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Penerapan Partial Least Squares Pada Data Gingerol
Multivariate calibration model aims to predict the expensive measures obtained by using the measures of a cheap and easy. There are several problems that often occur in the model calibration, among others, and multikolinear. To overcome these problems we used partial least squares method (PLS). The study was conducted to apply the PLS method on the data gingerol. Based on research conducted with the two components of the model obtained with the diversity of variable Y at 83.8032% and the diversity of variable X equal to 100%, and obtained for R2 = 83.8% and RMSE = 0.100891 calibration data group and R2 = 84.2 % and RMSEP = 0.199939 for the validation data.
Penerapan Partial Least Squares Pada Data Gingerol
Multivariate calibration model aims to predict the expensive measures obtained by using the measures of a cheap and easy. There are several problems that often occur in the model calibration, among others, and multikolinear. To overcome these problems we used partial least squares method (PLS). The study was conducted to apply the PLS method on the data gingerol. Based on research conducted with the two components of the model obtained with the diversity of variable Y at 83.8032% and the diversity of variable X equal to 100%, and obtained for R2 = 83.8% and RMSE = 0.100891 calibration data group and R2 = 84.2 % and RMSEP = 0.199939 for the validation data.
Penerapan Partial Least Squares Pada Data Gingerol
Margaretha Ohyver (author)
2010
Article (Journal)
Electronic Resource
Unknown
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The Universal Partial Least Squares, UNIPALS, Algorithm for Partial Least Squares, PLS, Regression
| British Library Conference Proceedings | 1993