Investigating microcrystalline cellulose crystallinity using Raman spectroscopy

<p>The decarboxylation of Δ9-tetrahydrocannabinolic acid (THCA) plays pivotal role in the potency of medical cannabis and its extracts. However, the literature data point out substantial variations in the process reaction rate and conversion efficacy due to variability of the temperature, heat transfer efficacy, raw material attributes, consequently resulting in incomplete decarboxylation, cannabinoid content decrease due to decomposition, evaporation, and possible side reactions. Our present work aims to draw attention to mid-infrared (MIR) spectroscopy for in-situ monitoring and decipher the THCA decarboxylation reaction in the solid state. The initial TG/DTG curves of THCA, for a first time outlined the solid-solid decarboxylation dynamics, defined the endpoint of the process and the temperature of the maximal conversion rate, which aided in the design of the further IR experiments. Temperature controlled IR spectroscopy experiments were performed on both THCA standard and cannabis flower by providing detailed band assignment and conducting spectra-structure correlations, based on the concept of functional groups vibrations. Moreover, a multivariate statistical analysis was employed to depict the spectral regions of utmost importance for the THCA→THC interconversion process. The principal component analysis model was reduced to two PCs, where PC1 explained 94.76% and 98.21% of the total spectral variations in the THCA standard and in the plant sample, respectively. The PC1 plot score of the THCA standard, as a function of the temperature, neatly complemented to the TG/DTG curve and enabled determination of rate constants for the decarboxylation reaction undertaken on several temperatures. Consequently, a progress in elucidation of kinetic models of THCA decarboxylation, fitting experimental data for both, solid state standard substance and a plant flower, was achieved. The results open the horizon to promote an appropriate process analytical technology (PAT) in the outgrowing medical cannabis industry.</p>

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Analysis and Classification of Liquid Samples Using Spatial Heterodyne Raman Spectroscopy

Applied Spectroscopy ◽

10.1177/0003702819863847 ◽

2019 ◽

Vol 73 (12) ◽

pp. 1409-1419

Author(s):

Ardian B. Gojani ◽

Dávid J. Palásti ◽

Andrea Paul ◽

Gábor Galbács ◽

Igor B. Gornushkin

Keyword(s):

Raman Spectra ◽

Michelson Interferometer ◽

Principal Component ◽

Resolving Power ◽

Calibration Plot ◽

Least Squares Regression ◽

Spatially Resolved ◽

Liquid Samples ◽

Diode Pumped

Spatial heterodyne spectroscopy (SHS) is used for quantitative analysis and classification of liquid samples. SHS is a version of a Michelson interferometer with no moving parts and with diffraction gratings in place of mirrors. The instrument converts frequency-resolved information into a spatially resolved one and records it in the form of interferograms. The back-extraction of spectral information is done by the fast Fourier transform. A SHS instrument is constructed with the resolving power 5000 and spectral range 522–593 nm. Two original technical solutions are used as compared to previous SHS instruments: the use of a high-frequency diode-pumped solid-state laser for excitation of Raman spectra and a microscope-based collection system. Raman spectra are excited at 532 nm at the repetition rate 80 kHz. Raman shifts between 330 cm−1 and 1600 cm−1 are measured. A new application of SHS is demonstrated: for the first time, it is used for quantitative Raman analysis to determine concentrations of cyclohexane in isopropanol and glycerol in water. Two calibration strategies are employed: univariate based on the construction of a calibration plot and multivariate based on partial least squares regression. The detection limits for both cyclohexane in isopropanol and glycerol in water are at a 0.5 mass% level. In addition to the Raman–SHS chemical analysis, classification of industrial oils (biodiesel, poly(1-decene), gasoline, heavy oil IFO380, polybutenes, and lubricant) is performed using the Raman–fluorescence spectra of the oils and principal component analysis. The oils are easily discriminated showing distinct non-overlapping patterns in the principal component space.

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Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

Journal of Spectroscopy ◽

10.1155/2016/1603609 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 14

Author(s):

Wenjing Liu ◽

Zhaotian Sun ◽

Jinyu Chen ◽

Chuanbo Jing

Keyword(s):

Colorectal Cancer ◽

Raman Spectroscopy ◽

Discriminant Analysis ◽

Diagnostic Accuracy ◽

Raman Spectra ◽

Principal Component ◽

Cancer Diagnostics ◽

Analysis Model ◽

Tissue Samples ◽

Linear Discriminant

Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.

