The classification performance of multivariate curve resolution-discriminant analysis: A comparative study

Pattern recognition models establish the relationship between the selected variables and objects' belonging to different classes. Variations in measured variables, different object patterns, and relationship models explain the need to develop different algorithms. Partial least squares-discriminant analysis (PLS-DA) is based on the pro-jection of object responses in the space of latent vectors of the PLS calibration model. Recently, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method has been introduced for discriminant analysis by our group. The possibility of incorporation of real physical and chemical information in the MCR decomposition of data leads to solutions that directly have meaning and simplifies the interpretation of results. Considering the new application of MCR in supervised pattern recognition, there is still a need to evaluate and compare the efficiency of MCR-DA with tested methods such as PLS-DA.Detection and discrimination of extra virgin olive oil from four other classes of pure edible oils and also their mixture with extra virgin olive oil, as adulteration was chosen just as a typical system to evaluate and compare the efficiency of MCR-DA and PLS-DA methods. Raman spectroscopy was used for response measurement of oil samples. Different classifications were made between extra virgin olive oil and diverse combinations of four other edible oils, and similar sets were used for the comparative evaluation of the two discrimination methods. Various criteria evaluated the performance of the two methods and in most cases the new MCR-DA method was com-parable to PLS-DA. However, MCR-DA can assign pure Raman spectra resulting from decomposition to different classes, which can provide direct information about the components of the studied system.

Automated summary

Pattern recognition models establish the relationship between variables and object classes. Different algorithms are needed to account for variations in measured variables, object patterns, and relationship models. MCR-ALS method, which incorporates physical and chemical information, simplifies result interpretation. The efficiency of MCR-DA and PLS-DA methods were evaluated and compared for discriminating olive oil samples.