scholarly journals Evaluating the dendroclimatological potential of blue intensity on multiple conifer species from Tasmania and New Zealand

2021 ◽  
Vol 18 (24) ◽  
pp. 6393-6421
Author(s):  
Rob Wilson ◽  
Kathy Allen ◽  
Patrick Baker ◽  
Gretel Boswijk ◽  
Brendan Buckley ◽  
...  
Keyword(s):  
New Zealand ◽  
High Frequency ◽  
Ring Width ◽  
Principal Component Regression ◽  
Principal Component ◽  
Signal Strength ◽  
Summer Temperature ◽  
Weak Signal ◽  
Conifer Species ◽  
Blue Intensity

Abstract. We evaluate a range of blue intensity (BI) tree-ring parameters in eight conifer species (12 sites) from Tasmania and New Zealand for their dendroclimatic potential, and as surrogate wood anatomical proxies. Using a dataset of ca. 10–15 trees per site, we measured earlywood maximum blue intensity (EWB), latewood minimum blue intensity (LWB), and the associated delta blue intensity (DB) parameter for dendrochronological analysis. No resin extraction was performed, impacting low-frequency trends. Therefore, we focused only on the high-frequency signal by detrending all tree-ring and climate data using a 20-year cubic smoothing spline. All BI parameters express low relative variance and weak signal strength compared to ring width. Correlation analysis and principal component regression experiments identified a weak and variable climate response for most ring-width chronologies. However, for most sites, the EWB data, despite weak signal strength, expressed strong coherence with summer temperatures. Significant correlations for LWB were also noted, but the sign of the relationship for most species is opposite to that reported for all conifer species in the Northern Hemisphere. DB results were mixed but performed better for the Tasmanian sites when combined through principal component regression methods than for New Zealand. Using the full multi-species/parameter network, excellent summer temperature calibration was identified for both Tasmania and New Zealand ranging from 52 % to 78 % explained variance for split periods (1901–1950/1951–1995), with equally robust independent validation (coefficient of efficiency = 0.41 to 0.77). Comparison of the Tasmanian BI reconstruction with a quantitative wood anatomical (QWA) reconstruction shows that these parameters record essentially the same strong high-frequency summer temperature signal. Despite these excellent results, a substantial challenge exists with the capture of potential secular-scale climate trends. Although DB, band-pass, and other signal processing methods may help with this issue, substantially more experimentation is needed in conjunction with comparative analysis with ring density and QWA measurements.

scholarly journals Evaluating the dendroclimatological potential of blue intensity on multiple conifer species from Australasia

2021 ◽  
Author(s):  
Rob Wilson ◽  
Kathy Allen ◽  
Patrick Baker ◽  
Sarah Blake ◽  
Gretel Boswijk ◽  
...  
Keyword(s):  
New Zealand ◽  
Tree Ring ◽  
High Frequency ◽  
Ring Width ◽  
Signal Strength ◽  
Summer Temperature ◽  
Weak Signal ◽  
Conifer Species ◽  
Blue Intensity ◽  
Blue Reflectance

Abstract. We evaluate a range of blue intensity (BI) tree-ring parameters in eight conifer species (12 sites) from Tasmania and New Zealand for their dendroclimatic potential, and as surrogate wood anatomical proxies. Using a dataset of ca. 10–15 trees per site, we measured earlywood maximum blue reflectance intensity (EWB), latewood minimum blue reflectance intensity (LWB) and the associated delta blue intensity (DB) parameter for dendrochronological analysis. No resin extraction was performed, impacting low frequency trends. Therefore, we focused only on the high frequency signal by detrending all tree-ring and climate data using a 20-year cubic smoothing spline. All BI parameters express low relative variance and weak signal strength compared to ring-width. Correlation analysis and principal component regression experiments identified a weak and variable climate response for most ring-width chronologies. However, for most sites, the EWB data, despite weak signal strength, expressed strong calibrations with summer temperatures. Significant correlations for LWB were also noted, but the sign of the relationship for most species is opposite to that reported for all conifer species in the Northern Hemisphere. DB performed well for the Tasmanian sites but explained minimal temperature variance in New Zealand. Using the full multi-species/parameter network, excellent summer temperature calibration was identified for both Tasmania and New Zealand ranging from 52 % to 78 % explained variance, with equally robust independent validation (Coefficient of Efficiency = 0.41 to 0.77). Comparison of the Tasmanian BI reconstruction with a wood anatomical reconstruction shows that these parameters record essentially the same strong high frequency summer temperature signal. Despite these excellent results, a substantial challenge exists with the capture of potential secular scale climate trends. Although DB, band-pass and other signal processing methods may help with this issue, substantially more experimentation is needed in conjunction with comparative analysis with ring density and quantitative WA measurements.

scholarly journals Inferred summer precipitation for southern Ontario back to AD 610, as reconstructed from ring widths of Thuja occidentalis

