Research Progress of Rapid Oil Detection Method Based on Near Infrared Spectroscopy

LI Ke; DU Biao; XIAO Zhe; CHEN Xiaoxiang; LI Qi; GUO Xiaoyan; LI Qingwu; ZHANG Zhengdong

doi:10.12338/j.issn.2096-9015.2022.0141

Volume 66 Issue 12

Feb. 2023

Turn off MathJax

Article Contents

Article Navigation > Metrology Science and Technology > 2022 > 66(12): 3-10, 26

LI Ke, DU Biao, XIAO Zhe, CHEN Xiaoxiang, LI Qi, GUO Xiaoyan, LI Qingwu, ZHANG Zhengdong. Research Progress of Rapid Oil Detection Method Based on Near Infrared Spectroscopy[J]. Metrology Science and Technology, 2022, 66(12): 3-10, 26. doi: 10.12338/j.issn.2096-9015.2022.0141

Citation:

LI Ke, DU Biao, XIAO Zhe, CHEN Xiaoxiang, LI Qi, GUO Xiaoyan, LI Qingwu, ZHANG Zhengdong. Research Progress of Rapid Oil Detection Method Based on Near Infrared Spectroscopy[J]. Metrology Science and Technology, 2022, 66(12): 3-10, 26. doi: 10.12338/j.issn.2096-9015.2022.0141

Citation:

LI Ke, DU Biao, XIAO Zhe, CHEN Xiaoxiang, LI Qi, GUO Xiaoyan, LI Qingwu, ZHANG Zhengdong. Research Progress of Rapid Oil Detection Method Based on Near Infrared Spectroscopy[J]. Metrology Science and Technology, 2022, 66(12): 3-10, 26. doi: 10.12338/j.issn.2096-9015.2022.0141

PDF( 1400 KB)

Research Progress of Rapid Oil Detection Method Based on Near Infrared Spectroscopy

doi: 10.12338/j.issn.2096-9015.2022.0141

1.
National Institute of Metrology, Beijing 100029, China
2.
Beijing Yixingyuan Petrochemical Technology Co. Ltd., Beijing 101301, China
3.
Liaoning Institute of Metrology, Shenyang 110004, China

Received Date: 2022-06-15
Accepted Date: 2022-06-20

Available Online: 2022-08-26

Publish Date: 2022-12-18

Abstract

Abstract

Gasoline and diesel are widely used petrochemical products in social production. The physicochemical property of gasoline and diesel determines whether the internal combustion engine can maintain normal operation and whether its exhaust emissions meet the standards. As a fast, efficient, accurate, and green analysis method, near-infrared spectroscopy (NIRS) has been applied to analyze the part of the physicochemical properties of gasoline and diesel. To improve the effect of NIRS in analyzing oil properties and promote the development of this technology, the application of NIRS technology in the detection of typical physicochemical properties of gasoline and diesel, data fusion method based on NIRS, and the development of NIR oil analyzer were reviewed, and the important development directions of NIRS analysis technology were described.
- near infrared spectroscopy,
- gasoline,
- diesel,
- data fusion,
- near infrared oil analyzer,
- oil rapid analysis

FullText(HTML)

References(55)

