An effective and rapid approach to predict molecular composition of naphtha based on raw NIR spectra

Zhu, Ling; Lu, Shao Hua; Zhang, Yao Heng; Yin, Bo; Mi, Jia

doi:10.1016/j.vibspec.2020.103071

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…Differentiation can be used to eliminate baseline drift in the spectrum, enhance spectral band characteristics, and overcome band overlap. Among them, 1D can remove the drift independent of wavelength, 27 and 2D can remove the drift that is linearly related to wavelength 28 . SG can improve the signal‐to‐noise ratio and reduce noise 29 .…”

Section: Resultsmentioning

confidence: 99%

Intelligent component detection of quaternary blended oil based on near infrared spectroscopy technology

Zhao

Sun

Bai

et al. 2023

Journal of Chemometrics

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In this study, near infrared spectroscopy (NIRS) technique was used for quantitative detection of quaternary blended oil. After a series of preprocessing, the prediction effects of the three models and their preprocessing combinations were compared. Taking soybean oil content prediction as an example, random forest (RF) model had better performance after second derivative (D2) optimization. In feature selection, a two‐step feature selection method was adopted to extract the feature wavelength. First, the elastic net (EN) was used for the initial screening of feature wavelengths, and most irrelevant features were eliminated. The number of feature wavelengths was reduced from 1048 to 134. After that, the competitive adaptive re‐weighted sampling (CARS) method was used to screen the remaining characteristic wavelengths more carefully, and 20 effective characteristic wavelengths were selected. Finally, a quantitative detection model was established based on 20 effective characteristic wavelengths selected by EN + CARS. Evaluated by the test set, The correlation coefficient of determination (R2), root‐mean‐square error of prediction (RMSEP) and Relative Percent Difference (RPD) values of 2D + EN + CARS + RF model were 0.97953, 1.34306 and 7.08875, respectively. The results showed that the two‐step feature selection method can effectively extract the feature wavelength, and the NIRS technology can realize the intelligent detection of blended oil components.

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Section: Resultsmentioning

confidence: 99%

Intelligent component detection of quaternary blended oil based on near infrared spectroscopy technology

Zhao

Sun

Bai

et al. 2023

Journal of Chemometrics

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“…A successful model for the classification of oils according to their geochemical origin was created using soft independent modeling of class analogy [31]. Calibration models have been created to determine density [32], viscosity [33], fractional composition [34], and water content [35] for different hydrocarbon systems using such methods as interval partial least squares, support vector regression, genetic algorithm, and others. In some cases, the goal is to predict an indicator that is not directly standardized, but is fundamentally important for explaining the patterns of processes such as crystallization of wax or the formation of asphalt, resin, and paraffin deposits-the temperature at which paraffin crystallization begins [5].…”

Section: Introductionmentioning

confidence: 99%

Determination of N-Paraffins Content in Crude Oil via Near-Infrared Spectroscopy Associated with Chemometric Approaches

Sarkisov,

Ivanova,

Primerova

et al. 2023

Processes

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This study explores the potential application of NIR spectroscopy coupled with different linear and nonlinear models for rapid evaluation of n-alkanes in crude oil. Samples for calibration were 30 model mixtures of n-eicosane in crude oil samples with a concentration of 1–15%. The prediction models were established based on 21 methods: linear regression, regression trees, support vector machines, Gaussian process regression, ensembles of trees, and neural networks. The spectral range 4500–9000 cm−1 was determined to be the most informative for prediction. The prediction capability of lineal regression methods turned out to be unsatisfactory. Nonlinear models were preferred over linear models; better results were obtained using the regression trees method, including «fine tree» (RMSE = 2.8635) and neural networks (RMSE = 2.0157). The LS-SVM model exhibited satisfactory prediction performance (R2 = 0.96, RMSE = 0.91), as did the Gaussian Process Regression Matern 5.2 GPR (R2 = 0.96, RMSE = 1.03) and Gaussian Process Regression (Rational Quadratic) (R2 = 0.95, RMSE = 1.04). Among the 21 chemometric algorithms, the best and weakest models were the LS-SVM and PLSR models, respectively. The LS-SVM model was the optimal model for the prediction of n-alkanes content in crude oil.

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“…NIR spectroscopy is a vibrational spectroscopic method that utilizes the absorbance or transmittance by the sample within the near-infrared region (wavenumbers ranging between 4,000 and ~ 15,000 cm −1 ) [6][7][8][9]. Fine chemicals, agricultural, food and dairy sector, pharmaceutical, cosmetics, pulp and paper, 3D printing and precision medications, petrochemicals, polymer synthesis, and oil industry are just a few of the industries that use NIRS to appropriately assess both chemical (related to functional groups) and physical characteristics of solid samples [4,[10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

Muthudoss¹,

Tewari

Rui³

et al. 2022

AAPS PharmSciTech

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NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model’s performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Graphical Abstract Impact of various data learning approaches on NIR spectral data

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An effective and rapid approach to predict molecular composition of naphtha based on raw NIR spectra

Cited by 14 publications

References 30 publications

Intelligent component detection of quaternary blended oil based on near infrared spectroscopy technology

Intelligent component detection of quaternary blended oil based on near infrared spectroscopy technology

Determination of N-Paraffins Content in Crude Oil via Near-Infrared Spectroscopy Associated with Chemometric Approaches

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

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