Predictive evaluation of multicomponent direct compress model tablets by integrating sphere UV-Vis spectroscopy and chemometrics

2021 ◽  
pp. 1-12
Author(s):  
Yuta Otsuka ◽  
Suvra Pal
Keyword(s):  
Regression Models ◽  
Process Analytical Technology ◽  
Compaction Pressure ◽  
Principal Component ◽  
Magnesium Stearate ◽  
Dicalcium Phosphate ◽  
Integrating Sphere ◽  
Pls Regression ◽  
Standard Normal Variate ◽  
Vis Spectroscopy

BACKGROUND: Control of the pharmaceutical manufacturing process and active pharmaceutical ingredients (API) is essential to product formulation and bioavailability. OBJECTIVE: The aim of this study is to predict tablet surface API concentration by chemometrics using integrating sphere UV-Vis spectroscopy, a non-destructive and contact-free measurement method. METHODS: Riboflavin, pyridoxine hydrochloride, dicalcium phosphate anhydrate, and magnesium stearate were mixed and ground with a mortar and pestle, and 100 mg samples were subjected to direct compression at a compaction pressure of 6 MPa at 7 mm diameter. The flat surface tablets were then analyzed by integrating sphere UV-Vis spectrometry. Standard normal variate (SNV) normalization and principal component analysis were applied to evaluate the measured spectral dataset. The spectral ranges were prepared at 300–800 nm and 500–700 nm with SNV normalization. Partial least squares (PLS) regression models were constructed to predict the API concentrations based on two previous datasets. RESULTS: The regression vector of constructed PLS regression models for each API was evaluated. API concentration prediction depends on riboflavin absorbance at 550 nm and the excipient dicalcium phosphate anhydrate. CONCLUSION: Integrating sphere UV-Vis spectrometry is a useful tool to process analytical technology.

scholarly journals Feasibility of UV-Vis Spectral Fingerprinting Combined with Chemometrics for Rapid Detection of Phyllanthus niruri Adulteration with Leucaena leucocephala

2021 ◽  
Vol 50 (4) ◽  
pp. 997-1006
Author(s):  
Mohamad Rafi Mohamad Rafi ◽  
Bayu Nurcahyo Bayu Nurcahyo ◽  
Wulan Tri Wahyuni ◽  
Zulhan Arif ◽  
Dewi Anggraini Septaningsih ◽  
...  
Keyword(s):  
Leucaena Leucocephala ◽  
Raw Materials ◽  
Principal Component ◽  
Phyllanthus Niruri ◽  
Standard Normal Variate ◽  
Vis Spectroscopy ◽  
Standard Normal ◽  
Materials Used ◽  
First Time ◽  
Absorbance Data

Phyllanthus niruri is widely used in Indonesia as immunostimulant. The morphology of Leucaena leucocephala leaves is similar to that of P. niruri leaves. L. leucocephala is easy to find and collect because it is widely distributed in the world. Therefore, it is likely P. niruri could be adulterated with L. leucocephala. Therefore, identification and authentication of P. niruri is important to ensure the raw materials used are original without any substitution or mixture with other similar plants causing inconsistencies in their efficacy. In this paper, we described feasibility used of UV-Vis spectral fingerprinting and chemometrics for rapid method for the identification and detection of P. niruri leaves adulterated with L. leucocephala leaves. UV-Vis spectra of samples measured in the interval of 200-800 nm and signal smoothing followed by standard normal variate were used for pre-processing the spectral data. Principal component analysis (PCA)with the absorbance data from the pre-processed UV-Vis spectra in the range of 250-700 nm as variables could distinguish P. niruri from L. leucocephala. PCA followed by discriminant analysis (DA) could successfully classified P. niruri mixed with 5, 25, and 50% L. luecocephala into their respective groups (96.81%). We also employed soft independent modelling of class analogy (SIMCA) for authentication of P. niruri and found that 88.3% of the samples were also correctly classified into their respective groups. A combination of UV-Vis spectroscopy with chemometrics, such as PCA-DA and SIMCA, were used for the first time for the identification and detection of P. niruri adulterated with L. leucocephala.

scholarly journals Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Molecules ◽  
2021 ◽  
Vol 26 (20) ◽  
pp. 6091
Author(s):  
Meinilwita Yulia ◽  
Diding Suhandy
Keyword(s):  
Regression Models ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Error Prediction ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Roasted Coffee ◽  
Vis Spectroscopy ◽  
Plsr Model

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

scholarly journals Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248956
Author(s):  
Elizabeth R. Lusczek ◽  
Nicholas E. Ingraham ◽  
Basil S. Karam ◽  
Jennifer Proper ◽  
Lianne Siegel ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Regression Models ◽  
Principal Component ◽  
Hospitalized Patients ◽  
Future Research ◽  
Ensemble Clustering ◽  
Multinomial Regression ◽  
Tailored Therapy ◽  
Clinical Phenotypes ◽  
Multivariable Models

