Near Infrared Spectroscopy An Overview Background Theory Uses -Industries * Agriculture * Chemical Industry - Production vs. Research * Centralized machine * Online Learning Objectives - List the needs to successfully predict sample composition using NIRS - Compare and contrast the advantages and disadvantage of using NIRS vs. wet chemistry - Apply NIRs to your project - Anticipate potential benefits, obstacles, and biases Caution : I am not a physicist but a user who has gathered this information through the course of my use!
Near Infrared Spectroscopy Theory Near Infrared Spectroscopy NIRS Uses the near-infrared region of the electromagnetic spectrum
Near Infrared Spectroscopy An Overview BASICS: - Specific chemical bonds absorb energy in the NIR spectrum. - The amount of energy absorbed by the compound is related to the amount in the sample (i.e. it is quantitative) - Samples are scanned using a near infrared spectrophotometer that both emits NIR light and detects it. - Samples are measured for composition of interest. - Statistically predictive equation(s) may be developed by combining the NIRS absorbance data with the chemical measurement. - Related to hyperspectral imaging but many more wavelengths - Any trait of interest (protein, micronutrients, etc.) can be attempted, but a successful calibration depends on many things. - Tradeoff in accuracy for speed and cost compared to wet chemistry
Near Infrared Spectroscopy An Overview Advantages: - No consumables other than lamps ($50 - $1000) - Non-destructive, non-invasive measurement - Sample can therefore be small - Nearly instantaneous measurement - Minimal sample preparation - Continuous real time data can be obtained - Can penetrate far into samples - Can positively ID a pure compound if a library of compounds is developed Disadvantages: - Machines are expensive ($5,000 - $100,000) - Must still measure reduced set of samples with wet chemistry - Calibration is less accurate than wet chemistry - Measurement outside of range of calibration samples is invalid - Small calibration sample sizes can lead to overconfidence
Generalized Different Approaches Taken Toward NIRS GENERALLY Chemistry, Plant Biology Agriculture (soil science, agronomy, plant breeding) Type of samples analyzed Pure compounds Complex, heterogeneous samples Calibrations made using Type of instrument used Reported as Peak height or peak area like HPLC / GC Fourier Transformed Near Infrared Spectroscopy (FT-NIRS) Wavenumbers (1 / wavelength) Complex statistical packages and algorithms Scanning monochrometer (FOSS generally) Wavelength (Nanometers, nm) Because of this, different people who work on NIRS can speak different languages
www.winisi.com/nirs_theory.htm NIRS Theory higher energy near-ir, approximately 14000 4000 cm 1 (0.8 2.5 μm wavelength) mid-infrared, approximately 4000 400 cm 1 (2.5 25 μm) far-infrared, approximately 400 10 cm 1 (25 1000 μm) Thermo Scientific
NIR light, approximately 14000 4000 cm 1 (0.8 2.5 μm wavelength) can excite overtone or harmonic vibrations in compounds.
NIRS Reflectance vs. Transmission Two primary modes - Reflectance * Bounces off samples * Best for solid objects (grain, soil, stover) - Transmission * Pass through samples * Must be liquid or at least not completely opaque * Usually much better calibrations may be developed than reflectance Penetration of samples is much deeper than visible light.
