 4. BLASTn alignment for E.coli sequenced 16S ribosomal RNA with 99 % identity to Escherichia coli strain NBRC 102203. https://doi.org/10.5599/admet.1650 A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 244 Figure 5. BLASTn alignment of S.aureus sequenced 16S ribosomal RNA with 100 % identity to Staphylococcus aureus ATCC 12600. ET data analysis LOD test of bacterial concentration The calibration curve of the PLS recognition model, for determining the limit of detection (LOD) test of bacterial concentration, has identified the presence of bacteria between the dilutions 10-11 and 10-10 for both bacteria E.coli (Figure 6) and S.aureus (Figure 7). Figure 6. PLS recognition model for E.coli LOD of different dilutions ranged from 10-14 to 10-4. ET can sense the presence of bacteria, in NB media, between dilutions 10-11 and 10-10. ADMET & DMPK 11(2) (2023) 237-250 Electronic tongue for pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 245 Figure 7. PLS recognition model for S.aureus LOD of different dilutions ranged from 10-14 to 10-4. ET can sense the presence of bacteria, in NB media, between dilutions 10-11 and 10-10. LOD test of bacterial earliest incubation period The calibration curve of the PLS recognition model for determining the LOD test of bacterial earliest incubation period after determining the concentration LOD (10-9) identified that ET can sense the presence of E.coli, in NB media, between 6 and 8 h of incubation (Figure 8) and S.aureus after 6 h of incubation (Figure 9). The results are summarized in Table 2. Figure 8. PLS recognition model for E.coli LOD of different incubation periods ranged from 4 to 24 h. ET can sense the presence of bacteria, in NB media, between incubation periods 6 and 8 h. https://doi.org/10.5599/admet.1650 A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 246 Figure 9. PLS recognition model for S.aureus LOD of different incubation periods ranged from 4 to 24 h. ET can sense the presence of bacteria, in NB media, at an incubation period of 6 h. Table 2. Limit of detection (LOD) results for S.aureus and E.coli. Bacterial type LOD of Concentration LOD of the incubation period S.aureus Between 10-11 and 10-10 After 6 h E.coli Between 10-11 and 10-10 Between 6 and 8 h ET classification test ET was able to identify two well-separated groups of E.coli and S.aureus in the same PCA scores plot, after joining the data for both recognized LOD tests (dilution greater than 10-10 and growth time greater than 8 h) in the same PCA score plot (Figure 10). The scoring model had 99 % PC-1 recognition power. Figure 10. PCA scores plot for both bacterial data at the recognized LOD tests (dilution greater than 10-10 and growth time greater than 8 h). E: E.coli, S: S.aureus. ET projection model A PCA model for E.coli and S.aureus were created using the resulting data for the projection test, where unknown samples of E.coli and S.aureus were incubated with a dilution of 10-9 for 10, 12 and 14 h, and S.agalactiae and P.aeruginosa as gram-positive and gram-negative bacteria were also incubated with a ADMET & DMPK 11(2) (2023) 237-250 Electronic tongue for pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 247 dilution of 10-9 and for 14 h in order to test the created models and to prove ET’s ability to recognise between different bacterial samples. E.coli PCA projection model projected unknown E.coli samples close enough to the created models’ data. Meanwhile, the unknown P.aeruginosa was out of the group, as well as the projected unknown S.aureus and S.agalactiae, which were far away from the model group (Figure 11). S.aureus PCA projection model projected samples of unknown S.aureus inside the model’s created group. Meanwhile, the unknown S.agalactiae was out of the group (at a distance), as well as the projected unknown E.coli and P.aeruginosa, which were far away from the model group (Figure 12). Figure 11. E.coli PCA projection model with projected unknown samples. A: a group of E.coli’s created data with projected unknown E.colis