Improving Near-Infrared Prediction Model Robustness with Support Vector Machine Regression: A Pharmaceutical Tablet Assay Example

2014 ◽  
Vol 68 (12) ◽  
pp. 1348-1356 ◽  
Author(s):  
Benoît Igne ◽  
James K. Drennen ◽  
Carl A. Anderson
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Near Infrared ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Pharmaceutical Tablet ◽  
Model Robustness

Fourier Transform Near-Infrared Spectroscopy and Chemometrics To Predict Zygosacchromyces rouxii in Apple and Kiwi Fruit Juices

2018 ◽  
Vol 81 (8) ◽  
pp. 1379-1385 ◽  
Author(s):  
CHEN NIU ◽  
HONG GUO ◽  
JIANPING WEI ◽  
MARINA SAJID ◽  
YAHONG YUAN ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fruit Juices ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Limit Of Quantification ◽  
Kiwi Fruit ◽  
Single Variety

ABSTRACT This study investigated the capability of near-infrared spectroscopy (NIRS) to predict the concentration of Zygosaccharomyces rouxii in apple and kiwi fruit juices. The yeast was inoculated in fresh kiwi fruit juice (n = 68), reconstituted kiwi juice (n = 85), and reconstituted apple juice (n = 64), followed by NIR spectra collection and plate counting. A principal component analysis indicated direct orthogonal signal correction preprocessing was suitable to separate spectral samples. Parameter optimization algorithms increased the performance of support vector machine regression models developed in a single variety juice system and a multiple variety juice system. Single variety juice models achieved accurate prediction of Z. rouxii concentrations, with the limit of quantification at 3 to 15 CFU/mL (R2 = 0.997 to 0.999), and the method was also feasible for Hanseniaspora uvarum and Candida tropicalis. The best multiple variety juice model obtained had a limit of quantification of 237 CFU/mL (R2 = 0.961) for Z. rouxii. A Bland-Altman analysis indicated good agreement between the support vector machine regression model and the plate counting method. It suggests that NIRS can be a high-throughput method for prediction of Z. rouxii counts in kiwi fruit and apple juices.

Virtual Screening acetylcholinesterase inhibitors based on machine learning combined with molecule docking methods

2020 ◽  
Vol 15 ◽  
Author(s):  
Jinyu Yan ◽  
Weiguang Huang ◽  
Chi Zhang ◽  
Haizhong Huo ◽  
Fuxue Chen
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Kernel Function ◽  
Acetylcholinesterase Inhibitors ◽  
Simulation Method ◽  
Quantitative Prediction ◽  
Support Vector ◽  
Classification Models ◽  
Support Vector Machine Regression ◽  
Molecule Docking

Objective: The aim of this study was to screen for compounds with relatively high inhibitory activity on acetylcholinesterase. Methods: Classification models for acetylcholinesterase inhibitors based on KNN(1-nearest neighbors), and a quantitative prediction model based on support vector machine regression were used. The interaction of the compounds and receptor were analyzed using the molecular simulation method. Results: The radial basis kernel function was selected as the kernel function for support vector machine regression, and a total of 19 descriptors were selected to construct the quantitative prediction model.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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