Near-infrared detection method for peanut quality and application

A technology for near-infrared detection and peanut quality, applied in measuring devices, material analysis through optical means, instruments, etc., can solve the problems of less prediction model reports, unclear spectral characteristic wavelengths, and more spectral redundant information

Active Publication Date: 2015-12-23
HUAZHONG AGRI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, near-infrared detection technology also has some applications in peanut detection. A non-destructive method for measuring the oil content of a single peanut seed (CN200310112331) only briefly introduces the establishment of near-infrared and oil content detection schemes. Near-infrared detection of amino acid content in peanuts The method (CN201210007425) introduced the wavelength range of peanut amino acids in the ...

Method used

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  • Near-infrared detection method for peanut quality and application
  • Near-infrared detection method for peanut quality and application
  • Near-infrared detection method for peanut quality and application

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0096] Example 1: Near-infrared Detection of Coated Peanut Protein

[0097] 1) Collect a total of 150 samples of the covered peanut samples in the above table 1;

[0098] 2) Determining the protein content of the covered peanut sample collected in step 1), the basic statistical data are shown in Table 3;

[0099] Table 3 The basic statistical data of the protein content of the coated peanut

[0100] Average (%)

Maximum (%)

Minimum value (%)

standard deviation

22.24

24.85

18.41

1.01

[0101] 3) Carry out near-infrared scanning to the peanut sample collected in step 1), the original spectrum is shown in the attached figure 1 ;

[0102] 4) For the absorbance value x obtained in step 3) ij Carry out denoising processing and preprocessing: use wavelet denoising method to x ij Perform denoising processing to obtain the denoising absorbance value, and then perform orthogonal signal correction (OSC) preprocessing on the denoising abs...

Embodiment 2

[0116] Example 2: Near-infrared Detection of Moisture in Coated Peanuts

[0117] 1) Collect a total of 150 samples of the covered peanut samples in the above table 1;

[0118] 2) Determining the moisture content of the coated peanut samples collected in step 1), the basic statistical data are shown in Table 4.

[0119] Table 4 Basic statistical data of moisture content of peanuts with coats

[0120] Average (%)

Maximum (%)

Minimum value (%)

standard deviation

5.48

5.69

5.21

0.11

[0121] 3) Carry out near-infrared scanning to the peanut sample collected in step 1), the original spectrum is shown in the attached figure 1 ;

[0122] 4) For the absorbance value x obtained in step 3) ij Perform denoising and preprocessing: use the threshold denoising method to x ij Perform denoising processing to obtain the denoising absorbance value, and then perform first-order derivative preprocessing on the denoising absorbance value to obtai...

Embodiment 3

[0137] Example 3: Near-infrared detection of peanut fat with coat

[0138] 1) Collect a total of 150 samples of the covered peanut samples in the above table 1;

[0139] 2) Determining the fat content of the coated peanut samples collected in step 1), the basic statistical data are shown in Table 6.

[0140] Table 6 Basic statistical data of fat content of peanuts with coat

[0141] Average (%)

Maximum (%)

Minimum value (%)

standard deviation

49.75

52.52

48.45

0.84

[0142] 3) Carry out near-infrared scanning to the peanut sample collected in step 1), the original spectrum is shown in the attached figure 1 ;

[0143] 4) For the absorbance value x obtained in step 3) ij Perform denoising and preprocessing: use the threshold denoising method to x ij Perform denoising processing to obtain the denoising absorbance value, and then perform standard normalization and multivariate scattering correction preprocessing on the denoising ...

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Abstract

The invention belongs to the technical field of agricultural product quality analysis, and particularly relates to a near-infrared detection method for the peanut quality and application. The method comprises the following steps that peanut samples are collected, physical and chemical testing is performed on the peanut samples, near-infrared scanning is performed on the peanut samples, denoising processing and preprocessing are performed on obtained light absorption values, obtained preprocessed light absorption values are analyzed, near-infrared spectrum characteristic wavelengths are obtained through screening, and a prediction model of the peanut quality is built through a stepwise regression method. According to the near-infrared detection method for the peanut quality and the application, the obtained information is intuitive and reliable, the characteristic wavelengths of the peanut quality are determined and are few in number, and the analytical method that the model is built through the characteristic wavelengths is applied, so that the model precision is improved; on the condition of the same prediction precision, the prediction speed is high; meanwhile, through the built near-infrared prediction model method for the moisture, protein, fat, total sugar and ash content of peanuts, the peanut quality can be analyzed more comprehensively, and usage and popularization are easy.

Description

technical field [0001] The invention belongs to the technical field of agricultural product quality analysis, and in particular relates to a near-infrared detection method and application of peanut quality. Background technique [0002] Peanut is an important source of edible vegetable oil and protein in China. In 2007, my country's total output has exceeded 130 million tons, accounting for about half of the total output of oil crops, ranking first in the world's total peanut output. In 2011, the unit yield reached 3.6 tons per hectare, and the unit yield, total output and export volume all ranked first among oil crops in my country. [0003] The fat content of most varieties of peanuts in my country is between 45% and 50%, the highest is more than 62%, and the range of variation is large. The protein content is more than 20%. Peanuts contain 8 kinds of essential amino acids for the human body, 90% of which can be absorbed by the human body. The sugar content is more than 3%...

Claims

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Application Information

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IPC IPC(8): G01N21/359G01N21/3563
Inventor 黄汉英赵思明胡月来熊善柏程明
Owner HUAZHONG AGRI UNIV
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