title: Optimization of Parameter Selection for Partial Least Squares Model Development.
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.
authors.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.
authors.forename: Na
authors.initials: N
authors.lastname: Zhao
authors.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.
authors.forename: Zhi-sheng
authors.initials: ZS
authors.lastname: Wu
authors.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.
authors.forename: Qiao
authors.initials: Q
authors.lastname: Zhang
authors.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.
authors.forename: Xin-yuan
authors.initials: XY
authors.lastname: Shi
authors.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.
authors.forename: Qun
authors.initials: Q
authors.lastname: Ma
authors.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.
authors.forename: Yan-jiang
authors.initials: YJ
authors.lastname: Qiao