Optimization of Salmonella detection in garlic, onion, cinnamon, red chili pepper powders and green tea

A Fluorescent Biosensor for Sensitive Detection of Salmonella Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network

In this study, a fluorescent biosensor was developed for the sensitive detection of Salmonella typhimurium using a low-gradient magnetic field and deep learning via faster region-based convolutional neural networks (R-CNN) to recognize the fluorescent spots on the bacterial cells. First, magnetic nanobeads (MNBs) coated with capture antibodies were used to separate target bacteria from the sample background, resulting in the formation of magnetic bacteria. Then, fluorescein isothiocyanate fluorescent microspheres (FITC-FMs) modified with detection antibodies were used to label the magnetic bacteria, resulting in the formation of fluorescent bacteria. After the fluorescent bacteria were attracted against the bottom of an ELISA well using a low-gradient magnetic field, resulting in the conversion from a three-dimensional (spatial) distribution of the fluorescent bacteria to a two-dimensional (planar) distribution, the images of the fluorescent bacteria were finally collected using a high-resolution fluorescence microscope and processed using the faster R-CNN algorithm to calculate the number of the fluorescent spots for the determination of target bacteria. Under the optimal conditions, this biosensor was able to quantitatively detect Salmonella typhimurium from 6.9 × 10¹ to 1.1 × 10³ CFU/mL within 2.5 h with the lower detection limit of 55 CFU/mL. The fluorescent biosensor has the potential to simultaneously detect multiple types of foodborne bacteria using MNBs coated with their capture antibodies and different fluorescent microspheres modified with their detection antibodies.

Sensitive detection of foodborne pathogens based on CRISPR-Cas13a

Salmonella, being one of the most widespread foodborne pathogens, is a compulsory test item required by national food safety standard of China and many other countries. More sensitive and specific Salmonella detection method is still needed since traditional methods are time consuming and highly dependent on enormous manpower and material resources. In this research, a bacteria detection method based on CRISPR-Cas13a system (where CRISPR is Clustered Regularly Interspaced Short Palindromic Repeats) was proposed. The target DNA was amplified by PCR and transcribed into RNA by T7 transcriptase, which can activate the RNase activity of the Cas13a protein. The self-folding quenched fluorescent probe can be cleaved by the activated Cas13a protein to generate fluorescent signal. We named this method as PCF detection (PCR-CRISPR-Fluorescence based nucleic acid detection). In this study, PCF detection showed excellent sensitivity, which can detect Salmonella genomic DNA with a minimum of 10¹ aM or 10° CFU/ml Salmonella bacteria in 2 hr. It also showed good specificity with no cross-reaction with other common foodborne bacteria. PRACTICAL APPLICATION: The PCF detection method proposed in this article can detect Salmonella sensitively and specifically, providing a novel strategy for the detection of foodborne pathogens in food and has great application potential in other microbial detection fields.