CN105158169A - Camellia seed component content software detection system and method - Google Patents
Camellia seed component content software detection system and method Download PDFInfo
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- CN105158169A CN105158169A CN201510299177.0A CN201510299177A CN105158169A CN 105158169 A CN105158169 A CN 105158169A CN 201510299177 A CN201510299177 A CN 201510299177A CN 105158169 A CN105158169 A CN 105158169A
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Abstract
The invention provides a camellia seed component content software detection system. The system comprises a software initialization module, a data processing module, a spectra preprocessing module, a model prediction module and a data storage module. The software initialization module invokes the data processing module, the spectra preprocessing module, the model prediction module and the data storage module. The system and method can fast, simply and effectively detect oleic acid, linoleic acid and palmitic acid contents of camellia seed aliphatic acid, is convenient for use, prevents seed destroy and environment pollution and has an important meaning for improving labor production efficiency.
Description
Technical field
The present invention relates to a kind of tea seed component content software detection systems and method thereof, belong to data processing and composition detection field.
Background technology
China is the production and consumption big country of edible oil, improves the leading indicator that oil crops oleaginousness is breeding man Crop Improvement always.Classic method measures oil plant oleaginousness to waste time and energy, and efficiency is lower, and contaminated environment, and, effort time-consuming to the mensuration of oil plant component content, is unfavorable for the quick detection of camellia seed oil quality, needs to destroy seed simultaneously, is unfavorable for the seed selection of improved seeds.
Along with the quickening day by day of development in science and technology and commodity circulation, particularly in biodiesel exploitation the screening of floorboard with high oil content energy crop also in the urgent need to fast, instruments and methods accurately and efficiently.
Summary of the invention
For solving the problems of the technologies described above, the invention provides a kind of tea seed component content software detection systems and method thereof, this tea seed component content software detection systems and method thereof simple, quick, effectively can detect oleic acid, linoleic acid, palmitic acid component content in tea seed fatty acid, are user-friendly to.
The present invention is achieved by the following technical programs.
A kind of tea seed component content software detection systems provided by the invention, comprises software initialization module, data processing module, Pretreated spectra module, model prediction module and data memory module; Described software initialization module calling data processing module, Pretreated spectra module, model prediction module and data memory module, wherein:
Described software initialization module realizes layout, the drafting tea seed EO-1 hyperion curvilinear coordinates of software interface and sets up data buffer, and generates visual analyzing result picture file folder;
Described data processing module reads file and extracts the valid data in spectroscopic data;
Described Pretreated spectra module adopts smoothing method complete Pretreated spectra and draw the curve of spectrum, at least comprises first differential or second-order differential process in smoothing method;
Described model prediction module from the single order of tea seed hyper spectral reflectance corresponding to the extracting data sensitive band after Pretreated spectra resume module or second-order differential value, and is predicted according to the corresponding forecast model of user's input selection;
Tea seed ingredient prediction result under the hyper spectral reflectance picture of tea seed sample, single order or second-order differential picture and corresponding model is stored as file and printout by described data memory module.
Described software initialization module, data processing module, Pretreated spectra module, model prediction module and data memory module form based on VisualBasic coding.
Described data processing module reads file and extracts the valid data in spectroscopic data, and the text referred to generating after the process of ViewspecPro software reads, and extracts the spectral effective data in text file.
This aspect is applied to and utilizes spectral analysis to detect oleic acid, linoleic acid, palmitic acid component content in tea seed fatty acid.
Present invention also offers a kind of tea seed component content software detecting method, comprise the steps:
1. initialization: software initialization module carries out initialization to software interface layout;
2. just process: data processing module reads the text generated after ViewspecPro process, and carries out Effective judgement to the data read, extracts wherein effective data;
3. pre-service: Pretreated spectra module carries out Savitzky-Golay smoothly to valid data, carries out first differential or second-order differential process, and draw the curve of spectrum to sharpening result;
4. model prediction: model prediction module reads the result of Pretreated spectra module, and single order or the second-order differential value of therefrom extracting spectral reflectivity corresponding to sensitive band, carry out tea seed ingredient prediction according to the forecast model that user selects at software interface;
5. data store: tea seed ingredient prediction result under the hyper spectral reflectance picture that Pretreated spectra module and model prediction resume module obtain by data memory module, single order or second-order differential picture and corresponding model, be stored as Word file or pdf document, and printout.
Described step 4. in forecast model used be defaulted as partial least squares regression when user does not select.
The described step formula that 3. middle Savitzky-Golay is level and smooth is:
Described step 3. in the formula of first differential process be: U
i[X
(ig)x
i]/g.
Described step 3. in the formula of second-order differential process be: U
i[X
(ig)2X
ix
(ig)]/g
2.
