Origination spectral reflectances

Among the fractionated meteorites, such as the eucrites, the lanthanides show some variation. These meteorites, the basaltic achondrites, are basalts, originating as lavas on small asteroidal bodies (Basaltic Volcanism Study Project 1981, p. 214). The eucrites form one important class. Since the asteroid 4 Vesta, 555km in diameter, has a basaltic-like surface from spectral reflectance data, it is a prime candidate for the source of such meteorites, although considerable dynamical problems remain (Gehrels 1979). 

UV transmittance analysers are used for QA in the textile industry. Identification of dyes on textile fibres by assessmenf of reflectance curves is difficult owing to the dependence of spectral reflectance on concentration and spectral interference due to the base colour of the substrate itself. A method of analysis has been proposed [42], Traces of Fe in textiles, such as linen, have been determined using ferrozine-mercaptoacetic acid reagent at pH 3.8 and rapid determination of Fe + from the reflectance spectrum at 570 nm of the purple-violet colour developed in the dry material [43], Derivative spectroscopy was employed to analyse pigments and a mixture of antioxidants in PE. With the fourth and fifth derivatives of the UV spectra, Irganox 1010 could be determined in PS the UV absorbance of this polymer makes evaluation of the original spectrum impossible [44]. A variety of chemical derivatisations in UV spectrophotometry have been described. 

Spectral reflectance data of 118 participants (22 females and 96 males) were measured in the study. Figures show the participant distribution based on ethnic origin. 

This method is commonly nsed on spectral data to correct for multiplicative variations between spectra. In spectroscopy, snch variations often originate from nnintended or uncontrolled differences in sample path length (or effective path length, in the case of reflectance spectroscopy), caused by variations in sample physical properties (particle size, thickness), sample preparation, sample presentation, and perhaps even variations in spectrometer optics. Snch variations can be particularly problematic because they are confounded with mnltiplicative effects from changes in component concentrations, which often constitute the signal in qnantitative applications. It is important to note that multiplicative variations cannot be removed by derivatives, mean-centering or variable-wise scaling. 

HCA is a common tool that is used to determine the natural grouping of objects, based on their multivariate responses [75]. In PAT, this method can be used to determine natural groupings of samples or variables in a data set. Like the classification methods discussed above, HCA requires the specification of a space and a distance measure. However, unlike those methods, HCA does not involve the development of a classification rule, but rather a linkage rule, as discussed below. For a given problem, the selection of the space (e.g., original x variable space, PC score space) and distance measure (e.g.. Euclidean, Mahalanobis) depends on the specific information that the user wants to extract. For example, for a spectral data set, one can choose PC score space with Mahalanobis distance measure to better reflect separation that originates from both strong and weak spectral effects. 

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