Chemometric Study of Excitation—Emission Matrix Fluorescence Data: Quantitative Analysis of Petrol—Kerosene Mixtures

Divya, O.; Mishra, Ashok Kumar

doi:10.1366/000370208784909454

Cited by 17 publications

(8 citation statements)

References 22 publications

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“…N-PLS is an extension of PLS to handle data in threedimensional arrays [19][20]. The principles are similar to the PLS algorithm.The advantage of N-PLS in comparison to PLS is not to obtain a better fit, but rather to obtain more adequate, robust, and interpretable models [21].…”

Section: Npls-damentioning

confidence: 99%

Application of 2D Correlation Infrared Spectroscopy to Identification of Adulterated Milk Using NPLS-DA Method

Liu¹,

Yang²,

Yang³

et al. 2013

2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control

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In this paper, a new method for discriminate adulterated milk was proposed by combining two-dimensional (2D) correlation infrared spectroscopy with multi-way partial least squares discriminate analysis (NPLS-DA). 64 pure milk samples were collected and four adulteration types of milk with urea, melamine, tetracycline and glucose were prepared, respectively. IR spectra of pure milk and adulterated milk samples in the region of 900-1700cm -1 were measured. Based on the characteristics of 2D correlation infrared spectra, the discriminant model of pure milk and adulterated milk was conducted by using NPLS-DA method. The prediction rate of unknown samples was 90.7%. Finally, compared with NPLS-DA and partial least squares discriminant analysis (PLS-DA) models, the NPLS-DA model showed excellent performance for prediction. This study demonstrates that 2D correlation spectra coupled with NPLS-DA is a promising way for rapid and non-expensive discrimination of adulterated milk.

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Section: Npls-damentioning

confidence: 99%

Application of 2D Correlation Infrared Spectroscopy to Identification of Adulterated Milk Using NPLS-DA Method

Liu¹,

Yang²,

Yang³

et al. 2013

2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control

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“…[12][13][14] In petroleum and petro-chemical industries, multivariate chemometric analyses have been used in combination with spectroscopic techniques such as Fourier transform infrared, near-infrared, and LIF to identify composition and impurities in fuel blends and chemical streams. [15][16][17][18][19] In the present work, we describe further FIO instrument improvements based on implementing multivariate, classical least square (CLS) calibration and analysis. Being a full-spectrum method, CLS model offers improved precision since there is a signal averaging effect and allows for estimation of errors in the measurement by evaluating the residuals of spectral fits.…”

Section: Introductionmentioning

confidence: 99%

An improved Method for Determining Transient Fuel Dilution of Oil in an Internal-Combustion Engine Using Laser-Induced Florescence and Multivariate Least Square Calibration

et al. 2021

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An optical diagnostic, based on laser-induced fluorescence (LIF), has been developed for on-engine measurements of real-time fuel dilution of engine oil, or fuel in oil (FiO). Fuel dilution of oil is broadly relevant to advancing engine technology including durability, calibration, and catalyst-system management, and believed to promote destructive stochastic pre-ignition (SPI) during high-load engine operations. While standard (e.g., ASTM D3524-90) methods are not capable of real-time transient measurements, the LIF technique resolves transient dilution on the minutes time scale. We have expanded on our original FiO instrument development by introducing an improved analysis based on multivariate least square chemometrics analysis. The measurement uses a fuel dye (180-1300ppm by mass), and monitors for its presence in the oil via 532-nm excitation and LIF. While the original FiO instrument utilized a two-color ratio method for analysis, the improved chemometric analysis uses the fully resolved LIF dye spectra to provide better predictive FiO accuracy (> 92%) over a wide FiO range (1.5-14%) typical of engine application. We also investigate the effect of oil temperature on the LIF signal. Limited engine applications for demonstrating and validating the improved FiO instrument are shown, and the related data used to quantify practical detection limit and sensitivity. The improved analysis is insensitive to laser power fluctuation and change in detector integration time, providing an excellent FiO sensitivity (1-2%) and detection limit (0.01 %FiO) over a wide range of loads and injection timings, illustrating this updated approach to be a promising tool for advancing engine technology.

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“…Therefore, N-PLS models have good ability of prediction [14][15] , which has been employed successfully for prediction model in various biological and biochemical applications [14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of melamine by multi-way partial least squares model with two-dimensional near-infrared correlation spectroscopy

2014

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A new approach for quantitative analysis of melamine in milk was proposed based on two-dimensional (2D) correlation near-infrared spectroscopy and multi-way partial least squares (N-PLS) in this paper. 40 pure milk samples and 40 milk samples adulterated with different contents of melamine were prepared. The near-infrared transmittance spectra of all samples were measured at room temperature. Then 2D NIR-NIR correlation spectroscopy under the perturbation of adulterant concentration was calculated and N-PLS model for the melamine concentration was established with 2D correlation spectra (28×51×51). For the prediction set, the root mean square errors of prediction (RMSEP) for melamine concentration was 0.067 g/L and the coefficient correlation between actual reference values and predicted values was 0.999, which means the model has good predictive ability. For comparison purpose, partial least squares (PLS) model was also built using the conventional one-dimensional near-infrared spectra (28×51), where the RMSEP and the coefficient correlation were 0.079 g/L and 0.998, respectively. The average relative prediction error was 22.9% for N-PLS model; whereas it was 122.4% for PLS model. The N-PLS models yielded relatively low RMSEP and average relative prediction error as compared to PLS model. Therefore, N-PLS method was more robust than PLS method for accurate quantification of the concentration of melamine in milk.

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Chemometric Study of Excitation—Emission Matrix Fluorescence Data: Quantitative Analysis of Petrol—Kerosene Mixtures

Cited by 17 publications

References 22 publications

Application of 2D Correlation Infrared Spectroscopy to Identification of Adulterated Milk Using NPLS-DA Method

Application of 2D Correlation Infrared Spectroscopy to Identification of Adulterated Milk Using NPLS-DA Method

An improved Method for Determining Transient Fuel Dilution of Oil in an Internal-Combustion Engine Using Laser-Induced Florescence and Multivariate Least Square Calibration

Quantitative analysis of melamine by multi-way partial least squares model with two-dimensional near-infrared correlation spectroscopy

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