The document discusses optical sensing techniques for soil macronutrients, highlighting the limitations of traditional laboratory methods and field measurements due to soil variability. It emphasizes the advantages of near-infrared (NIR) spectroscopy in assessing soil properties like moisture and nutrient content with minimal preparation and non-destructive analysis. Additionally, the document outlines challenges and chemometric methods used for analyzing spectral data to improve soil nutrient predictions.

1. Optical Sensing of Soil
Macronutrient
Presented by:
Jyoti Singh
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2. Introduction
• Great Demand for Soil Property Data.
• Methods for soil attributes measurement relies mainly upon
the use of laboratory methods (More samples and
measurements, time consuming).
• Problem with field measurement is the variation in soil
moisture content and surface roughness.
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3. Soil NPK Sensing Techniques
• Standard Soil Testing Laboratory
– time consuming, Laborious, use of chemical and
reagents which effect human health and
environment, costly, do not consider spatial
variation in the field.
• Electrochemical Sensing
– Ion Selective Electrodes
– Ion Sensitive Field Effect Transistor
• Optical Spectroscopy
– NIR Spectroscopy
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4. Optical Spectroscopy
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5. The Electromagnetic Spectrum
Electron Electron Molecular Molecular
Excitation Transition Vibration Rotation
Wavelength λ µm 0.0001 0.01 0.1 1 10 100 1000
Wavenumber cm-1 106 105 10000 1000 100 10 1
Spectral region X-ray UV Vis Infrared Microwave
NIR MIR FIR
Wavelength (nm) 0.7 2.5 25 100
III II I
Overtones Overtones Combination
2nd N-H 1st C-H, N-H C-H, N-H, O-H
3rd C-H 2nd C-H C=O
Weak Strong
Absorbance
)(10)( 41
mcm  
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6. NIR and Light Interaction with matter
If the frequency of
radiation matches with
vibrational frequency
of molecule, then
radiation will be
absorbed causing
change in amplitude of
molecule vibration.
Symmetrical
Stretching
Antisymmetrical
Stretching
Bending
• Utilizes the absorbance of NIR light (700 - 2500 nm) by
vibrating bonds between atoms in molecules.
• O-H, C-H, C-N, C-O, P-O, S-O.
• Compositional information on samples (n~>100) is
correlated with the spectral information to develop
statistical calibration models.
• The calibrations “train” the instrument to analyze future
unknown samples.
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7. Extracting information from
spectral data

8. • Signal processing is used to transform spectral data prior to analysis.
• Data pretreatment
-Local filters
-Smoothing
-Derivatives
-Baseline correction
-Multiplicative Scatter Correction (MSC)
-Orthogonal Scatter Correction (OSC)
1. Qualitative information grouping and classification of spectral objects
from samples into supervised and non-supervised learning methods.
2. Quantitative information relationships between spectral data and
parameter(s) of interest.
How to extract the information?
1. Multivariate analysis (MVA)
Principal Component Analysis (PCA), Projection to Latent
Structures (PLS), PLS-Discriminant Analysis (PLS-DA), …
2. Two dimensional correlation spectroscopy
Homo-correlation, Hetero-correlation

12. Regression by data
compression
Regression on scores
PC1
t-score
y
q
ti
PCA
to compress data
x1
x2
x3

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*Prediction of soil content using near-infrared spectroscopy
Yong He and Haiyan Song
2006 SPIE—The International Society for Optical Engineering
N, PCA P, PCA K, PCA
Correlation between measured and predicted values of N, P and K

14. NIR Spectroscopy
Advantages
• Minimal to no sample preparation.
• Able to measure many constituents
simultaneously with high scan
Speed ( < 1sec).
• Quantitative and Qualitative
results.
• No phase constraints – gas, liquid
or solid.
• Non Destructive, non contact.
• Faster, safer working environment
that does not require chemicals.
• The availability of efficient
chemometric evaluation tools and
software as well as light-fiber optics
has made NIRS to an invaluable tool
for academic research and
industrial quality control.
Disadvantages
• Less information contained in
spectra and Spectra is affected by
particle size, moisture etc.
• Combination and overtone bands
make association with individual
chemical groups more difficult.
• Generally can’t indentify
components of less than 1% in
product hence need more robust
calibration techniques.
• Chemometrics – PCA, PLS.
• Robustness of calibrations needs to
be monitored.
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15. Soil Properties Predicted with NIR
• Sand, silt, clay
• Organic C, organic matter, total C
• C:N ratio
• Biomass
• Exchangeable Ca, Mg
• Fe
• N,P,K
• pH
• Water content
• Electrical conductivity
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16. UV-Vis & UV-Vis-NIR Systems Cary
5000 UV-Vis-NIR4/24/2017 16
Spectrometers

17. RED-Wave Micro MEMS
NIR spectrometer from
Stellar Net
Micro NIR Spectrometer
High resolution spectral data
from a ruggedized field-
portable spectroradiometer4/24/2017 17

18. Block Diagram
r
Light Source (Tungsten
Halogen lamp)
Monochromator
Soil
Sample
Photodetector
Processor
Display
Spectrometer
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20. References
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near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous
assessment of various soil properties”, Geoderma, Vol. 131, No. 1–2, pp. 59–75, 2006.
[2] Y. Qiao and S. Zhang, “Near-infrared spectroscopy technology for soil nutrients detection based
on LS-SVM”, IFIP Advances in Information and Communication Technology, Vol. 368, pp. 325–335,
2012.
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influential variables”, Analtical Methods, Vol. 4, No. 2, p. 467, Feb. 2012.
[7] D. Bertrand and C. N. G. Scotter, “Application of Multivariate Analyses to NIR Spectra of
Gelatinized Starch”, Applied Spectroscopy, Vol. 46, No. 9, pp. 1420–1425, 1992.
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21. References
[8] M. Urbano-Cuadrado, M. D. Luque De Castro, P. M. Pérez-Juan, J. García-Olmo, and M. A.
Gómez-Nieto, “Near infrared reflectance spectroscopy and multivariate analysis in enology:
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[9] K. Wiesner, K. Fuchs, A. M. Gigler, and R. Pastusiak, “Trends in near infrared spectroscopy and
multivariate data analysis from an industrial perspective”, Procedia Engineering, Vol. 87, pp.
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[10]P. R. G. Hein, “Multivariate regression methods for estimating basic density in Eucalyptus
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[11]V. Baeten and P. Dardenne, “Spectroscopy: Developments in instrumentation and analysis”,
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[12]C. Pasquini, “Near Infrared Spectroscopy: fundamentals, practical aspects and analytical
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[13]N. A. O’Brien, C. A. Hulse, D. M. Friedrich, F. J. Van Milligen, M. K. von Gunten, F. Pfeifer, and
H. W. Siesler, “Miniature near-infrared (NIR) spectrometer engine for handheld applications”,
in SPIE Defense, Security, and Sensing, Vol. 8374 , 2012.
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Corporation, and S. Rosa, “Miniature near-infrared spectrometer for point-of-use chemical
analysis”, SPIE proceedings Vol. 8992, 2014.
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