samples (incubated with a dilution of 10-9 and periods at 10, 12 and 14 h), B: projected unknown P.aeruginosa (incubated with a dilution of 10-9 and 14 h), C: projected unknown S.aureus and S.agalactiae that incubated with a dilution of 10-9 and periods at 10, 12 and 14 h. Figure 12. S.aureus PCA projection model with projected unknown samples. A: projected unknown E.coli samples (incubated with a dilution of 10-9 and periods at 10, 12 and 14 h), B: projected unknown P.aeruginosa (incubated with a dilution of 10-9 and 14 h), C: a group of all S.aureus’s created data with projected unknown S.aureus samples (incubated with a dilution of 10-9 and periods at 10, 12 and 14 h), D: projected S.agalactiae (incubated with a dilution of 10-9 and 14 h). The 99 % homology for E. coli may be due to mutations throughout the subsequent culturing or the sequencing process. It can also be attributed that E. coli used in this study is a different strain from strain NBRC 102203. https://doi.org/10.5599/admet.1650 A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 248 The ET was able to classify the two types of bacteria according to their gram-negative and gram-positive strains (i.e., E.coli and S.aureus). Moreover, ET could sense the difference between the same strains (i.e., E.coli and P.aeruginosa as gram-negative, and S.aureus and S.agalactiae as gram-positive). This can be due to bacteria’s different characteristics. Conclusions Astree II ET was an efficient technique for tracking bacterial growth and following their metabolic changes in NB media. It was able to create a categorization model that is specific for some strains of microorganisms. Moreover, ET was able to detect these food-borne bacterial strains just a few hours after inoculation up to only 8 h and even 6 h in some strains such as S.aureus. ET’s sensitivity was also confirmed for identifying microorganisms’ proliferation even with a very low concentration of an original inoculum (such as a dilution factor up to 10-10). According to these statements, ET can be considered a powerful tool for early identification and fast classification of harmful food-borne microorganisms by creating other subsequent steps to create microorganisms’ models and save patients’ lives. In the long term, this will open a wide door for using these sensors as an alternative assessment and fast monitoring technique in industrial, categorizing, fermentable and other applications. ET ease of use in tracking microorganism footprints coupled with distinguishing these microorganisms in the native state (in vitro assessment) and being contained in a complex system is important. However, combining ET with other technologies can provide a powerful combination in a wide range of applications. Further studies should be carried out to monitor sensors' temperature dependence and charge transfer affected by the adsorption of solution components. Also, enlarging the specified foot-printing databases of microorganisms that needs the first step of full work. Conflict of interest: The authors declare no conflict of interest. Acknowledgements: The authors acknowledge the infrastructure and support of Palestine Technical University—Kadoorie (PTUK) through the master’s program in “Agricultural Biotechnology”. We acknowledge the collaboration of TELENATURA EBT, SL enterprise, Spain. References [1] N.M. Aljamali. Review on food poisoning (Types, Causes, Symptoms, Diagnosis, Treatment). Global Academic Journal of Pharmacy and Drug Research 3 (2021) 54-61. https://doi.org/10.36348/- gajpdr.2021.v03i04.001 [2] S.M. Parry, S.R. Palmer, J. Slader, T. Humphrey. Risk factors for Salmonella food poisoning in the domestic kitchena case control study. Epidemiology & Infection 129 (2001) 277-285. https://doi.org/10.1017/s0950268802007331 [3] N. Nordin, N.A. Yusof, J. Abdullah, S. Radu, R. Hushiarian. Sensitive detection of multiple pathogens using a single DNA probe. Biosensors and Bioelectronics 86 (2016) 398-405. https://doi.org/- 