Improved Methods for PCA-Based Reconstructions: Case Study Using the Steig et al. (2009) Antarctic Temperature Reconstruction

Abstract A detailed analysis is presented of a recently published Antarctic temperature reconstruction that combines satellite and ground information using a regularized expectation–maximization algorithm. Though the general reconstruction concept has merit, it is susceptible to spurious results for both temperature trends and patterns. The deficiencies include the following: (i) improper calibration of satellite data; (ii) improper determination of spatial structure during infilling; and (iii) suboptimal determination of regularization parameters, particularly with respect to satellite principal component retention. This study proposes two methods to resolve these issues. One utilizes temporal relationships between the satellite and ground data; the other combines ground data with only the spatial component of the satellite data. Both improved methods yield similar results that disagree with the previous method in several aspects. Rather than finding warming concentrated in West Antarctica, the authors find warming over the period of 1957–2006 to be concentrated in the peninsula (≈0.35°C decade−1). This study also shows average trends for the continent, East Antarctica, and West Antarctica that are half or less than that found using the unimproved method. Notably, though the authors find warming in West Antarctica to be smaller in magnitude and find that statistically significant warming extends at least as far as Marie Byrd Land. This study also finds differences in the seasonal patterns of temperature change, with winter and fall showing the largest differences and spring and summer showing negligible differences outside of the peninsula.

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Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-357 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 357-1-357-6

Author(s):

Luisa F. Polanía ◽

Raja Bala ◽

Ankur Purwar ◽

Paul Matts ◽

Martin Maltz

Keyword(s):

Independent Component Analysis ◽

Spectral Reflectance ◽

Source Separation ◽

Principal Component ◽

Component Analysis ◽

Previous Method ◽

Independent Component ◽

Light Transport ◽

Camera Model ◽

Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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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

Journal of AOAC International ◽

10.1093/jaoac/90.2.391 ◽

2007 ◽

Vol 90 (2) ◽

pp. 391-404 ◽

Cited By ~ 9

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.

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Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer

Applied Spectroscopy ◽

10.1177/00037028211032930 ◽

2021 ◽

pp. 000370282110329

Author(s):

Ling Wang ◽

Mario O. Vendrell-Dones ◽

Chiara Deriu ◽

Sevde Doğruer ◽

Peter de B. Harrington ◽

...

Keyword(s):

Raman Spectroscopy ◽

Principal Component ◽

Surface Enhanced Raman Spectroscopy ◽

Quantitative Detection ◽

Least Squares Regression ◽

Surface Enhanced ◽

Surface Enhanced Raman ◽

Sers Detection ◽

Current Screening

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

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Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽

10.3390/molecules26133983 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3983

Author(s):

Ozren Gamulin ◽

Marko Škrabić ◽

Kristina Serec ◽

Matej Par ◽

Marija Baković ◽

...

Keyword(s):

Raman Spectra ◽

Principal Component ◽

Support Vector ◽

Gender Recognition ◽

Proof Of Concept ◽

Male And Female ◽

Tooth Type ◽

Tooth Apex ◽

The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

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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

Open Chemistry ◽

10.1515/chem-2021-0022 ◽

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.

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Determination of Agarwood oil’s significant chemical compounds using principal component analysis

10.1063/5.0035224 ◽

2020 ◽

Author(s):

Mohamad Hushnie Haron ◽

Mohd Nasir Taib ◽

Nurlaila Ismail ◽

Nor Azah Mohd Ali ◽

Saiful Nizam Tajuddin

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Chemical Compounds ◽

Component Analysis ◽

Significant Chemical

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Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-04-2013-347 ◽

2014 ◽

Vol 41 (2) ◽

pp. 203-212 ◽

Cited By ~ 13

Author(s):

Suyong Yeon ◽

ChangHyun Jun ◽

Hyunga Choi ◽

Jaehyeon Kang ◽

Youngmok Yun ◽

...