Purpose Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Methods This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. Results The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11–17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10–6.00), p = 0.03) increases in hazard of death relative to phenotype III. Conclusion We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

scholarly journals Unravelling Plant-Pathogen Interactions: Proximal Optical Sensing as an Effective Tool for Early Detect Plant Diseases

2021 ◽  
Vol 5 (1) ◽  
pp. 18
Author(s):  
Mafalda Reis-Pereira ◽  
Rui C. Martins ◽  
Aníbal Filipe Silva ◽  
Fernando Tavares ◽  
Filipe Santos ◽  
...  
Keyword(s):  
Optical Fibers ◽  
Optical Sensing ◽  
Principal Component ◽  
Plant Diseases ◽  
Bacterial Suspension ◽  
Control Group ◽  
Post Inoculation ◽  
Early Disease Detection ◽  
Run Off ◽  
Vis Spectroscopy

This study analyzed the potential of proximal optical sensing as an effective approach for early disease detection. A compact, modular sensing system, combining direct UV–Vis spectroscopy with optical fibers, supported by a principal component analysis (PCA), was applied to evaluate the modifications promoted by the bacteria Xanthomonas euvesicatoria in tomato leaves (cv. cherry). Plant infection was achieved by spraying a bacterial suspension (108 CFU mL−1) until run-off occurred, and a similar approach was followed for the control group, where only water was applied. A total of 270 spectral measurements were performed on leaves, on five different time instances, including pre- and post-inoculation measurements. PCA was then applied to the acquired data from both healthy and inoculated leaves, which allowed their distinction and differentiation, three days after inoculation, when unhealthy plants were still asymptomatic.

Towards reliable estimation of an “electronic tongue” predictive ability from PLS regression models in wine analysis

Talanta ◽  
2012 ◽  
Vol 90 ◽  
pp. 109-116 ◽  
Author(s):  
Dmitry Kirsanov ◽  
Olga Mednova ◽  
Vladimir Vietoris ◽  
Paul A. Kilmartin ◽  
Andrey Legin
Keyword(s):  
Regression Models ◽  
Electronic Tongue ◽  
Predictive Ability ◽  
Pls Regression ◽  
Reliable Estimation ◽  
Wine Analysis

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>

An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

2013 ◽  
pp. 273-289
Author(s):  
Jiansheng Wu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Principal Component ◽  
Linear Regression Models ◽  
Rainfall Forecasting ◽  
Forecasting Accuracy ◽  
Parametric Regression ◽  
Nonlinear Regression Models ◽  
Linear And Nonlinear ◽  
Artificial Neural Network Ann

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

scholarly journals Long-Lead Seasonal Prediction of China Summer Rainfall Using an EOF–PLS Regression-Based Methodology*,+

2016 ◽  
Vol 29 (5) ◽  
pp. 1783-1796 ◽  
Author(s):  
Wen Xing ◽  
Bin Wang ◽  
So-Young Yim
Keyword(s):  
Air Temperature ◽  
Dynamic Models ◽  
Principal Component ◽  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Rainfall Anomaly ◽  
Pls Regression ◽  
Rainfall Prediction ◽  
Forecast Validation ◽  
Forecast Models

Abstract Considerable year-to-year variability of summer rainfall exposes China to threats of frequent droughts and floods. Objective prediction of the summer rainfall anomaly pattern turns out to be very challenging. As shown in the present study, the contemporary state-of-the-art dynamical models’ 1-month-lead prediction of China summer rainfall (CSR) anomalies has insignificant skills. Thus, there is an urgent need to explore other ways to improve CSR prediction. The present study proposes a combined empirical orthogonal function (EOF)–partial least squares (PLS) regression method to offer a potential long-lead objective prediction of spatial distribution of CSR anomalies. The essence of the methodology is to use PLS regression to predict the principal component (PC) of the first five leading EOF modes of CSR. The preceding December–January mean surface temperature field [ST; i.e., SST over ocean and 2-m air temperature (T2m) over land] is selected as the predictor field for all five PCs because SST and snow cover, which is reflected by 2-m air temperature, are the most important factors that affect CSR and because the correlation between each mode and ST during winter is higher than in spring. The 4-month-lead forecast models are established by using the data from 1979 to 2004. A 9-yr independent forward-rolling prediction is made for the latest 9 yr (2005–13) as a strict forecast validation. The pattern correlation coefficient skill (0.32) between the observed and the 4-month-lead predicted patterns during the independent forecast period of 2005–13 is significantly higher than the dynamic models’ 1-month-lead hindcast skill (0.04), which indicates that the EOF–PLS regression is a useful tool for improving the current seasonal rainfall prediction. Issues related to the EOF–PLS method are also discussed.

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