scholarly journals Ultraviolet-Visible Spectroscopy and Chemometric Strategy Enable the Classification and Detection of Expired Antimalarial Herbal Medicinal Product in Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jacob N. Mensah ◽  
Abena A. Brobbey ◽  
John N. Addotey ◽  
Isaac Ayensu ◽  
Samuel Asare-Nkansah ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Medicinal Products ◽  
Herbal Medicinal Products ◽  
Support Vector Machine Classification ◽  
Herbal Medicinal

To meet the growing demand for complementary and alternative treatment for malaria, manufacturers produce several antimalarial herbal medicinal products. Herbal medicinal products regulation is difficult due to their complex chemical nature, requiring cumbersome, expensive, and time-consuming methods of analysis. The aim of this study was to develop a simple spectroscopic method together with a chemometric model for the classification and the identification of expired liquid antimalarial herbal medicinal products. Principal component analysis model was successfully used to distinguish between different herbal medicinal products and identify expired products. Principal component analysis showed a clear class separation between all five herbal medicinal products (HMP) studied, with explained variance for first and second principal components as 37.51% and 26.38%, respectively, while the third principal component had 18.74%. Support vector machine classification gave specificity and accuracy of 1.00 (100%) for training set data for all the products. The validation set HMP1, HMP2, and HMP3 had sensitivity, specificity, and accuracy of 1.00. HMP4 and HMP5 had sensitivity and specificity of 0.90 and 1.00, respectively, and an accuracy of 0.98. The support vector machine classification and principal component analysis models were successfully used to identify expired herbal medicinal products. This strategy can be used for rapid field detection of expired liquid antimalarial herbal medicinal products.

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

2022 ◽  
pp. 146808742110707
Author(s):  
Aran Mohammad ◽  
Reza Rezaei ◽  
Christopher Hayduk ◽  
Thaddaeus Delebinski ◽  
Saeid Shahpouri ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Data Driven ◽  
Support Vector ◽  
Emission Models ◽  
Emission Modeling

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

Sign in / Sign up

Export Citation Format

Share Document