SIMCA pattern recognition classification of five infauna taxonomic groups using non-polar compounds analysed by high resolution gas chromatography

Polycyclic aromatic hydrocarbons in soil and air: statistical analysis and classification by the SIMCA method

“…Interpretation of object (sample) grouping and variable correlation are made on object score and variable loading plots, respectively (33). The SIMCA method is an extension of principal component analysis to include supervised modeling (9,30,39). The calculation of principal components depends on the data that are being analyzed.…”

Rapid Profiling of Swiss Cheese by Attenuated Total Reflectance (ATR) Infrared Spectroscopy and Descriptive Sensory Analysis

“…The class projection plot provides a visual representation of well‐separated classes based on a 3‐factor principal component analysis (PCA) performed on the entire training set (Infometrix 2003). The interclass distances (IDR), a measure of separation between the classes (Vogt and Knutsen 1985), ranged from 5.7 to 31.2 (Table 3). Classes with IDR greater than 3 are regarded as significant to identify 2 groups of samples as different classes (Wold and others 1981; Kvalheim and Karstang 1992).…”

The use of infrared spectrometers to predict quality parameters of cornmeal (corn grits) and differentiate between organic and conventional practices

“…In addition, residuals provide valuable information regarding class homogeneity, separation between classes (interclass distance), and the relative strength of any given variable to model the structure of a class or to discriminate between classes (discriminating power). SIMCA's interclass distance (ICD) describes quantitatively the similarity or dissimilarity of the different classes, being generally accepted that samples can be differentiated when ICD > 3 ( Vogt and Knutsen, 1985). The discriminating power of variables may be used to eliminate noise from the data set, such that variables having both low discriminating power and modeling power can be eliminated.…”

“…SIMCA calibration models developed using all three instruments tested in this research had interclass distances (ICDs) above 3, which indicates that cornmeal samples can be separated into different classes. Any interclass distance that is greater than 3 is considered significant to identify the two classes as different (Vogt and Knutsen, 1985). The best separation between conventional and organic sample groups in terms of ICD was obtained using portable MIR spectra.…”