Analyzing spectral data is a bit like trying to decode a rainbow -- it’s beautiful but full of tricky surprises.  Spectral data presents unique challenges due to its highly correlated nature, which renders many conventional techniques ineffective.

In this talk, we identify these challenges and explore advanced methods tailored for handling such data. Specifically, we dive into three powerful techniques: principal component analysis (PCA), partial least squares (PLS), and functional data analysis (FDA). By comparing these methods, we highlight their strengths, limitations, and practical applications, offering insights into choosing the best approach for analyzing highly correlated spectral data. We show you how to transform your data into a vibrant spectrum of success even if you never look at a rainbow the same way.