Keyword(s):

Principal Component ◽

Point Clouds ◽

Previous Method ◽

Divide And Conquer ◽

Sensor Data ◽

Estimation Accuracy ◽

Content Type ◽

Extraction Algorithm ◽

Plane Extraction ◽

Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

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Simultaneous determination of various platycosides in Four Platycodon grandiflorum cultivars by UPLC-QTOF/MS

Applied Biological Chemistry ◽

10.1186/s13765-019-0457-x ◽

2019 ◽

Vol 62 (1) ◽

Author(s):

Dae Young Lee ◽

Bo-Ram Choi ◽

Jae Won Lee ◽

Yurry Um ◽

Dahye Yoon ◽

...

Keyword(s):

Principal Component ◽

Ultra Performance Liquid Chromatography ◽

Platycodon Grandiflorum ◽

Pentacyclic Triterpenoid ◽

Flight Mass Spectrometry ◽

Platycodi Radix ◽

Isomeric Pairs ◽

Different Levels

Abstract In Platycodi Radix (root of Platycodon grandiflorum), there are a number of platycosides that consist of a pentacyclic triterpenoid aglycone and two sugar moieties. Due to the pharmacological activities of platycosides, it is critical to assess their contents in PR, and develop an effective method to profile various platycosides is required. In this study, an analytical method based on ultra performance liquid chromatography coupled with quadrupole time-of-flight/mass spectrometry (UPLC-QTOF/MS) with an in-house library was developed and applied to profile various platycosides from four different Platycodi Radix cultivars. As a result, platycosides, including six isomeric pairs, were successfully analyzed in the PRs. In the principal component analysis, several platycosides were represented as main variables to differentiate the four Platycodi Radix cultivars. Their different levels of platycosides were also represented by relative quantification. Finally, this study indicated the proposed method based on the UPLC-QTOF/MS can be an effective tool for identifying the detail characterization of various platycosides in the Platycodi Radix.

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Tropospheric NO<sub>2</sub> column densities deduced from zenith-sky DOAS measurements in Shanghai, China, and their application to satellite validation

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-3641-2009 ◽

2009 ◽

Vol 9 (11) ◽

pp. 3641-3662 ◽

Cited By ~ 51

Author(s):

D. Chen ◽

B. Zhou ◽

S. Beirle ◽

L. M. Chen ◽

T. Wagner

Keyword(s):

Satellite Data ◽

Urban Site ◽

Optical Absorption Spectroscopy ◽

Vertical Column ◽

Error Sources ◽

Satellite Validation ◽

Tropospheric No2 ◽

Realistic Information ◽

Good Agreement

Abstract. Zenith-sky scattered sunlight observations using differential optical absorption spectroscopy (DOAS) technique were carried out in Shanghai, China (31.3° N, 121.5° E) since December 2006. At this polluted urban site, the measurements provided NO2 total columns in the daytime. Here, we present a new method to extract time series of tropospheric vertical column densities (VCDs) of NO2 from these observations. The derived tropospheric NO2 VCDs are important quantities for the estimation of emissions and for the validation of satellite observations. Our method makes use of assumptions on the relative NO2 height profiles and the diurnal variation of stratospheric NO2 VCDs. The main error sources arise from the uncertainties in the estimated stratospheric slant column densities (SCDs) and the determination of tropospheric NO2 air mass factor (AMF). For a polluted site like Shanghai, the accuracy of our method is conservatively estimated to be <25% for solar zenith angle (SZA) lower than 70°. From simultaneously performed long-path DOAS measurements, the NO2 surface concentrations at the same site were observed and the corresponding tropospheric NO2 VCDs were estimated using the assumed seasonal NO2 profiles in the planetary boundary layer (PBL). By making a comparison between the tropospheric NO2 VCDs from zenith-sky and long-path DOAS measurements, it is found that the former provides more realistic information about total tropospheric pollution than the latter, so it's more suitable for satellite data validation. A comparison between the tropospheric NO2 VCDs from ground-based zenith-sky measurements and SCIAMACHY was also made. Satellite validation for a strongly polluted area is highly needed, but exhibits also a great challenge. Our comparison shows good agreement, considering in particular the different spatial resolutions between the two measurements. Remaining systematic deviations are most probably related to the uncertainties of satellite data caused by the assumptions on aerosol properties as well as the layer heights of aerosols and NO2.

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