Standardization of NIR data to identify adulteration in ethanol fuel

Milanez, Karla Danielle Tavares de Melo; Silva, Adenilton Camilo; Paz, José Eduardo Matos; Medeiros, Everaldo S.; Pontes, Márcio José Coelho

doi:10.1016/j.microc.2015.08.013

Cited by 20 publications

(7 citation statements)

References 29 publications

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“…One approach adopts the model transfer methodology that utilises data of standard samples, such as the external parameter orthogonalization method (EPO) [ 25 ] which projects the soil spectra orthogonal to that of the moisture spectrum thus removing the moisture contribution from the data effectively. Other method utilises the direct standardisation (DS) [ 38 , 41 , 42 , 43 ] methodology which removes environmental factors (e.g., temperature, texture of soil surface, etc) from the spectroscopic database to make the library data more suitable for field data usage. Similar techniques such as the piecewise direct standardisation (PDS) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ], the slope/bias (S/B) correction [ 50 ] and the orthogonal signal correction (OSC) [ 50 ] have also been applied and better prediction of SOM have been achieved when it is compared directly with that using the raw spectral data for the analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Other method utilises the direct standardisation (DS) [ 38 , 41 , 42 , 43 ] methodology which removes environmental factors (e.g., temperature, texture of soil surface, etc) from the spectroscopic database to make the library data more suitable for field data usage. Similar techniques such as the piecewise direct standardisation (PDS) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ], the slope/bias (S/B) correction [ 50 ] and the orthogonal signal correction (OSC) [ 50 ] have also been applied and better prediction of SOM have been achieved when it is compared directly with that using the raw spectral data for the analysis. Other stream of model transfer approach that utilises non-standard samples, such as the standardisation of spectra through finite impulse response (FIR) method [ 51 ], has been an alternative way to achieve similar result as that of the PDS technique.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Wang

Zheng

et al. 2020

Sensors

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This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Wang

Zheng

et al. 2020

Sensors

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“…Common methods include direct standardization [12], piecewise direct standardization (PDS) [12,13,14], spectral space transformation [15], generalized least squares (GLS) [16], and so on. These methods have been extensively used to transfer quantitative calibration models [17,18,19,20], but very few studies were focused on the transfer of classification models [21,22]. Although these methods are very useful, they can only deal with calibration transfer from one instrument to another at a time and require transfer datasets to be collected from the same physical samples with both instruments.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Direct Model Transferability Using Miniature Near-Infrared Spectrometers

2019

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Recent developments in compact near infrared (NIR) instruments, including both handheld and process instruments, have enabled easy and affordable deployment of multiple instruments for various field and online or inline applications. However, historically, instrument-to-instrument variations could prohibit success when applying calibration models developed on one instrument to additional instruments. Despite the usefulness of calibration transfer techniques, they are difficult to apply when a large number of instruments and/or a large number of classes are involved. Direct model transferability was investigated in this study using miniature near-infrared (MicroNIR™) spectrometers for both classification and quantification problems. For polymer classification, high cross-unit prediction success rates were achieved with both conventional chemometric algorithms and machine learning algorithms. For active pharmaceutical ingredient quantification, low cross-unit prediction errors were achieved with the most commonly used partial least squares (PLS) regression method. This direct model transferability is enabled by the robust design of the MicroNIR™ hardware and will make deployment of multiple spectrometers for various applications more manageable.

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“…Milanez and coauthors studied the applicability of classification transfer methods for adulteration control of hydrated ethyl alcohol fuel (HEAF) samples. They successfully developed classification models by applying the multivariate tools linear discriminant analysis coupled to the successive projections algorithm (SPA-LDA) and partial least-squares discriminant analysis (PLS-DA) …”

Section: Introductionmentioning

confidence: 99%

Classification of Fuel Blends Using Exploratory Analysis with Combined Data from Infrared Spectroscopy and Stable Isotope Analysis

2016

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Chemometric tools were applied for exploratory analysis and classification of fuel blends using the combined information on Fourier transform infrared spectroscopy and stable isotope analysis through isotope ratio mass spectrometry. Principal component analysisand hierarchical clustering analysis were applied for exploratory analysis, while support vector machine (SVM) was used to classify the biodiesel/diesel blends. All of the chemometric models used present better results from the combination of spectral information with isotopic data for biodiesel contents of over 10% in the mixture, with the best results being obtained from the SVM classification. Therefore, the development presented in this paper could become an important technique to improve the discrimination of the feedstock used in biodiesel production and a resource for quality control in industry.

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Standardization of NIR data to identify adulteration in ethanol fuel

Cited by 20 publications

References 29 publications

Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

Investigation of Direct Model Transferability Using Miniature Near-Infrared Spectrometers

Classification of Fuel Blends Using Exploratory Analysis with Combined Data from Infrared Spectroscopy and Stable Isotope Analysis

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