Near Infrared Spectroscopy Integrating Sphere Hyperspectral imaging uses filters (like on a camera) to take pictures that include only certain wavelengths NIRS cover all wavelengths but only at the one point on a detector. If you are scanning a heterogeneous sample (such as corn) if the detector is on the embryo you will get a different result than if it is on the endosperm. To average the light over a larger region an integrating sphere is used. Integrating spheres are optical cavities that provide diffuse reflectivity, they diffuse light. The more perfect your integrating sphere the higher your signal-to noise ratio will be http://www.thermo.com/ ethermo/cma/pdfs/prod uct/productpdf_7545.pdf
Near Infrared Spectroscopy Uses and Applications Crops - Forage quality and composition - Pasture management http://cnrit.tamu.edu/ganlab/ganlab_webpage_files/analysis.htm - Fecal nitrogen (FN) and phosphorus (FP) http://cnrit.tamu.edu/ganlab/ganlab_webpage_files/analysis.htm - Approved method of the American Association of Cereal Chemists to measure to moisture and protein content in wheat (AACC. 2002. Approved methods of the AACC, Methods 39-10, 39-11, 46-30 and 08-01. St. Paul, Minn.: American Association of Cereal Chemists.) - Ability to measure carotenoids in maize Brenna OV, Berardo N (2004) Application of near-infrared reflectance spectroscopy (NIRS) to the evaluation of carotenoids content in maize. J Agric Food Chem 52:5577 5582. - Sugars in grapes and other fruits Jarén C, Ortuño JC, Arazuri S, Arana JI, Salvadores MC. Sugar determination in grapes using NIR technology. Int J Infrared Milli 2001; 22: 1521 1530. - Oleic acid in single peanut seeds Tillman, B. L.; Gorbet, D. W.; Person, G.Predicting oleic and linoleic acid content of single peanut seeds using near-infrared reflectance spectroscopy Crop Sci. 2006, 46 ( 5) 2121 2126. - Starch gelatinization temperature in rice. J. S. Bao, H. Corke: Pasting properties of γ-irradiated rice starches as affected by ph. J. Agric. Food Chem. 2002, 50, 336 341. - Malting quality in barley Ratcliffe, M., and Panozzo, J. F. 1999. The application of near infrared spectroscopy to evaluate malting quality. J. Inst. Brew. 105:85-88. - ADF, NDF, lignin, protein in sorghum leaf and stem Murray, S.C., Rooney, W.L., Mitchell, S.E., Sharma, A., Klein, P.E., Mullet, J.E., and Kresovich, S. 2008. Genetic Improvement of Sorghum as a Biofuel Feedstock: II. QTL for Stem and Leaf Structural Carbohydrates. Crop Science 48: 2180-2193. - Starch, oil, protein, ADF, phosphorus in sorghum grain Murray, S.C., Sharma, A., Rooney, W.L., Klein, P.E., Mullet, J.E., Mitchell, S.E., and Kresovich, S. 2008. Genetic Improvement of Sorghum as a Biofuel Feedstock: I. QTL for Stem Sugar and Grain Nonstructural Carbohydrates. Crop Science 48: 2165-2179. - Greenseeker technology also uses NIR light - Etc.
Near Infrared Spectroscopy FORAGE VERSUS http://www.dairyone.com/
Near Infrared Spectroscopy GRAIN VERSUS http://wardlab.com/
Near Infrared Spectroscopy Uses and Applications Soils - Soil calibrations have generally not performed well across soil types, but accounting for soil organic matter has improved calibrations. Russell, C.A. (2003) Sample preparation and prediction of soil organic matter properties by near infra-red reflectance spectroscopy. Communications in Soil Science and Plant Analysis, 34, 1557-1572. - Reasonable results have been found with P, Ca, Mg, K, Fe, Mn, S and Na but not ph within Canadian soils. Malley, D. F., Yesmin, L., Wray, D., and Edwards, S. (1999). Application of near-infrared spectroscopy in analysis of soil mineral nutrients. Comm. Soil. Sci. Plant Anal. 30, 999 1012. Soil mineralization of nitrogen. Russell, C. A., Angus, J. F., Batten, G. D., Dunn, B. W. and Williams, R. L. (2002) The potential of NIR spectroscopy to predict nitrogen mineralization in rice soils. Plant Soil 247, pp. 243-252. -Soil moisture and organic matter. Hummel et al., 2001. J.W. Hummel, K.A. Sudduth and S.E. Hollinger, Soil moisture and organic matter prediction of surface and subsurface soils using a NIR sensor. Computers and Electronics in Agriculture 32 2001 (2001), pp. 149 165. Water -Comparatively few publications - Phosphorus, total organic carbon, color, and ph performed marginally. E. Dåbakk, M. Nilsson, P. Geladi, S. Wold and I. Renberg. Water Res. 34 (2000), p. 1666. - Monitoring bacteria species in water. http://www.icpmf.org/pp/s06_3_am1010_camara_martosetal%28icpmf09%29.pdf - May benefit from spectral subtraction in aqueous solutions.
