Visible, near- and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality

Vis-NIRS, MIRS, and a combination of both coupled with PLS machine learning were applied to i) trace the composed proportions different apple varieties in formulated purees ii) predict quality characteristics from spectral information initial puree cultivars. The models could estimate each cultivar mixtures using MIR spectra (RMSEP<8.1%, RPD> 3.6), especially for Granny Smith (RMSEP = 2.7%, RPD 11.4). concentration profiles multivariate curve resolution-alternative least squares (MCR-ALS) made possible reconstruct purees. MIRS technique was evidenced final quality, such as viscosity (RPD>4.0), contents soluble solids (RPD 4.1), malic acid 4.7) glucose 4.3), based only on data Infrared should be powerful tool traceability, even multicriteria optimization products cultivars before formulation.