Dina Salah Arabi (D S Arabi)

Rapid and successful clinical diagnosis and bacterial infection treatment depend on accurate identification and differentiation between different pathogenic bacterial species. A lot of efforts have been made to utilize modern techniques which avoid the laborious work and time-consuming of conventional methods to fulfill this task. Among such techniques, laser-induced breakdown spectroscopy (LIBS) can tell much about bacterial identity and functionality. In the present study, a sensitivity-improved version of LIBS, i.e. nano-enhanced LIBS (NELIBS), has been used to discriminate between two different bacteria (Pseudomonas aeruginosa and Proteus mirabilis) belonging to different taxonomic orders. Biogenic silver nanoparticles (AgNPs) are sprinkled onto the samples’ surface to have better discrimination capability of the technique. The obtained spectroscopic results of the NELIBS approach revealed superior differentiation between the two bacterial species compared to the results of the conventional LIBS. Identification of each bacterial species has been achieved in light of the presence of spectral lines of certain elements. On the other hand, the discrimination was successful by comparing the intensity of the spectral lines in the spectra of the two bacteria. In addition, an artificial neural network (ANN) model has been created to assess the variation between the two data sets, affecting the differentiation process. The results revealed that NELIBS provides higher sensitivity and more intense spectral lines with increased detectable elements. The ANN results showed that the accuracy rates are 88% and 92% for LIBS and NELIBS, respectively. In the present work, it has been demonstrated that NELIBS combined with ANN successfully differentiated between both bacteria rapidly with high precision compared to conventional microbiological discrimination techniques and with minimum sample preparation.

Fish products are essential sources of animal proteins and numerous nutrients required for healthy human nutrition worldwide. However, some types of low-priced fish may look very similar to some other expensive types, and usually, it is not easy to differentiate between them for inexperienced customers. Moreover, in some markets, adulterating such high-priced fish types through its substitution by cheaper ones or mixing with bacterially spoiled ones, mostly when sold as fish fillets, is sometimes common. Certainly, fish microbial contamination in open markets represents serious hazards for people’s public health. Accordingly, seeking easy and fast fish fraud detection methods and their microbial contamination disclosure is crucial. Currently, available techniques are costly, time-consuming, and requiring special laboratories. In the present work, laser-induced fluorescence (LIF), as a spectrochemical analytical technique and diffuse optical measurements, has been used to discriminate between fillets of low-priced Tilapia and expensive Nile Perch and disclose microbial contamination in any. The experimental data have been analyzed and evaluated using the principal component analysis (PCA), partial least square regression (PLSR), and receiver operatic characteristic (ROC) methods. The results demonstrated the high advantages of optical and spectrochemical techniques in the fast and accurate discrimination between the two fish species. Moreover, LIF spectral band obtained at 490 nm showed a difference in microbial load between both species.

The present work reports on the evaluation of laser induced fluorescence (LIF) for the discrimination between different microbial strains. Pseudomonas aeruginosa and Staphylococcus aureus are important pathogenic bacteria for which therapeutic options are lacking nowadays. These microbial strains were selected due to their medical relevance as they are commonly found in human diseases infections. LIF is a spectrochemical analytical technique that was used in the present study to obtain bacteria spectral fingerprints in the liquid phase. Two laser wavelengths, 266nm (UV) and 405nm (violet), have been used as excitation light sources delivering output power 5mW and 100mW, respectively. The results of LIF analysis showed that the differences in fluorescence bands intensity can be used as a fingerprint for each bacterial species. In addition, the fluorescence emission intensities of the two strains were exponentially related to the concentration of the bacteria. Confocal laser scanning microscopy was used successfully to visualize the fluorescence emission of the cells in comparison with the LIF measurements. The obtained results demonstrate the potential of LIF as a fast, noninvasive, and easy technique for bacterial discrimination. The technique can be also used for the determination of bacteria concentration after performing proper calibration.