# A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
## 1 Introduction  
The early and fast detection of foodstuffs' contamination with pathogenic bacteria is of major relevance and can be used as a quality and/or safety diagnosis tool throughout the entire food chain. Conventional culture-based methods, immunological assays and polymerase chain reaction methods are widely used as reliable, specific, and sensitive standard techniques despite being time-consuming, expensive, labor-intensive technicians, and being not suitable as point-of-use sensing devices [1, 2, 3]. Alternative strategies for microorganisms' screening (e.g., fungi, bacteria, etc.) consider the detection of specific volatile organic compounds (VOCs) generated during themicroorganisms' primary and secondary metabolisms, like alcohols, aldehydes, hydrocarbons, acids, ethers, esters, ketones, and terpenes [3]. Thus, gas chromatography coupled with mass spectrometry (GC-MS) is the standard analytical technique, allowing identifying and quantifying the volatiles related to each microorganism [4; 5; 6; 7; 8; 9]. During their growth, each microorganism emits characteristic volatile compounds, generating distinctive odor fingerprints, which can be used for discrimination purposes [10]. The volatile pattern and amounts released by each microorganism may differ with the strain, incubation time, and growth conditions (i.e., substrate, nutrients, pH, humidity, and temperature) [3; 11]. However, different bacteria can produce the same VOCs during their growth (ammonia is emitted by _Pseudomonas aeruginosa_ or _Staphylococcus aureus_; methanol, 1-propanol, 1-butanol, and indole are associated with _Escherichia coli_; and acetoin and diacetyl with _Enterococcus faecalis_) [4; 5; 6; 7; 8; 9; 10; 11]. In this sense, the use of electronic noses (E-noses), capable of encoding high-dimension VOCs' patterns into a smaller-dimension pattern of sensors signals, has emerged as a practical and feasible non-invasive alternative for detecting bacteria, as recently reviewed by [PERSON] et al. [10]. Different gas sensors have been used namely, metal oxide (MOX) or metal oxide semiconductors (MOS), and polymer sensors. To extract the information from the signals profiles, different chemometric tools have been applied, like principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), uncorrelated LDA (ULDA), and/or support vector machines (SVM). E-noses coupled with multivariate statistical techniques, have been successfully used for bacteria detection (e.g., _Bacillus cereus_, _E. coli_, _E. faecalis_, _Listeria innocua_, _P. aeruginosa_, _S. aureus_, or _Salmonella_ spp.) in different culture media or foods [10]. It should be remarked that, besides this particular type of application, the use of E-noses in food analysis has been widely exploited for different matrices [12; 13; 14]. Regarding microorganisms' evaluation, it was possible to discriminate (predictive accuracies from 84 to 97.5%) different bacteria (_B. cereus_, _E. coli_, _E. faecalis_, _Listeria_ spp., and/or _Salmonella_ spp.) grown in solid or liquid synthetic media using E-nose-MOS devices coupled with ULDA or SVM models [1; 2; 15; 16]. It was possible to discriminate viable from non-viable bacteria (_E. coli_ and _Yersinia enterociltica_) using an E-nose-MOS-LDA model (predictive accuracies of 100%) grown in a liquid synthetic medium or in skimm milk samples [17]. Other researchers also showed that an E-nose-MOS coupled with discriminant function analysis or probabilistic neural network could discriminate among _E. coli_, _P. aeruginosa_, and _Klebsiella oxytoca_ in water samples (predictive accuracy of 87% [18]. Commercial E-noses (Cyranse 320 from Cyranse Sciences; BH-114, from Bloodhound; or eNOSGE 4000, from Marconi Applied Technology), comprising arrays of polymer sensors, allowed the correct classification (98%) of different bacteria (_S. aureus_, _Haemophilus influenza_, _Streptococcus pneumoniae_, _E. coli_, _P.