Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

Abstract A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200–950 cm–1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), rp increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.