10.1016/j.bios.2016.06.077. [4] Q. Yu, L. Zhai, X. Bie, Z. Lu, C. Zhang, T. Tao, J. Li, F. Lv, H. Zhao. Survey of five food-borne pathogens in commercial cold food dishes and their detection by multiplex PCR. Food Control 59 (2016) 862-869. https://doi.org/10.1016/j.foodcont.2015.06.027. [5] J. Qin, Y. Cui, X. Zhao, H. Rohde, T. Liang, M. Wolters, D. Li, C. Belmar Campos, M. Christner, Y. Song. Identification of the Shiga toxin-producing Escherichia coli O104: H4 strain responsible for a food poisoning outbreak in Germany by PCR. Journal of Clinical Microbiology 49 (2011) 3439-3440. https://journals.asm.org/doi/10.1128/JCM.01312-11. https://doi.org/10.36348/gajpdr.2021.v03i04.001 https://doi.org/10.36348/gajpdr.2021.v03i04.001 https://doi.org/10.1017/s0950268802007331 https://doi.org/10.1016/j.bios.2016.06.077 https://doi.org/10.1016/j.bios.2016.06.077 https://doi.org/10.1016/j.foodcont.2015.06.027 https://journals.asm.org/doi/10.1128/JCM.01312-11 ADMET & DMPK 11(2) (2023) 237-250 Electronic tongue for pathogenic bacteria doi: https://doi.org/10.5599/admet.1650 249 [6] X. Zhao, M. Li, Z. Xu. Detection of foodborne pathogens by surface enhanced raman spectroscopy. Frontiers in Microbiology 9 (2018) 1236-1249. https://doi.org/10.3389/fmicb.2018.01236. [7] A. Rohde, J. A. Hammerl, I. Boone, W. Jansen, S. Fohler, G. Klein, R. Dieckmann, S. Al Dahouk. Overview of validated alternative methods for the detection of foodborne bacterial pathogens. Trends in Food Science & Technology 62 (2017) 113-118. https://doi.org/10.1016/j.tifs.2017.02.006. [8] A. Fernandez-Lopez, M. Ferrández-Villena, M. Oates, C. Cabrera, C. Conesa, R. J. Agustín, N. Abu- Khalaf, A. Ruiz-Canales. Use of lowcost electronic nose, tongue and eye for monitoring agri-food processes. Proceeding of The II University Congress on Food Innovation and Sustainability (CUISA), at The Higher Polytechnic School of Orihuela (EPSO) of the Miguel Hernández University of Elche, Spain, 2021 (Abstract in English, Paper in Spanish). [9] S. Kumar, V. Kumar, P. Suthar, R. Saini, T. Negi. Crop Improvement, CRC Press., Florida, USA, 2021, p. 225-236. [10] K. R. Srivastava, S. Awasthi, P. K. Mishra, P. K. Srivastava. Waterborne Pathogens, Elsevier, Amsterdam, Netherland, 2020, 237-277. [11] N. Abu-Khalaf. Identification and quantification of olive oil quality parameters using an electronic nose. Agriculture 11 (2021) 674-685. https://doi.org/10.3390/agriculture11070674. [12] N. Abu-Khalaf, W. Masoud. Electronic Nose for Differentiation and Quantification of Yeast Species in White Fresh Soft Cheese. Applied Bionics and Biomechanics, (2022) Article ID 8472661, 1-5. https://doi.org/10.1155/2022/8472661. [13] N. Christodoulides, M.P. McRae, G.W. Simmons, S.S. Modak, J. T. McDevitt. Sensors that learn: The evolution from taste fingerprints to patterns of early disease detection. Micromachines 10 (2019) 251- 264. https://doi.org/10.3390/mi10040251. [14] D. Ha, Q. Sun, K. Su, H. Wan, H. Li, N. Xu, F. Sun, P. Wang. Recent achievements in electronic tongue and bioelectronic tongue as taste sensors. Sensors and Actuators B: Chemical 207 (2015) 1136-1146. https://doi.org/10.1016/j.snb.2014.09.077. [15] W. Masoud, A. Al-Qaisi, N. Abu-Khalaf. Growth Prediction of the Food Spoilage Yeast Debaryomyces Hansenii using Multivariate Data Analysis. Palestine Technical University Research Journal 9 (2021) 22- 32. https://doi.org/10.53671/ptukrj.v9i1.160. [16] S. Mudalal, N. Abu-Khalaf. Electronic nose to differentiate between several drying techniques for Origanum syriacum leaves. Food Research 5 (2021) 260-265. https://doi.org/10.26656/fr.2017.5(6).125. [17] M. Peris, L. Escuder-Gilabert. Electronic noses and tongues to assess food authenticity and adulteration. Trends in Food Science & Technology 58 (2016) 40-54. https://doi.org/10.1016/j.tifs.