Near Infrared Spectroscopy Uses and Applications Industrial / pharmaceutical -Place probe in bioreactors to measure: - Target blends in pharmaceutical industry - Fermentation progess - Oil and gas industry Health - Used to detect tissue oxygenation since 1977 - Human skull easily penetrated by NIR light and can be used for neuroimaging of brain parameters Strangman G, Boas D A and Sutton J P 2002a Non-invasive neuroimaging using near-infrared light Biol. Psychiatry. 52 679 93 - Pediatric Critical Care and Cardiac Intensive Care https://www.acep.org/workarea/downloadasset.aspx?id=44550 http://www.thermoscientific.com/
How Do I Build a Calibration? 1. Identify your goals and the types of machines available. 2. Scan all samples into machine using appropriate settings (TBD) and under similar conditions. 3. Use integrated software features to select most informative samples for wet chemistry. 4. Re-scan those same samples to make sure you are accurate and have two spectra per sample if wet chemistry is expensive. 5. Submit samples for wet chemistry. 6. Combine actual values from wet chemistry with spectra. 7. Run statistical analysis (YOU CAN NOT TELL FROM LOOKING AT SPECTRA!) 1. Examine derivatives not raw spectrum (usually) 2. Multivariate modeling for multiple wavelengths 3. Partial least-squares regression, principal component analysis (PCA) or other calibration techniques. 8. Use best calibration to select new samples to include (return to step 3). 9. Include validation samples that are not used to develop the calibration to determine the fit and usefulness of the calibration.
All Samples: Stem (cellulose, hemi-cellulose, lignin, protein) Leaf (cellulose, hemi-cellulose, lignin, protein) Grain (starch, oil, protein) Grind to 1mil with UDYcyclone High-Throughput Phenotyping: NIRS Analysis for Composition Wet Chemistry performed on ~ 10% of Samples www.dairyone.com www.udyone.com FOSS Feed and Forage system (1100-2500nm) www.foss.dk ANKOM A200 Filter Bag Technique Forage Method Acid Detergent Fiber (ADF) Neutral Detergent Fiber (NDF) Acid Detergent Lignin (ADL) Cellulose = ADF - ADL Hemi-Cellulose= NDF - ADF Lignin = ADL Find samples to use to develop calibration equations Develop calibration equations and test fit Stem Type Fit - R 2 %ADF 0.94 %NDF 0.95 %Cellulose 0.82 %Hemi - 0.46 Cellulose %Lignin 0.77 %Crude Protein 0.94 Find outliers
Thermo FT-NIRS on Ground Grain
Thermo Ground Grain FT-NIRS Results Rsq. = 0.887 Using 300 calibration samples and 150 validation samples Consistently under predicts higher levels
FOSS Whole Grain NIRS Results Rsq. = 0.807 Equation built using toxigenic type only with a red. (3/4) and val. (1/4) sets 2,5,5,1 SCVD both vis and NIR (Rsq = 0.8071) Plot predicted vs. Full non-ag Small sample/ limited cultivars
NIRS Results - Aflaguard (atoxigenic strain) Samples NIRS over-predicts contamination levels on atoxigenic samples Suggests that there is a molecular signature that NIRS is picking up not necessarily aflatoxin per se. Help to develop A. flavus resistant germplasm
What Else Do I Need to Know? Particle size: NIR reflectance is influenced by the particle size properties of ground or powdered materials. Thus using proper procedures and calibration, NIR reflectance spectroscopy can be used for particle size analysis - Pasikatan, M.C., Steele, J.L., Spillman, C.K. and Haque, E. (2001) Near infrared reflectance spectroscopy for online particle size analysis of powders and ground materials. Journal of Near Infrared Spectroscopy 9, pp. 153-164. Conversely, changing particle size can then disturb your calibration! Sample number for wet chemistry: General rule of thumb that I have always heard: - 100 samples minimum - >10% total samples BUT - Studies are routinely published with 50 or 60 samples total for developing a calibration. - Can only predict within the diversity of samples that you have. Sample detection limitations: General rule of thumb is constituents can only be detected at 0.1% or 1% of sample composition. Need adequate variance / variability in samples for best calibration.
