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Optimization of Parameter Selection for Partial Least Squares Model Development.

Zhao N, Wu ZS, Zhang Q, Shi XY, Ma Q, Qiao YJ - Sci Rep (2015)

Bottom Line: In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection.In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model.The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

View Article: PubMed Central - PubMed

Affiliation: 1] Beijing University of Chinese Medicine, Beijing 100102, China [2] Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing, 100102, China [3] Key Laboratory of TCM-information Engineer of State Administration of TCM, Beijing, China, 100102.

ABSTRACT
In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal(2)) and validation (Rpre(2)) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

No MeSH data available.


Related in: MedlinePlus

Raw NIR spectra of corn sample (a), Yinhuang granules sample (b) and pharmaceutical tablets sample (c).
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f1: Raw NIR spectra of corn sample (a), Yinhuang granules sample (b) and pharmaceutical tablets sample (c).

Mentions: The NIR spectra (1,100–2,498 nm) of typical corn samples are shown in Fig. 1a. There were several broad peaks located near 1,190, 1,450, and 1,940 nm. These represent the characteristic peaks regarding the water in the corn spectral dataset. The strong absorption bands of water appeared around 1,400–1,450 nm and 1,900–1,950 nm due to the first overtone of OH stretching and combination of the OH stretching band with OH bending.


Optimization of Parameter Selection for Partial Least Squares Model Development.

Zhao N, Wu ZS, Zhang Q, Shi XY, Ma Q, Qiao YJ - Sci Rep (2015)

Raw NIR spectra of corn sample (a), Yinhuang granules sample (b) and pharmaceutical tablets sample (c).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4499800&req=5

f1: Raw NIR spectra of corn sample (a), Yinhuang granules sample (b) and pharmaceutical tablets sample (c).
Mentions: The NIR spectra (1,100–2,498 nm) of typical corn samples are shown in Fig. 1a. There were several broad peaks located near 1,190, 1,450, and 1,940 nm. These represent the characteristic peaks regarding the water in the corn spectral dataset. The strong absorption bands of water appeared around 1,400–1,450 nm and 1,900–1,950 nm due to the first overtone of OH stretching and combination of the OH stretching band with OH bending.

Bottom Line: In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection.In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model.The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

View Article: PubMed Central - PubMed

Affiliation: 1] Beijing University of Chinese Medicine, Beijing 100102, China [2] Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing, 100102, China [3] Key Laboratory of TCM-information Engineer of State Administration of TCM, Beijing, China, 100102.

ABSTRACT
In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal(2)) and validation (Rpre(2)) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

No MeSH data available.


Related in: MedlinePlus