Complementarity of Raman and Infrared spectroscopy for rapid characterization of fucoidan extracts.

BACKGROUND: Fucoidans are sulfated polysaccharides from the cell-wall of brown algae. They have a wide range of applications in medicine, including regenerative medicine, ophthalmology, cancer, and autoimmune disease. Biological activity of fucoidans directly depends on their structure, which remains poorly understood. This is primarily because the polymeric nature of these molecules limits the use of nuclear magnetic resonance and mass spectrometry, classical tools of structural biology for their structural characterization. Raman and Infrared spectroscopies are non-invasive and non-destructive techniques that can be used to probe the structural organization of biological specimens. In this study, we investigate the potential of Raman and Infrared spectroscopy for structural analysis of several fucoidan extracts. RESULTS: Our results show that Infrared and Raman provide different but complimentary information about the structure of crude extracts of fucoidans, revealing the presence of minor impurities from co-extractants. We also found that at high extraction temperatures acidic conditions limit formation of melanoidins, while also yielding relatively high sulfate ester fucoidan. However, at high temperatures, water extraction may potentially result in formation of advanced glycation end products. Their presence could be problematic for fucoidan extracts intended for medicinal use, as advanced glycation end products have been linked to endocrine interruption mechanisms in vivo by crosslinking to and permanently altering extracellular matrix proteins. CONCLUSION: Raman and Infrared can be used as complementary tools for rapid screening of crude fucoidan extracts, which can be a valuable tool for assessing impurities that remain after extraction.

Raman-Based Diagnostics of Stalk Rot Disease of Maize Caused by Colletotrichum graminicola.

Stalk rot caused by Colletotrichum graminicola is a disease of worldwide importance. Stalk rot is difficult to detect at the early stages of infection because the fungus colonizes the tissues inside the maize stem. Current diagnostic methods are time-consuming, laborious, and destructive to the stem tissue. We utilized Raman spectroscopy to follow the development of stalk rot in three different maize genotypes grown either in the field or the greenhouse. We then used the acquired spectra to calibrate statistical models to differentiate amongst the different disease timepoints and the genotypes themselves. This non-invasive spectroscopic method enabled high-accuracy identification of stalk rot based on both stalk and leaf spectra. We additionally found that leaf spectra were favorable for identifying maize by genotype. Finally, we identified Raman bands that showed correlation with the sizes of stalk rot-associated lesions in the stems. We demonstrated that Raman spectroscopy is a viable tool for detection of stalk rot disease, as well as potent for the differentiation of maize genotypes.

Biochemical Origin of Raman-Based Diagnostics of Huanglongbing in Grapefruit Trees.

Biotic and abiotic stresses cause substantial changes in plant biochemistry. These changes are typically revealed by high-performance liquid chromatography (HPLC) and mass spectroscopy-coupled HPLC (HPLC-MS). This information can be used to determine underlying molecular mechanisms of biotic and abiotic stresses in plants. A growing body of evidence suggests that changes in plant biochemistry can be probed by Raman spectroscopy, an emerging analytical technique that is based on inelastic light scattering. Non-invasive and non-destructive detection and identification of these changes allow for the use of Raman spectroscopy for confirmatory diagnostics of plant biotic and abiotic stresses. In this study, we couple HPLC and HPLC-MS findings on biochemical changes caused by Candidatus Liberibacter spp. (Ca. L. asiaticus) in citrus trees to the spectroscopic signatures of plant leaves derived by Raman spectroscopy. Our results show that Ca. L. asiaticus cause an increase in hydroxycinnamates, the precursors of lignins, and flavones, as well as a decrease in the concentration of lutein that are detected by Raman spectroscopy. These findings suggest that Ca. L. asiaticus induce a strong plant defense response that aims to exterminate bacteria present in the plant phloem. This work also suggests that Raman spectroscopy can be used to resolve stress-induced changes in plant biochemistry on the molecular level.

Raman Spectroscopy Can Distinguish Glyphosate-Susceptible and -Resistant Palmer Amaranth (Amaranthus palmeri).

