Model Optimization for the Prediction of Red Wine Phenolic Compounds Using Ultraviolet–Visible Spectra

The primary objective of this work was to optimize red wine phenolic prediction with models built from wine ultraviolet–visible absorbance spectra. Three major obstacles were addressed to achieve this, namely algorithm selection, spectral multicollinearity, and phenolic evolution over time. For algorithm selection, support vector regression, kernel ridge regression, and kernel partial least squares regression were compared. For multicollinearity, the spectrum of malvidin chloride was used as an external standard for spectral adjustment. For phenolic evolution, spectral data were collected during fermentation as well as once a week for four weeks after fermentation had ended. Support vector regression gave the most accurate predictions among the three algorithms tested. Additionally, malvidin chloride proved a useful standard for phenolic spectral transformation and isolation. As for phenolic evolution, models needed to be calibrated and validated throughout the aging process to ensure predictive accuracy. In short, red wine phenolic prediction by the models built in this work can be realistically achieved, although periodic model re-calibration and expansion from data obtained using known phenolic assays is recommended to maintain model accuracy.

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Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

Engineering Construction & Architectural Management ◽

10.1108/ecam-08-2020-0618 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Emmanuel Adinyira ◽

Emmanuel Akoi-Gyebi Adjei ◽

Kofi Agyekum ◽

Frank Desmond Kofi Fugar

Keyword(s):

Machine Learning ◽

Support Vector Regression ◽

Cash Flow ◽

Predictive Accuracy ◽

Model Development ◽

Construction Project ◽

Support Vector ◽

Sensitivity Index ◽

Content Type ◽

Hyperparameter Selection

PurposeKnowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.Design/methodology/approachThe study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.FindingsThe developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.Originality/valueThe developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

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Determination of Cefoperazone Sodium in Presence of Related Impurities by Linear Support Vector Regression and Partial Least Squares Chemometric Models

Journal of Analytical Methods in Chemistry ◽

10.1155/2015/593892 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Ibrahim A. Naguib ◽

Eglal A. Abdelaleem ◽

Hala E. Zaazaa ◽

Essraa A. Hussein

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Multivariate Calibration ◽

Drug Product ◽

Support Vector ◽

Control Analysis ◽

Least Squares Regression ◽

Significant Difference ◽

Cefoperazone Sodium

A comparison between partial least squares regression and support vector regression chemometric models is introduced in this study. The two models are implemented to analyze cefoperazone sodium in presence of its reported impurities, 7-aminocephalosporanic acid and 5-mercapto-1-methyl-tetrazole, in pure powders and in pharmaceutical formulations through processing UV spectroscopic data. For best results, a 3-factor 4-level experimental design was used, resulting in a training set of 16 mixtures containing different ratios of interfering moieties. For method validation, an independent test set consisting of 9 mixtures was used to test predictive ability of established models. The introduced results show the capability of the two proposed models to analyze cefoperazone in presence of its impurities 7-aminocephalosporanic acid and 5-mercapto-1-methyl-tetrazole with high trueness and selectivity (101.87 ± 0.708 and 101.43 ± 0.536 for PLSR and linear SVR, resp.). Analysis results of drug products were statistically compared to a reported HPLC method showing no significant difference in trueness and precision, indicating the capability of the suggested multivariate calibration models to be reliable and adequate for routine quality control analysis of drug product. SVR offers more accurate results with lower prediction error compared to PLSR model; however, PLSR is easy to handle and fast to optimize.

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A New Approach to Cutting Temperature Prediction Using Support Vector Regression and Ant Colony Optimization

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.4.145 ◽

2012 ◽

Vol 4 ◽

pp. 145-152

Author(s):

De Huai Zeng ◽

Yuan Liu ◽

Lian Bo Jiang ◽

Li Li ◽

Gang Xu

Keyword(s):

Support Vector Regression ◽

Ant Colony Optimization ◽

Predictive Accuracy ◽

Back Propagation ◽

Cutting Temperature ◽

Cutting Parameters ◽

Back Propagation Neural Network ◽

Ant Colony ◽

Support Vector ◽

Interface Temperature

In this paper, support vector regression with ant colony optimization is presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. Ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function ε and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model tool–chip interface temperature with good predictive accuracy.

