The second lab managers’ meeting of the South-East Asia Laboratory NETwork (SEALNET) took place on 19 - 23 November 2018 in ICAR-IISS (Indian Institute of Soil Science), Bhopal, India. Dr. K M Hati, Principal Scientist, ICAR-IISS, Bhopal, India (1st Day)

1. Infrared Spectroscopy and its potential for
estimation of soil properties
Kuntal Mouli Hati
Principal Scientist (Soil Physics)
ICAR-Indian Institute of Soil Science, Bhopal

2. Need for soil analysis
• Different soil properties and processes are estimated through standard
laboratory analysis
• These are usually costly, labour intensive, time consuming and also
utilizes many chemicals not friendly to the environment.
• Information on availability of soil nutrient and water to plant, soil
mechanical impedance and microbial environment are required for soil
health analysis
• For environmental monitoring, climate change modelling and precision
agriculture nowadays there is a great demand for larger amounts of good
quality, inexpensive soil data.
• There is a global thrust towards the development of more time- and cost
efficient methodologies for soil analysis

3. Need for soil analysis
• The Knowledge of spatial variability of soil attributes within an agricultural field is
critical for successful site-specific crop management or precision farming.
• Large number of sample analysis at a short period is imperative for site-specific
nutrient management
• The potential of precision agriculture is limited by the lack of appropriate
measurement and analysis techniques for agronomically important factors
• Soil sensing techniques to assess this variability on the go are being developed as
an alternative to tedious manual soil sampling and laboratory testing.
• While the concept of precision farming is sound, our understanding of the physical
and biological aspects of the cropping system is incomplete due to limitations in the
current sensing and data processing technologies.

4. • Infrared spectroscopy (IR) is a proven technology for rapid, non-
destructive characterization of the composition of materials based
on the interaction of electromagnetic energy with matter.
• IR is now routinely used for analyses of a wide range of materials in
laboratory and process control applications in agriculture, food and
feed technology, geology, biomedicine and space exploration .
• Both the visible–near-infrared (VNIR, 0.35-2.5 µm) and mid–infrared
(MIR, 2.5-25 µm) wavelength regions have been investigated for
non-destructive analyses of soils and can be applied to predict a
number of important soil properties (Shepherd & Walsh, 2002;
McBratney et al 2006; Brown et al. 2006).

5. The electromagnetic spectrum highlighting the
visible and infrared portions

6. How Infrared Spectroscopy function
IR radiation does not have enough energy to induce electronic transitions as
seen with UV.
Absorption of IR is restricted to compounds with small energy differences in
the possible vibrational and rotational states.
For a molecule to absorb IR, the vibrations or rotations within a molecule
must cause a net change in the dipole moment of the molecule.
The alternating electrical field of the radiation interacts with fluctuations in
the dipole moment of the molecule.
If the frequency of the radiation matches the vibrational frequency of the
molecule then radiation will be absorbed, causing a change in the amplitude
of molecular vibration.
Molecular vibrations comes under two catagories of stretching and bending.

7. • FTIR (Fourier Transform Infra-red) spectroscopy uses polychromatic radiation to measure
the excitation of molecular bonds whose relative absorbances provide an index of the
abundance of various functional groups
• Absorption of IR light occurs when photon transfer to the molecule excites it to a higher
energy state. These “excited states” result in vibrations of molecular bonds, rotations,
and translations.
• The IR spectra contain peaks representing the absorption of IR light by specific molecular
bonds at specific frequencies (i.e. wavenumbers) due to stretching, bending, and
wagging vibrations in the molecules.
• Intense fundamental molecular frequencies related to soil components occur in the MIR
between wavelengths 2500 and 25,000 nm.
• Weak overtones and combinations of these fundamental vibrations due to the
stretching and bending of NH, OH and CH groups dominate the NIR (700–2500 nm)
and electronic transitions dominates the VIS (400–700 nm) portions of the
electromagnetic (EM) spectrum.
• While not all molecules lend themselves to FTIR analysis, the majority of inorganic
and organic compounds in the environment are IR active.

