Online NIR-Spectroscopy for theLive Monitoring of … · Jérôme Kaeslin, Emanuel Billeter ,...

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Online NIR-Spectroscopy for the Live Monitoring ofHydrocarbon Index (HI) in Contaminated Soil Material

Jérôme Kaeslin, Emanuel Billeter, Manuel Kober-Czerny

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§ It is a cumulative parameter§ no absolute method

§ Includes all hydrocarbons between C10H22 and C40H82 that...§ boil between 175 and 525 °C§ do are not absorbed by magnesium silicate (non-polar)

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The Hydrocarbon Index (HI)Definition

20.12.2016Jérôme Kaeslin, Emanuel Billeter, Manuel Kober-Czerny

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§ contaminated waste is burned in cement factory to reduce amount of toxichydrocarbons§ only hydocarbons between C10H22 and C40H82, with boiling points of 175 - 525 °C§ only apolar substancesØSelectivity

§ Online/Process Analysis before the oven§ method should be fast and exact§ no sample preparation should be needed§ no sample destruction

§ the instrument of choice should be robust and prefereably cheap

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The TaskDescription and Requirements


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§ Cement factory is used, because their ovens have T ~1500 °C

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The TaskSome Background Information

20.12.2016„Kohlenwasserstoff-Index (KW-Index)“, AWEL Gewasserschutzlabor, 2006 Jérôme Kaeslin, Emanuel Billeter, Manuel Kober-Czerny

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The Old MethodsFID/GC

20.12.2016

> Altlasten / Abfall> Umwelt-Vollzug2013

> Analysenmethoden im Abfall- und Altlastenbereich

Stand 2013


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§ extraction of grinded/powdered sample§ with hexane/acetone and water

§ filtration over Florisil (magnesium silicate)§ LOQ = 20 mg/kg (20 ppm)§ Area between n-Decan and n-Tetracontan used

used for HI-calculation§ Calibration beforehand with standards/spikes

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Requirement GCSelectivity for C10-C40 ✔

exclude polar substances ✔

online/process Analysis ❌

robust ❌

fast measurement ❌


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Near Infrared SpectroscopyAn Overview


§ range: 12800 – 4000 cm-1 (780 – 2500 nm)§ shows Overtone & Combination Bands -> lower intensities

§ CH, OH, NH functionalities

Burns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press,Third Edition. 2007, p. 7-19

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Near Infrared SpectroscopyAn Overview


Band Range Wavelength (C-H)[nm]

Relative Intensity[%]

Fundamental Mid-IR 3380-3510 100

1st overtone Mid-IR or NIR 1690-1755 1

2nd overtone NIR 1127-1170 0.1

3rd overtone NIR 845-878 0.01

4th overtone NIR 690-770 0.005

Schwanninger, M.; Rodrigues, M.; Fackler, M.; J. Near Infrared Spectrosc. 2011, 19, p. 287–308Workman, J.; Weyer, L.; „Practical Guide and Spectral“, CRC Press, Second Edition, 2012, p. 8

§ Intensities much lower§ Changes in concentration harder to detect§ LOD = 0.1–0.5%

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§ „fingerprint“§ signature of the physical state of order observable

§ no sample preparation needed§ penetration depth up to a few cm§ less scattering than in UV-Vis

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Near Infrared SpectroscopyImportant Features

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Burns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press,Third Edition. 2007, p. 7-19


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Near Infrared SpectroscopyThe Spectrum


Vis NIR

1st C-Hcombination

2200-2450 nm

1st C-Hovertone

1650-1800 nm

2nd C-Hcomb.

1300-1420 nm

2nd C-Hover.

1100-1225 nm

3rdC-Hover.

850-950 nm

Monograph – NIR Spectroscopy; Metrohm

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20.12.2016Jérôme Kaeslin, Emanuel Billeter, Manuel Kober-CzernyMonograph – NIR Spectroscopy; Metrohm

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20.12.2016Jérôme Kaeslin, Emanuel Billeter, Manuel Kober-CzernyWorkman, J.; Weyer, L.; „Practical Guide and Spectral“, CRC Press, Second Edition, 2012, p. 208

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§ The spectrum is influenced by§ particle size§ porosity and surface§ refractive index§ packing density§ temperature§ water content

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Near Infrared SpectroscopyMeasurement Techniques


Burns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press,Third Edition. 2007, p. 71Wetzel, D.L.; Analytical Chemistry, 1983, 55(12), p. 1166

