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.
These variables are often referred to as latent variables, principal components, factors, eigenvectors, etc, and are vectors. These variables 是指哪100个变量呢还是后面指的几个重要变量即主成分？
一句话说说参加原创大赛对您的影响和帮助
油罐混装，这样的食用油你还敢吃吗？
色谱耗材品限时大促，火热进行中！
色谱耗材品限时大促，火热进行中！
