scholarly journals Comparison of Individual and Integrated Inline Raman, Near-Infrared, and Mid-Infrared Spectroscopic Models to Predict the Viscosity of Micellar Liquids

2020 ◽  
Vol 74 (7) ◽  
pp. 819-831 ◽  
Author(s):  
Kiran Haroon ◽  
Ali Arafeh ◽  
Stephanie Cunliffe ◽  
Philip Martin ◽  
Thomas Rodgers ◽  
...  
Keyword(s):  
Consumer Satisfaction ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Process Efficiency ◽  
Initial Assessment ◽  
Data Sets ◽  
Spectroscopic Techniques ◽  
Signal To Noise ◽  
Data Set ◽  
Mid Infrared

In many industries, viscosity is an important quality parameter which significantly affects consumer satisfaction and process efficiency. In the personal care industry, this applies to products such as shampoo and shower gels whose complex structures are built up of micellar liquids. Measuring viscosity offline is well established using benchtop rheometers and viscometers. The difficulty lies in measuring this property directly in the process via on or inline technologies. Therefore, the aim of this work is to investigate whether proxy measurements using inline vibrational spectroscopy, e.g., near-infrared (NIR), mid-infrared (MIR), and Raman, can be used to predict the viscosity of micellar liquids. As optical techniques, they are nondestructive and easily implementable process analytical tools where each type of spectroscopy detects different molecular functionalities. Inline fiber optic coupled probes were employed; a transmission probe for NIR measurements, an attenuated total reflectance probe for MIR and a backscattering probe for Raman. Models were developed using forward interval partial least squares variable selection and log viscosity was used. For each technique, combinations of pre-processing techniques were trialed including detrending, Whittaker filters, standard normal variate, and multiple scatter correction. The results indicate that all three techniques could be applied individually to predict the viscosity of micellar liquids all showing comparable errors of prediction: NIR: 1.75 Pa s; MIR: 1.73 Pa s; and Raman: 1.57 Pa s. The Raman model showed the highest relative prediction deviation (RPD) value of 5.07, with the NIR and MIR models showing slightly lower values of 4.57 and 4.61, respectively. Data fusion was also explored to determine whether employing information from more than one data set improved the model quality. Trials involved weighting data sets based on their signal-to-noise ratio and weighting based on transmission curves (infrared data sets only). The signal-to-noise weighted NIR–MIR–Raman model showed the best performance compared with both combined and individual models with a root mean square error of cross-validation of 0.75 Pa s and an RPD of 10.62. This comparative study provides a good initial assessment of the three prospective process analytical technologies for the measurement of micellar liquid viscosity but also provides a good basis for general measurements of inline viscosity using commercially available process analytical technology. With these techniques typically being employed for compositional analysis, this work presents their capability in the measurement of viscosity—an important physical parameter, extending the applicability of these spectroscopic techniques.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

scholarly journals Comparison of Spectroscopic Techniques Combined with Chemometrics for Cocaine Powder Analysis

2020 ◽  
Vol 44 (8) ◽  
pp. 851-860
Author(s):  
Joy Eliaerts ◽  
Natalie Meert ◽  
Pierre Dardenne ◽  
Vincent Baeten ◽  
Juan-Antonio Fernandez Pierna ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Near Infrared ◽  
Evaluation Criteria ◽  
Classification Model ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Data Set ◽  
Promising Tool ◽  
Powder Analysis ◽  
Mir Spectra

Abstract Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC–MS) and gas chromatography with flame-ionization detection (GC–FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

scholarly journals 3-D visualisation of palaeoseismic trench stratigraphy and trench logging using terrestrial remote sensing and GPR – a multiparametric interpretation

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 323-340 ◽  
Author(s):  
Sascha Schneiderwind ◽  
Jack Mason ◽  
Thomas Wiatr ◽  
Ioannis Papanikolaou ◽  
Klaus Reicherter
Keyword(s):  
Seismic Hazard ◽  
Near Infrared ◽  
Normal Faults ◽  
Data Sets ◽  
Data Set ◽  
Backscatter Signal ◽  
Multispectral Data ◽  
Sedimentary Architecture ◽  
Hazard Assessments ◽  
Multispectral Analysis

Abstract. Two normal faults on the island of Crete and mainland Greece were studied to test an innovative workflow with the goal of obtaining a more objective palaeoseismic trench log, and a 3-D view of the sedimentary architecture within the trench walls. Sedimentary feature geometries in palaeoseismic trenches are related to palaeoearthquake magnitudes which are used in seismic hazard assessments. If the geometry of these sedimentary features can be more representatively measured, seismic hazard assessments can be improved. In this study more representative measurements of sedimentary features are achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of ISO (iterative self-organising) cluster analysis of a true colour photomosaic representing the spectrum of visible light. Photomosaic acquisition disadvantages (e.g. illumination) were addressed by complementing the data set with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D interpretation of attached 2-D ground-penetrating radar (GPR) profiles collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements, and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. This manuscript combines multiparametric approaches and shows (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GPR techniques, and (ii) how a multispectral digital analysis can offer additional advantages to interpret palaeoseismic and stratigraphic data. The multispectral data sets are stored allowing unbiased input for future (re)investigations.

