Food and oil grain acoustic signal feature extraction method and system based on wavelet transformation

A technology of wavelet transformation and signal characteristics, which is applied in the processing of detection response signals and the use of sound waves/ultrasonic waves/infrasonic waves to analyze solids, etc., can solve the problems of high cost, time-consuming, and inability to measure in real time, and achieve the effect of low-cost detection

Inactive Publication Date: 2012-05-16
HENAN UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

There are already many methods and instruments for measuring grain and oil quality. For example, methods for measuring wheat hardness include pressure method, grinding method, particle size index method, near-infrared method, etc. These methods are time-consuming, laborious, and cannot be measured in real time. Cost is too high to be widespread

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  • Food and oil grain acoustic signal feature extraction method and system based on wavelet transformation
  • Food and oil grain acoustic signal feature extraction method and system based on wavelet transformation
  • Food and oil grain acoustic signal feature extraction method and system based on wavelet transformation

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Embodiment 1

[0025] Measuring the hardness index of wheat. Such as image 3 As shown, the present invention uses the acoustic characteristics of wheat grains, combined with wavelet transform technology, to carry out non-destructive testing of wheat hardness, the specific process is as follows:

[0026] (1) put the wheat grain into the signal collection box, and make it hit the metal target at the bottom of the collection box to generate a sound signal;

[0027] (2) The sound sensor receives the sound signal of wheat grain impact, after filtering and amplifying, the data acquisition card performs A / D conversion on the sound signal, and the computer saves the collected sound signal data;

[0028] (3) Utilize the endpoint detection method to detect the starting point and the end point of the sound signal produced by the impact of each wheat grain. In implementing this step, an amplitude threshold can be determined according to the difference between the wheat grain sound signal and the backg...

Embodiment 2

[0044] Measuring the thousand-grain weight index of wheat. Such as image 3 As shown, the present invention utilizes the acoustic properties of wheat grains, combined with wavelet transform technology, to carry out non-destructive detection of wheat thousand-grain weight, and the specific process is as follows:

[0045] (1) put the wheat grain into the signal collection box, and make it hit the metal target at the bottom of the collection box to generate a sound signal;

[0046] (2) The sound sensor receives the sound signal of wheat grain impact, after filtering and amplifying, the data acquisition card performs A / D conversion on the sound signal, and the computer saves the collected sound signal data;

[0047](3) Utilize the endpoint detection method to detect the starting point and the end point of the sound signal produced by the impact of each wheat grain. In implementing this step, an amplitude threshold can be determined according to the difference between the wheat gr...

Embodiment 3

[0063] As shown in the figure, put the wheat into the sound signal generation system to generate sound signals, use the signal acquisition system to collect the sound signals, send the collected sound data to the signal processing system for processing, and establish the regression equation of wheat acoustic characteristics and moisture , to achieve the determination of wheat moisture.

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Abstract

The invention discloses a food and oil grain acoustic signal feature extraction method and a food and oil grain acoustic signal feature extraction system based on wavelet transformation. The method comprises steps that: food or oil grains are delivered grain-by-grain into an acoustic signal collection box; the grains impact a metal target in the collection box, such that acoustic signals are produced; the impaction acoustic signals are received by a sound transducer; the received acoustic signals are processed through filtering and amplifying, and are transmitted to a data collection card; the acoustic signals are subject to A / D conversion in the data collection card, and are stored in the data collection card; the acoustic signals are decomposed with the wavelet transformation technology; the acoustic signals are reconstructed by using a selected specific wavelet coefficient; energy values of reconstructed signals are extracted as features, and are associated with quality features ofthe food or oil grains. According to the invention, with the acoustic features of the food and oil grains, and combined with the wavelet transformation technology, a non-destructive test is carried out upon the quality features of food and oil grains. The method is convenient and fast. The testing result is stable, and the resolution is relatively high.

Description

technical field [0001] The invention relates to a non-destructive testing method for grain and oil quality. Background technique [0002] The quality of grains and oilseeds is directly related to their uses, so it is also closely related to the price of grains and oilseeds. There are already many methods and instruments for measuring grain and oil quality. For example, methods for measuring wheat hardness include pressure method, grinding method, particle size index method, near-infrared method, etc. These methods are time-consuming, laborious, and cannot be measured in real time. The cost is too high to be widely used. Therefore, it is necessary to find a simple, non-destructive and low-cost method for determining the quality of grain and oil. In recent decades, with the rapid development of information science and technology, useful information can be extracted from the impact sound signals of grain and oil seeds by using signal processing technology, and the quality cha...

Claims

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Application Information

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IPC IPC(8): G01N29/44G01N29/04
Inventor 杨红卫王爱民李齐超丁宁
Owner HENAN UNIVERSITY OF TECHNOLOGY
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