Hyperspectral-image-technology-based multi-quality nondestructive testing method for dried green soybeans

Abstract

The invention relates to a hyperspectral-image-technology-based multi-quality nondestructive testing method for dried green soybeans. According to the technical scheme, the method comprises the steps of: a. selecting dried green soybeans; b. collecting hyperspectral images of the dried green soybeans by utilizing a hyperspectral image collecting system; c. extracting contour information of the dried green soybeans by utilizing a threshold segmenting method; d. extracting image entropy characteristic parameters by utilizing the obtained contour information; e. collecting the color, moisture rate, hardness and shrinkage rate indexes of the dried green soybeans by utilizing a destructive instrument; f. constructing an evaluation prediction model of the dried green soybeans by utilizing a partial least-squares regression algorithm; g. collecting the hyperspectral images of the dried green soybeans and inputting the hyperspectral images of the dried green soybeans to the evaluation prediction model to obtain the evaluation results on the color, moisture rate, hardness and shrinkage rate indexes of the dried green soybeans. Through the evaluation prediction model and the hyperspectral image collecting system, a multi-quality evaluation result can be obtained under the situation of being nondestructive for the majority dried green soybeans; the hyperspectral-image-technology-based multi-quality nondestructive testing method is easy to operate, good in real-time property and high in reliability.

Description

technical field [0001] The invention relates to a method for non-destructive detection of dry soybean quality, in particular to a method for synchronous non-destructive detection of multiple quality indicators of dried soybean based on hyperspectral image technology. Background technique [0002] Edamame (edamame) is a kind of soybean (Glycine Max (L.) Merr.), also known as vegetable soybean or green soybean. Because edamame is rich in protein, vitamins, dietary fiber, carotene and other nutrients that are beneficial to the human body, it is more and more favored by consumers. Edamame is very easy to yellow after picking. In order to prolong the storage and shelf life of edamame kernels, edamame can be made into a snack food with a unique flavor by drying. Due to the diversity of drying methods, the quality detection of dried edamame is extremely important. The quality inspection indicators of dried edamame samples mainly include the detection of color, moisture content, h...

AI Technical Summary

Problems solved by technology

The quality inspection indicators of dried edamame samples mainly include the detection of color, moisture content, hardness, and shrinkage rate. Edamame that is too dark in color, too hard in hardness, or cracked will seriously affect the vision and taste of edamame, and is difficult to be accepted by consumers.

In the quality testing of dried edamame currently used, different standard instruments are mainly used to conduct destructive testing on multiple indicators respectively, which has the problems of time-consuming testing, sample damage, and difficulty in large-scale testing

With the development of non-destructive testing technology, machine vision technology has been studied in the color detection of agricultural products, and visible / near-infrared spectroscopy in the detection of moisture content, but these two technologies cannot simultaneously obtain the detection of multiple quality indicators

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