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Sensory and Volatile Profiles of Korean Commercially Distilled Soju Using Descriptive Analysis and HS-SPME-GC-MS

Foods ◽

10.3390/foods9091330 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1330

Author(s):

Jung-Min Hong ◽

Tae-Wan Kim ◽

Seung-Joo Lee

Keyword(s):

Solid Phase ◽

Descriptive Analysis ◽

Principal Component ◽

Raw Material ◽

Gas Chromatography Mass Spectrometry ◽

Least Squares Regression ◽

Sensory Data ◽

Oak Barrels ◽

Positive Correlations ◽

Ethyl Hexadecanoate

Volatile compositions and sensory characteristics of 11 commercially distilled soju samples were investigated using headspace solid-phase microextraction (HS-SPME) with gas chromatography-mass spectrometry (GC-MS) and sensory descriptive analysis. A total of 59 major volatile compounds, consisting of 32 esters, 10 alcohols, 2 acids, 5 aldehydes, 3 ketones, 1 hydrocarbon, 1 furan, 2 phenols, and 3 miscellaneous compounds, were identified. From the principal component analysis (PCA) of volatile data, MSJ made by atmospheric distillation showed a clear distinction in volatile compositions compared to that of other samples made by vacuum distillation. Based on PCA of the sensory data determined by a panel of ten judges, MSJ was associated with a large amount of longer chain esters that showed high intensities in bitter taste and yeast/nuruk-related flavor attributes. HYJ, LPJ, and HAJ made with rice as a raw material were associated with lower intensities of the alcohol aroma, while JRJ and OKJ aged in oak barrels were associated with fruit flavor, sweet flavor, and brandy aroma. In the partial least squares regression (PLSR) analysis to see any relationship between volatile and sensory data, longer chain esters like ethyl tetradecanoate, and ethyl hexadecanoate were highly associated with bleach aroma. In contrast, positive correlations were seen with barley aroma and yeast flavor with hexanal, nonanal, benzaldehyde, and 2-methoxy-phenol.

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Synergistic Use of FTIR Spectroscopy and TG to Elucidate the Solid State THCA Decarboxylation Reaction Kinetics in THCA Standard and Cannabis Flower

10.26434/chemrxiv.14216561 ◽

2021 ◽

Author(s):

Olga Gigopulu ◽

Nikola Geskovski ◽

Gjoshe Stefkov ◽

Veronika Stoilkovska Gjorgievska ◽

Irena Slaveska Spirevska ◽

...

Keyword(s):

Solid State ◽

Process Analytical Technology ◽

Principal Component ◽

Raw Material ◽

In Situ Monitoring ◽

Side Reactions ◽

Plant Sample ◽

Medical Cannabis ◽

Analysis Model ◽

Decarboxylation Reaction

<p>The decarboxylation of Δ9-tetrahydrocannabinolic acid (THCA) plays pivotal role in the potency of medical cannabis and its extracts. However, the literature data point out substantial variations in the process reaction rate and conversion efficacy due to variability of the temperature, heat transfer efficacy, raw material attributes, consequently resulting in incomplete decarboxylation, cannabinoid content decrease due to decomposition, evaporation, and possible side reactions. Our present work aims to draw attention to mid-infrared (MIR) spectroscopy for in-situ monitoring and decipher the THCA decarboxylation reaction in the solid state. The initial TG/DTG curves of THCA, for a first time outlined the solid-solid decarboxylation dynamics, defined the endpoint of the process and the temperature of the maximal conversion rate, which aided in the design of the further IR experiments. Temperature controlled IR spectroscopy experiments were performed on both THCA standard and cannabis flower by providing detailed band assignment and conducting spectra-structure correlations, based on the concept of functional groups vibrations. Moreover, a multivariate statistical analysis was employed to depict the spectral regions of utmost importance for the THCA→THC interconversion process. The principal component analysis model was reduced to two PCs, where PC1 explained 94.76% and 98.21% of the total spectral variations in the THCA standard and in the plant sample, respectively. The PC1 plot score of the THCA standard, as a function of the temperature, neatly complemented to the TG/DTG curve and enabled determination of rate constants for the decarboxylation reaction undertaken on several temperatures. Consequently, a progress in elucidation of kinetic models of THCA decarboxylation, fitting experimental data for both, solid state standard substance and a plant flower, was achieved. The results open the horizon to promote an appropriate process analytical technology (PAT) in the outgrowing medical cannabis industry.</p>