EXPRESS: Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

2020 ◽  
pp. 000370282097470
Author(s):  
Joshua M. Ottaway ◽  
J. Chance Carter ◽  
Kristl L Adams ◽  
Joseph Camancho ◽  
Barry Lavine ◽  
...  
Keyword(s):  
Olive Oil ◽  
Peroxide Value ◽  
Near Infrared ◽  
Edible Oils ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Calibration Models

The peroxide value (PV) of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine PVs using field portable and process instrumentation; those efforts presented ‘best-case’ scenarios with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique, or combination thereof, is best for predicting PVs. Following PV assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression (PLSR) calibration models to predict the PV of unknown oil samples. A global PV model based on near-infrared (8 mm optical path length – OPL) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm OPL near infrared (5.1), Raman (6.9) and 50 μm OPL mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global PV models based on low-level fusion of the NIR (8 and 24 mm OPL) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes.

The Effect of Chain Length on Mid-Infrared and Near-Infrared Spectra of Aliphatic 1-Alcohols

2017 ◽  
Vol 72 (2) ◽  
pp. 288-296 ◽  
Author(s):  
Michał Kwaśniewicz ◽  
Mirosław A. Czarnecki
Keyword(s):  
Correlation Analysis ◽  
Chain Length ◽  
Near Infrared ◽  
Hydrophobic Interactions ◽  
Principal Component ◽  
Butyl Alcohol ◽  
Two Dimensional ◽  
Data Set ◽  
Mid Infrared ◽  
Spectral Changes

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH3, CH2, and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.

Near-infrared and Mid-infrared Spectroscopic Techniques for a Fast and Nondestructive Quality Control of Thymi herba

Planta Medica ◽  
2017 ◽  
Vol 84 (06/07) ◽  
pp. 420-427 ◽  
Author(s):  
Cornelia Pezzei ◽  
Stefan Schönbichler ◽  
Shah Hussain ◽  
Christian Kirchler ◽  
Verena Huck-Pezzei ◽  
...  
Keyword(s):  
Antioxidant Capacity ◽  
Rosmarinic Acid ◽  
Near Infrared ◽  
Coefficient Of Determination ◽  
Attenuated Total Reflectance ◽  
Spectroscopic Techniques ◽  
Test Set ◽  
Mid Infrared ◽  
Capacity Coefficient ◽  
Infrared Spectroscopic

AbstractIn this study, novel near-infrared and attenuated total reflectance mid-infrared spectroscopic methods coupled with multivariate data analysis were established enabling the determination of thymol, rosmarinic acid, and the antioxidant capacity of Thymi herba. A new high-performance liquid chromatography method and UV-Vis spectroscopy were applied as reference methods. Partial least squares regressions were carried out as cross and test set validations. To reduce systematic errors, different data pretreatments, such as multiplicative scatter correction, 1st derivative, or 2nd derivative, were applied on the spectra. The performances of the two infrared spectroscopic techniques were evaluated and compared. In general, attenuated total reflectance mid-infrared spectroscopy demonstrated a slightly better predictive power (thymol: coefficient of determination = 0.93, factors = 3, ratio of performance to deviation = 3.94; rosmarinic acid: coefficient of determination = 0.91, factors = 3, ratio of performance to deviation = 3.35, antioxidant capacity: coefficient of determination = 0.87, factors = 2, ratio of performance to deviation = 2.80; test set validation) than near-infrared spectroscopy (thymol: coefficient of determination = 0.90, factors = 6, ratio of performance to deviation = 3.10; rosmarinic acid: coefficient of determination = 0.92, factors = 6, ratio of performance to deviation = 3.61, antioxidant capacity: coefficient of determination = 0.91, factors = 6, ratio of performance to deviation = 3.42; test set validation). The capability of infrared vibrational spectroscopy as a quick and simple analytical tool to replace conventional time and chemical consuming analyses for the quality control of T. herba could be demonstrated.

scholarly journals Quantifying instrument errors in macromolecular X-ray data sets

2010 ◽  
Vol 66 (6) ◽  
pp. 733-740 ◽  
Author(s):  
Kay Diederichs
Keyword(s):  
Data Reduction ◽  
Spindle Speed ◽  
Experimental Setup ◽  
Data Sets ◽  
Signal To Noise ◽  
Instrument Error ◽  
Acta Cryst ◽  
Data Set ◽  
X Ray ◽  
The Stability

