Silkworm cocoon surface pollution detection method

By combining Raman spectroscopic analysis technology and adaptive spectroscopic analysis technology, high sensitivity, accuracy and rapid detection of microbial contamination on the surface of silkworm cocoons is achieved, and the problems of long detection cycle, low efficiency and inevitable destructive treatment in the prior art are solved.

CN119985445AInactive Publication Date: 2025-05-13四川奥特丝纺织有限公司
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Patent Information

Application Number
CN202510476946.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-16
Publication Date
2025-05-13
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing microbial pollution detection methods on the surface of silkworm cocoons have problems such as long detection cycle, low efficiency, inevitable destructive treatment and low signal-to-noise ratio, which cannot meet the real-time, efficient and non-destructive detection needs.

Method used

The confocal Raman spectral analysis system based on Raman spectral analysis technology is combined with adaptive spectral analysis technology to generate a three-dimensional model that is highly consistent with the surface of the silkworm cocoon through three-dimensional scanning and adaptive surface reconstruction, achieving non-destructive detection.

Benefits of technology

It achieves high sensitivity and accuracy, rapid and automated detection, and can detect microbial spores as low as 0.5 μm, providing efficient, non-destructive and accurate detection results.

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Abstract

The invention is applicable to the technical field of silkworm cocoon quality detection, and provides a silkworm cocoon surface pollution detection method, which is used for performing non-destructive detection on silkworm cocoon surface pollution by combining a high-sensitivity confocal Raman spectrum analysis system and a self-adaptive spectrum analysis technology based on a Raman spectrum analysis technology. According to the self-adaptive spectrum analysis technology, firstly, a probe on a scanning platform is used for curved surface reconstruction based on pre-scanning point cloud data based on an irradiation sampling surface of a sample; screening out pre-scanning points which do not contain microbial spores, and generating a three-dimensional data model which is highly matched with the distribution of microorganisms on the silkworm cocoons; corresponding to the reconstructed three-dimensional coordinates of the remaining multiple points, analyzing the corresponding Raman spectrograms one by one by utilizing Raman detection to obtain an identification result. Therefore, the method can significantly reduce the operation complexity and accelerate the overall detection rate.
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