scholarly journals The Effect of Principal Component Analysis Parameters on Solar-Induced Chlorophyll Fluorescence Signal Extraction

2021 ◽  
Vol 11 (11) ◽  
pp. 4883
Author(s):  
Zhongqiu Sun ◽  
Songxi Yang ◽  
Shuo Shi ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Chlorophyll Fluorescence ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Infrared Region ◽  
Fluorescence Signal ◽  
Ecological Diversity ◽  
Parameter Setting ◽  
Retrieval Method

Solar-induced chlorophyll fluorescence (SIF), one of the three main releasing pathways of vegetation-absorbed photosynthetic active radiation, has been proven as an effective monitoring implementation of leaf photosynthesis, canopy growth, and ecological diversity. There exist three categories of SIF retrieval methods, and the principal component analysis (PCA) retrieval method is obtrusively eye-catching due to its brief, data-driven characteristics. However, we still lack a lucid understanding of PCA’s parameter settings. In this study, we examined if principal component numbers and retrieval band regions could have effects on the accuracy of SIF inversion under two controlled experiments. The results revealed that the near-infrared region could remarkably boost SIF’s retrieval accuracy, whereas red and near-infrared bands caused anomalous values, which subverted a traditional view that more retrieval regions might provide more photosynthetic information. Furthermore, the results demonstrated that three principal components would benefit more in PCA-based SIF retrieval. These arguments further help elucidate the more in-depth influence of the parameters on the PCA retrieval method, which unveil the potential effects of different parameters and give a parameter-setting foundation for the PCA retrieval method, in addition to assisting retrieval achievements.

Study of Vis-NIR and NIR spectral data of black inkjet prints using principal component analysis for forensic discrimination

NIR news ◽  
2017 ◽  
Vol 28 (2) ◽  
pp. 7-12 ◽  
Author(s):  
Michal Oravec ◽  
Lukáš Gál ◽  
Michal Čeppan
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Near Infrared ◽  
Molecular Spectroscopy ◽  
Principal Component ◽  
Invasive Procedure ◽  
Component Analysis ◽  
Infrared Region ◽  
Forensic Analysis ◽  
Fibre Optics

The aim of this work was to prepare spectral data for principal component analysis and to examine 19 samples of six different brands. Samples consisted of the same type of office paper with black areas printed in black ink only. The spectral data were acquired by fibre optics reflection spectroscopy in Vis-NIR and only NIR (Vis-NIR FORS) directly on paper. The black inkjet-printed samples were analysed with regard to the forensic analysis of documents. The method used is based on the combination of molecular spectroscopy in the visible (Vis) and near infrared region (NIR) combined with a chemometric method, – principal component analysis (PCA). The PCA method divides the inkjet inks sample into clusters. It was found out that by a combination of spectrum pre-processing methods and principal component analysis, it is possible to separate inks containing carbon black from the other inks using other organic colourants. This method appears to be a useful tool for forensic examination of printed documents containing inkjet inks. Spectra of inkjet inks were acquired without any destructive or invasive procedure, for example cutting sample or for extraction with the possibility to measure out of the laboratory.

scholarly journals Pre-processing of inkjet prints NIR spectral data for principal component analysis

2015 ◽  
Vol 8 (2) ◽  
pp. 191-196 ◽  
Author(s):  
Michal Oravec ◽  
Lukáš Gál ◽  
Michal Čeppan
Keyword(s):  
Principal Component Analysis ◽  
Spectral Data ◽  
Near Infrared ◽  
Molecular Spectroscopy ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Infrared Region ◽  
Ocean Optics ◽  
Novel Approach

Abstract This paper presents a novel approach in non-destructive analysis of inkjet-printed documents. Our method is based on the combination of molecular spectroscopy in the Near Infrared Region (NIR) and a chemometric method - principal component analysis (PCA). The aim of this work was to prepare spectral data for the analysis of the interrelationships between 19 samples consisting of the same type of office paper on which black squares were full printed in black ink only. The spectra were obtained separately using the Ocean Optics System in two spectral regions, i.e., overtones: 1000-1600 nm and combination bands: 1600-2300 nm, with the paper base. Experimental results confirmed the high reliability of the proposed approach despite the sparse dataset.

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.

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.

scholarly journals ANÁLISIS DE COMPONENTES PRINCIPALES DE IMÁGENES MULTIESPECTRALES EN EL ÁMBITO DEL ARTE RUPESTRE

2017 ◽  
Author(s):  
Berta Carrión-Ruiz ◽  
José Luis Lerma
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Rock Art ◽  
Visible Region ◽  
Principal Component ◽  
Optical Filters ◽  
Infrared Region ◽  
Spectral Behaviour ◽  
Near Infrared Region ◽  
Art Analysis

This paper tackles principal component analysis (PCA) in images that include wavelengths between 380-1000 nm. Our approach is focussed on taking advantage of the potencial of ultraviolet and infrarred images, in combination with the visible ones, to improve documentation process and rock art analysis. In this way, we want to improve the discrimination between pigment and support rock, and analyse the spectral behaviour of rock art paintings in the ultraviolet and infrared regions. Three images were used, one image from the ultraviolet (UV) region, one from the visible region (VIS) and another one from the near infrared region (NIR). Optical filters coupled to the camera optics were used to take the images. These filters capture specific wavelengths excluding radiation that we are not interested in registering. Finally, PCA is applied to the acquired images. The results obtained demonstrate the PCA usefulness with imagery in this field and also it is possible to extract some conclusions about the correspondent paint pigments.http://dx.doi.org/10.4995/CIGeo2017.2017.6597

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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