The Differentiation of Illicit Methaqualone Tablet Formulations Using Principal Component and Soft Independent Modeling of Class Analogy Analysis of Their Near-Infrared Reflectance Spectra

1995 ◽  
Vol 40 (6) ◽  
pp. 13879J
Author(s):  
Etienne Francios Van Zyl ◽  
Mercia Louw
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Reflectance Spectra ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Tablet Formulations

Principal Component Analysis of Diffuse Near-Infrared Reflectance Data from Paper Currency

1989 ◽  
Vol 43 (8) ◽  
pp. 1399-1405 ◽  
Author(s):  
John M. Dale ◽  
Leon N. Klatt
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Test System ◽  
Reflectance Spectra ◽  
Sample Population ◽  
Infrared Reflectance ◽  
K Nearest Neighbor ◽  
Near Infrared Reflectance

Product tampering and product counterfeiting are increasing the need for methods to quickly determine product authenticity. One of the concepts that we are investigating for the detection of counterfeit objects involves the use of pattern recognition techniques to analyze multivariant data acquired from properties intrinsic to the object. The near-infrared reflectance spectra of currency and other paper stock were used as a test system. The sample population consisted of authentic currency, circulated and uncirculated, and cotton and rag paper stock as stand-ins for counterfeit currency. Reflectance spectra were obtained from a spot that was essentially void of printing on both sides of the currency specimens. Although the reflectance spectra for all of the samples were very similar, principal component analysis separated the samples into distinct classes without there being any prior knowledge of their chemical or physical properties. Class separation was achieved even for currency bills that differed only in their past environment. Leave-One-Out procedures resulted in 100% correct classification of each member of the sample set. A K-Nearest-Neighbor test or a linear discriminate can be used to correctly classify unknown samples.

Effects of Particle Size on the Near-Infrared Reflectance Spectra of Wheat and Rape Seed Meal Mixtures

1995 ◽  
Vol 49 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Marie-Françoise Devaux ◽  
Nathalie Nathier-Dufour ◽  
Paul Robert ◽  
Dominique Bertrand
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Reflectance Spectra ◽  
Seed Meal ◽  
Infrared Reflectance ◽  
Rape Seed ◽  
Near Infrared Reflectance

Northwest Africa 6232: Visible-near infrared reflectance spectra variability of an olivine diogenite

2018 ◽  
Vol 53 (10) ◽  
pp. 2228-2242 ◽  
Author(s):  
Cristian Carli ◽  
Giovanni Pratesi ◽  
Vanni Moggi-Cecchi ◽  
Francesca Zambon ◽  
Fabrizio Capaccioni ◽  
...  
Keyword(s):  
Near Infrared ◽  
Reflectance Spectra ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Northwest Africa

Analysis of artificial mixtures of pure chemicals by near-infrared reflectance

1990 ◽  
Vol 114 (3) ◽  
pp. 253-257 ◽  
Author(s):  
G. Z. Wetherill ◽  
I. Murray ◽  
C. A. Glasbey
Keyword(s):  
Near Infrared ◽  
Compositional Analysis ◽  
Principal Component ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Chemical Methods ◽  
Wet Chemical ◽  
Linear Regressions ◽  
Unexplained Variation ◽  
Artificial Mixtures

SUMMARYCompositional analysis of feeds and other materials by near-infrared reflectance (NIR) has been proposed as a cheap and rapid alternative to traditional wet chemical methods. A theoretical basis for NIR measurements is needed and may be obtained from the study of artificial mixtures of pure chemicals.Mixtures of lactose, casein and sodium oleate, in widely differing concentrations, were analysed by NIR. Principal component analysis was used to study the variations between spectra, and multiple linear regressions gave predictors of sample compositions from the spectra. Optical densities at most combinations of wavelengths gave good predictions of sample compositions because there was much less unexplained variation between NIR spectra than would occur between natural samples.

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