OPLS AND PLS MULTIVARIATE REGRESSION OF <sup>1</sup>H NMR SPECTRA OF DIESEL/MAFURRA BIODIESEL BLENDS FOR ESTIMATION OF BIODIESEL CONTENT

Máquina, Ademar D. V.; Sitoe, Baltazar V.; Ferreira, Maria T. C.; Santos, Douglas Q.; Borges Neto, Waldomiro

doi:10.21577/0100-4042.20170559

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Artigo • Quím. Nova 43 (7) • July 2020 • https://doi.org/10.21577/0100-4042.20170559 copy

OPLS AND PLS MULTIVARIATE REGRESSION OF ¹H NMR SPECTRA OF DIESEL/MAFURRA BIODIESEL BLENDS FOR ESTIMATION OF BIODIESEL CONTENT

Authorship SCIMAGO INSTITUTIONS RANKINGS

Two methodologies were developed to monitor the biodiesel content of mafurra in mixtures with diesel using hydrogen nuclear magnetic resonance (¹H NMR) Spectroscopy combined with the multivariate regression by orthogonal projections to latent structure (OPLS) and partial least squares (PLS). The efficiency of these methodologies was analyzed based on the figures of merit and the fit of the models through the correlation of the measured and predicted values of the calibration and prediction sets. The results of the figures of merit in the OPLS model were better than in the PLS model. A high correlation between the measured and predicted values was evident in the OPLS model, with a correlation coefficient (R²) greater than 0.99, demonstrating a better fit of the OPLS model in relation to the PLS model which presented a correlation coefficient (R²) less than 0.98. The OPLS model is more robust and has good predictive capacity than the PLS model because it obtained a higher Q 2 value. The excellent results of the application of ¹H NMR spectroscopy combined with multivariate regression by OPLS suggest that this analytical methodology is ideal, feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.

Keywords:
mafurra Biodiesel; ¹H NMR spectrometry; Monitoring; OPLS; PLS

Figura de mérito	Equação
Seletividade	$S Ê L_{i} = \frac{n â s_{i}}{x_{i}}$
Sensibilidade	$S Ê N = \frac{1}{b_{k}}$
Sensibilidade analítica	$γ = \frac{S Ê N}{δ_{x}}$
Limite de detecção	$LQ = 10 δ_{x} \frac{1}{S Ê N}$
Limite de quantificação	$LQ = 10 δ_{x} \frac{1}{S Ê N}$
bias	$bias = \frac{\sum_{i = 1}^{n_{val}} (y_{i} - {\hat{y}}_{i})}{n_{val}}$
t _bias	$t_{bias} = \frac{\|bias\| \sqrt{n_{val}}}{SDV}$
SDV	$SDV = \sqrt{\frac{\sum {[(y_{i} - {\hat{y}}_{i}) - bias]}^{2}}{n_{val} - 1}}$
Q2	$Q 2 = 1 - \frac{\sum_{i = 1}^{n} {(y_{i} - {\hat{y}}_{icv})}^{2}}{\sum_{i = 1}^{n} {(y_{i} - \bar{y})}^{2}}$
R2	$R 2 = 1 - \frac{\sum_{i = 1}^{n} {(y_{i} - {\hat{y}}_{ical})}^{2}}{\sum_{i = 1}^{n} {(y_{i} - \bar{y})}^{2}}$

Parâmetros/Figuras de mérito	Valores do modelo
Parâmetros/Figuras de mérito	OPLS	PLS
RMSECV / % (v/v)	1,04	1,48
RMSEC / % (v/v)	0,10	0,67
RMSEP / % (v/v)	0,78	1,32
Seletividade	0,70	0,18
Sensibilidade / % (v/v)^-1	5,73×10⁷	3,77 ×10⁷
Sensibilidade analítica / % (v/v)	2,47 ×10³	1,63 ×10³
Inverso da sensibilidade analítica / (v/v)^-1	4,03×10^-4	6,13 ×10^-4
Limite de detecção / % (v/v)	0,0013	0,0020
Limite de quantificação / % (v/v)	0,0040	0,0061
t _bias	0,32	0,41
t_crítico	2,0639	2,0639
R²	0,995	0,977
Q2	0,970	0,940

	Wilcoxon		Sign Test		Rand t-test
	OPLS	PLS	OPLS	PLS	OPLS	PLS
Y-column: 1
Self-Pred (RMSEC)	0.000	0.000	0.000	0.000	0.005	0.005
Cross-Val (RMSECV)	0.000	0.000	0.000	0.000	0.005	0.005
Y-column: 2
Self-Pred (RMSEC)	0.000	0.000	0.000	0.000	0.005	0.02
Cross-Val (RMSECV)	0.000	0.000	0.000	0.000	0.005	0.01

	Modelo OPLS	Modelo PLS	F_calculado	F_tabelado (0,05,24,24)
RMSEP	0,776	1,319	2,889	2,027

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OPLS AND PLS MULTIVARIATE REGRESSION OF 1H NMR SPECTRA OF DIESEL/MAFURRA BIODIESEL BLENDS FOR ESTIMATION OF BIODIESEL CONTENT

OPLS AND PLS MULTIVARIATE REGRESSION OF ¹H NMR SPECTRA OF DIESEL/MAFURRA BIODIESEL BLENDS FOR ESTIMATION OF BIODIESEL CONTENT