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An Input–Process–Output Model of Pilot Core Competencies

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000120 ◽

2017 ◽

Vol 7 (2) ◽

pp. 78-85 ◽

Cited By ~ 5

Author(s):

Heikki Mansikka ◽

Don Harris ◽

Kai Virtanen

Keyword(s):

Path Analysis ◽

Team Performance ◽

Core Competencies ◽

Core Competency ◽

Principal Component ◽

Analysis Model ◽

Input Process ◽

The Core ◽

Performance Framework ◽

Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

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The Utilization of Ftir (Fourier Transform Infra-Red) Method Combined with Chemometrics For Authentication of Indonesian Coffee Powder

International Journal of Pharmaceutical and Clinical Research ◽

10.25258/ijpcr.v9i3.8320 ◽

2017 ◽

Vol 9 (3) ◽

Author(s):

Firmansyah A. ◽

Winingsih W. ◽

Soebara Y S

Keyword(s):

Fourier Transform ◽

Natural Product ◽

High Performance ◽

Principal Component ◽

Raw Material ◽

Attenuated Total Reflectance ◽

Coffee Bean ◽

Product Analysis ◽

Total Reflectance ◽

And Cluster Analysis

Analysis of natural product remain challenging issues for analytical chemist, since natural products are complicated system of mixture. The most popular methods of choice used for quality control of raw material and finished product are high performance liquid chromatography (HPLC), gas chromatography (GC) and mass spectrometry (MS). The utilization of FTIR-ATR (Fourier Transform Infrared-Attenuated Total Reflectance) method in natural product analysis is still limited. This study attempts to expand the use of FTIR spectroscopy in authenticating Indonesian coffee powder.The coffee samples studied were taken from nine regions in Indonesia, namely Aceh Gayo, Flores, Kintamani, Mandheling, Papua, Sidikalang, Toraja, Kerinci and Lampung.The samples in the form of coffee bean from various regions were powdered . The next step conducted was to determine the spectrum using the FTIR-ATR (Attenuated Total Reflectance) using ZnSe crystal of 8000 resolution. Spectrum samples, then, were analyzed using chemometrics. The utilized chemometric model was the principal component analysis (PCA) and cluster analysis (CA). Based on the chemometric analysis, there are similarities between Aceh Gayo coffee with Toraja coffee, Mandailing coffee, Kintamani coffee and Flores coffee. Sidikalang coffee has a similarity to Flores coffee; Papua coffee has a similarity to Sidikalang coffee; Lampung coffee has a similarity to Sidikalang coffee, while Kerinci coffee has a similarity to Papua coffee.

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Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

Nordic Pulp & Paper Research Journal ◽

10.3183/npprj-1997-12-04-p276-281 ◽

1997 ◽

Vol 12 (4) ◽

pp. 276-281 ◽

Cited By ~ 5

Author(s):

Gunnar Forsgren ◽

Joana Sjöström

Keyword(s):

Data Analysis ◽

Multivariate Data Analysis ◽

Food Packaging ◽

Multivariate Data ◽

Principal Component ◽

Raw Material ◽

Experimental Conditions ◽

The Third ◽

Headspace Gas ◽

Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analysis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only virgin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selection of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "fingerprints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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Multivariate micropollutants analysis in marine waters

Water Science & Technology ◽

10.2166/wst.1995.0701 ◽

1995 ◽

Vol 32 (9-10) ◽

pp. 341-348

Author(s):

V. Librando ◽

G. Magazzù ◽

A. Puglisi

Keyword(s):

Multivariate Data Analysis ◽

Partial Least Squares Regression ◽

Environmental Chemistry ◽

Sampling Site ◽

Principal Component ◽

Least Squares Regression ◽

Organic Micropollutants ◽

Suspended Material ◽

The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

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