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

Transcription

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

2 The Hydrocarbon Index (HI) Definition It is a cumulative parameter no absolute method Includes all hydrocarbons between C 10 H 22 and C 40 H 82 that... boil between 175 and 525 C do are not absorbed by magnesium silicate (non-polar)

3 The Task Description and Requirements contaminated waste is burned in cement factory to reduce amount of toxic hydrocarbons only hydocarbons between C 10 H 22 and C 40 H 82, with boiling points of C only apolar substances Ø Selectivity 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

4 The Task Some Background Information Cement factory is used, because their ovens have T ~1500 C Kohlenwasserstoff-Index (KW-Index), AWEL Gewa sserschutzlabor,

5 The Old Methods FID/GC 2013 > Umwelt-Vollzug > Altlasten / Abfall > Analysenmethoden im Abfall- und Altlastenbereich Stand

6 The Old Methods FID/GC 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

7 The Old Methods FID/GC Requirement Selectivity for C 10 -C 40 exclude polar substances online/process Analysis robust fast measurement GC

8 Near Infrared Spectroscopy An Overview range: cm -1 ( 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

9 Near Infrared Spectroscopy An Overview Band Range Wavelength (C-H) [nm] Relative Intensity [%] Fundamental Mid-IR st overtone Mid-IR or NIR nd overtone NIR rd overtone NIR th overtone NIR Intensities much lower Changes in concentration harder to detect LOD = % Schwanninger, M.; Rodrigues, M.; Fackler, M.; J. Near Infrared Spectrosc. 2011, 19, p Workman, J.; Weyer, L.; Practical Guide and Spectral, CRC Press, Second Edition, 2012, p

10 Near Infrared Spectroscopy Important Features 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 Burns, D.A.; Ciurczak, E.W., Handbook ofnear-infrared Analysis, CRC Press,Third Edition. 2007, p

11 Near Infrared Spectroscopy The Spectrum Vis NIR 3rd C-H over nm 2nd C-H over nm 2nd C- H comb nm 1st C-H overtone nm 1st C-H combination nm Monograph NIR Spectroscopy; Metrohm

12 Near Infrared Spectroscopy The Spectrum Monograph NIR Spectroscopy; Metrohm

13 Near Infrared Spectroscopy The Spectrum Workman, J.; Weyer, L.; Practical Guide and Spectral, CRC Press, Second Edition, 2012, p

14 Near Infrared Spectroscopy The Spectrum The spectrum is influenced by particle size porosity and surface refractive index packing density temperature water content

15 Near Infrared Spectroscopy Measurement Techniques NIR scheme Burns, D.A.; Ciurczak, E.W., Handbook ofnear-infrared Analysis, CRC Press,Third Edition. 2007, p. 71 Wetzel, D.L.; Analytical Chemistry, 1983, 55(12), p

16 Factors that Influence NIR Spectra Water NaOH in water (0-50 %) top: pure water bottom: 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 content Workman, J.; Weyer, L.; Practical Guide and Spectral, CRC Press, Second Edition, 2012, p. 56ff Burns, D.A.; Ciurczak, E.W., Handbook ofnear-infrared Analysis, CRC Press,Third Edition. 2007, p

17 Factors that Influence NIR Spectra Particle Size cellulose samples a: 24 µm b: 45.8 µm c: 93.4 µm d: 261 µm e: 406 µm baseline shifts with particle size larger particles absorb more radiation Monograph NIR Spectroscopy; Metrohm

18 Interesting Example egg samples 1: 20 min 2: 40 min 3: 60 min 4: 120 min 5: 240 min eggs measured directly after certain times after laying Norris, K.H.; J. NearInfrared Spectrosc., 1996, 4, p

19 Feasability Study Experimental Part 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 Data evaluation and calibration works by pattern recognition instead of single peak observation

20 Statistical Techniques Principle Component Analysis Linear data transformation Goals: Information about relevance (variance) Independent from each other (orthogonal) 1. Centering & Scaling 2. Matrix transformation X = t $ p & $ + t ( p & ( + So that all t x are orthogonal and each t describes the maximum variance 3. The Eigenvalue of p x describes the fraction of the total variance 4. Selection of relevant Eigenvalues & plotting in the corresponding dimensions (t x ) Norgaard, L.; Rasmus, B.; Engelsen, S.B.; Principal ComponentAnalysis and Near Infrared Spectroscopy, paperfrom FOSS A/S Analytical

21 NIR of glucose/fructose/sucrose mixture Norgaard, L.; Rasmus, B.; Engelsen, S.B.; Principal ComponentAnalysis and Near Infrared Spectroscopy, paperfrom FOSS A/S Analytical

22 Statistical Techniques Principle Component Analysis Norgaard, L.; Rasmus, B.; Engelsen, S.B.; Principal ComponentAnalysis and Near Infrared Spectroscopy, paperfrom FOSS A/S Analytical

23 Statistical Techniques Principle Component Analysis Norgaard, L.; Rasmus, B.; Engelsen, S.B.; Principal ComponentAnalysis and Near Infrared Spectroscopy, paperfrom FOSS A/S Analytical

24 Statistical Techniques Univariate Linear Regression

25 Statistical Techniques Multiple Linear Regression (MLR) Burns, D.A.; Ciurczak, E.W., Handbook ofnear-infrared Analysis, CRC Press,Third Edition. Boca Raton,

26 Statistical Techniques Partial Least Squares (PLS) Burns, D.A.; Ciurczak, E.W., Handbook ofnear-infrared Analysis, CRC Press, Third Edition. Boca Raton, Lindon, J.C.; Encyclopedia ofspectroscopy and Spectrometry, Elsevier Academic Press, 2. Edition, Mark, H.; Workman, J.; Chemometrics in spectroscopy, Academic Press, London,

27 Statistical Techniques Comparison MLR - PLS MLR Proper choice of absorbances at different wavelengths is required. Not too many should be considered. In particular no collinear absorbances. Struggles with multicollinearity i.e. strong correlations between absorbances at different wavelengths. PLS Visual results (loadings) might help for the interpretation. However physical meaning of the new coordinates are hard (if not impossible) to interpret, since they are linear combinations of the initial coordinates. Can deal with multicollinearity. Model requires more observations than predictor variables (absorbances). Burns, D.A.; Ciurczak, E.W., Handbook ofnear-infrared Analysis, CRC Press, Third Edition. Boca Raton, Lindon, J.C.; Encyclopedia ofspectroscopy and Spectrometry, Elsevier Academic Press, 2. Edition, Mark, H.; Workman, J.; Chemometrics in spectroscopy, Academic Press, London,

28 Alternative Methods Requirement NIR IR Raman GC Selectivity for C 10 -C 40 exclude polar substances online/process Analysis ( ) robust fast measurement no sample preparation ( ) limitations/problems no structure elucidation water interference broad fluorescent baseline saturation of detector time-consuming

29 Thank you for your attention