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Wheat is one of the oldest and most widely cultivated staple food crops worldwide. Wheat encounters an array of biotic and abiotic stresses during its growth that significantly impact the crop yield and consequently global food security. Molecular and imaging methods that can be used to detect such stresses are laborious and have numerous limitations. This catalyzes the search for alternative techniques that can be used to monitor plant health. Raman spectroscopy (RS) is a modern analytical technique that is capable of probing structure and composition of samples non-invasively and non-destructively. In this study, we investigate the accuracy of RS in confirmatory diagnostics of biotic and abiotic stresses in wheat. Specifically, we modelled nitrogen deficiency (ND) and drought, key abiotic stresses, and Russian wheat aphid (Diuraphis noxia) infestation and viral diseases: wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), economically significant biotic stresses in common bread wheat. Raman spectra as well as high pressure liquid chromatography (HPLC)-based analyses revealed drastically distinct changes in the intensity of carotenoid vibration (1185 cm-1) and in the concentration of lutein, chlorophyll, and pheophytin biomolecules of wheat, triggered in response to aforementioned biotic and abiotic stresses. The biochemical changes were reflected in unique vibrational signatures in the corresponding Raman spectra, which, in turn could be used for ~100% accurate identification of biotic and abiotic stresses in wheat. These results demonstrate that a hand-held Raman spectrometer could provide an efficient, scalable, and accurate diagnosis of both biotic as well as abiotic stresses in the field.

Metal toxicities can be detrimental to a plant health, as well as to the health of animals and humans that consume such plants. Metal content of plants can be analyzed using colorimetric, atomic absorption- or mass spectroscopy-based methods. However, these techniques are destructive, costly and laborious. In the current study, we investigate the potential of Raman spectroscopy (RS), a modern spectroscopic technique, for detection and identification of metal toxicities in rice. We modeled medium and high levels of iron and aluminum toxicities in hydroponically grown plants. Spectroscopic analyses of their leaves showed that both iron and aluminum toxicities can be detected and identified with ∼100% accuracy as early as day 2 after the stress initiation. We also showed that diagnostics accuracy was very high not only on early, but also on middle (day 4–day 8) and late (day 10–day 14) stages of the stress development. Importantly this approach only requires an acquisition time of 1 s; it is non-invasive and non-destructive to plants. Our findings suggest that if implemented in farming, RS can enable pre-symptomatic detection and identification of metallic toxins that would lead to faster recovery of crops and prevent further damage.

Research activities involving the use of human subjects, vertebrate animals, and/or biohazards must be reviewed and approved by the appropriate Texas A&M University regulatory research committee (i.e., IRB, IACUC, IBC) before the activity can commence. This requirement applies to activities conducted at Texas A&M and to activities conducted at non-Texas A&M facilities or institutions. In both cases, students are responsible for working with the relevant Texas A&M research compliance program to ensure and document that all Texas A&M compliance obligations are met before the study begins. I, Samantha Higgins, certify that all research compliance requirements related to this Undergraduate Research Scholars thesis have been addressed with my Research Faculty Advisor prior to the collection of any data used in this final thesis submission.

Lyme disease (LD), the leading tick-borne disease in the Northern hemisphere, is caused by spirochetes of several genospecies of the Borreliella burgdorferi sensu lato complex. LD is a multi-systemic and highly debilitating illness that is notoriously challenging to diagnose. The main drawbacks of the two-tiered serology, the only approved diagnostic test in the United States, include poor sensitivity, background seropositivity, and cross-reactivity. Recently, Raman spectroscopy (RS) was examined for its LD diagnostic utility by our earlier proof-of-concept study. The previous investigation analyzed the blood from mice that were infected with 297 and B31 strains of Borreliella burgdorferi sensu stricto (s.s.). The selected strains represented two out of the three major clades of B. burgdorferi s.s. isolates found in the United States. The obtained results were encouraging and prompted us to further investigate the RS diagnostic capacity for LD in this study. The present investigation has analyzed blood of mice infected with European genospecies, Borreliella afzelii or Borreliella garinii, or B. burgdorferi N40, a strain of the third major class of B. burgdorferi s.s. in the United States. Moreover, 90 human serum samples that originated from LD-confirmed, LD-negative, and LD-probable human patients were also analyzed by RS. The overall results demonstrated that blood samples from Borreliella-infected mice were identified with 96% accuracy, 94% sensitivity, and 100% specificity. Furthermore, human blood samples were analyzed with 88% accuracy, 85% sensitivity, and 90% specificity. Together, the current data indicate that RS should be further explored as a potential diagnostic test for LD patients.

Food security is an emerging problem that is faced by our civilization. There are millions of people around the world suffering from various kinds of malnutrition. The number of people that starve will only increase considering the continuous growth of the world’s population. The problem of food security can be addressed by timely detection and identification biotic and abiotic stresses in plants that drastically reduce the crop yield. A growing body of evidence suggests that Raman spectroscopy (RS), an emerging analytical technique, can be used for the confirmatory and non-invasive diagnostics of plant stresses. However, it remains unclear whether RS can efficiently disentangle biotic and abiotic stresses, as well as detect both of them simultaneously in plants. In this work, we modeled a stalk rot disease in corn by inoculating the plant stalks with Colletotrichum graminicola. In parallel, we subjected plants to salt stress, as well as challenging plants with both stalk rot disease and salinity stress simultaneously. After the stresses were introduced, Raman spectra were collected from the stalks to reveal stress-specific changes in the plant biochemistry. We found that RS was able to differentiate between stalk rot disease and salinity stresses with 100% accuracy, as well as predict presence of both of those stresses in plants on early and late stages. These results demonstrate that RS is a robust and reliable approach that can be used for confirmatory, non-destructive and label-free diagnostics of biotic and abiotic stresses in plants.

Scalp hairs are readily present at most crime scenes because an average person sheds around 100 hairs a day. Forensic experts analyze hair found at crime scenes to identify suspects involved in a crime. Many people color their hair on a regular basis. Therefore, confirmatory analysis of hair colorants can be extremely useful in forensic investigation of hair evidence. However, most currently available methods for analysis of hair colorants are invasive, destructive, or not reliable. Surface enhanced Raman spectroscopy (SERS) is a minimally invasive, fast, and highly accurate technique that can be used to identify colorants present on hair. SERS is based on 106–108 enhancement of Raman scattering from molecules present in the close proximity to noble metal nanostructures. In this study, we investigate the extent to which SERS can be used to reveal coloration history of hair. We found that SERS enables nearly 100% identification of dyes of different color if those were applied on hair in the sequential order. The same accuracy was observed for colorants of different brand and type. Furthermore, SERS was capable of revealing the order in which two colorants were applied on hair. Finally, we demonstrated that SERS could be used to reveal hair coloration history if two randomly selected dyes of different color, brand and type were used to color the hair. These findings facilitate the need for forensic experts to account for hair that has been redyed and can be identified against a library of the same colorant combinations.