Beneficial effect of the present invention is: simple, quick, effectively can detect oleic acid, linoleic acid, palmitic acid component content in tea seed fatty acid, be user-friendly to, have and do not need to destroy seed, the advantage such as free from environmental pollution, have important meaning to raising labor productivity etc.
Accompanying drawing explanation
Fig. 1 is principle schematic of the present invention.
Embodiment
Further describe technical scheme of the present invention below, but described in claimed scope is not limited to.
A kind of tea seed component content software detection systems as shown in Figure 1, comprises software initialization module, data processing module, Pretreated spectra module, model prediction module and data memory module; Described software initialization module calling data processing module, Pretreated spectra module, model prediction module and data memory module, wherein:
Described software initialization module realizes layout, the drafting tea seed EO-1 hyperion curvilinear coordinates of software interface and sets up data buffer, and generates visual analyzing result picture file folder; Specifically, visual analyzing result picture file folder is generated for automatically generating Spectrum_Picture file at D dish;
Described data processing module reads file and extracts the valid data in spectroscopic data;
Described Pretreated spectra module adopts smoothing method complete Pretreated spectra and draw the curve of spectrum, at least comprises first differential or second-order differential process in smoothing method;
Described model prediction module from the single order of tea seed hyper spectral reflectance corresponding to the extracting data sensitive band after Pretreated spectra resume module or second-order differential value, and is predicted according to the corresponding forecast model of user's input selection;
Tea seed ingredient prediction result under the hyper spectral reflectance picture of tea seed sample, single order or second-order differential picture and corresponding model is stored as file and printout by described data memory module.
Described software initialization module, data processing module, Pretreated spectra module, model prediction module and data memory module form based on VisualBasic coding, specifically, are based on VisualBasic6.0.
Described data processing module reads file and extracts the valid data in spectroscopic data, and the text referred to generating after the process of ViewspecPro software reads, and extracts the spectral effective data in text file.
This aspect is applied to and utilizes spectral analysis to detect oleic acid, linoleic acid, palmitic acid component content in tea seed fatty acid.
The present invention also provides a kind of tea seed component content software detecting method, comprises the steps:
1. initialization: software initialization module carries out initialization to software interface layout;
2. just process: data processing module reads the text generated after ViewspecPro process, and carries out Effective judgement to the data read, extracts wherein effective data;
3. pre-service: Pretreated spectra module carries out Savitzky-Golay smoothly to valid data, carries out first differential or second-order differential process, and draw the curve of spectrum to sharpening result;
4. model prediction: model prediction module sensitive band choose and on basis that the principal component analysis model of each component content, Partial Least-Squares Regression Model are set up, from the result of Pretreated spectra module, extract single order or the second-order differential value of spectral reflectivity corresponding to sensitive band, carry out tea seed ingredient prediction according to the forecast model that user selects at software interface;
5. data store: tea seed ingredient prediction result under the hyper spectral reflectance picture that Pretreated spectra module and model prediction resume module obtain by data memory module, single order or second-order differential picture and corresponding model, be stored as Word file or pdf document, and printout.
Generally speaking, the process of Pretreated spectra module comprises first differential process, second-order differential process and multiplicative scatter correction process.
Described step 4. in forecast model used be defaulted as partial least squares regression when user does not select.
The described step formula that 3. middle Savitzky-Golay is level and smooth is:
Y
i---for i wave band level and smooth after reflectance value;
X
i---be the level and smooth front reflectance value of i wave band.。
Described step 3. in the formula of first differential process be: U
i[X
(ig)x
i]/g;
Described step 3. in the formula of second-order differential process be: U
i[X
(ig)2X
ix
(ig)]/g
2;
In formula:
U
i---be the reflectivity differential value of spectrum i-th wave band;
X
i---be the reflectance value of spectrum i-th wave band;
G---be the width of window.
Thus, present invention achieves the follow-up work of tea seed EO-1 hyperion after professional software ViewspecPro process, comprise the reading to the text generated after the process of ViewspecPro software, the extraction of valid data, level and smooth, the first differential of spectrum or second-order differential process, extract specific band reflectance value for the preservation of model prediction and file and printing etc.This software detecting method simple, quick, effectively can detect oleic acid, linoleic acid, palmitic acid component content in tea seed fatty acid, be user-friendly to, have and do not need to destroy seed, the advantage such as free from environmental pollution, have important meaning to raising labor productivity etc.
