Multicomponent Analysis of Near-Infrared Spectra of Anesthetized Rat Head: (I) Estimation of Component Spectra by Principal Component Analysis

1989 ◽  
pp. 3-10 ◽  
Author(s):  
Ryuichiro Araki ◽  
Ichiro Nashimoto
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Multicomponent Analysis ◽  
Near Infrared Spectra

106 Assessment of fecal near infrared reflectance spectroscopy to detect and monitor the reproductive status of endangered Amur and Snow leopard females

2019 ◽  
Vol 31 (1) ◽  
pp. 179
Author(s):  
M. Santos-Rivera ◽  
L. Johnson-Ulrich ◽  
A. Graham ◽  
E. Willis ◽  
A. J. Kouba ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component ◽  
Component Analysis ◽  
Chemometric Analysis ◽  
Reproductive Status ◽  
Fecal Samples ◽  
Near Infrared Spectra

Feces from captive and wild carnivores can yield valuable information about an individuals’ physiological and reproductive status, diet, and ecology. Near infrared spectroscopy (NIRS) is a rapid, noninvasive, cost-efficient technique widely used in the agricultural, pharmaceutical, and chemical industries that has gained traction in diagnostic and ecological field applications for herbivore species, such as wild deer, antelope, and giant panda. The aim of this study was to test the transferability of NIRS to measuring reproductive status in feces from 2 endangered carnivore species, the Snow (Panthera uncia) and Amur (Panthera pardus orientalis) leopards. Fecal near infrared spectra analysed with multivariate statistics were used to generate prediction models for estrone-3-glucuronide (E1G) and progesterone (P4). In the E1G NIRS model, fecal samples (n=93) were obtained from 5 female leopards (3 Amur, 2 Snow) at 5 different zoo facilities, whereas for the P4 NIRS model fecal samples (n=51) from only 1 pregnant Amur leopard was available. The hormones were extracted with methanol and quantified by enzyme-linked immunosorbent assays (C. Munroe), where the sample range for E1G was 0.20-2.17 μg/g and the range for P4 was 0.06-61.89 μg/g. The near infrared spectra (350-2500nm) were acquired with an ASD FieldSpec®3 portable spectrometer (Malvern Panalytical, Malvern, UK), and the chemometric analysis was realised using the Unscrambler® X v.10.4 (CAMO Software AS, Oslo, Norway). Hormone reference values were log transformed before chemometric analysis to account for the heterogeneity of variance. Spectral pretreatment of standard normal variate was applied to the truncated wavelength range 700-240 0nm in order to remove interference from the visible region (350-700nm) due to individual diets that can confer colour variants that alter spectral signatures. Initial principal component analysis for the E1G and P4 datasets models showed >95% of the variation was explained by 4 factors, with no separation of principal component analysis scores between species or reproductive status. Quantitative prediction models using partial least-squares regression on selected wavelength ranges yielded a coefficient of determination for E1G and P4 of 0.10-0.04 and 0.35-0.19 for calibrations and validations, respectively. These near infrared models require further mathematical processing and consideration of sample variation due to diet complexity in carnivores in order to accurately assess hormone levels and monitor reproductive cycles in these species. This work was supported by USDA-ARS Biophotonics Initiative grant #58-6402-3-018.

Identification of textiles by handheld near infrared spectroscopy: Protecting customers against product counterfeiting

2018 ◽  
Vol 26 (5) ◽  
pp. 311-321 ◽  
Author(s):  
Hui Yan ◽  
Heinz W Siesler
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Discrimination Performance ◽  
Rapid Test ◽  
Principal Component ◽  
Component Analysis ◽  
Textile Materials ◽  
Discrimination Capability ◽  
Near Infrared Spectra

Textiles are extremely important materials for everyday life with a broad range of applications and properties. Due to the large variations in quality on the one hand and the increasing quality awareness and price consciousness of customers on the other hand, the availability of a simple tool for a rapid test of the correct identity of the purchased textile article would be a significant progress in customer protection. Miniaturization of near infrared spectrometers has advanced to the point where handheld instruments could provide reliable and affordable means to serve this purpose. One objective of the present communication was to scrutinize the identification and discrimination performance for textile materials for four real-handheld (<200 g) near infrared spectrometers based on different monochromator principles. The second focus was to show that in the near future these handheld instruments can be used by a non-expert user community to protect themselves against fraud in textile purchase situations. For this purpose, diffuse reflection spectra of 72 textile samples of synthetic and natural origin were measured. While in simple situations, test samples can readily be authenticated by visual inspection of their near infrared spectra only, for a more comprehensive identification of unknown samples principal component analysis in combination with soft independent modeling of class analogies was applied. In the present work, this approach provided a suitable analytical tool for the correct assignment of the investigated different types of textile materials. Moreover, the evaluation of the mean Euclidian distances in the principal component analysis score plots derived from the near infrared spectra of the textile classes under investigation allowed to compare the identification performance and discrimination capability of the different handheld instruments.

scholarly journals Principal Component Analysis(PCA) Applied to Near Infrared Spectra for Classifying Wheat Flours.

1995 ◽  
Vol 42 (10) ◽  
pp. 796-801 ◽  
Author(s):  
Minoru CHIBA ◽  
Masatoshi MINAMISAWA ◽  
Sumio KAWANO ◽  
Mutsuo IWAMOTO
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Near Infrared Spectra ◽  
Wheat Flours
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