Starch particle identification method based on Mask R-CNN

A technology of starch granules and identification methods, applied in the field of food science and engineering, can solve the problems of different edge shapes, colors and granule size, inconsistent granule evaluation standards, and limit starch gelatinization indicators, etc., to improve segmentation efficiency, detection The effect of high accuracy and high degree of automation

Pending Publication Date: 2022-01-04
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

At present, most of the research on the number of granules in the starch gelatinization process is focused on manual recognition, which has a slow recognition rate and incomplete recognition indicators, which affect the gelatinization evaluation.
Due to the non-unifor...

Method used

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  • Starch particle identification method based on Mask R-CNN
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  • Starch particle identification method based on Mask R-CNN

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Embodiment 1

[0072] Such as Figure 4 Shown, a kind of starch granule recognition method based on Mask R-CNN comprises the following steps:

[0073] (1) Data collection of starch gelatinization map: Weigh 0.60 g of potato starch and mix it with 10 ml of water to prepare a potato starch suspension with a mass ratio of starch to water of 6%. Mix well, use a pipette to absorb the starch suspension on the basis of mixing, drop it into the center of the round glass slide, and after the starch suspension is dispersed, seal it with a cover glass and glass glue. After sealing, the starch suspension should be evenly dispersed with spaces between starch granules. Place the sealed potato starch suspension glass slide on the hot stage equipment (THMS600, Linkam, UK), heat up, adjust the starch granule image to an appropriate position, adjust the microscope so that the eyepiece is 10 times, and the objective lens is 20 times. Adjust the focal length and exposure time of the digital camera, and use th...

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Abstract

The invention discloses a Mask R-CNN-based starch particle identification method, and the method combines hot stage-polarized light microscopic observation with a computer image identification technology, and intelligently identifies the change of the number of particles in a starch gelatinization process: collecting starch gelatinization image data based on hot stage-polarized light microscopic observation; sequentially carrying out starch gelatinization data set construction, Mask R-CNN model construction and training, starch gelatinization process particle detection and starch particle number statistics through an algorithm, wherein the algorithm comprises a starch image binarization algorithm, a starch gelatinization data set construction algorithm, a neural network training algorithm, a starch particle detection algorithm and the like. According to the method, the Mask R-CNN is used for intelligently detecting starch gelatinization, the automation degree is high, the detection efficiency is good, and the particle detection accuracy rate reaches 95% or above. The invention provides a novel quantitative method for intelligently detecting starch particles and evaluating starch gelatinization.

Description

technical field [0001] The invention belongs to the technical field of food science and engineering, and in particular relates to a method for identifying starch granules based on Mask R-CNN. Background technique [0002] In the traditional food industry, starch is an important component that undertakes the structure and function of food. In recent years, starch has been widely used in food, material, chemical, medical and other fields. Gelatinization is an intuitive expression of component morphology, supramolecular structure, multi-component structure-activity relationship and behavioral response in starch systems. During processing, under the action of relevant physical fields, the intramolecular and intermolecular interactions of starch molecules (chains) are weakened, resulting in changes in multi-scale structures (granule structure, ultrastructure, crystal structure and molecular structure), specifically manifested as swelling, Phase transition behaviors such as gela...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T7/11G06T7/13G06N3/04G06N3/08
CPCG06T7/11G06T7/13G06N3/084G06T2207/10061G06T2207/20081G06T2207/20084G06T2207/20112G06N3/045
Inventor 董仁涛朱芷仪刘宏生牛雅惠潘博廖静欣
Owner SOUTH CHINA UNIV OF TECH
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