- 2016.10.014. [18] E. Legin, O. Zadorozhnaya, M. Khaydukova, D. Kirsanov, V. Rybakin, A. Zagrebin, N. Ignatyeva, J. Ashina, S. Sarkar, S. Mukherjee. Rapid evaluation of integral quality and safety of surface and waste waters by a multisensor system (Electronic Tongue). Sensors 19 (2019) 1-15. https://doi.org/10.1016/- j.bios.2016.06.077. [19] J.K. Lorenz, J.P. Reo, O. Hendl, J.H. Worthington, V.D. Petrossian. Evaluation of a taste sensor instrument (electronic tongue) for use in formulation development. International Journal of Pharmaceutics 367 (2009) 65-72. https://doi.org/10.1016/j.ijpharm.2008.09.042. [20] W. Wang, Y. Liu. Evaluation technologies for food quality. Elsevier, Amsterdam, Netherland, 2019, p. 23-36. [21] M. Kumar, S. Kumar, A. Gupta, A. Ghosh. Development of electronic interface for sensing applications with voltammetric electronic tongue. IEEE Sensors (2018) 1-4. https://doi.org/10.1109/ICSENS.20 18.8589506. [22] W. Masoud, N. Abu-Khalaf. Food and Agricultural Engineering. Proceedings of IRES international Conference, Putrajaya, 2022, p. 16-18. [23] A. Veloso, M. Sousa, L. Estevinho, L. Dias, A. Peres. Honey evaluation using electronic tongues: An Overview. Chemosensors 6 (2018) 28-53. https://doi.org/10.3390/chemosensors6030028. https://doi.org/10.5599/admet.1650 https://doi.org/10.3389/fmicb.2018.01236 https://doi.org/10.1016/j.tifs.2017.02.006 https://doi.org/10.3390/agriculture11070674 https://doi.org/10.1155/2022/8472661 https://doi.org/10.3390/mi10040251 https://doi.org/10.1016/j.snb.2014.09.077 https://doi.org/10.53671/ptukrj.v9i1.160 https://doi.org/10.26656/fr.2017.5(6).125 https://doi.org/10.1016/j.tifs.2016.10.014 https://doi.org/10.1016/j.tifs.2016.10.014 https://doi.org/10.1016/j.bios.2016.06.077 https://doi.org/10.1016/j.bios.2016.06.077 https://doi.org/10.1016/j.ijpharm.2008.09.042 https://doi.org/10.1109/ICSENS.20‌18.8589506. https://doi.org/10.1109/ICSENS.20‌18.8589506. https://doi.org/10.3390/chemosensors6030028 A. Abu Rumaila et al. ADMET & DMPK 11(2) (2023) 237-250 250 [24] M. Wesoly, P. Ciosek. Comparison of various data analysis techniques applied for the classification of pharmaceutical samples by electronic tongue. Sensors and Actuators B: Chemical 267 (2018) 570-580. https://doi.org/10.1016/j.snb.2018.04.050. [25] A.N. Zaid, R. Al Ramahi, A. AlKilany, N. Abu-Khalaf, M. El Kharouf, D.A. Dayeh, L. Al-omari, M. Yaqoup. Following Drug Degradation and Consequent Taste Deterioration of an Oral Reconstituted Paediatric Suspension during dosing interval via Electronic Tongue. Saudi Pharmaceutical Journal 30 (2022) 555- 561. https://doi.org/10.1016/j.jsps.2022.02.016. [26] R. Al-Ramahi, A.N. Zaid, N. Abu-Khalaf. Evaluating the potential use of electronic tongue in early identification and diagnosis of bacterial infections. Infection and Drug Resistance 12 (2019) 2445-2457. https://doi.org/10.2147/IDR.S213938. [27] E.I. Mohamed, S.M. Abdel-Mageed. The electronic tongue basic principles and medical applications. Journal of Biophysics and Biomedical Sciences 3 (2010) 290-295. [28] T. Wasilewski, D. Migon, J. Gebicki, W. Kamysz. Critical review of electronic nose and tongue instruments prospects in pharmaceutical analysis. Analytica Chimica Acta 1077 (2019) 14-29. https://doi.org/10.1016/j.aca.2019.05.024. [29] E. Oleneva, M. Khaydukova, J. Ashina, I. Yaroshenko, I. Jahatspanian, A. Legin, D. Kirsanov. A simple procedure to assess limit of detection for multisensor systems. Sensors 19 (2019) 1359. https://doi.org/10.3390/s19061359. [30] N. Abu-Khalaf, K. F. Haselmann. Characterization of Odorants in an Air Wet Scrubber Using Direct Aqueous Injection-Gas Chromatography-Mass Spectrometry (DAI-GC-MS) and Solid Phase Extraction (SPE-GC). American Journal of Environmental Engineering 2 (2012) 58-68. https://doi.org/10.5923/- j.ajee.20120203.04. ©2023 by the authors; licensee IAPC, Zagreb, Croatia. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/) https://doi.org/10.1016/j.snb.2018.04.050 https://doi.org/10.1016/j.jsps.2022.02.016 https://doi.org/10.2147/idr.s213938 https://doi.org/10.1016/j.aca.2019.05.024 https://doi.org/10.3390/s19061359 https://doi.org/10.5923/j.ajee.20120203.04 https://doi.org/10.5923/j.ajee.20120203.04 http://creativecommons.org/licenses/by/3.0/