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Anwendung spektroskopischer Messverfahren und Multivariater Datenanalyse zur Bewertung und Beobachtung von Bioprozessen

10.51202/9783186293176 ◽

2017 ◽

Author(s):

Jan-Patrick Voß

Keyword(s):

Pichia Pastoris ◽

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Process Analytical Technology ◽

Support Vector ◽

Least Squares Regression ◽

Analytical Technology

Die vorliegende Arbeit befasst sich mit dem Bioprozessmonitoring unter Verwendung spektroskopischer Messverfahren und multivariater Datenanalyse nach den Grundsätzen von PAT – Process Analytical Technology. Mit NIR-Spektroskopie und dem Verfahren Soft Independent Modelling of Class Analogy (SIMCA) wurde eine Quali¬tätsbewertung von Hefeextrakten realisiert. Im Vordergrund stand jedoch die Quanti¬fizierung nicht direkt messbarer Größen aus NIR-, Raman- und 2D-Fluoreszenzspektren in pharmazeutischen Produktionsprozessen mit Pichia pastoris. Eine entsprechende Online-Bestimmung mit der Methode Partial Least Squares Regression (PLSR) kam weiterführend zur Regelung der Glycerolkonzentration zum Einsatz. Darüber hinaus wurde die Verwendung nichtspektraler Online-Daten zur Prozessbeobachtung erprobt. Dabei gelang mit Hilfe des nichtlinearen Verfahrens Support Vector Regression (SVR) unter anderem die Bestimmung zellspezifischer Reaktionsraten. ...

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Comparison on quantitative inversion of characteristic ions in salinized soils with hyperspectral based on support vector regression and partial least squares regression

European Journal of Remote Sensing ◽

10.1080/22797254.2020.1854622 ◽

2020 ◽

Vol 53 (1) ◽

pp. 340-348

Author(s):

Jingyi Wang ◽

Xiaoming Li

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Support Vector ◽

Least Squares Regression

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Stability indicating analysis of bisacodyl by partial least squares regression, spectral residual augmented classical least squares and support vector regression chemometric models: A comparative study

Bulletin of Faculty of Pharmacy Cairo University ◽

10.1016/j.bfopcu.2011.10.001 ◽

2011 ◽

Vol 49 (2) ◽

pp. 91-100 ◽

Cited By ~ 5

Author(s):

Ibrahim A. Naguib

Keyword(s):

Comparative Study ◽

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Support Vector ◽

Least Squares Regression ◽

Stability Indicating ◽

Spectral Residual

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A study on prediction performance of the mechanical properties of rubber using Fourier-transform near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033518805277 ◽

2018 ◽

Vol 26 (6) ◽

pp. 351-358 ◽

Cited By ~ 3

Author(s):

Rattapol Pornprasit ◽

Philaiwan Pornprasit ◽

Pruet Boonma ◽

Juggapong Natwichai

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Prediction Models ◽

Scatter Correction ◽

Support Vector ◽

Least Squares Regression ◽

Multiplicative Scatter Correction ◽

Orthogonal Signal Correction

Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r2 values were greater than 0.9, the bias values were among ±0.5, and the RMSEP values were lower than 5.

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Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms

Beverages ◽

10.3390/beverages7040078 ◽

2021 ◽

Vol 7 (4) ◽

pp. 78

Author(s):

Harrison Fuller ◽

Chris Beaver ◽

James Harbertson

Keyword(s):

Raman Spectroscopy ◽

Alcoholic Fermentation ◽

Red Wine ◽

White Wine ◽

Total Sugar ◽

Machine Learning Algorithms ◽

Support Vector ◽

Least Squares Regression ◽

Spontaneous Raman Scattering ◽

Fermentation Monitoring

In the following study, total sugar concentrations before and during alcoholic fermentation, as well as ethanol concentrations and pH levels after fermentation, of red and white wine grapes were successfully predicted using Raman spectroscopy. Fluorescing compounds such as anthocyanins and pigmented phenolics found in red wine present one of the primary limitations of enological analysis using Raman spectroscopy. Unlike the spontaneous Raman effect, fluorescence is a highly efficient process and consequently emits a much stronger signal than spontaneous Raman scattering. For this reason, many enological applications of Raman spectroscopy are impractical as the more subtle Raman spectrum of any red wine sample is in large part masked by fluorescing compounds present in the wine. This work employs a simple extraction method to mitigate fluorescence in finished red wines. Ethanol and total sugars (fructose plus glucose) of wines made from red (Cabernet Sauvignon) and white (Chardonnay, Sauvignon Blanc, and Gruner Veltliner) varieties were modeled using support vector regression (SVR), partial least squares regression (PLSR) and Ridge regression (RR). The results, which compared the predicted to measured total sugar concentrations before and during fermentation, were excellent (R2SVR = 0.96, R2PLSR = 0.95, R2RR = 0.95, RMSESVR = 1.59, RMSEPLSR = 1.57, RMSERR = 1.57), as were the ethanol and pH predictions for finished wines after phenolic stripping with polyvinylpolypyrrolidone (R2SVR = 0.98, R2PLSR = 0.99, R2RR = 0.99, RMSESVR = 0.23, RMSEPLSR = 0.21, RMSERR = 0.23). The results suggest that Raman spectroscopy is a viable tool for rapid and trustworthy fermentation monitoring.

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