Rapid and sensitive bacteria detection and identification are becoming increasingly important for a wide range of areas including the control of food safety, the prevention of infectious diseases, and environmental monitoring. Raman spectroscopy is an emerging technology which provides comprehensive information for the analysis of bacteria in a short time and with high sensitivity. Raman spectroscopy offers many advantages including relatively simple operation, non‐destructive analysis, and information on molecular differences between bacteria species and strains. A variety of biochemical properties can be measured in a single spectrum. This short review covers the recent advancements and applications of Raman spectroscopy for bacteria analysis with specific focuses on bacteria detection, bacteria identification and discrimination, as well as bacteria antibiotic susceptibility testing in 2022. The development of novel substrates, the combination with other techniques, and the utilization of advanced data processing tools for the improvement of Raman spectroscopy and future directions are discussed.

The monitoring of drinking water for indicators of fecal contamination is crucial for ensuring a safe supply. In this study, a novel electrochemical method was developed for the rapid and sensitive detection of Escherichia coli (E. coli) in drinking water. This strategy is based on the use of engineered bacteriophages (phages) to separate and concentrate target E. coli when conjugated with magnetic beads, and to facilitate the detection by expressing gold binding peptides fused alkaline phosphatase (GBPs-ALP). The fusion protein GBPs-ALP has both the enzymatic activity and the ability to directly bind onto a gold surface. This binding-peptide mediated immobilization method provided a novel and simple approach to immobilize proteins on a solid surface, requiring no post-translational modifications. The concentration of E. coli was determined by measuring the activity of the ALP on gold electrodes electrochemically using linear sweep voltammetry (LSV). This approach was successfully applied in the detection of E. coli in drinking water. We were able to detect 10⁵ CFU mL⁻¹ of E. coli within 4 hours. After 9 hours of preincubation, 1 CFU of E. coli in 100 mL of drinking water was detected with a total assay time of 12 hours. This approach compares favorably to the current EPA method and has the potential to be applied to detect different bacteria in other food matrices.

Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D ¹H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer (“deep”) artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.

Despite enhanced sanitation implementations, foodborne bacterial pathogens still remain a major threat to public health and generate high costs for the food industry. Reporter bacteriophage (phage) systems have been regarded as a powerful technology for diagnostic assays for their extraordinary specificity to target cells and cost-effectiveness. Our study introduced an enzyme-based fluorescent assay for detecting the presence of E. coli using the T7 phage engineered with the lacZ operon which encodes beta-galactosidase (β-gal). Both endogenous and overexpressed β-gal expression was monitored using a fluorescent-based method with 4-methylumbelliferyl β-d-galactopyranoside (MUG) as the substrate. The infection of E. coli with engineered phages resulted in a detection limit of 10 CFU/mL in ground beef juice after 7 h of incubation. In this study, we demonstrated that the overexpression of β-gal coupled with a fluorogenic substrate can provide a straightforward and sensitive approach to detect the potential biological contamination in food samples. The results also suggested that this system can be applied to detect E. coli strains isolated from environmental samples, indicating a broader range of bacterial detection.