The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations. The glyphosate-resistant populations used in this study were 11-, 32-, and 36-fold more resistant compared to the susceptible standard. The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number for these resistant populations ranged from 86 to 116. We found that Raman spectroscopy could be used to differentiate herbicide-treated and non-treated susceptible populations based on changes in the intensity of vibrational bands at 1156, 1186, and 1525 cm-1 that originate from carotenoids. The partial least squares discriminant analysis (PLS-DA) model indicated that within 1 day of glyphosate treatment (D1), the average accuracy of detecting herbicide-treated and non-treated susceptible populations was 90 and 73.3%, respectively. We also found that glyphosate-resistant and -susceptible populations of Palmer amaranth can be easily detected with an accuracy of 84.7 and 71.9%, respectively, as early as D1. There were relative differences in the concentration of carotenoids in plants with different resistance levels, but these changes were not significant. The results of the study illustrate the utility of Raman spectra for evaluation of herbicide resistance and stress response in plants under field conditions.

Raman Spectroscopy Enables Non-invasive and Confirmatory Diagnostics of Salinity Stresses, Nitrogen, Phosphorus, and Potassium Deficiencies in Rice.

Proper management of nutrients in agricultural systems is critically important for maximizing crop yields while simultaneously minimizing the health and environmental impacts of pollution from fertilizers. These goals can be achieved by timely confirmatory diagnostics of nutrient deficiencies in plants, which enable precise administration of fertilizers and other supplementation in fields. Traditionally, nutrient diagnostics are performed by wet-laboratory analyses, which are both time- and labor-consuming. Unmanned aerial vehicle (UAV) and satellite imaging have offered a non-invasive alternative. However, these imaging approaches do not have sufficient specificity, and they are only capable of detecting symptomatic stages of nutrient deficiencies. Raman spectroscopy (RS) is a non-invasive and non-destructive technique that can be used for confirmatory detection and identification of both biotic and abiotic stresses on plants. Herein, we show the use of a hand-held Raman spectrometer for highly accurate pre-symptomatic diagnostics of nitrogen, phosphorus, and potassium deficiencies in rice (Oryza sativa). Moreover, we demonstrate that RS can also be used for pre symptomatic diagnostics of medium and high salinity stresses. A Raman-based analysis is fast (1 s required for spectral acquisition), portable (measurements can be taken directly in the field), and label-free (no chemicals are needed). These advantages will allow RS to transform agricultural practices, enabling precision agriculture in the near future.

Raman Spectroscopy vs Quantitative Polymerase Chain Reaction In Early Stage Huanglongbing Diagnostics.

Raman spectroscopy (RS) is an emerging analytical technique that can be used to develop and deploy precision agriculture. RS allows for confirmatory diagnostic of biotic and abiotic stresses on plants. Specifically, RS can be used for Huanglongbing (HLB) diagnostics on both orange and grapefruit trees, as well as detection and identification of various fungal and viral diseases. The questions that remain to be answered is how early can RS detect and identify the disease and whether RS is more sensitive than qPCR, the "golden standard" in pathogen diagnostics? Using RS and HLB as case study, we monitored healthy (qPCR-negative) in-field grown citrus trees and compared their spectra to the spectra collected from healthy orange and grapefruit trees grown in a greenhouse with restricted insect access and confirmed as HLB free by qPCR. Our result indicated that RS was capable of early prediction of HLB and that nearly all in-field qPCR-negative plants were infected by the disease. Using advanced multivariate statistical analysis, we also showed that qPCR-negative plants exhibited HLB-specific spectral characteristics that can be distinguished from unrelated nutrition deficit characteristics. These results demonstrate that RS is capable of much more sensitive diagnostics of HLB compared to qPCR.

Raman Spectroscopy Enables Non-Invasive Identification of Peanut Genotypes and Value-Added Traits.

Identification of specific genotypes can be accomplished by visual recognition of their distinct phenotypical appearance, as well as DNA analysis. Visual identification (ID) of species is subjective and usually requires substantial taxonomic expertise. Genotyping and sequencing are destructive, time- and labor-consuming. In this study, we investigate the potential use of Raman spectroscopy (RS) as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of peanut genotypes. We show that chemometric analysis of peanut leaflet spectra provides accurate identification of different varieties. This same analysis can be used for prediction of nematode resistance and oleic-linoleic oil (O/L) ratio. Raman-based analysis of seeds provides accurate genotype identification in 95% of samples. Additionally, we present data on the identification of carbohydrates, proteins, fiber and other nutrients obtained from spectroscopic signatures of peanut seeds. These results demonstrate that RS allows for fast, accurate and non-invasive screening and selection of plants which can be used for precision breeding.