References

[1]	HONG F W, CHIA K S. A review on recent near infrared spectroscopic measurement setups and their challenges[J]. Measurement, 2021, 171: 108732. doi: 10.1016/j.measurement.2020.108732
[2]	OZAKI Y. Infrared Spectroscopy-Mid-infrared, Near-infrared, and Far-infrared/Terahertz Spectroscopy[J]. Analytical Sciences, 2021, 37(9): 1193-1212. doi: 10.2116/analsci.20R008
[3]	MORO M K, DOS SANTOS F D, FOLLI G S, et al. A review of chemometrics models to predict crude oil properties from nuclear magnetic resonance and infrared spectroscopy[J]. Fuel, 2021, 303: 121283. doi: 10.1016/j.fuel.2021.121283
[4]	WANG H, CHU X, CHEN P, et al. Partial least squares regression residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy[J]. Fuel, 2022, 309: 122224. doi: 10.1016/j.fuel.2021.122224
[5]	YUN Y H, LI H D, DENG B C, et al. An overview of variable selection methods in multivariate analysis of near-infrared spectra[J]. TrAC Trends in Analytical Chemistry, 2019, 113: 102-115. doi: 10.1016/j.trac.2019.01.018
[6]	KELLY J J, BARLOW C H, JINGUJI T M, et al. Prediction of gasoline octane numbers from near-infrared spectral features in the range 660~1215 nm[J]. Analytical Chemistry, 1989, 61(4): 313-320. doi: 10.1021/ac00179a007
[7]	HONG S Y, WANG Y Z, CHEN A, et al. Rapid assessment of gasoline quality by near-infrared (NIR) deep learning model combined with fractional derivative pretreatment[J]. Analytical Letters, 2021, 55(11): 1745-1756.
[8]	LEE Y, CHUNG H E, KIM N. Spectral range optimization for the near-infrared quantitative analysis of petrochemical and petroleum products: Naphtha and gasoline[J]. Appl Spectrosc, 2006, 60(8): 892-897. doi: 10.1366/000370206778062219
[9]	ZHAN B S, YANG J G, LIU X M, et al. Research on rapid determination of diesel cetane with near-infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(6): 1749-1753.
[10]	XU J S, ZHANG J, YANG D J. Determination of Diesel Group Composition by Near Infrared Spectroscopy[J]. JOURNAL OF THE CHEMICAL SOCIETY OF PAKISTAN, 2013, 35(1): 180-187.
[11]	ZHANG J, XU J S, CUI Y, et al. Determination of diesel cetane number by near infrared spectroscopy[J]. Journal of The Chemical Society of Pakistan, 2013, 35(2): 369-371.
[12]	LI K, ZHANG C, DU B, et al. Selection of the Effective characteristic spectra based on the chemical structure and its application in rapid analysis of ethanol content in gasoline[J]. ACS Omega, 2022, 7: 20291-20297. doi: 10.1021/acsomega.2c02282
[13]	LI M G, YAN C H, XUE J, et al. Rapid quantitative analysis of methanol content in methanol gasoline by near infrared spectroscopy coupled with wavelet transform-random forest[J]. Chinese Journal of Analytical Chemistry, 2019, 47(12): 1995-2003.
[14]	FERNANDES D D S, GOMES A A, DA COSTA G B, et al. Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection[J]. Talanta, 2011, 87: 30-34. doi: 10.1016/j.talanta.2011.09.025
[15]	LIU S, WANG S, HU C, et al. Determination of alcohols-diesel oil by near infrared spectroscopy based on gramian angular field image coding and deep learning[J]. Fuel, 2022, 309: 122121. doi: 10.1016/j.fuel.2021.122121
[16]	PALOU A, MIRO A, BLANCO M, et al. Calibration sets selection strategy for the construction of robust PLS models for prediction of biodiesel/diesel blends physico-chemical properties using NIR spectroscopy[J]. Spectrochimica Acta part A-Molecular and Biomolecular Spectroscopy, 2017, 180: 119-126. doi: 10.1016/j.saa.2017.03.008
[17]	WANG S T, LIU S Y, YUAN Y Y, et al. Simultaneous detection of different properties of diesel fuel by near infrared spectroscopy and chemometrics[J]. Infrared Physics & Technology, 2020, 104: 103111.
[18]	LITANI-BARZILAI I, SELA I, BULATOV V, et al. On-line remote prediction of gasoline properties by combined optical methods[J]. Analytica Chimica Acta, 1997, 339(1): 193-199.
[19]	FEL CIO C C, BR S L P, LOPES J A, et al. Comparison of PLS algorithms in gasoline and gas oil parameter monitoring with MIR and NIR[J]. Chemometrics and Intelligent Laboratory Systems, 2005, 78(1): 74-80.