NIR scheme

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Factors that Influence NIR SpectraWater


NaOH in water (0-50 %)top: pure waterbottom: 50 %-solution

§ wavelength shift due to water (also H-bonds)§ peak shape varies with water content

§ broadness increases with water content§ baseline shift due to water content

§ absorption increases with water contentWorkman, J.; Weyer, L.; „Practical Guide and Spectral“, CRC Press, Second Edition, 2012, p. 56ffBurns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press,Third Edition. 2007, p. 334

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Factors that Influence NIR SpectraParticle Size


cellulose samplesa: 24 µmb: 45.8 µmc: 93.4 µmd: 261 µme: 406 µm

Monograph – NIR Spectroscopy; Metrohm

§ baseline shifts with particle size§ larger particles absorb more radiation

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Interesting Example


egg samples1: 20 min2: 40 min3: 60 min4: 120 min5: 240 min eggs measured directly

after certain times after laying

Norris, K.H.; J. Near Infrared Spectrosc., 1996, 4, p. 31-37

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§ The calibration is the most important part for quantitative analysis§ take several hundred samples (inhomogeneous)

§ cover large concentration region§ grind and dry samples§ spike samples with oil of known concentration§ measure and assign bands of importance§ use data to get a calibration curve

§ cross-check HI with GC method

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Feasability StudyExperimental Part


Data evaluation and calibration works by pattern recognitioninstead of single peak observation.

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Statistical TechniquesPrinciple Component Analysis


§ Linear data transformation§ Goals:

§ Information about relevance (variance)§ Independent from each other (orthogonal)

1. Centering & Scaling2. Matrix transformation 𝑋 = 𝑡$𝑝$& + 𝑡(𝑝(& +⋯

So that all tx are orthogonal and each t describes the maximum variance3. The Eigenvalue of px describes the fraction of the total variance4. Selection of relevant Eigenvalues & plotting in the corresponding

dimensions (tx)Norgaard, L.; Rasmus, B.; Engelsen, S.B.; Principal Component Analysis and Near Infrared Spectroscopy, paper from FOSS A/S Analytical.

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NIR of glucose/fructose/sucrose mixture


Norgaard, L.; Rasmus, B.; Engelsen, S.B.; Principal Component Analysis and Near Infrared Spectroscopy, paper from FOSS A/S Analytical.

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Statistical TechniquesUnivariate Linear Regression


Statistical TechniquesMultiple Linear Regression (MLR)

Burns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press,Third Edition.Boca Raton, 2008.


Statistical TechniquesPartial Least Squares (PLS)

Burns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press, Third Edition.Boca Raton, 2008.Lindon, J.C.; „Encyclopedia ofSpectroscopy and Spectrometry“, Elsevier Academic Press, 2. Edition, 2010.Mark, H.; Workman, J.; „Chemometrics in spectroscopy“,Academic Press, London, 2007.


Statistical TechniquesComparison MLR - PLS

MLR PLSProper choice of absorbances at different wavelengths is required. Not too many should be considered. In particular no collinear absorbances.

Visual results (loadings) might help for theinterpretation. However physical meaning of thenew coordinates are hard (if not impossible) tointerpret, since they are linear combinations ofthe initial coordinates.

Struggles with multicollinearity i.e. strong correlations between absorbances at different wavelengths.

Can deal with multicollinearity.

Model requires more observations than predictorvariables (absorbances).

Burns, D.A.; Ciurczak, E.W., “Handbook ofNear-Infrared Analysis“, CRC Press, Third Edition.Boca Raton, 2008.Lindon, J.C.; „Encyclopedia ofSpectroscopy and Spectrometry“, Elsevier Academic Press, 2. Edition, 2010.Mark, H.; Workman, J.; „Chemometrics in spectroscopy“,Academic Press, London, 2007.

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Alternative Methods


Requirement NIR IR Raman GCSelectivity for C10-C40 ✔ ✔ ✔ ✔

exclude polar substances ✔ ✔ ✔ ✔

online/process Analysis ✔ (❌ ) ✔ ❌

robust ✔ ✔ ✔ ❌

fast measurement ✔ ✔ ✔ ❌

no sample preparation ✔ (❌ ) ✔ ❌

limitations/problems no structureelucidation

water interference broad fluorescentbaseline

time-consuming

saturation ofdetector

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Thank youfor your attention

Online NIR-Spectroscopy for theLive Monitoring of … · Jérôme Kaeslin, Emanuel Billeter ,...

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