Application of principal-component analysis on near-infrared spectroscopic data of vegetable oils for their classification

1994 ◽  
Vol 71 (3) ◽  
pp. 293-298 ◽  
Author(s):  
Tetsuo Sato
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Spectroscopic Data ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Infrared Spectroscopic

scholarly journals A Near Infrared Spectroscopic Discrimination of Noodle Flours Using a Principal-Component Analysis Coupled with Chemical Information.

2002 ◽  
Vol 18 (10) ◽  
pp. 1145-1150 ◽  
Author(s):  
Masanori KUMAGAI ◽  
Kikuko KARUBE ◽  
Tomoaki SATO ◽  
Naganori OHISA ◽  
Toshio AMANO ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Information ◽  
Infrared Spectroscopic

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Principal component analysis of measured quantities during degradation of hydroperoxides in oxidized vegetable oils

Lipids ◽  
1999 ◽  
Vol 34 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Károly Héberger ◽  
Ágnes Keszler ◽  
Michael Gude
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Principal Component ◽  
Component Analysis

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

Discrimination of Plastics Using a Portable near Infrared Spectrometer

2002 ◽  
Vol 10 (4) ◽  
pp. 247-255 ◽  
Author(s):  
Masanori Kumagai ◽  
Hideto Suyama ◽  
Tomoaki Sato ◽  
Toshio Amano ◽  
Nobuaki Ogawa
Keyword(s):  
Principal Component Analysis ◽  
Absorption Band ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Rapid Identification ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Methylene Group ◽  
Absorbance Spectra

A portable near infrared (NIR) spectrometer was used to accomplish rapid identification of plastics. Thirteen kinds of plastics were collected and their NIR spectra were measured. Standardised normalisation treatment of the original spectra reveals the differences of the spectra more clearly. Absorbance spectra can be used to distinguish easily between polyethylene (PE) and polypropylene (PP) with the absorption band of the methylene group at 1410 nm. The result can be used to discriminate PE, PP and the copolymer. This suggestion is supported by a principal component analysis of the second-order derivatives spectrum.

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