[20]	BAPTISTA P, FELIZARDO P, MENEZES J C, et al. Multivariate near infrared spectroscopy models for predicting the methyl esters content in biodiesel[J]. Analytica Chimica Acta, 2008, 607(2): 153-159. doi: 10.1016/j.aca.2007.11.044
[21]	BALABIN R M, SMIRNOV S V. Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data[J]. Analytica Chimica Acta, 2011, 692(1-2): 63-72. doi: 10.1016/j.aca.2011.03.006
[22]	BALABIN R M, SAFIEVA R Z. Near-infrared (NIR) spectroscopy for biodiesel analysis: fractional composition, iodine value, and cold filter plugging point from one vibrational spectrum[J]. Energy & Fuels, 2011, 25(5): 2373-2382.
[23]	RONG H T, SONG C F, YUAN H F, et al. Rapid quantitative analysis of content of the additive in gasoline for motor vehicles by near-infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2015, 35(10): 2757-2760.
[24]	CORONA F, ZHU Z X, DE SOUZA A H, et al. Supervised distance preserving projections: applications in the quantitative analysis of diesel fuels and light cycle oils from NIR spectra[J]. Journal of Process Control, 2015, 30: 10-21. doi: 10.1016/j.jprocont.2014.11.005
[25]	LEAL A L, SILVA A M S, RIBEIRO J C, et al. Using spectroscopy and support vector regression to predict gasoline characteristics: A comparison of H-1 NMR and NIR[J]. Energy & Fuels, 2020, 34(10): 12173-12181.
[26]	MISHRA P, MARINI F, BIANCOLILLO A, et al. Improved prediction of fuel properties with near-infrared spectroscopy using a complementary sequential fusion of scatter correction techniques[J]. Talanta, 2021, 223: 121693. doi: 10.1016/j.talanta.2020.121693
[27]	LEAL A L, SILVA A M S, RIBEIRO J C, et al. Data driven models exploring the combination of NIR and H-1 NMR spectroscopies in the determination of gasoline properties[J]. Microchemical Journal, 2022, 175: 107217. doi: 10.1016/j.microc.2022.107217
[28]	BALABIN R M, SAFIEVA R Z, LOMAKINA E I. Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques[J]. Analytica Chimica Acta, 2010, 671(1): 27-35.
[29]	BALABIN R M, SAFIEVA R Z. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data[J]. Fuel, 2008, 87(7): 1096-1101. doi: 10.1016/j.fuel.2007.07.018
[30]	MABOOD F, GILANI S A, ALBROUMI M, et al. Detection and estimation of Super premium 95 gasoline adulteration with Premium 91 gasoline using new NIR spectroscopy combined with multivariate methods[J]. Fuel, 2017, 197: 388-396. doi: 10.1016/j.fuel.2017.02.041
[31]	OUYANG A G, LIU J. Classification and determination of alcohol in gasoline using NIR spectroscopy and the successive projections algorithm for variable selection [J]. Measurement Science and Technology 2013, 24(2): 25502.
[32]	MENDES G, BARBEIRA P J S. Detection and quantification of adulterants in gasoline using distillation curves and multivariate methods[J]. Fuel, 2013, 112: 163-171. doi: 10.1016/j.fuel.2013.04.077
[33]	KANYATHARE B, ASAMOAH B, PEIPONEN K E. Imaginary optical constants in near-infrared (NIR) spectral range for the separation and discrimination of adulterated diesel oil binary mixtures[J]. Optical Review, 2019, 26(1): 85-94. doi: 10.1007/s10043-018-0481-9
[34]	ALEME H G, CORGOZINHO C N C, BARBEIRA P J S. Diesel oil discrimination by origin and type using physicochemical properties and multivariate analysis[J]. Fuel, 2010, 89(11): 3151-3156. doi: 10.1016/j.fuel.2010.05.010
[35]	LIU Z, LUO N N, SHI J L, et al. Quantitative analysis of fuel blends based on raman and near infrared absorption spectroscopy[J]. Spectroscopy and Spectral Analysis, 2020, 40(6): 1889-1894.
[36]	ROCHA W F C, VAZ B G, SARMANHO G F, et al. Chemometric techniques applied for classification and quantification of binary biodiesel/diesel blends[J]. Analytical Letters, 2012, 45(16): 2398-2411. doi: 10.1080/00032719.2012.686135
[37]	VERAS G, GOMES A D, DA SILVA A C, et al. Classification of biodiesel using NIR spectrometry and multivariate techniques[J]. Talanta, 2010, 83(2): 565-568. doi: 10.1016/j.talanta.2010.09.060
