Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration.
Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method.
Xu
Lu
L
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
Zhou
Yan-Ping
YP
Tang
Li-Juan
LJ
Wu
Hai-Long
HL
Jiang
Jian-Hui
JH
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D002138:Calibration; D016208:Databases, Factual; D005504:Food Analysis / Q000379:methods; D008460:Meat / Q000032:analysis; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results; D012639:Seeds / Q000737:chemistry; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D019265:Spectroscopy, Near-Infrared / Q000295:instrumentation / Q000379:methods*; D014908:Triticum / Q000737:chemistry