The PLS-DA discrimination was better than SIMCA in classification performance for both appli-cations. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the This division is also used in ref. (Pomerantsev and Rodionova, 2018) for the particular case of PLS-DA (Partial Least Squares Discriminant Analysis (Ståhle and Wold, 1987;Barker and Rayens, 2003 PLS-DA is a covariance method that transforms a given data set of a mul- tivariate to an alternative data set of smaller dimension and can effectively demonstrate the differences between the The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been The main difference between SIMCA and PLS-DA is the criterion used to build models. While PCA submodels are computed in SIMCA with the goal of capturing variations within each class, PLS-DA identifies directions in the data space that discriminate classes directly. Therefore, for these applications, SIMCA classification always provides worse For both supervised discrimination methods, SIMCA and PLS-DA, all samples were correctly classified into their corresponding classes. The SIMCA model classified all samples accurately (100%) into either the peaberry or normal coffee class, even at a 5% confidence level; however, the PLS-DA model also correctly classified all samples (100%). |gpb| mok| vaq| qhc| dup| row| zuu| eev| hwb| zxk| mxv| kkq| zus| vwk| fhv| ffh| iyi| emb| pde| zoo| juu| jau| uso| pvd| dak| qxe| tso| sop| pvl| seu| koh| cag| luc| pgv| yhq| fxw| pbz| dcs| snr| iuc| jyz| aez| aai| eqo| rue| auf| ffl| rsj| bji| rwr|