http://rdf.ncbi.nlm.nih.gov/pubchem/patent/AU-2020101288-A4
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d6a6f422b091ba12ea61d4adbf1b0e8e |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N21-718 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N21-31 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N21-3586 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N29-50 |
| filingDate | 2020-07-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2020-08-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_583e4e5c4e731a7d6c29bc7351dc91c7 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_54c4376a1e175ecd6d1553883747c719 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_75dc83a367807c4861fc4239b13fc5b8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_49b9ebb964b6f7714b3763bdbf6c936d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f08e939d702885489bd71fe9673eb035 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_da9686987cdf64bd22af779fe55839c3 |
| publicationDate | 2020-08-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | AU-2020101288-A4 |
| titleOfInvention | Method and System for Quantitatively Detecting Copper in Rice Leaves |
| abstract | The present invention provides a method and system for quantitatively detecting copper (Cu) ncontent in rice leaves. The method includes: obtaining rice leaf samples, laser-induced breakdown nspectroscopy (LIBS) data and a true Cu content Y; establishing a simple linear regression (SLR) nmodel of Cu emission line intensity and Cu content, and calculating a predicted Cu content Yi nbased on the model; establishing an exponential regression (ER) model of Cu emission line nintensity and Cu content, and obtaining a maximum ReJ; calculating a predicted Cu content Yj nbased on the model; establishing a multiple linear regression (MLR) model of Cu emission line nintensity and Cu content Yd, and calculating a predicted Cu content based on the model; and nestablishing a linear regression model of multi-equation combination predicted content and true nCu content, and finally determining a Cu content. The method of the present invention contains nmathematical relationships such as linear, exponential and non-linear relationships between the nLIBS spectra and the Cu content of the rice leaves to the maximum extent. This method retards nmatrix interference information and thus achieves accurate quantification of the Cu content.n1/2 n101 nObtain rice leaf samples n102 nAcquire laser-induced breakdown spectroscopy (LIBS) data of the rice leaf samples n103 nMeasure a true copper (Cu) content y in the rice leaf samples by using inductively ncoupled plasma mass spectrometry (ICP-MS) nObtain a characteristic band with a highest correlation with the true content from the 104 nspectral data by using a characteristic variable screening method n105 nQuickly locate n Cu emission lines in the characteristic band nUse a simple linear regression (SLR) method to establish n SLR models of Cu emission 106 nline intensity and Cu content of the test set samples, and obtain correlations RI, R2, ... , Rn nbetween the true Cu content y and predicted contents yl, y2, ... , yn n107 nObtain a maximum correlation Ri among RI, R2, ... , Rn n108 nCalculate a predicted Cu content yi based on an SLR model of Cu emission line nintensity and Cu content corresponding to the maximum correlation Ri nUse an exponential regression (ER) equation to establish n ER models of Cu emission 109 nline intensity and Cu content of the test set samples, and obtain correlations Rel, Re2, nRen between the true Cu content y and predicted contents yl, y2, ... , yn n110 nObtain a maximum correlation Rej among Re, Re2, ... , Ren n111 nCalculate a predicted Cu content yj of an ER model of Cu emission line intensity and nCu content corresponding to the maximum correlation Rej nUse a multiple linear regression (MLR) method to establish n MLR models of Cu 112 nemission line intensity and Cu content of the test set samples, and obtain a correlation Rd nbetween the true Cu content y and a predicted content yd; n113 nCalculate the predicted Cu content yd based on an MLR model of Cu emission line nintensity and Cu content corresponding to the correlation Rd; nEstablish a linear regression model of test multi-equation combination predicted content 114 and true Cu content by using a linear regression method by taking Cu contents yi, yj and yd nas an input vector and the true content y as an output vector n115 nDetermine a Cu content based on the linear regression model of multi-equation ncombination predicted content and true Cu content nFIG. 1 |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112649383-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114636689-A |
| priorityDate | 2020-03-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID23978 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID418354341 |
Total number of triples: 23.