What Else Do I Need to Know? Signal to noise ratio: The most important measurement according to NIRS sales people is signal to noise ratio, each salesperson say s their instrument is the best. The longer the pathlength the more light is lost (usually). The lower the signal the higher the noise. More scans per sample will average out noise - Noise adds up as the square root of the number of scans - Signal adds up linearly
Scanning Monochrometers vs. Fourier Transformed Scanning monochrometers - Measure NIR wavelengths with a mirror and prism that scans over the spectrum of interest one at a time. http://www.chemwiki.ucdavis.edu/ http://www.foss.dk/solutions/productsdirect/fo odscanmeatanalyser/technology.aspx# Fourier Transformed NIRS - No diagram form and I am unqualified to explain.
Why Should I Care Which NIRS Instrument I Use? Assumptions: - You will spend a lot of time and effort scanning samples. - You will spend a lot of money collecting wet chemistry data. Challenges: - Can you bring it to the field? - How available is it? - Use (Dr. Rooney s Foss is in constant use) - Sharing atmosphere / charges for use? - How accurate is it? - How sensitive is it to moisture and vibrations? Why can t I transfer calibrations? - In theory you CAN however: - Each make and model has a different pathlength - Each instrument has its own quirks (many are custom built) - Need standards that have been scanned on both machines - Transferring calibrations is painful on the agricultural side, maybe not so much if you are looking at pure or nearly pure compounds.
NIRS Available Equipment in SCSC / at TAMU ASDI AgriSpec Advantages: - Portable and rugged - Many scanning accessories (hand held probe and mug lamp) - Provides own white light source - 350-2500 nm spectra range (VisNIR) - high signal to noise ratio for soils compared to other specs Disadvantages: - 3 detectors for whole spectral range make it expensive - no spining disk for homogenizing samples - long pathlength of probe may decrease resolution/ accuracy Software used: - Unscrambler ($900) - R (free) Approximate cost: - $45,000 Currently being used for: - Lab Soil characterization (clay, fine clay, organic carbon, inorganic carbon, most soil properties related to clay fraction) - In situ mapping of soil profiles (In R&D Phase) Owner/ responsible person: Cristine Morgan (SCSC College Station) cmorgan@ag.tamu.edu
NIRS Available Equipment in SCSC / at TAMU ASD LabSpec Model 5000 Advantages: - Portable - Rugged http://www.asdi.com/news/asd- releases-labspec-5000-5100- spectrometer - Diffuse Reflectance / transmission probe and Muglight Diffuse Reflectance - NIRS and visible spectral ranges 250 25000 nm Disadvantages: - long pathlength of probe may decrease resolution/ accuracy Software used: - Indico Pro and Unscrambler Approximate cost: - $57,000 Currently being used for: Model Development: Wet chemistry complete for large range of land covers in watersheds in Scotland, Puerto Rico, Czech Republic, New Hampshire, Maine - Soil CO 2 evolution NIR picking up labile C fractions - Soil Organic Carbon and Total Nitrogen analysis done, scanning soil for model - Free and organically-bound Iron, Aluminum and Manganese - Water Extractable soil DOC and DON Model Development: Texas - Stream water signatures and non-point source inputs to fresh waters - Grave soil: Identification of grave soil signature Owner/ responsible person: Jacqueline Ann Aitkenhead-Peterson (SCSC College Station) jpeterson@ag.tamu.edu
NIRS Available Equipment in SCSC / at TAMU Unity Model SpectraStar 2400 Advantages: - NIRS and visible spectral ranges - Accuracy - Calibrations already developed and available for soil and forage - No splits over spectral range - Can take different sample cups - Easy to use software Disadvantages: - No probe Software used: - Scanstar, new software coming soon. Approximate cost: - $47,000 Currently being used for: - Soil and forage analysis in service laboratory http://www.unityscientific.com/products/ni R-at-line/spectrastar-rtw.asp Owner/ responsible person: Tony Provin (SCSC College Station) t-provin@tamu.edu