[38]	TAN A L, BI W H. Identification of spilled oils by NIR spectroscopy technology based on KPCA and LSSVM [C]. SPIE Proceedings. Beijing: International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, 2011: 81933k1-81933k6.
[39]	TAN A L, BI W H. Quantitative analysis model of multi-component complex oil spill source based on near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2012, 32(12): 3203-3207.
[40]	TAN A L, BI W H, ZHAO Y. Identification of spilled oil by NIR spectroscopy technology based on sparse nonnegative matrix factorization and support vector machine[J]. Spectroscopy and Spectral Analysis, 2011, 31(5): 1250-1253.
[41]	FLUMIGNAN D L, SEQUINEL R, HATANAKA R R, et al. Carbon nuclear magnetic resonance spectroscopic profiles coupled to partial least-squares multivariate regression for prediction of several physicochemical parameters of Brazilian commercial gasoline[J]. Energy & Fuels, 2012, 26(9): 5711-5718.
[42]	VALENTIN ORTEGA C, ANDREAS W, WERNER S, et al. Spectral monitoring of toluene and ethanol in gasoline blends using Fourier-Transform Raman spectroscopy [C]. SPIE Proceedings. Munich: Optical Measurement Systems for Industrial Inspection VIII, 2013: 8788341-8788348.
[43]	VOIGT M, LEGNER R, HAEFNER S, et al. Using fieldable spectrometers and chemometric methods to determine RON of gasoline from petrol stations: A comparison of low-field 1H NMR@80 MHz, handheld RAMAN and benchtop NIR[J]. Fuel, 2019, 236: 829-835. doi: 10.1016/j.fuel.2018.09.006
[44]	LI Y, ZHANG J Y, WANG Y Z. FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng[J]. Analytical and Bioanalytical Chemistry, 2018, 410(1): 91-103. doi: 10.1007/s00216-017-0692-0
[45]	GAYDOU V, KISTER J, DUPUY N. Evaluation of multiblock NIR/MIR PLS predictive models to detect adulteration of diesel/biodiesel blends by vegetal oil[J]. Chemometrics and Intelligent Laboratory Systems, 2011, 106(2): 190-197. doi: 10.1016/j.chemolab.2010.05.002
[46]	ZHOU K P, BI W H, XING Y H, et al. Multi spectral detection of ethanol content in gasoline based on SiPLS feature extraction and information fusion[J]. Spectroscopy and Spectral Analysis, 2017, 37(2): 429-434.
[47]	CHEN H K, ZHANG Y J, QI H B, et al. Detection of ethanol content in ethanol diesel based on PLS and multispectral method[J]. Optik, 2019, 195: 162861. doi: 10.1016/j.ijleo.2019.05.067
[48]	LI M G, XUE J, DU Y, et al. Data fusion of raman and near-infrared spectroscopies for the rapid quantitative analysis of methanol content in methanol-gasoline[J]. Energy & Fuels, 2019, 33(12): 12286-12294.
[49]	LEGNER R, VOIGT M, WIRTZ A, et al. Using compact proton nuclear magnetic resonance at 80 MHz and vibrational spectroscopies and data fusion for research octane number and gasoline additive determination[J]. Energy & Fuels, 2020, 34(1): 103-110.
[50]	GONZAGA F B, PASQUINI C. A low cost short wave near infrared spectrophotometer: Application for determination of quality parameters of diesel fuel[J]. Analytica Chimica Acta, 2010, 670(1): 92-97.
[51]	WU J, DU Z H, LIU J, et al. Research on shortwave NIR spectroscopy and its application to in situ flammable liquid detection[J]. Spectroscopy and Spectral Analysis, 2008, 28(9): 2087-2089.
[52]	CORREIA R M, DOMINGOS E, CAO V M, et al. Portable near infrared spectroscopy applied to fuel quality control[J]. Talanta, 2018, 176: 26-33. doi: 10.1016/j.talanta.2017.07.094
[53]	BROUILLETTE C, SMITH W, SHENDE C, et al. Analysis of twenty-two performance properties of diesel, gasoline, and jet fuels using a field-portable near-infrared (NIR) analyzer[J]. Appl Spectrosc, 2016, 70(5): 746-755. doi: 10.1177/0003702816638279
[54]	PAIVA E M, RIBESSI R L, ROHWEDDER J J R. Near-infrared spectra of liquid and gas samples by diffuse reflectance employing benchtop and handheld spectrophotometers[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy, 2022, 264: 120302. doi: 10.1016/j.saa.2021.120302
[55]	WAN S K, LU B, ZHANG H M, et al. Quick measurement method of condensation point of diesel based on temperature-compensation model[J]. Spectroscopy and Spectral Analysis, 2021, 41(10): 3111-31116.