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Chemical analysis via NIR spectroscopy

  1. Chemical Analysis via NIR Spectroscopy Yonas Asmare, ILRI Identifying Investment Opportunities for Livestock Feed Resources Development in the Eastern Africa Sub-Region, ILRI Addis, 13–15 December 2017
  2. Purpose • Introduce Near-infrared Spectroscopy (NIRS) • Describe: – Benefits to use NIRS – How NIRS can be used for feed/Food Nutrient content analysis – Advances on NIRS instrumentation
  3. Spectroscopy Spectroscopy is the study of the interaction between matter and electromagnetic radiation. Historically, spectroscopy originated through the study of visible light dispersed according to its wavelength, by a prism.
  4. Electromagnetic Spectrum and NIRS
  5. Organic compounds absorb NIR energy To have an absorption in the near infrared region, the constituent of interest must have covalent bonds such as: O-H, C-H, C=0 C-N, S=O, P-O N-H and S-H. Inorganic measurements are indirect measurement. Interaction, Absorption and Measurement
  6. Pre-Dispersive Monochrometer: Grating PbS Detector Polychromatic Light Monochromatic Light
  7. R² =0.999 A= 83.205C +0.0167 NIR absorbances obey the Beer/Lambert law Beers-Lambert Law: A = abC Data (spectra) of 7 samples from Visible Spectrometer, Scanning range 300nm-600n
  8. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1100 1300 1500 1700 1900 2100 2300 Wavelength, nm Log1/R 15 % Fat 10 % Fat 6 % Fat Effect of Constituent Concentration On NIR Spectra: Fat Content in Fishmeal
  9. Advantages of NIRS
  10. The Traditional Wet-Chemistry Method Example: Analysis of Nitrogen Kjeldahl Procedure: Other Running Costs; Chemicals Big Space Time required for 18 sample is 1 day Skilled and trained Personnel S.N Procedure 18 Sample Per Run Required Input Cost (USD) 1 Sample grinding Sample mill >1000 2 Sample weighing analytical balance >2000 sampleTubes >100 3 Add Chemical chemicals 4 Heating the samples at 350C Block digester and scrubber >1000 6 Dilution with distilled water water distiller >2000 7 Mixing vortex mixer >200 8 Steam distillation and titration Kjeltec equipment >30000 9 Data analysis computer >300 Sum total> 36,600
  11. Qualitative trait prediction in plant breeding based on Near Infrared Spectroscopy (NIRS) Non-evasive c. 200 samples/d >30 traits Physico-chemical c. 60 000 USD Calibration Validation NIRS equations sharable across compatible instruments At ILRI
  12. Disadvantages of Wet-chemistry and Advantages of NIRS Wet Chemistry NIRS Slow =Time delay 200sample per month per 8 component Fast 200sample per day for >30component Destructive Non Destructive Costly Only initial investment and calibration development is costly Personal health and safely Issue No issue Environmental issues No issue Single component per day per person per 18 sample Multi component per day per person per 200 sample Real time data-on site Equation Sharable across similar platforms
  13. How can NIRS work for you?
  14. Model Development and Prediction Software for Chemo-metrics • WinISI, • ISI scan, • Matlab
  15. First step: Get NIR spectra (1000 sample)
  16. Second step: Sample selection Objective: Sample selection for wet-chemistry NIRS can be used to select sample sets from a large group of samples which: Retain a maximum representation of overall sample population variability Samples selected better than random because: Greater recovery of range Higher variance Better Kurtosis (more even distribution)
  17. • Do Wet chemistry only on those selected sample set • Calibrations are developed by combining Wet- chemistry data and NIRS Spectra(Data) • These calibrations can be used to predict the remaining samples. Requires large sample sets ncalibration :100 samples recommended Third step: Model development
  18. Methionine prediction by NIRS
  19. ILRI-Lab-ID DM% ASH% N(%) NDF% ADF% ADL% ME(MJ/k g) IVOMD% 83708 96.09 11.82 0.82 77.18 51.70 6.53 6.64 46.42 83709 95.91 12.58 0.84 74.86 50.59 6.14 6.76 47.26 83710 96.22 11.81 0.79 77.89 52.36 6.65 6.66 46.45 83711 96.19 12.23 0.81 75.85 50.79 6.27 6.67 46.71 83712 96.18 10.35 0.76 77.44 51.06 6.43 6.69 46.46 83713 95.52 12.35 1.03 74.52 49.44 6.36 6.59 46.50 83714 96.14 12.19 0.92 75.09 49.74 6.18 6.69 47.06 83715 96.53 12.22 0.87 76.33 51.40 6.50 6.67 46.83 83716 95.91 12.26 1.04 73.52 48.49 6.09 6.70 47.39 83717 96.04 12.03 0.86 75.94 51.37 6.38 6.63 46.46 83718 96.10 11.29 0.84 76.97 51.13 6.53 6.62 46.31 83719 96.11 11.68 0.80 77.14 52.24 6.67 6.70 46.80 83720 96.35 11.02 0.74 78.81 52.78 6.68 6.61 46.07 NIRS prediction result NIRS -Up to 30 parameter in 1minutes; -200 samples per day. -3USD per sample Traditional wet- chemistry -it takes 1month to analyze 200 sample. -USD68 per sample for 8 parameters
  20. Tools
  21. The Tools Foss NIRS system model 5000 & 6500 Stationary NIRS
  22. Mobile handheld NIRS • About USD 40 000 but price decreasing • Application currently developed and validated at ILRI India and Ethiopia Brimrose Luminar 5030 Phazir Mobile NIRS medium size
  23. Real time application of mobile-NIRS
  24. Miniaturized NIRS • Scio, Pocket Molecular Sensor startup company o 900nm -1052 nm o Need internet connection o Data saved on the cloud • NIR Scan Nano; Texa Instruments o 900-1700nm
  25. Limitations of NIR Technology • The most important limitation of the technology lies in the development of calibrations. • Different calibrations apply to different products such as: o corn leaf vs. sugarcane leaf. • At present NIRS is not practical for small sample groups
  26. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ilri.org ILRI thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system
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