Early Preview·This tool is in active development. Results may need verification.
Principal component analysis for multi-spectrum datasets — no coding required
Principal Component Analysis (PCA) is a multivariate statistical technique that transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components. Each component captures the maximum remaining variance in the data, producing an ordered set of axes that reveal the dominant patterns in a dataset.
In spectroscopy, each spectrum is a high-dimensional observation — hundreds or thousands of intensity values at different wavenumbers. PCA compresses this information into a handful of components that capture the chemical differences between samples. Two spectra that appear nearly identical by eye may separate clearly in PC space because PCA amplifies subtle but systematic differences across the full spectral range.
SpectralBench computes PCA entirely in your browser. Drop your spectral files, and the tool automatically aligns them to a common wavenumber grid, mean-centers the data, and computes the principal components using a dual-formulation eigendecomposition optimized for the case where the number of spectra is much smaller than the number of wavenumbers.
The score plot displays each spectrum as a point in principal component space — typically PC1 vs PC2. Points that cluster together represent spectra with similar chemical composition. The distance between clusters reflects the magnitude of spectral differences. Use the axis selectors to view other PC combinations (e.g., PC2 vs PC3) to examine variance not captured by the first two components.
The loading plot shows the contribution of each wavenumber to a given principal component. Peaks in the loading plot correspond to spectral bands that drive the separation seen in the score plot. Positive loadings indicate features that increase along the positive direction of that PC; negative loadings indicate the opposite. SpectralBench uses a two-tone fill — cyan for positive, amber for negative — to make interpretation intuitive.
The scree plot shows the fraction of total variance explained by each principal component, both individually (bars) and cumulatively (line). The “elbow” in the cumulative line indicates where additional components add diminishing information. For well-separated groups, PC1 and PC2 often capture 80–95% of the variance; for noisy or highly similar spectra, more components may be needed.
SpectralBench's PCA tool works with any spectral data that can be represented as intensity vs. wavenumber (or wavelength). Common use cases include:
Learn PCA step by step with real spectral data? Read the PCA Tutorial →
Need to preprocess spectra before PCA? Spectral Preprocessing Tool →
Principal Component Analysis (PCA) is a multivariate statistical method that reduces spectral data to a small number of principal components capturing the most variance. In spectroscopy, PCA reveals groupings, trends, and outliers across multiple spectra — for example, separating different polymer types by their FTIR fingerprints or detecting contaminated samples in a batch of Raman measurements.
PCA requires a minimum of 2 spectra, but 3 or more are recommended for meaningful results. For classification or grouping tasks, aim for at least 5–10 spectra per group. The more spectra you include, the more robust the principal components and the clearer the separation between clusters in the score plot.
SpectralBench's PCA tool accepts all spectral file formats supported by the platform — JCAMP-DX (.jdx, .dx), SPC, Bruker OPUS, Thermo SPA, Renishaw WDF, Horiba NGS/L6S, PerkinElmer SP, JASCO JWS, CSV, TXT, and many more. Drop files from any vendor and SpectralBench aligns them to a common wavenumber grid automatically.
SpectralBench finds the overlapping wavenumber range across all input spectra and resamples each spectrum onto a common, evenly-spaced grid using linear interpolation. The data matrix is then mean-centered (the average spectrum is subtracted from each row) before computing the principal components. This alignment is fully automatic — no manual preprocessing required.
A score plot shows each spectrum as a point in principal component space. Spectra that cluster together are chemically similar; outliers appear as isolated points. A loading plot shows which wavenumbers (spectral features) contribute most to each principal component — peaks in the loading plot correspond to the spectral bands that drive the separation you see in the scores.
No. PCA computation runs entirely in your browser using JavaScript. Your spectral files are parsed and analyzed client-side — nothing is uploaded to any server. This makes SpectralBench safe for proprietary samples, pre-publication research, and regulated environments.
Yes. PCA is widely used in FTIR quality control to detect batch-to-batch variation, identify contaminated or out-of-spec samples, and monitor production consistency. Drop your reference and test spectra into SpectralBench, run PCA, and check whether test samples cluster with references or appear as outliers in the score plot.
View and overlay multiple spectra with color-coded traces and spectral math.
Compare spectra with overlay, difference, mirror, and split views plus HQI scoring.
AI-powered spectral interpretation with material identification and confidence ratings.