Raman-Based Differentiation of Hemp, Cannabidiol-Rich Hemp, and Cannabis.

Hemp (Cannabis sativa) has been used to treat pain as far back as 2900 B.C. Its pharmacological effects originate from a large variety of cannabinols. Although more than 100 different cannabinoids have been isolated from Cannabis plants, clear physiological effects of only a few of them have been determined, including delta-9 tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabigerol (CBG). While THC is an illicit drug, CBD and CBG are legal substances that have a variety of unique pharmacological properties such as the reduction of chronic pain, inflammation, anxiety, and depression. Over the past decade, substantial efforts have been made to develop Cannabis varieties that would produce large amounts of CBD and CBG. Ideally, such plant varieties should produce very little (below 0.3%) if any THC to make their cultivation legal. The amount of cannabinoids in the plant material can be determined using high performance liquid chromatography (HPLC). This analysis, however, is nonportable, destructive, and time and labor consuming. Our group recently proposed to use Raman spectroscopy (RS) for confirmatory, noninvasive, and nondestructive differentiation between hemp and cannabis. The question to ask is whether RS can be used to detect CBD and CBG in hemp, as well as enable confirmatory differentiation between hemp, cannabis, and CBD-rich hemp. In this manuscript, we show that RS can be used to differentiate between cannabis, CBD-rich plants, and regular hemp. We also report spectroscopic signatures of CBG, cannabigerolic acid (CBGA), THC, delta-9-tetrahydrocannabinolic acid (THCA), CBD, and cannabidiolic acid (CBDA) that can be used for Raman-based quantitative diagnostics of these cannabinoids in plant material.

Non-invasive diagnostics of Liberibacter disease on tomatoes using a hand-held Raman spectrometer.

MAIN CONCLUSION: Hand-held Raman spectroscopy can be used for confirmatory, non-invasive and non-destructive detection and identification of two haplotypes of Liberibacter disease on tomatoes. Using this spectroscopic approach, structural changes in carotenoids, xylan, cellulose and pectin that are associ-ated with this bacterial disease can be determined. 'Candidatus Liberibacter solanacearum' (Lso) is a phloem-limited Gram-negative bacterium that infects crops worldwide. In North America, two haplotypes of Lso (LsoA and LsoB) are transmitted by the potato psyllid, Bactericera cockerelli (Sulc), and infect many solanaceous crops such as potato and tomato. Infected plants exhibit chlorosis, severe stunting, leaf cupping, and scorching. Polymerase chain reaction (PCR) and potato tuber frying are commonly used methods for diagnostics of the plant disease caused by Lso. However, they are time-consuming, costly, destructive to the sample, and often not sensitive enough to detect the pathogen in the early infection stage. Raman spectroscopy (RS) is a noninvasive, nondestructive, analytical technique, which probes chemical composition of analyzed samples. In this study, we demonstrate that Lso infection can be diagnosed by non-invasive spectroscopic analysis of tomato leaves three weeks following infection, before the development of aerial symptoms. In combination with chemometric analyses, Raman spectroscopy allows for 80% accurate diagnostics of Liberibacter disease caused by each of the two different haplotypes. This diagnostics approach is portable and sample agnostic, suggesting that it could be utilized for other crops and could be conducted autonomously.

Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a hand-held Raman spectrometer.

Cannabis is a generic term that is used to denote hemp plants (Cannabis sativa) that produce delta-9-tetrahydrocannabinolic acid (THCA) in amounts higher than industrial hemp. While THCA itself is not considered psychoactive, it is the source of the psychoactive delta-9 tetrahydrocannabinol (THC) that forms from its oxidation. About 147 million people, which is around 2.5% of the world population, consume cannabis. This makes cannabis by far the most widely cultivated and trafficked illicit drug in the world. Such enormous popularity of cannabis requires substantial effort by border control and law enforcement agencies to control illegal trafficking and distribution. Confirmatory diagnostics of cannabis is currently done by high pressure liquid chromatography (HPLC), which requires sample transportation to a certified laboratory, making THC diagnostics extremely time and labor consuming. This catalyzed a push towards development of a portable, confirmatory, non-invasive and non-destructive approach for cannabis diagnostics that could be performed by a police officer directly in the field to verify illicit drug possession or transport. Raman spectroscopy (RS) is a modern analytical technique that meets all these strict expectations. In this manuscript, we show that RS can be used to determine whether plant material is hemp or cannabis with 100% accuracy. We also demonstrate that RS can be used to probe the content of THCA in the analyzed samples. These findings suggest that a hand-held Raman spectrometer can be an ideal tool for police officers and hemp breeders to enable highly accurate diagnostics of THCA content in plants.

