Neural network algorithm-based bacterial microbial sensor and microbial detection method

Abstract

A microbubble cavity bacterial microbial sensor based on a neural network algorithm comprises 1, a wide-spectrum optical fiber laser, 2, microbubble cavity, 3, an optical fiber taper, 4, a spectrum analyzer, and 5, a processor technology, wherein the wide-spectrum optical fiber laser carries out electro-excitation on the wide-spectrum laser, is low in cost and easy to integrate, and is used for generating the wide-spectrum laser; the microbubble cavity is prepared by processing a silicon dioxide capillary tube with a hollow middle part through high-power laser, has high quality factors, is used for a fluid to pass through, and can trap a light field; the optical fiber taper is formed by drawing glass fibers and is used for coupling the optical fiber laser into the microbubble cavity and guiding the optical fiber laser into a spectrum detector; the spectrum analyzer can analyze light intensities of different frequencies; and the processor technology is based on a neural network deep learning algorithm.

Description

technical field [0001] The invention relates to the fields of optical microbubble cavity waveguide coupling and microorganism detection, in particular to a microbubble cavity bacteria and microorganism sensor and a microorganism detection method based on a neural network algorithm. Background technique [0002] Escherichia coli in fluid poses a great hidden danger to people's health, so the detection of microorganisms such as Escherichia coli is extremely important for food safety. However, the existing Escherichia coli sensor is detected by combining chemical reagents and other manual methods in practical applications, which has many defects such as low detection efficiency, inflexible detection, and high labor cost. However, a microbubble cavity bacterial microbial sensor based on neural network algorithm proposed in this scheme can effectively solve the above problems of traditional detection, and realize real-time, accurate, fast and efficient detection of microorganisms...

AI Technical Summary

Problems solved by technology

However, the existing Escherichia coli sensor is detected by combining chemical reagents and other manual methods in practical applications, which has many defects such as low detection efficiency, inflexible detection, and high labor costs.

Images