Raman microspectroscopic analysis of triterpenoids found in plant cuticles

Contemporary applications of vibrational spectroscopy in plant stresses and phenotyping

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

Steam distillation, supercritical fluid extraction, and anti-Trypanosoma cruzi activity of compounds from pink pepper (Schinus terebinthifolius Raddi)

Anti-Trypanosoma cruzi activity of compounds from fruits of Schinus terebinthifolius Raddi (pink pepper) were evaluated, using sustainable techniques such as steam distillation (SD) and supercritical fluid extraction (SFE). SD was optimised using a design of experiment and SFE was carried out using supercritical CO2 solvent (300 bar and 60 °C). Results of the anti-T. cruzi activity showed that the essential oil presented high activity (IC50 = 4.5 ± 0.3 μg/mL), whereas the supercritical extract had a moderate effect (IC50 = 19.7 ± 2.9 μg/mL). The differences in the anti-T. cruzi activity can be attributed to the extraction of non-volatile compounds in the SFE, such as moronic and (Z)-masticadienoic acids. In contrast, SD extracted only volatile compounds such as monoterpenes and sesquiterpenes. Therefore, these results suggest that the volatile compounds from pink pepper are involved with the anti-T. cruzi activity.

Harnessing Raman spectroscopy for the analysis of plant diversity

Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

Vibrational spectroscopic profiling of biomolecular interactions between oak powdery mildew and oak leaves

Oak powdery mildew, caused by the biotrophic fungus Erysiphe alphitoides, is a prevalent disease affecting oak trees, such as English oak (Quercus robur). While mature oak populations are generally less susceptible to this disease, it can endanger young oak seedlings and new leaves on mature trees. Although disruptions of photosynthate and carbohydrate translocation have been observed, accurately detecting and understanding the specific biomolecular interactions between the fungus and the leaves of oak trees is currently lacking. Herein, via hybrid Raman spectroscopy combined with an advanced artificial neural network algorithm, the underpinning biomolecular interactions between biological soft matter, i.e., Quercus robur leaves and Erysiphe alphitoides, are investigated and profiled, generating a spectral library and shedding light on the changes induced by fungal infection and the tree's defence response. The adaxial surfaces of oak leaves are categorised based on either the presence or absence of Erysiphe alphitoides mildew and further distinguishing between covered or not covered infected leaf tissues, yielding three disease classes including healthy controls, non-mildew covered and mildew-covered. By analysing spectral changes between each disease category per tissue type, we identified important biomolecular interactions including disruption of chlorophyll in the non-vein and venule tissues, pathogen-induced degradation of cellulose and pectin and tree-initiated lignification of cell walls in response, amongst others, in lateral vein and mid-vein tissues. Via our developed computational algorithm, the underlying biomolecular differences between classes were identified and allowed accurate and rapid classification of disease with high accuracy of 69.6% for non-vein, 73.5% for venule, 82.1% for lateral vein and 85.6% for mid-vein tissues. Interfacial wetting differences between non-mildew covered and mildew-covered tissue were further analysed on the surfaces of non-vein and venule tissue. The overall results demonstrated the ability of Raman spectroscopy, combined with advanced AI, to act as a powerful and specific tool to probe foliar interactions between forest pathogens and host trees with the simultaneous potential to probe and catalogue molecular interactions between biological soft matter, paving the way for exploring similar relations in broader forest tree-pathogen systems.

Raman developmental markers in root cell walls are associated with lodging tendency in tef

MAIN CONCLUSION

Using Raman micro-spectroscopy on tef roots, we could monitor cell wall maturation in lines with varied genetic lodging tendency. We describe the developing cell wall composition in root endodermis and cylinder tissue. Tef [Eragrostis tef (Zucc.) Trotter] is an important staple crop in Ethiopia and Eritrea, producing gluten-free and protein-rich grains. However, this crop is not adapted to modern farming practices due to high lodging susceptibility, which prevents the application of mechanical harvest. Lodging describes the displacement of roots (root lodging) or fracture of culms (stem lodging), forcing plants to bend or fall from their vertical position, causing significant yield losses. In this study, we aimed to understand the microstructural properties of crown roots, underlining tef tolerance/susceptibility to lodging. We analyzed plants at 5 and 10 weeks after emergence and compared trellised to lodged plants. Root cross sections from different tef genotypes were characterized by scanning electron microscopy, micro-computed tomography, and Raman micro-spectroscopy. Lodging susceptible genotypes exhibited early tissue maturation, including developed aerenchyma, intensive lignification, and lignin with high levels of crosslinks. A comparison between trellised and lodged plants suggested that lodging itself does not affect the histology of root tissue. Furthermore, cell wall composition along plant maturation was typical to each of the tested genotypes independently of trellising. Our results suggest that it is possible to select lines that exhibit slow maturation of crown roots. Such lines are predicted to show reduction in lodging and facilitate mechanical harvest.

Using Raman spectroscopy for early detection of resistance-breaking strains of tomato spotted wilt orthotospovirus in tomatoes

Tomato spotted wilt (TSW) disease caused by tomato spotted wilt orthotospovirus (TSWV, Orthotospovirus tomatomaculae) poses a significant threat to specialty and staple crops worldwide by causing over a billion dollars in crop losses annually. Current strategies for TSWV diagnosis heavily rely on nucleic acid or protein-based techniques which require significant technical expertise, and are invasive, time-consuming, and expensive, thereby catalyzing the search for better alternatives. In this study, we explored the potential of Raman spectroscopy (RS) in early detection of TSW in a non-invasive and non-destructive manner. Specifically, we investigated whether RS could be used to detect strain specific TSW symptoms associated with four TSWV strains infecting three differentially resistant tomato cultivars. In the acquired spectra, we observed notable reductions in the intensity of vibrational peaks associated with carotenoids. Using high-performance liquid chromatography (HPLC), we confirmed that TSWV caused a substantial decrease in the concentration of lutein that was detected by RS. Finally, we demonstrated that Partial Least Squares-Discriminant Analysis (PLS-DA) could be used to differentiate strain-specific TSW symptoms across all tested cultivars. These results demonstrate that RS can be a promising solution for early diagnosis of TSW, enabling timely disease intervention and thereby mitigating crop losses inflicted by TSWV.