An indicator which is calculated after the data reduction of a test data set may be used to estimate the (systematic) instrument error at a macromolecular X-ray source. The numerical value of the indicator is the highest signal-to-noise [I/σ(I)] value that the experimental setup can produce and its reciprocal is related to the lower limit of the mergingRfactor. In the context of this study, the stability of the experimental setup is influenced and characterized by the properties of the X-ray beam, shutter, goniometer, cryostream and detector, and also by the exposure time and spindle speed. Typical values of the indicator are given for data sets from the JCSG archive. Some sources of error are explored with the help of test calculations usingSIM_MX[Diederichs (2009),Acta Cryst.D65, 535–542]. One conclusion is that the accuracy of data at low resolution is usually limited by the experimental setup rather than by the crystal. It is also shown that the influence of vibrations and fluctuations may be mitigated by a reduction in spindle speed accompanied by stronger attenuation.

scholarly journals Characterization of the umbra–penumbra boundary by the vertical component of the magnetic field

2020 ◽  
Vol 638 ◽  
pp. A25
Author(s):  
P. Lindner ◽  
R. Schlichenmaier ◽  
N. Bello González
Keyword(s):  
Magnetic Field ◽  
Near Infrared ◽  
Statistical Study ◽  
Vertical Component ◽  
Fundamental Property ◽  
Field Vector ◽  
Data Sets ◽  
Data Set ◽  
Geometric Difference ◽  
The Magnetic Field

Context. The vertical component of the magnetic field was found to reach a constant value at the boundary between penumbra and umbra of stable sunspots in a recent statistical study of Hinode/SP data. This finding has profound implications as it can serve as a criterion to distinguish between fundamentally different magneto-convective modes operating in the sun. Aims. The objective of this work is to verify the existence of a constant value for the vertical component of the magnetic field (B⊥) at the boundary between umbra and penumbra from ground-based data in the near-infrared wavelengths and to determine its value for the GREGOR Infrared Spectrograph (GRIS@GREGOR) data. This is the first statistical study on the Jurčák criterion with ground-based data, and we compare it with the results from space-based data (Hinode/SP and SDO/HMI). Methods. Eleven spectropolarimetric data sets from the GRIS@GREGOR slit-spectograph containing fully-fledged stable sunspots were selected from the GRIS archive. SIR inversions including a polarimetric straylight correction are used to produce maps of the magnetic field vector using the Fe I 15648 Å and 15662 Å lines. Averages of B⊥ along the contours between penumbra and umbra are analyzed for the 11 data sets. In addition, contours at the resulting B⊥const are drawn onto maps and compared to intensity contours. The geometric difference between these contours, ΔP, is calculated for each data set. Results. Averaged over the 11 sunspots, we find a value of B⊥const = (1787 ± 100) gauss. The difference from the values previously derived from Hinode/SP and SDO/HMI data is explained by instrumental differences and by the formation characteristics of the respective lines that were used. Contours at B⊥ = B⊥const and contours calculated in intensity maps match from a visual inspection and the geometric distance ΔP was found to be on the order of 2 pixels. Furthermore, the standard deviation between different data sets of averages along umbra–penumbra contours is smaller for B⊥ than for B∥ by a factor of 2.4. Conclusions. Our results provide further support to the Jurčák criterion with the existence of an invariable value B⊥const at the umbra–penumbra boundary. This fundamental property of sunspots can act as a constraining parameter in the calibration of analysis techniques that calculate magnetic fields. It also serves as a requirement for numerical simulations to be realistic. Furthermore, it is found that the geometric difference, ΔP, between intensity contours and contours at B⊥ = B⊥const acts as an index of stability for sunspots.

Rapid Epidemiological Assessment of Health Status in Displaced Populations—An Evolution toward Standardized Minimum Essential Data Sets

2002 ◽  
Vol 17 (4) ◽  
pp. 178-185
Author(s):  
David A. Bradt ◽  
Christina M. Drummond
Keyword(s):  
Health Status ◽  
Needs Assessment ◽  
Best Practice ◽  
Evaluation Criteria ◽  
Monitoring And Evaluation ◽  
Initial Assessment ◽  
Data Sets ◽  
Data Set ◽  
Displaced Populations ◽  
Post Disaster

AbstractRapid epidemiological assessment (REA) has evolved over the past 30 years into an essential tool of disaster management. Small area survey and sampling methods are the major application. While REA is protocol driven, needs assessment of displaced populations remains highly non-standardized. The United Nations and other international organizations continue to call for the development of standardized instruments for post-disaster needs assessment.This study examines REA protocols from leading agencies in humanitarian health assistance across an evaluation criteria of best-practice attributes. Analysis of inconsistencies and deficits leads to the derivation of a Minimum Essential Data Set (MEDS) proposed for use by relief agencies in post-disaster REA of health status in displaced populations. This data set lends itself to initial assessment, ongoing monitoring, and evaluation of relief efforts. It is expected that the task of rapid epidemiological assessment, and more generally, the professional practice of post-disaster health coordination, will be enhanced by development, acceptance, and use of standardized Minimum Essential Data Sets (MEDS).

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