Method for rapid detection of sulfur fumigation of wolfberry based on multi-feature fusion of spectrum and sensor

By using a detection method that integrates spectral and sensor features, the problem of distinguishing the proportion of free and bound sulfur in wolfberries and tracing the source of sulfur fumigation processes in existing technologies has been solved, enabling rapid, non-destructive detection and accurate process inference.

CN122385488APending Publication Date: 2026-07-14NINGXIA UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NINGXIA UNIVERSITY
Filing Date
2026-05-20
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing methods are insufficient to distinguish the difference in the proportion of free sulfur and bound sulfur in wolfberries, making it impossible to trace the source of different sulfur fumigation processes in reverse, and the detection process is difficult to achieve rapid and non-destructive screening.

Method used

A detection method based on spectral and sensor multi-feature fusion was adopted to randomly divide wolfberry samples into a destruction detection group and a non-consumption detection group. Through hyperspectral imaging and gas phase sensor data, volatility index, spatial spectral cubic data and distribution category data were extracted, a label detection map was drawn, and a multivariate regression model was constructed to infer the chemical form and process category of sulfur.

Benefits of technology

It enables rapid, non-destructive screening of sulfur fumigation traces, provides high-dimensional physical information support, accurately infers the type of sulfur fumigation process, and improves the accuracy and efficiency of detection.

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Abstract

The present application relates to the technical field of traditional Chinese medicine detection, and discloses a rapid detection method for fumigation of wolfberry based on spectrum and sensor multi-feature fusion, which comprises the following steps: determining the volatilization rate index of each fumigation wolfberry in each damage detection group, the spatial spectrum cube data of each fumigation wolfberry in the non-consumption detection group, and the distribution category data of each distribution label in each batch; collecting the spectrum data and gas sensor data of each distribution label in each batch, determining the morphology index, response time lag and peak temperature difference of each distribution label in each batch; constructing a wolfberry fumigation detection model; collecting the spectrum detection data and sensor detection data of the sample to be detected, and determining the predicted fumigation process, predicted distribution label and predicted volatilization rate index of the sample to be detected. The complete judgment from non-consumption detection dynamic response feature extraction to sulfur chemical form and process category inference is realized.
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