2004 ◽  
Vol 34 (12) ◽  
pp. 2541-2553 ◽  
Author(s):  
Brendan M Buckley ◽  
Robert JS Wilson ◽  
Peter E Kelly ◽  
Douglas W Larson ◽  
Edward R Cook
Keyword(s):  
Time Scales ◽  
Ring Width ◽  
Principal Component Regression ◽  
Principal Component ◽  
Summer Precipitation ◽  
Severity Index ◽  
Drought Severity ◽  
Southern Ontario ◽  
Thuja Occidentalis ◽  
Decadal Scale

We present a network of seven ring-width chronologies of eastern white-cedar (Thuja occidentalis L.) from the Niagara Escarpment in southern Ontario, Canada. Using principal component regression, a 350-year June-July precipitation reconstruction (SOR) is developed for the region. Prior to the 20th century, the SOR series shows reasonable coherence, particularly at the decadal scale, with an independent tree-ring-based reconstruction of the Palmer drought severity index (PDSI) for roughly the same region. A weakening of the tree-growth – climate relationship in recent decades results in a regression model explaining 21% of the variance in the original climate series when the recent data are used for calibration. We therefore compromise with a model, calibrated for the period 1900–1960, which explains 33% of the variance. The model, while not terribly strong, does pass verification tests, indicating some degree of predictive skill. The longest chronology in our network, the 2787-year Flowerpot Island (FLOW) chronology, also exhibits common variability with the PDSI reconstruction, particularly on decadal and longer time scales and was used to infer hydroclimatic conditions back to AD 610. The combined information of the SOR, PDSI, and FLOW series suggests that dry conditions existed for the periods 1700–1725, 1750–1800, and 1840–1900, and wet conditions for the periods 1675–1700, 1730–1750, and 1810–1840. Over longer time scales, the FLOW chronology shows that summer precipitation was particularly variable during the 7th, 9th, 13th, and 16th centuries.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Application of Derivative, Derivative Ratio, and Multivariate Spectral Analysis and Thin-Layer Chomatography-Densitometry for Determination of a Ternary Mixture Containing Drotaverine Hydrochloride, Caffeine, and Paracetamol

2007 ◽  
Vol 90 (2) ◽  
pp. 391-404 ◽  
Author(s):  
Fadia H Metwally ◽  
Yasser S El-Saharty ◽  
Mohamed Refaat ◽  
Sonia Z El-Khateeb
Keyword(s):  
Thin Layer ◽  
Least Squares ◽  
Ternary Mixture ◽  
Principal Component Regression ◽  
Pharmaceutical Preparations ◽  
Principal Component ◽  
Standard Addition ◽  
Drotaverine Hydrochloride ◽  
Assay Precision

Abstract New selective, precise, and accurate methods are described for the determination of a ternary mixture containing drotaverine hydrochloride (I), caffeine (II), and paracetamol (III). The first method uses the first (D1) and third (D3) derivative spectrophotometry at 331 and 315 nm for the determination of (I) and (III), respectively, without interference from (II). The second method depends on the simultaneous use of the first derivative of the ratio spectra (DD1) with measurement at 312.4 nm for determination of (I) using the spectrum of 40 μg/mL (III) as a divisor or measurement at 286.4 and 304 nm after using the spectrum of 4 μg/mL (I) as a divisor for the determination of (II) and (III), respectively. In the third method, the predictive abilities of the classical least-squares, principal component regression, and partial least-squares were examined for the simultaneous determination of the ternary mixture. The last method depends on thin-layer chromatography-densitometry after separation of the mixture on silica gel plates using ethyl acetatechloroformmethanol (16 + 3 + 1, v/v/v) as the mobile phase. The spots were scanned at 281, 272, and 248 nm for the determination of (I), (II), and (III), respectively. Regression analysis showed good correlation in the selected ranges with excellent percentage recoveries. The chemical variables affecting the analytical performance of the methodology were studied and optimized. The methods showed no significant interferences from excipients. Intraday and interday assay precision and accuracy values were within regulatory limits. The suggested procedures were checked using laboratory-prepared mixtures and were successfully applied for the analysis of their pharmaceutical preparations. The validity of the proposed methods was further assessed by applying a standard addition technique. The results obtained by applying the proposed methods were statistically analyzed and compared with those obtained by the manufacturer's method.

scholarly journals Principal component regression in GAMLSS applied to Greek–German government bond yield spreads

2021 ◽  
pp. 1471082X2110229
Author(s):  
D. Stasinopoulos Mikis ◽  
A. Rigby Robert ◽  
Georgikopoulos Nikolaos ◽  
De Bastiani Fernanda
Keyword(s):  
Additive Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Complex Nature ◽  
Government Bond ◽  
German Government ◽  
Yield Spreads ◽  
Explanatory Variables ◽  
Interaction Terms ◽  
Bond Yield

A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale and shape (GAMLSS) is given here using as an example the Greek–German government bond yield spreads from 25 April 2005 to 31 March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

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