8. Vibrations
Modes of vibration
C—HStretching
Bending C
O
H
H
H
Symmetrical
2853 cm-1
H
H
Asymmetrical
2926 cm-1
H
H
H
H
Scissoring
1450 cm-1
Rocking
720 cm-1
H
H
H
H
Wagging
1350 cm-1
Twisting
1250 cm-1
Stretching
frequency
Bending
frequency

9. • Bonds subject to vibrational
energy changes => continually
vibrate in different ways:
• Energy absorption in IR
region then occurs &
translated into absorption
spectrum.
Antisymmetrical
stretching
Symmetrical
stretching
Rocking
Wagging Twisting
Scissoring
Source: www.wikipedia.org/
9
Types of vibration in molecules occur due to
adsorption of IR radiation

10. Infrared techniques commonly used for soil analysis
VIS-NIR Spectroscopy (400-2500 nm range)
MIR Spectroscopy (2500-25000 nm range)
• Diffused reflectance mode (DRIFTS)
• Attenuated total reflectance (ATR)
• Photo-acoustic spectroscopy (Fourier
transform infrared photoacoustic
spectroscopy, FTIR-PAS)

12. Standard reflectance spectra in NIR region for soils of
different texture classes.
Reflectance is generally lower in the visible range (400-700 nm) and higher in the near
infrared (700-2500 nm) region
Three specific bands around at 1400, 1900 and 2200 nm associated with clay minerals,
OH features of free water at 1400 and 1900 nm, and lattice OH features at 1400 and
2200 nm
In addition, the spectra show a small reflectance peak around 2250 nm, -due to
organic molecules (e.g., CH2, CH3, and NH3), SiOH bonds, cation OH bonds in
phyllosilicate minerals (e.g., kaolinite, montmorillonite).

13. Average Vis-NIR spectral reflectance for the selected soils of
India (Das et al., 2015)

14. MIR spectrum with assignment of principal bands and the spectral width
regions of soils.

15. The MIR spectrum can be divided into four regions:
• the X-H (O-H, C-H, and N-H) stretching region (4,000-2,500 cm-1)
• the triple-bond (C≡C and C≡N) region (2,500-2,000 cm-1)
• the double-bond (C=C, C=O and C=N) region (2,000-1,500 cm-1)
• the fingerprint region (1,500-600 cm-1)

16. Absorption bands in the MIR range and functional groups or soil
components ( denotes stretching vibration and  bending vibration)
Wave number
(cm-1)
Vibration Functional group or component
3620  O-H Clay mineral
3600-2800 O-H,
 N-H
Water, alcohols, phenols; carboxyl,
hydroxyl groups, amides
3000-2800  C-H Aliphatic methyl and methylene groups
2520 CO3
-2 Carbonates
1610  N-H Amine
1100-1000,
1030-950
Si-O
Si-O
Silicates (Quartz)
Clay minerals
800, 700 Si-O Quartz
700-600 Iron oxides

17. Spectra of some Vertisols taken in the MIR region

18.  MIR region dominated by intense vibration fundamentals, whereas the NIR
region is dominated by much weaker and broader signals from vibration
overtones and combination bands
 The MIR region provides more robust calibrations for a soil set with diverse
properties
 Light diffusion is higher in NIR than in MIR
 NIR spectra will be more affected by factors which affect the diffusion of
light, such as the physical structure (size of aggregates, porosity), but also the
presence of water, which changes the refractive index and therefore the
diffusion of light
Spectroscopy
NIR
800-2500 nm
MIR
2500-25000 nm
Why, MIR??

19. Near-infrared (NIR) spectrometers and spectroradiometers

20. FT-MIR

21. Review of the quantitative predictions of various soil attributes
using spectral response in different regions of the EM spectrum
Soil attribute Spectral
region
Spectral
range (nm)
Multivariate
method
R2 Authors
Acid (exch.)
cmol/kg
VIS–NIR 400–2498 PCR (11) 0.65 Chang et
al. (2001)
Al (exch.);
cmol/kg
MIR 2500-
25000
PLSR 0.64 Janik et al.
(1998)
C (inorg.);
g/kg
MIR 2500-
25000
PLSR 0.98 McCarty et
al. (2002)
C (inorg.);
g/kg
NIR 1100-2498 PLSR 0.87 -do-

