Raman Spectroscopy Applications in Agriculture: From Early Plant Stress Diagnostics to Animal Diet Predictions
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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.
Altangerel, Narangerel (2017). Raman Spectroscopy Applications in Agriculture: From Early Plant Stress Diagnostics to Animal Diet Predictions. Doctoral dissertation, Texas A&M University. Available electronically from