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I am performing PCA analysis of NIR spectra for the analysis of the progression of a film coating process I have noticed that one of my components goes up with the film coating process (with upwards loadings mirroring the entire spectrum) while the other goes down during the progression of the film coating process (with positive loadings mirroring the entire spectrum) When performing linear regression with the dependent variable (film thickness) they are the two most highest corelated PCs

Why are the loadings inverse (the peaks of interest should increase according to domain knowledge)

How would I go about explaining this inverse spectrum in the loadings and how are they orthogonal to each other?

Thanks in advance,

phil27
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    Not sure I fully understand your question (e.g. are you using the principal components as independent variables in your regression model?), but this other question and answers might be useful: https://stats.stackexchange.com/questions/88880/does-the-sign-of-scores-or-of-loadings-in-pca-or-fa-have-a-meaning-may-i-revers – J-J-J Mar 30 '24 at 20:51

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