22. Statistics on the predictive ability of NIR and DRIFT-
MIR spectrometry for soil and crop parameters.
Property NIR MIR
R2 R2
Minerizable N 0.46*** 0.21*
Olsen P 0.71*** 0.55**
eCEC 0.83*** 0.56**
Exch. K 0.11 0.36***
Exch. Ca 0.80 0.60***
Exch. Mg 0.82 0.61***
Groenigen et al., 2003 Plant Soil
Alluvial Soil, California, USA

23. Scatter-plots of predicted (PSLR prediction model) vs. measured
soil textural fractions (Sand, Silt & Clay) for the calibration (a) and
validation (b) data sets using NIRS for Italian Soils
Conforti et al., 2015

24. Methodology3
Laboratory
soil data
VNIR or MIR spectral
data
Pre-treatment:
Log-normalization using base-10 logarithm
Testing of 30 different preprocessing
transformations
Identify relationships
Complete
dataset
Methods:
Stepwise Multiple Linear Regression (SMLR)
Principal Components Regression (PCR)
Partial Least-Squares Regression (PLSR)
Regression Tree (RT)
Random Forest (RF) Regression
~70%
of data
Model
dataset
~ 30% of data
used to
test accuracy
of model
predictions
Validation
dataset
predictions R2, RMSE

25. Spectral Pre-processing
Spectral reflectance consists of information on both the composition
(absorption) and scattering (Rayleigh, Mie, and geometric scattering) of
incident EMR.
The scattering component is of least significance in the context of soil
compositional analysis, as it does not have energy transfer with the soil
sample. But it may cause undesirable variations in the spectra. Thus, the
scattering component has to be effectively eliminated from the reflectance
signal. Also, accuracy of prediction may improve with pre-processing.

26. The most widely used pre-processing techniques can be divided into two
categories: scatter-correction methods and spectral derivatives.
The spectral derivative method consists of first derivatives (FD) and
second derivatives (SD) of the reflectance spectrum.
Spectral Pre-processing Continuo…..

27. 0
0.2
0.4
0.6
0.8
EC
EC
0
100
200
300
400
500
600
Av_N(kg/ha)
Av-N
0
100
200
300
400
500
600
700
800
900
1000
Av._K(kg/ha)
Av-K
3
4
5
6
7
8
9pH pH
0
0.2
0.4
0.6
0.8
1
1.2
1.4
SOC(%)
SOC
Scattergram of soil properties of samples collected from various
Alfisols region of India
0
50
100
150
200
Av_P(kh/ha)
Av-P

28. MIR Spectra of soil samples recorded using alpha-FT-MIR spectrometer

29. y = 0.7319x + 0.1236
R² = 0.8838, N=35
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 0.5 1 1.5Predicted
Measured
Calibration Validation
Soil organic carbon (%)

30. 4.00
4.50
5.00
5.50
6.00
6.50
7.00
4.00 4.50 5.00 5.50 6.00 6.50 7.00
Predicted
Measured
Calibration Validation
Y= 0.5689x +2.3722
R2 = 0.722 ( N= 35)
Soil pH

31. y = 0.4529x + 120.7
R² = 0.5666, N=33
100
150
200
250
300
350
400
450
100 200 300 400 500
Predicted
Measured
Calibration Validation
Available N (Kg ha-1)

32. y = 0.5188x + 20.119
R² = 0.5294, N= 33
0
10
20
30
40
50
60
70
80
0 20 40 60 80
Predicted
Measured
Available P (Kg ha-1)
Calibration
Validation

33. Property
Random Forest Regression
Validation of Model
R2R2 RMSE
pH 0.94 0.57 0.72
SOC 0.94 0.20 0.88
Av._N. 0.96 59.23 0.57
Av._P 0.94 19.73 0.53
Av._K 0.94 84.46 0.23
Prediction co-efficient of different soil properties for Alfisols

34. Conclusion:
• IR spectroscopy (both the NIRS and MIRS) has great potential
for simultaneous estimation of number of soil properties and
useful for soil health studies
• Need to develop spectral library and chemometric models
useful for Indian soils and applicable over various soil types
• Scope for development of MIR or NIR spectroscopes
indigenously to make them more cost effective and adapted
for local condition
• Suitable spectral bands in the MIR and NIR region can be
used for development of sensors for soil property estimation
in-situ
• NIR spectroscopes requires less soil preparation and the
spectroscopes are more rugged for field use