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

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>

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

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>

scholarly journals Investigating microcrystalline cellulose crystallinity using Raman spectroscopy

Cellulose ◽  
2021 ◽  
Author(s):  
Ana Luiza P. Queiroz ◽  
Brian M. Kerins ◽  
Jayprakash Yadav ◽  
Fatma Farag ◽  
Waleed Faisal ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Microcrystalline Cellulose ◽  
Web Application ◽  
Principal Component ◽  
Raw Material ◽  
Least Squares Regression ◽  
Analysis Model ◽  
General Reference ◽  
Material Source ◽  
Univariate Model

AbstractMicrocrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine MCC crystallinity index (%CI) from Raman spectra of 30 commercial batches using Raman probes with spot sizes of 100 µm (MR probe) and 6 mm (PhAT probe). A principal component analysis model separated Raman spectra of the same samples captured using the different probes. The %CI was determined using a previously reported univariate model based on the ratio of the peaks at 380 and 1096 cm−1. The univariate model was adjusted for each probe. The %CI was also predicted from spectral data from each probe using partial least squares regression models (where Raman spectra and univariate %CI were the dependent and independent variables, respectively). Both models showed adequate predictive power. For these models a general reference amorphous spectrum was proposed for each instrument. The development of the PLS model substantially reduced the analysis time as it eliminates the need for spectral deconvolution. A web application containing all the models was developed. Graphic abstract

A unique approach for in-situ monitoring of the THCA decarboxylation reaction in solid state

2021 ◽  
pp. 120471
Author(s):  
Olga Gigopulu ◽  
Nikola Geskovski ◽  
Gjoshe Stefkov ◽  
Veronika Stoilkovska Gjorgievska ◽  
Irena Slaveska Spirevska ◽  
...  
Keyword(s):  
Solid State ◽  
In Situ Monitoring ◽  
Decarboxylation Reaction ◽  
Unique Approach

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
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.

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

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.

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

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
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.

scholarly journals Grill Workers Exposure to Polycyclic Aromatic Hydrocarbons: Levels and Excretion Profiles of the Urinary Biomarkers

2020 ◽  
Vol 18 (1) ◽  
pp. 230
Author(s):  
Marta Oliveira ◽  
Sílvia Capelas ◽  
Cristina Delerue-Matos ◽  
Simone Morais
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
High Performance ◽  
Principal Component ◽  
Fold Increase ◽  
Urinary Biomarkers ◽  
Analysis Model ◽  
Exposed Workers ◽  
Polycyclic Aromatic ◽  
The Impact

Grilling activities release large amounts of hazardous pollutants, but information on restaurant grill workers’ exposure to polycyclic aromatic hydrocarbons (PAHs) is almost inexistent. This study assessed the impact of grilling emissions on total workers’ exposure to PAHs by evaluating the concentrations of six urinary biomarkers of exposure (OHPAHs): naphthalene, acenaphthene, fluorene, phenanthrene, pyrene, and benzo(a)pyrene. Individual levels and excretion profiles of urinary OHPAHs were determined during working and nonworking periods. Urinary OHPAHs were quantified by high-performance liquid-chromatography with fluorescence detection. Levels of total OHPAHs (∑OHPAHs) were significantly increased (about nine times; p ≤ 0.001) during working comparatively with nonworking days. Urinary 1-hydroxynaphthalene + 1-hydroxyacenapthene and 2-hydroxyfluorene presented the highest increments (ca. 23- and 6-fold increase, respectively), followed by 1-hydroxyphenanthrene (ca. 2.3 times) and 1-hydroxypyrene (ca. 1.8 times). Additionally, 1-hydroxypyrene levels were higher than the benchmark, 0.5 µmol/mol creatinine, in 5% of exposed workers. Moreover, 3-hydroxybenzo(a)pyrene, biomarker of exposure to carcinogenic PAHs, was detected in 13% of exposed workers. Individual excretion profiles showed a cumulative increase in ∑OHPAHs during consecutive working days. A principal component analysis model partially discriminated workers’ exposure during working and nonworking periods showing the impact of grilling activities. Urinary OHPAHs were increased in grill workers during working days.

scholarly journals A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1411
Author(s):  
José Luis P. Calle ◽  
Marta Ferreiro-González ◽  
Ana Ruiz-Rodríguez ◽  
Gerardo F. Barbero ◽  
José Á. Álvarez ◽  
...  
Keyword(s):  
Random Forest ◽  
Raw Materials ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Raw Material ◽  
Support Vector ◽  
Protected Designation Of Origin ◽  
Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals Effect of Water and Ethanol Extracts from Hericium erinaceus Solid-State Fermented Wheat Product on the Protection and Repair of Brain Cells in Zebrafish Embryos

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3297
Author(s):  
Shun-Kuo Sun ◽  
Chun-Yi Ho ◽  
Wei-Yang Yen ◽  
Su-Der Chen
Keyword(s):  
Solid State ◽  
Brain Cell ◽  
Hot Water ◽  
Raw Material ◽  
Brain Cells ◽  
Zebrafish Embryos ◽  
Hericium Erinaceus ◽  
Water Extracts ◽  
The Brain ◽  
Wheat Product

Extracts from Hericium erinaceus can cause neural cells to produce nerve growth factor (NGF) and protect against neuron death. The objective of this study was to evaluate the effects of ethanol and hot water extracts from H. erinaceus solid-state fermented wheat product on the brain cells of zebrafish embryos in both pre-dosing protection mode and post-dosing repair mode. The results showed that 1% ethanol could effectively promote zebrafish embryo brain cell death. Both 200 ppm of ethanol and water extracts from H. erinaceus solid-state fermented wheat product protected brain cells and significantly reduced the death of brain cells caused by 1% ethanol treatment in zebrafish. Moreover, the zebrafish embryos were immersed in 1% ethanol for 4 h to cause brain cell damage and were then transferred and soaked in the 200 ppm of ethanol and water extracts from H. erinaceus solid-state fermented wheat product to restore the brain cells damaged by the 1% ethanol. However, the 200 ppm extracts from the unfermented wheat medium had no protective and repairing effects. Moreover, 200 ppm of ethanol and water extracts from H. erinaceus fruiting body had less significant protective and restorative effects on the brain cells of zebrafish embryos. Both the ethanol and hot water extracts from H. erinaceus solid-state fermented wheat product could protect and repair the brain cells of zebrafish embryos damaged by 1% ethanol. Therefore, it has great potential as a raw material for neuroprotective health product.

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