PCA is a method for reducing the 100 variables (wavelength data) in each spectrum down to just a few important variables. These variables are often referred to as latent variables, principal components, factors, eigenvectors, etc, and are vectors. This manual will refer to them as PC’s. The dot product of these vectors with the spectral data yields scalars called “PC scores”. Unknowns can be identified by comparing the PC scores of unknown materials to those of the model.
PCA is a method for reducing the 100 variables (wavelength data) in each spectrum down to just a few important variables. These variables are often referred to as latent variables, principal components, factors, eigenvectors, etc, and are vectors. This manual will refer to them as PC’s. The dot product of these vectors with the spectral data yields scalars called “PC scores”. Unknowns can be identified by comparing the PC scores of unknown materials to those of the model.
我的社区
签到
赛默飞实验室产品焕新计划有奖调研！
【七月征文】不一YOUNG实验“猿”
报名开启：ICS2024第十三届光谱网络会议！
推荐收藏！盘点中药材及饮片检测解决方案
