New insights to improve resolution and reliability of Raman spectral analysis using higher-density multiscale regression

Abstract

When paired with sophisticated multivariate calibration methods, spectroscopy techniques stand out conspicuously for high-throughput analysis of complex samples. However, despite the identification capability of multivariate analysis, fluctuant spectral interference and complicated chemometrics procedure often combine to limit conventional spectroscopy techniques. In this regard, we proposed a novel strategy, higher-density multiscale regression (HDMR), to adaptively process Raman spectra for quantitative analysis. HDMR firstly splits the Raman spectra into frequency components at different scales using higher-density discrete wavelet transform (HDDWT), yielding a larger number of subbands to benefit a good separation of spectral bands. Parallel member models constructed with these decomposed components are then fused into a final prediction through partial least square (PLS) reweighting strategy. With HDMR, the pre-treatment process and multivariate calibration are integrated into a unified calibration model for Raman spectra at hand, regardless of its structural characteristics. This would definitely avoid of information leakage in multivariate calibration, thus promoting the application of Raman spectroscopy technique as a general tool for analytical chemistry. To validate the performance of the HDMR, two industrial Raman spectral data sets were investigated to yield challenges representative of those encountered in Raman spectroscopy. This proposed strategy has improved calibration performance through the reweighting way. Satisfactory calibration results suggest that HDMR provides a universal tool for reliable Raman spectral analysis, which may well extend to other spectral data sets.