Claims (9)
1. a tea seed component content software detection systems, comprise software initialization module, data processing module, Pretreated spectra module, model prediction module and data memory module, it is characterized in that: described software initialization module calling data processing module, Pretreated spectra module, model prediction module and data memory module, wherein:
Described software initialization module realizes layout, the drafting tea seed EO-1 hyperion curvilinear coordinates of software interface and sets up data buffer, and generates visual analyzing result picture file folder;
Described data processing module reads file and extracts the valid data in spectroscopic data;
Described Pretreated spectra module adopts smoothing method complete Pretreated spectra and draw the curve of spectrum, at least comprises first differential or second-order differential process in smoothing method;
Described model prediction module from the single order of tea seed hyper spectral reflectance corresponding to the extracting data sensitive band after Pretreated spectra resume module or second-order differential value, and is predicted according to the corresponding forecast model of user's input selection;
Tea seed ingredient prediction result under the hyper spectral reflectance picture of tea seed sample, single order or second-order differential picture and corresponding model is stored as file and printout by described data memory module.
2. tea seed component content software detection systems as claimed in claim 1, is characterized in that: described software initialization module, data processing module, Pretreated spectra module, model prediction module and data memory module form based on VisualBasic coding.
3. tea seed component content software detection systems as claimed in claim 1, it is characterized in that: described data processing module reads file and extracts the valid data in spectroscopic data, the text referred to generating after the process of ViewspecPro software reads, and extracts the spectral effective data in text file.
4. tea seed component content software detection systems as claimed in claim 1, is characterized in that: this aspect is applied to and utilizes spectral analysis to detect oleic acid, linoleic acid, palmitic acid component content in tea seed fatty acid.
5. a tea seed component content software detecting method, is characterized in that: comprise the steps:
1. initialization: software initialization module carries out initialization to software interface layout;
2. just process: data processing module reads the text generated after ViewspecPro process, and carries out Effective judgement to the data read, extracts wherein effective data;
3. pre-service: Pretreated spectra module carries out Savitzky-Golay smoothly to valid data, carries out first differential or second-order differential process, and draw the curve of spectrum to sharpening result;
4. model prediction: model prediction module reads the result of Pretreated spectra module, and single order or the second-order differential value of therefrom extracting spectral reflectivity corresponding to sensitive band, carry out tea seed ingredient prediction according to the forecast model that user selects at software interface;
5. data store: tea seed ingredient prediction result under the hyper spectral reflectance picture that Pretreated spectra module and model prediction resume module obtain by data memory module, single order or second-order differential picture and corresponding model, be stored as Word file or pdf document, and printout.
6. tea seed component content software detection systems as claimed in claim 1 and method thereof, is characterized in that: described step 4. in forecast model used be defaulted as partial least squares regression when user does not select.
7. tea seed component content software detection systems as claimed in claim 1 and method thereof, is characterized in that: the described step formula that 3. middle Savitzky-Golay is level and smooth is:
8. tea seed component content software detection systems as claimed in claim 1 and method thereof, is characterized in that: described step 3. in the formula of first differential process be: U
i[X
(ig)x
i]/g.
9. tea seed component content software detection systems as claimed in claim 1 and method thereof, is characterized in that: described step 3. in the formula of second-order differential process be: U
i[X
(ig)2X
ix
(ig)]/g
2.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001040776A1 (en) * | 1999-12-02 | 2001-06-07 | Johns Hopkins University | Method of measuring tissue hemoglobin saturation using gaussian decomposition |
| US20020113210A1 (en) * | 2000-10-13 | 2002-08-22 | Treado Patrick J. | Near infrared chemical imaging microscope |
| CN102353643A (en) * | 2011-06-22 | 2012-02-15 | 中国林业科学研究院林产化学工业研究所 | Method for rapid determination of oil content in Camellia oleifera seeds by using near-infrared diffuse reflectance spectroscopy (NIRS) |
| CN103743703A (en) * | 2013-12-20 | 2014-04-23 | 贵州省分析测试研究院 | Method for detecting main components in tea leaves by adopting near infrared spectrum |
-
2015
- 2015-06-03 CN CN201510299177.0A patent/CN105158169A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001040776A1 (en) * | 1999-12-02 | 2001-06-07 | Johns Hopkins University | Method of measuring tissue hemoglobin saturation using gaussian decomposition |
| US20020113210A1 (en) * | 2000-10-13 | 2002-08-22 | Treado Patrick J. | Near infrared chemical imaging microscope |
| CN102353643A (en) * | 2011-06-22 | 2012-02-15 | 中国林业科学研究院林产化学工业研究所 | Method for rapid determination of oil content in Camellia oleifera seeds by using near-infrared diffuse reflectance spectroscopy (NIRS) |
| CN103743703A (en) * | 2013-12-20 | 2014-04-23 | 贵州省分析测试研究院 | Method for detecting main components in tea leaves by adopting near infrared spectrum |
Non-Patent Citations (1)
| Title |
|---|
| 郭敦军: "基于高光谱的油茶籽内部品质检测方法研究", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
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Application publication date: 20151216 |