Combination of the Mahalanobis distance and residual variance pattern recognition techniques for classification of near-infrared reflectance spectra

Abstract

465proaches agree well, a consequence of the area parameter of eq 2 ( N w J dominating at short times (i.e., in the time range where eq 1 was applied). The values of ro and N in Table I11 characterize the random (site overlap allowed) disk electrode ensemble that would display transient diffusion current equivalent to the corresponding composite. It is both surprising and encouraging that the transient diffusion current at these composite electrodes, whose surface morphologies are essentially impossible to defiie geometrically, can be predicted from model ensembles consisting of well-defined (disk) active site geometry. CONCLUSIONKel-F/ precious metal composite electrodes with low volume percent conductor have been shown to possess high electrical conductivities and to exhibit behavior typical of microelectrode ensembles. The enhanced current densities displayed by these composites should result in higher signal-to-noise ratios than obtained at the corresponding macro disk electrodes, making them attractive alternative electrode materials for electroanalytical measurements, including amperometric detection in flowing streams (24). Furthermore, the enhanced current density coupled with the high conductivity and low precious metal content of these composites may make them attractive for energy generation and storage applications, particularly where weight and cost are considerations. Further optimization of the fabrication and composition of Kel-F/precious metal composites should lead to improvements in the advantageous properties reported here for these first generation Kelgold and Kelplat electrodes. Work in these areas is in progress. Principal component analysis of near-infrared reflectance (NIR) spectra is used for the calcuiatlon of Mahalanoblseilng Of 'lass ('IMCA) ciasslfication models. The Mahaianobls dlstance classlflcatlon and the space spanned by the secondary vectors 1 s used in SIMCA residual variance ciasslfkation. The complementary behavior of these two classlflcatlon methods Is discussed and a new classification rule based on a combination of the two methods IS described.The application of NIR spectroscopy and the pattern recognltion technique for ldentlfylng and classifying raw materials used in pharmaceutical Industry is also discussed.

Organic substrate, clay type, texture, and water influence on NIR carbon measurements

“…Mahalanobis distance is too large), its' spectral characteristics are considered dissimilar. It is then assumed to be an outlier and predictive accuracy may be poor (Shah and Gemperline, 1990). A drawback of this screening method is that users determine which samples should be excluded only after being taken, processed, and scanned.…”

A real time method of contaminant classification using conventional water quality sensors

“…Objects in any one cluster share some similarity. Although definitions of similarity vary from one clustering model to another, in most of these models, the concept of similarity is based on distances, e.g., Mahalanobis distance (Shah and Gemperline, 1990). In other words, similar objects are assumed to have close values for at least a set of dimensions.…”

Discriminant Analysis of Shodhana (Processing) on Baliospermum Montanum Muell (Danti) Root Samples Based on Near Infrared Spectroscopy and Multivariate Chemometric Technique

“…On the other hand, near-infrared spectroscopy (NIR) encompasses acquisition of a large number of absorbencies in a spectral region typically ranging from 1000 to 2500 nm, where organic matter has distinct spectral fingerprints related to functional groups [3]. The forthcoming trend, NIR spectroscopy has taken wide acceptance within the pharmaceutical industry for raw material testing, intermediate product, finished product quality control and process monitoring [4,5]. Along with the availability of chemometric evaluation procedures for qualitative discrimination and quantitative determination is new opportunity to overcome data evaluation.…”