Raw spectral signals collected by optical sensors are seldom ready for immediate interpretation; they contain ambient sensor noise, dark current, fluorescence backgrounds, and overlapping peak convolutions that confound manual review.
Computational spectroscopy merges advanced mathematical signal processing—such as asymmetric least squares baseline subtraction, Savitzky-Golay smoothing, and Gaussian deconvolution—with machine learning classifiers trained on thousands of verified spectral reference standards.
This intelligent software pipeline automates the entire analytical workflow: within milliseconds of photon capture, the system evaluates peak centroids, ratios, and full-width half-maximum (FWHM) values to deliver a simple, unequivocal PASS or REFER result to operators.