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Predicting method for fruit maturity

A maturity and fruit technology, which is applied in the field of fruit maturity prediction, can solve the problems of damage and unrealistic detection, and achieve the effects of reducing interference, improving reliability and repeatability, and expanding the detection range

Inactive Publication Date: 2007-06-27
ZHEJIANG UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

This method is to use a steel indenter with a certain diameter to perform a compression test on the fruit at a certain compression speed, and measure the compression force at the same time, which belongs to destructive testing, and it is unrealistic to test a large number of samples one by one.

Method used

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Embodiment

[0033] The invention establishes a multiple linear regression model (MLR) and an inverse propagation neural network model (ANN) of peach odor. The sensor response value database obtained by the electronic nose was used as the independent variable, and the firmness, sugar content and acidity of peaches were respectively used as the dependent variables to establish a multiple regression model. 10 sample data were substituted into the model to test the predictive ability of the model. The multiple linear regression model between the 8 sensor responses to peach odor (S1,...,S8) and the maturity index is as follows:

[0034] For firmness, the peach odor multiple linear regression model is:

[0035] CF=72.76-26.73×S1+12.02×S2-357.37×S3+7.91×S4+314.82×S5-3.92×S6-45.3×S7-1.06×S8 For Brix, the multiple linear regression model for peach odor is:

[0036] SSC=3.81+3.99×S1-0.87×S2+4.94×S3+7.84×S4-9.65×S5+7.25×S6+5.44×S7-7.64×S8 For acidity, the multiple linear regression model for peach...

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Abstract

The method includes steps: (1) placing fruit sample inside sealed container; after top air portion reaches balance, sampled top air portion is inducted to sensor array reaction chamber; response signal is obtained when reaction takes place between sensor array and top air portion; response signal of sensor is ratio between resistance R after sensor contacts top air and resistance R0 when sensor passes through clean air, i.e. S=R / R0; (2) carrying out detections including compactness, sugar degree, and acidity for detected fruit; (3) using multiple linear regression, major constituent regression, least squares regression, and artificial neural network to build mathematical model of relation between the response signal of sensor and compactness, sugar degree, acidity of fruit sample. The invention extends detection range, lowers interference, increase sensitivity, reliability, and repeatability.

Description

technical field [0001] The invention relates to a method for predicting fruit maturity. Background technique [0002] In recent years, with the globalization of the international market, people have higher and higher requirements for fruit quality, and the maturity of fruit is the main factor determining fruit quality. The degree of maturity in the process of fruit harvesting, storage and circulation determines the degree of satisfaction of consumers, so it is very important to detect and control the degree of fruit maturity. At present, the research on fruit maturity detection technology has been continuously developed, but most of them use damage detection. [0003] The smell of fruit is an important means to evaluate its quality, and it is also one of the main factors affecting consumers' purchase. Each fruit has a different fragrance and special smell, which is determined by the aromatic substances contained in them. The content of aromatic substances varies in differ...

Claims

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Application Information

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IPC IPC(8): G01N33/02G01N27/02G01N27/00G06F19/00
Inventor 王俊张红梅叶盛
Owner ZHEJIANG UNIV
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