Confirmatory non-invasive and non-destructive identification of poison ivy using a hand-held Raman spectrometer.

Poison ivy (Toxicodendron radicans) is a forest understory plant that grows throughout the United States, Canada and Mexico. The plant contains urushiol oils, a mixture of pentadecylcatechols, that cause severe allergic reactions on skin including reddish inflammation, uncoloured bumps and blistering. Such allergic reactions develop within hours or days, which facilitates unknowing spread of the urushiol inside the house. This enables continuous contact with urushiol extending the length of time of the rash. It should be noted that apart from extensive washing with soap and cold water, there is no direct way to treat urushiol-induced allergic reactions. In these circumstances, the best practice is to avoid contact with the plant. However, differentiating poison ivy from other plants requires sophisticated botanical experience that is not possessed by a vast majority of people. To overcome this limitation, we developed a confirmatory, label-free, non-invasive and non-destructive approach for detection and identification of poison ivy. We show that using a hand-held Raman spectrometer, 100% accurate identification of this species can be performed in only one second. We also demonstrate that in combination with partial least square discriminant analysis (PLS-DA), Raman spectroscopy is capable of distinguishing poison ivy from more than fifteen different plant species, including weeds, grasses and trees. The use of a hand-held spectrometer on a motorized robotic platform or an unmanned aerial vehicle (UAV) can be used for automated surveillance of household and agricultural spaces enabling confirmatory detection and identification of this dangerous plant species.

Rapid and noninvasive diagnostics of Huanglongbing and nutrient deficits on citrus trees with a handheld Raman spectrometer.

Huanglongbing (HLB) or citrus greening is a devastating disease of citrus trees that is caused by the gram-negative Candidatus Liberibacter spp. bacteria. The bacteria are phloem limited and transmitted by the Asian citrus psyllid, Diaphorina citri, and the African citrus psyllid, Trioza erytreae, which allows for a wider dissemination of HLB. Infected trees exhibit yellowing of leaves, premature leaf and fruit drop, and ultimately the death of the entire plant. Polymerase chain reaction (PCR) and antibody-based assays (ELISA and/or immunoblot) are commonly used methods for HLB diagnostics. However, they are costly, time-consuming, and destructive to the sample and often not sensitive enough to detect the pathogen very early in the infection stage. Raman spectroscopy (RS) is a noninvasive, nondestructive, analytical technique which provides insight into the chemical structures of a specimen. In this study, by using a handheld Raman system in combination with chemometric analyses, we can readily distinguish between healthy and HLB (early and late stage)-infected citrus trees, as well as plants suffering from nutrient deficits. The detection rate of Raman-based diagnostics of healthy vs HLB infected vs nutrient deficit is ~ 98% for grapefruit and ~ 87% for orange trees, whereas the accuracy of early- vs late-stage HLB infected is 100% for grapefruits and ~94% for oranges. This analysis is portable and sample agnostic, suggesting that it could be utilized for other crops and conducted autonomously. Graphical abstract.

Noninvasive and Nondestructive Detection of Cowpea Bruchid within Cowpea Seeds with a Hand-Held Raman Spectrometer.

Insect damage to crops is a serious issue, in particular when the pest dwells within its host. The cowpea bruchid ( Callosobruchus maculatus) is an herbivore of legumes including beans and peas. The bruchid lays its eggs on the seeds themselves; after hatching, the larvae burrow into and develop inside the seed, complicating detection and treatment. Left unchecked, two insects could destroy up to 50% of 1 ton of harvest cowpea ( Vigna unguiculata) after several months of storage. In this study, we investigated the possibility of using a hand-held Raman spectrometer to detect the pest during its development within intact cowpeas. Our results show that Raman spectroscopy can detect chemical signatures of bruchid larvae as well as their excrements inside the intact seeds. Additionally, using chemometric methods, we distinguished between healthy and infested seeds as well as among seeds hosting developmentally early or late-stage larvae with high accuracy. This study demonstrates Raman spectroscopy's efficacy in not only detection of pathogens and pests present on the surface of plant leaves and the grain but also inside the seeds. This Raman-based method may prove useful as a rapid means of screening crops for internal pests.