Near-infrared spectroscopic prediction of chemical composition of a series of petrochemical process streams for aromatics production

Rebouças, Márcio das Virgens; Santos, Jamile Batista dos; Domingos, Daniela; Massa, Ana Rosa C.G.

doi:10.1016/j.vibspec.2009.09.006

Cited by 30 publications

(8 citation statements)

References 16 publications

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“…Raman spectroscopy, another commonly used vibrational spectroscopy, shows a disadvantage in this respect since the high-energy laser source and the weak Raman scattering effect make switching difficult. Due to the above mentioned reasons, NIR spectroscopy has been widely used for nondestructive measurements in a variety of domains, such as food, [13,14] pharmacy, [15,16] petrochemical, [17,18] and chemical engineering. [19][20][21] Recently, chemometrics and machine learning methods further push forward the development of NIR spectroscopy, [22][23][24][25] which help to extract feature information from the spectra and serve as a basic tool for classification or regression.…”

Section: Introductionmentioning

confidence: 99%

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

Zhang

Lei

et al. 2020

Polymer Engineering & Sci

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Properties of an immiscible polymer blend have been proved to be closely related to dispersion uniformity of the minor phase. At present, dispersion uniformity is difficult to evaluate during the blending process, resulting in hysteretic feedback. Aiming at this problem, this work utilized near‐infrared (NIR) spectroscopy to in‐line characterize dispersion uniformity evolution during a twin‐screw extrusion. A multichannel NIR measurement system was set up and applied to evaluate the blending process of polypropylene and polyolefin elastomer (POE). Based on the NIR spectra collected at different positions of the extruder, five prediction models of POE content were established using the light gradient boosting machine algorithm. Dispersion uniformity was characterized through the fluctuation of the predicted content. The evolution of dispersion under such processing parameters was consistent with scanning electron microscopy.

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Section: Introductionmentioning

confidence: 99%

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

Zhang

Lei

et al. 2020

Polymer Engineering & Sci

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“…The method has already become a popular analytical technique in many fields, including agriculture, 7 pharmaceuticals, 8 and petrochemicals. 9 NIR spectra primarily reflect the vibration information of the hydrogen-contained groups (C-H, O-H, N-H). Since these functional groups present the basic components in biomass, it is possible for NIR technology to analyze the properties of a biomass both chemically and physically.…”

Section: Introductionmentioning

confidence: 99%

Near infrared spectroscopy to evaluate change in color and chemical composition in heat-treated bamboo

Zhang

et al. 2020

Journal of Near Infrared Spectroscopy

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This study investigated the feasibility of using near infrared spectroscopy technology to predict color and chemical composition in the heat-treated bamboo processing industry. The quantitative presentations of the changes in the chemical components were discussed using the difference spectra method of the 2nd derivative NIR spectra of the heat-treated bamboo samples. Then, the relationships between the color changes of the heat-treated bamboo and its near infrared spectra were constructed using the changes in the chemical components of the bamboo samples during the heating process. The prediction of color and chemical composition of both the outer and inner sides of the heat-treated bamboo surface were constructed using partial least squares regression method combined with a leave-one-out cross-validation process. Then, the results were validated by independent sample sets. The proposed prediction models were found to produce high r2P (above 0.93), RPD (above 3.13), and low RMSEP for both the outer and inner sides of the heat-treated bamboo samples. These studies’ results confirmed that the proposed models, especially outer side models, were perfectly suitable for the in-process inspections of the color and chemical content changes of heat-treated bamboo.

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“…Near infrared (NIR) spectroscopy is a fast, accurate, and nondestructive technique requiring minimal sample processing before analysis. Coupled with chemometric techniques, it appears to be an effective and powerful analytical tool widely used in different fields, such as agricultural food [14,15], petrochemical [16], pharmaceutical [17], environment [18], metabolomic profiling [19], etc. The NIR region spans the wavelength range between 780 and 2500 nm.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

Wen

et al. 2019

Molecules

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Near infrared (NIR) spectroscopy with chemometric techniques was applied to discriminate the geographical origins of crude drugs (i.e., dried ripe fruits of Trichosanthes kirilowii) and prepared slices of Trichosanthis Fructus in this work. The crude drug samples (120 batches) from four growing regions (i.e., Shandong, Shanxi, Hebei, and Henan Provinces) were collected, dried, and used and the prepared slice samples (30 batches) were purchased from different drug stores. The raw NIR spectra were acquired and preprocessed with multiplicative scatter correction (MSC). Principal component analysis (PCA) was used to extract relevant information from the spectral data and gave visible cluster trends. Four different classification models, namely K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and support vector machine-discriminant analysis (SVM-DA), were constructed and their performances were compared. The corresponding classification model parameters were optimized by cross-validation (CV). Among the four classification models, SVM-DA model was superior over the other models with a classification accuracy up to 100% for both the calibration set and the prediction set. The optimal SVM-DA model was achieved when C =100, γ = 0.00316, and the number of principal components (PCs) = 6. While PLS-DA model had the classification accuracy of 95% for the calibration set and 98% for the prediction set. The KNN model had a classification accuracy of 92% for the calibration set and 94% for prediction set. The non-linear classification method was superior to the linear ones. Generally, the results demonstrated that the crude drugs from different geographical origins and the crude drugs and prepared slices of Trichosanthis Fructus could be distinguished by NIR spectroscopy coupled with SVM-DA model rapidly, nondestructively, and reliably.

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Near-infrared spectroscopic prediction of chemical composition of a series of petrochemical process streams for aromatics production

Cited by 30 publications

References 16 publications

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

Near infrared spectroscopy to evaluate change in color and chemical composition in heat-treated bamboo

Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

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