Penerapan Partial Least Squares Pada Data Gingerol
DOI:
https://doi.org/10.21512/comtech.v1i1.2166Keywords:
gingerol, multivariate calibration, partial least squaresAbstract
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.Plum Analytics
References
Abdi, H. (2003). Partial least squares regression. Encyclopedia of Social Sciences Research Methods (online), 1-7. Retrieved from http://www.utdallas.edu/~herve.
Arnita. (2005). Koreksi pencaran pada data kalibrasi rimpang jahe (Zingiber offcinale). Tesis tidak diterbitkan, Bogor: Program Pascasarjana, Institut Pertanian Bogor.
Atok, R. M., dan Notodiputro, K. A. (2004). Metode NN (Neural Network) dengan principle component sebagai pre-processing pada data. Proceeding Seminar Nasional Statistika, Bogor: Institut Pertanian Bogor.
Boulesteix, A., and Strimmer, K. (2006). Partial least squares: A versatile tool for the analysis of high-dimensional genomic data (online). Retrieved from
http://www.slcmsr.net/boulesteix/papers/review.
Chung, H. et al. (2004). Investigation of partial least squares calibration performance based on different resolutions of near infrared spectra. Bull. Korean Chem. Soc, 25 (5), 647-651.
Garthwaite, P. H. (1994). An interpretation of partial least squares. Journal of the American Statistical Association, 89, 122-127.
Martens, H., and Naes, T. (1989). Multivariate calibration, New York: John Wiley & Sons, Inc.
Naes, T. et al. (2002). Multivariate calibration and classification, Chichester: NIR Publications.
Neter, J., Wasserman, W., and Kutner, M. H. (1990). Applied linear statistical models, Illinois: Irwin.
Shao, X., and Zhuang, Y. (2004). Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing. Analytical Sciences, 20, 451-454.
Sunaryo, S. (2005). Model kalibrasi dengan transformasi wavelet sebagai metode pra-pemrosesan. Disertasi tidak diterbitkan. Bogor: Program Pascasarjana, Institut Pertanian Bogor.
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