NIRS Available Equipment in SCSC / at TAMU Thermo Advantages: - NIRS and visible spectral ranges - Accuracy - Calibrations already developed and available for soil and forage Disadvantages: - no probe Software used: -???? Approximate cost: - $$$$$ Currently being used for: - Peanut Analysis Owner/ responsible person: Mark Burow(SCSC Lubbock) mburow@tamu.edu
NIRS Available Equipment in SCSC / at TAMU Unity Model SpectraStar RTW Advantages: - NIRS spectral ranges - Accuracy - Calibrations already developed and available for soil and forage - Open cup Disadvantages: - no probe Software used: -???? Approximate cost: - $$$$$ Currently being used for: - Sugarcane biomass - Sugarcane agronomy http://www.unityscientific.com/products/ni R-at-line/spectrastar-rtw.asp Owner/ responsible person: Nael El-Hout (Formally SCSC Weslaco) I do not know who is now responsible. Try Robert McGee, Operations Manager r-mcgee@tamu.edu
NIRS Available Equipment in SCSC / at TAMU Infratec 1226 Grain Analyzer (bought out by FOSS) Advantages: - NIRS spectral ranges - Calibrations already developed and available for cornand soybeans - Ease of use Disadvantages: - Old technology no longer supported - Few options - Poor resolution and accuracy Software used: - Integrated into machine no computer needed Approximate cost: - $?, free donated from companies Currently being used for: - Corn and wheat composition http://agronomyday.cropsci.illinois.edu/2002/extractablestarch/index.html Owner/ responsible person: Lloyd Rooney (SCSC College Station) lrooney@tamu.edu
NIRS Available Equipment in SCSC / AgriLife Foss Model 6500 Advantages: - NIRS and visible spectral ranges - Calibrations already developed and available for many products Disadvantages: - No probe - Slow - Expensive given value Software used: - WinISI, ISI scan Approximate cost: - ~$65,000 Currently being used for: - Botanical composition for ruminants - Fecal analysis of ruminant diets http://dubuque.ebayclassifieds.com/busines s-industrial/dyersville/foss-5000-nirinstrument/?ad=5653757 Owner/ responsible person: John Walker (Rangeland San Angelo) jwalker@ag.tamu.edu
NIRS Available Equipment in SCSC / at TAMU Foss Model XDS Advantages: - NIRS and visible spectral ranges - Calibrations already developed and available for many products - Liquids module Disadvantages: - No probe - Slow - Expensive given value Software used: - WinISI, ISI scan Approximate cost: - ~$75,000 + $25,000 for liquids module Currently being used for: - Sorghum biomass composition FOSS XDS Scanning Monochrometer Owner/ responsible person: William "Bill" Rooney (SCSC College Station) wlr@tamu.edu Thomas Stefaniak (SCSC College Station) tstefaniak@ag.tamu.edu
NIRS Available Equipment in SCSC / at TAMU Thermo Anteris II Fourier Transformed NIRS Advantages: - NIRS spectral ranges - Integrating Sphere, liquids module, probe - Fast, accurate and precise (FT) Disadvantages: - Expensive - Heavy and bulky Software used: - TQanalyst, Omnic Approximate cost: - ~$62,000 + $8,000 for probe Currently being used for: - Corn aflatoxin and composition analysis - Epicuticular wax analysis - Cotton gossypol analysis - Wheat composition Thermo Antaris II FT-Interferometer Owner/ responsible person: Seth Murray (SCSC College Station) sethmurray@tamu.edu
Combining NIRS with High Speed Seed Sorting - Removing corn kernels infected by mycotoxins would be useful for food safety and research reasons. - Certain NIR bands are correlated with infection. - A high speed seed sorter can separate contaminated kernels from clean kernels. T.C. Pearson, D.L. Brabec and S. Haley, Color image based sorter for separating red and white wheat, Sensing and Instrumentation for Food Quality and Safety 2008 (2) (2008), pp. 280 288.