Dear Praveen Kumar Yadav

Loading plots indicate the actual dimension of the problems that you transformed by PCA. It will help you to decide how many data dimension will you take and exclude for your further analysis. Since PCA and PLSR are used for data dimension reduction or noise cancellation, the key direction you will get from the loading plots.

Regards!

Dear Praveen Kumar Yadav ,

I will give you some useful information related to PCA.

Scores: describe the properties of the samples and are usually shown as a map of one PC plotted against another.

Samples with close scores along the same PC are similar (they have close values for the corresponding variables). Conversely, samples for which the scores differ greatly are quite different from each other with respect to those variables. The relative importance of each principal component is expressed in terms of how much variance of the original data it describes.

Loadings: describe the relationships between variables and may be plotted as a line (commonly used in spectral data interpretation) or a map (commonly used in process or sensory data analysis).

For each PC, look for variables with high loadings (i.e. close to +1 or –1); this indicates that the loading is interpretable.

To study variable correlations, one studies the relative location of variables in the loadings space. Variables that lie close together are highly correlated. For instance, if two variables have high loadings along the same PC, it means that their angle is small, which in turn means that the two variables are highly correlated. If both loadings have the same sign, the correlation is positive (when one variable increases, so does the other). If the loadings have opposite signs, the correlation is negative (when one variable increases, the other decreases).

But remember: Loadings cannot be interpreted without Scores, and vice versa.

Loadings. For that reason the BI-PLOT is the best plot for analyzing PCA.

About the significance of each score and loadings, it depends of the software that you have used for processing your data. In the result, you should be able to find the P values of each score and/or loadings,

Wish you all the best,

Thank you for both of your valuable comments.

I agree with the above two answers.

It may help you to look at my article Rapid Quantification of Methamphetamine: Using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Chemometrics on ResearchGate and particularly figure 2. This study used similar techniques to what you are describing, and figure 2 shows the benefits of a bi-plot, and illustrates visually what Edenio Olivares Diaz stated in his 6th paragraph( To study...).

I hope this helps you with your study

Thank you for your reply. It was very helpful.

There is some free program for quantification??? For ATR FTIR??

Dear Carlos Fernando Odir Rodrigues Melo

Python, python, and python....everything is possible with python, just do a google search,

For example https://www.essentialftir.com/eftirScripting.html

All the best

Dear Edenio Olivares Diaz

Thank you for the help.

I will check it!!!

best regards

My pleasure

Just try to find the most suitable and understandable tool for you. I said python because it offers lot of possibilities and package or tools since every day more and more persons are using it to solve their task. And it is free, you could create your own functions.

All the best,

First, it shows the loading significance extracted from the PCA which means the degree of correlation and at the same time the direction of relationship with the dimension with which it varies. namely, a dimension or principal component is a combination of variables correlated with each other two by two. Thus, the loading plot shows how each variable behaves in terms of correlation with each dimension.