This work is mainly devoted to development of Raman spectroscopic techniques
for in vivo detection of abiotic plant stress and animal diet prediction by Raman
spectra of their feces. The ability to measure plant stress in vivo responses is
becoming increasingly vital as we consider human population growth and climate
change reports. In the first study, Raman spectroscopy was utilized to nondestructively
detect abiotic stress responses during 48 hours of plant response to multiple
stresses. Coleus Solenostemon scutellarioides plants were subjected to four common
abiotic stress conditions, individually: high soil salinity, drought, chilling exposure,
and light saturation and examined post stress induction by Raman microscopic and
spectroscopic systems, and chemical analytical methods. While anthocyanin levels
increased, carotenoid levels decreased under exposure to these stress conditions by
in vivo Raman measurements and the chemical analysis. This unique negative correlated relationship shows that plant stress response is fine-tuned to protect against
stress-induced damage. In the next study, we utilized a Raman spectroscopy as detection tool to predict cow diets by their feces. The objective of this study was to
compare near infrared reflectance spectroscopy (NIRS) to Raman spectroscopy of
fecal samples for predicting the percentage of Honey mesquite Prosopis glandulosa
Torr. in the diet of ruminally fistulated cattle fed three different base hay diets and
to compare them for their ability to discriminate among the three base diets. Spectra
were collected from fecal materials from a feeding trial with mesquite fed at 0, 1, 3
and 5% of the diet and base hay diets of timothy hay Phleum pratense L., Sudan
hay Sorghum sudanense (Piper) Stapf, or a 50 : 50 combination of Bermudagrass
hay Cynodon dactylon (L.) Pers. and beardless wheat hay Triticum aestivum L.. NIRS and Raman spectra were used for partial least squares regression calibrations
with the timothy and Sudan hays and validated with the Bermudagrass beardless
wheat hay diets. NIRS spectra provided useful calibrations (R²=0.88, slope=1.03,
intercept=1.88, root mean square error=2.09, bias=1.95, ratio of performance to deviation=2.6), but Raman spectra did not. Stepwise discriminant analysis was used
to select wavenumbers for discriminant among the three hays. Fifteen of 350 possible
wavenumbers for NIRS spectra and 29 of 300 possible wavenumbers for Raman
spectra met the P≤0.05 entry and staying criteria. Canonical discriminant analysis
using these wavenumbers resulted in 100% correct classification for all three base diets
and the Raman spectra provided greater separation than NIRS spectra. Discrimination
using Raman spectra was primarily associated with wavenumbers associated
with undigestible constituents of the diet, i.e., lignin. In contrast, discrimination
using NIRS spectra was primarily associated with wavenumbers associated with digestible constituents in the diet, i.e., protein, starch and lipid. At last, coherent
Raman scattering spectroscopy is studied specifically, with the Gaussian ultrashort
pulses as a hands-on elucidatory extraction tool of the clean coherent Raman resonant
spectra from the overall measured data contaminated with the non-resonant
four wave mixing background. The integral formulae for both the coherent anti-
Stokes and Stokes Raman scattering are given in the semiclassical picture, and the
closed-form solutions in terms of a complex error function are obtained. An analytic
form of maximum enhancement of pure coherent Raman spectra at threshold
time delay depending on bandwidth of probe pulse is also obtained. The observed
experimental data for pyridine in liquid-phase are quantitatively elucidated and the
inferred time-resolved coherent Raman resonant results are reconstructed with a new
insight
