Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, have developed a groundbreaking method published in the journal LWT - Food Science and Technology has revolutionized the way we identify lactic acid bacteria (LAB) colonies. This innovative approach overcomes the challenges of fluorescence background interference and slow detection speeds, enabling the swift and accurate identification of bacterial colonies.

Bacterial colonies play a crucial role in various fields, including microbiological research, food industry, medical diagnostics, and environmental monitoring. However, traditional methods for bacterial colony identification, such as mass spectrometry and polymerase chain reaction (PCR), are time-consuming and often require complex procedures. Raman spectroscopy, a rapid and non-invasive technique, has shown promise in bacterial identification but suffers from weak signal strength and fluorescence interference.

To address these issues, the research team combined droplet microcavity with label-free SERS technology. They first prepared hydrophobic aluminized slides to minimize background signals and enhance the SERS signal. Silver nanoparticles (AgNPs) were synthesized and used to enhance the Raman scattering intensity of the bacterial colonies. Single colonies were then transferred to centrifuge tubes, mixed with AgNPs, and applied as droplets onto the hydrophobic slides. The SERS spectra were collected using a Raman spectrometer, and the data were analyzed using machine learning algorithms, including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN).

The results demonstrated an improvement in SERS signal intensity and stability after hydrophobic treatment. Compared to conventional Raman spectra, the SERS spectra exhibited clear peaks, a higher signal-to-noise ratio, and noticeable spectral differences between various LAB colonies. The detection speed was also notably enhanced, with each SERS spectrum requiring only 0.5 seconds, allowing the acquisition of 100 spectral data points for a bacterial colony in less than one minute.

The machine learning algorithms achieved high recognition rates, with SVM exceeding 95% and KNN surpassing 90%. These results highlight the effectiveness of the droplet microcavity label-free SERS technology in rapid and accurate identification of bacterial colonies.

This research has important implications for various industries. In the food industry, rapid identification of LAB colonies can improve quality control and ensure the safety of fermented products. In medical diagnostics, the technology can facilitate the early detection of pathogens, enabling timely treatment. Additionally, the technique can be applied to environmental monitoring, aiding in the detection of harmful microorganisms in water and soil.

The combination of droplet microcavity and label-free SERS technology presents a promising solution for rapid and robust identification of bacterial colonies. The high recognition rates achieved through machine learning algorithms underscore the practical significance of this approach. With further development, this technology has the potential to revolutionize bacterial identification in various fields.