An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss
We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component i...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/306937 |
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| author | Yan Zhou Hui Cao |
| author_facet | Yan Zhou Hui Cao |
| author_sort | Yan Zhou |
| collection | DOAJ |
| description | We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR. |
| format | Article |
| id | doaj-art-e8b6cb5fb12844adaeb27f651731cc10 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e8b6cb5fb12844adaeb27f651731cc102025-02-03T06:12:33ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/306937306937An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information LossYan Zhou0Hui Cao1School of Energy & Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaWe propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.http://dx.doi.org/10.1155/2013/306937 |
| spellingShingle | Yan Zhou Hui Cao An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss The Scientific World Journal |
| title | An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss |
| title_full | An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss |
| title_fullStr | An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss |
| title_full_unstemmed | An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss |
| title_short | An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss |
| title_sort | augmented classical least squares method for quantitative raman spectral analysis against component information loss |
| url | http://dx.doi.org/10.1155/2013/306937 |
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