Method for constructing colony count regression prediction model and method for detecting freshness of fish by using model
The invention relates to a method for constructing a colony count regression prediction model and a method for detecting the freshness of fish by using the model. The construction method of the colonycount regression prediction model comprises the steps of performing hyperspectral image detection on...
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creator | YE RONGKE DUAN QINGLING LIU CHUNHONG LI DAOLIANG GUO YUCHEN WEI YAOGUANG |
description | The invention relates to a method for constructing a colony count regression prediction model and a method for detecting the freshness of fish by using the model. The construction method of the colonycount regression prediction model comprises the steps of performing hyperspectral image detection on a fish sample, preprocessing a hyperspectral image, obtaining a training sample and establishing the colony count regression prediction model; after the colony count regression prediction model is constructed, acquiring a hyperspectral image of the to-be-detected fish sample through a hyperspectral sorter, and predicting the colony count of the to-be-detected fish sample so as to determine the freshness of the to-be-detected fish sample. According to the invention, a kernel extreme learning machine is optimized by using the differential evolution algorithm to further establish the fish colony count regression model; according to the model, nondestructive detection can be carried out on thecolony count content of t |
format | Patent |
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The construction method of the colonycount regression prediction model comprises the steps of performing hyperspectral image detection on a fish sample, preprocessing a hyperspectral image, obtaining a training sample and establishing the colony count regression prediction model; after the colony count regression prediction model is constructed, acquiring a hyperspectral image of the to-be-detected fish sample through a hyperspectral sorter, and predicting the colony count of the to-be-detected fish sample so as to determine the freshness of the to-be-detected fish sample. 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The construction method of the colonycount regression prediction model comprises the steps of performing hyperspectral image detection on a fish sample, preprocessing a hyperspectral image, obtaining a training sample and establishing the colony count regression prediction model; after the colony count regression prediction model is constructed, acquiring a hyperspectral image of the to-be-detected fish sample through a hyperspectral sorter, and predicting the colony count of the to-be-detected fish sample so as to determine the freshness of the to-be-detected fish sample. 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The construction method of the colonycount regression prediction model comprises the steps of performing hyperspectral image detection on a fish sample, preprocessing a hyperspectral image, obtaining a training sample and establishing the colony count regression prediction model; after the colony count regression prediction model is constructed, acquiring a hyperspectral image of the to-be-detected fish sample through a hyperspectral sorter, and predicting the colony count of the to-be-detected fish sample so as to determine the freshness of the to-be-detected fish sample. According to the invention, a kernel extreme learning machine is optimized by using the differential evolution algorithm to further establish the fish colony count regression model; according to the model, nondestructive detection can be carried out on thecolony count content of t</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS TESTING |
title | Method for constructing colony count regression prediction model and method for detecting freshness of fish by using model |
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