Automated principal component-based orthogonal signal correction applied to fused near infrared-mid-infrared spectra of French olive oils.

An approach for automating the determination of the number of components in orthogonal signal correction (OSC) has been devised. In addition, a novel principal component OSC (PC-OSC) is reported that builds softer models for removing background from signals and is much faster than the partial least-squares (PLS) based OSC algorithm. These signal correction methods were evaluated by classifying fused near- and mid-infrared spectra of French olive oils by geographic origin. Two classification methods, partial least-squares-discriminant analysis (PLS-DA) and a fuzzy rule-building expert system (FuRES), were used to evaluate the signal correction of the fused vibrational spectra from the olive oils. The number of components was determined by using bootstrap Latin partitions (BLPs) in the signal correction routine and maximizing the average projected difference resolution (PDR). The same approach was used to select the number of latent variables in the PLS-DA evaluation and perfect classification was obtained. Biased PLS-DA models were also evaluated that optimized the number of latent variables to yield the minimum prediction error. Fuzzy or soft classification systems benefit from background removal. The FuRES prediction results did not differ significantly from the results that were obtained using either the unbiased or biased PLS-DA methods, but was an order of magnitude faster in the evaluations when a sufficient number of PC-OSC components were selected. The importance of bootstrapping was demonstrated for the automated OSC and PC-OSC methods. In addition, the PLS-DA algorithms were also automated using BLPs and proved effective.