Multivariate control charts based on NAS and mid‐infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

Abstract

aIn this work, mid-infrared spectroscopy and multivariate control charts based on net analyte signal were applied for quality control of B5 blends of biodiesel/diesel (5% biodiesel/95% diesel). Control charts were constructed using instrumental signal decomposition, generating three charts: the net analyte signal chart for monitoring the analyte of interest (methyl soybean biodiesel); the interference chart, which corresponds to the contribution of all other compounds in the diesel sample (diesel); and the residual chart, which corresponds to non-systematic variations. Statistical limits were established for each developed chart, using samples inside quality specifications (normal operation conditions). To validate multivariate control charts, new samples were analyzed. The new samples represented samples in-control and samples out-of-control in relation to the content of biodiesel, adulterated biodiesel with severe vegetable oils and adulterated diesel with residual automotive lubricant oil, kerosene, and gasoline. The results obtained show an excellent distinction between the samples inside and out of the quality specifications, with 91% and 100% correctly classified, respectively, which demonstrates that the methodology developed is a viable alternative for quality monitoring of this type of fuel.

Identificação De Adulterantes De Biodiesel Metílico De Crambe Em Misturas Com Diesel, Usando Ft-Mir E Cartas De Controle Multivariadas Baseadas No Sinal Analítico Líquido

“…35,36 Porém, trabalhos publicados para monitorar o teor do biodiesel e a presença de adulterantes nas misturas biodiesel/diesel usando cartas de controle multivariadas e espectrometria MIR são muito escassos. [37][38][39] Além disso, ainda não existem trabalhos publicados para identificar adulterantes no biodiesel metílico de Crambe usando este método. Por isso, o objetivo deste trabalho foi construir e validar modelo de cartas de controle multivariadas baseadas no NAS aliadas à espectrometria MIR para monitorar a qualidade de misturas B10 do biodiesel metílico de Crambe em relação ao teor e à adição direta de adulterantes (óleo de soja e óleo de fritura residual).…”

Detection of BCG bacteria using a magnetoresistive biosensor: A step towards a fully electronic platform for tuberculosis point-of-care detection

“…Moreover, specific interference tests, cross-reactivity tests, as well as, automatizing the magnetic separation and washing steps during functionalization should greatly reduce systema- tic errors. Also, random sampling tests could be done, using process analytical techniques, such as multi-variate control charts, which can be implemented for controlling the quality of different MR-biochips batches (Mitsutake et al, 2015).…”

Characterization of Biodiesel from Animal Fat, Vegetable Oil, and Adulterants by Infrared Spectroscopy Combined with Chemometric Methods

“…In CC-NAS, the graphs are considered a semiquantitative method since they detect the presence or absence of the analyte or the property of interest and provide the representation of the quantitative limit for that analyte. 46 PLS-DA associates principles of regression analysis (a quantitative method) of the set of instrumental signals with the categorization of the analyte (e.g., class 1 if present or class 0 if absent). 47 The two algorithms provide preliminary and important data for the classification of samples with different compositions and the presence or absence of adulterants by means of Fouriertransform infrared spectroscopy (FTIR).…”

“…Control charts based on the net analyte signal (CC-NAS) and partial least squares discriminant analysis (PLS-DA) are among the classification data analysis algorithms applied in this context. In CC-NAS, the graphs are considered a semiquantitative method since they detect the presence or absence of the analyte or the property of interest and provide the representation of the quantitative limit for that analyte . PLS-DA associates principles of regression analysis (a quantitative method) of the set of instrumental signals with the categorization of the analyte (e.g., class 1 if present or class 0 if absent) .…”