Elucidation of sex from mature Palmer amaranth (Amaranthus palmeri) leaves using a portable Raman spectrometer

Palmer amaranth (Amaranthus palmeri) is a pervasive and troublesome weed species that poses significant challenges to agriculture in the United States. Identifying the sex of Palmer amaranth plants is crucial for developing tailored control measures due to the distinct characteristics and reproductive strategies exhibited by male and female plants. Traditional methods for sex determination are expensive and time-consuming, but recent advancements in spectroscopic techniques offer new possibilities. This study explores the potential of portable Raman spectroscopy for determining the sex of mature Palmer amaranth plants in-field. Raman analysis of the plant leaves reveals spectral differences associated with nitrate salts, lipids, carotenoids, and terpenoids, allowing for high accuracy and reliable identification of the plant's sex; male plants had higher concentrations of these compounds compared to females. It was also found that male plants had higher concentrations of these compounds compared to the females. Raman spectra were analyzed using a machine learning tool, partial least squares discriminant analysis (PLS-DA), to generate accuracies of no less than 83.7% when elucidating sex from acquired spectra. These findings provide insights into the sex-specific characteristics of Palmer amaranth and suggest that Raman analysis, combined with PLS-DA, can be a promising, non-destructive, and efficient method for sex determination in field settings. This approach has implications for developing sex-specific management strategies to monitor and control this invasive weed in real-world environments, benefiting farmers, agronomists, researchers, and master gardeners.

Raman-based diagnostics of drought, heat and light-induced stresses in three different varieties of hemp

MAIN CONCLUSION

Hand-held Raman spectroscopy can be used for highly accurate differentiation between drought, heat and light-triggered stresses in hemp. The differentiation is based on the changes in the biochemistry of plants caused by such stresses. Hemp farming is a rapidly growing industry. This dioecious plant is primarily cultivated for its fibers, seeds, and cannabinoid-rich oils. The yield of these materials can be drastically lowered by many abiotic stresses, such as drought, heat and light. It becomes critically important to develop robust and reliable approaches that can be used to diagnose such abiotic stresses in hemp. In this study, we investigate the accuracy of Raman spectroscopy, an emerging tool within crop monitoring, in the confirmatory identification of drought, heat, and light-induced stresses in three varieties of hemp. Our results showed that mono, double and triple stresses uniquely alter plant biochemistry that results in small spectroscopic changes detected in the Raman spectra acquired from the hemp leaves. These changes could be used for the 80-100% accurate identification of individual abiotic stresses and their combinations in plants. These results demonstrate that a hand-held Raman spectrometer can be used for highly accurate, non-invasive, non-destructive, and label-free diagnostics of hemp stresses directly in the greenhouse or in the field.

Detection and differentiation of herbicide stresses in roses by Raman spectroscopy

Herbicide application is a critical component of modern horticulture. Misuse of herbicides can result in damage to economically important plants. Currently, such damage can be detected only at symptomatic stages by subjective visual inspection of plants, which requires substantial biological expertise. In this study, we investigated the potential of Raman spectroscopy (RS), a modern analytical technique that allows sensing of plant health, for pre-symptomatic diagnostics of herbicide stresses. Using roses as a model plant system, we investigated the extent to which stresses caused by Roundup (Glyphosate) and Weed-B-Gon (2, 4-D, Dicamba and Mecoprop-p (WBG), two of the most commonly used herbicides world-wide, can be diagnosed at pre- and symptomatic stages. We found that spectroscopic analysis of rose leaves enables ~90% accurate detection of Roundup- and WBG-induced stresses one day after application of these herbicides on plants. Our results also show that the accuracy of diagnostics of both herbicides at seven days reaches 100%. Furthermore, we show that RS enables highly accurate differentiation between the stresses induced by Roundup- and WBG. We infer that this sensitivity and specificity arises from the differences in biochemical changes in plants that are induced by both herbicides. These findings suggest that RS can be used for a non-destructive surveillance of plant health to detect and identify herbicide-induced stresses in plants.

Non-invasive identification of combined salinity stress and stalk rot disease caused by Colletotrichum graminicola in maize using Raman spectroscopy

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.

3D (x-y-t) Raman imaging of tomato fruit cuticle: Microchemistry during development

The cuticle is a protective extracellular matrix that covers the above-ground epidermis of land plants. Here, we studied the cuticle of tomato (Solanum lycopersicum L.) fruits in situ using confocal Raman microscopy. Microsections from cuticles isolated at different developmental stages were scanned to visualize cuticle components with a spatial resolution of 342 nm by univariate and multivariate data analysis. Three main components, cutin, polysaccharides, and aromatics, were identified, with the latter exhibiting the strongest Raman scattering intensity. Phenolic acids and flavonoids were differentiated within the cuticle, and three schematic cuticle models were identified during development. Phenolic acids were found across the entire cuticle at the earliest stage of development, i.e. during the formation of the procuticle layer. Based on a mixture analysis with reference component spectra, the phenolic acids were identified as mainly esterified p-coumaric acid together with free p-hydroxybenzoic acid. During the cell expansion period of growth, phenolic acids accumulated in an outermost layer of the cuticle and in the middle region of the pegs. In these stages of development, cellulose and pectin were detected next to the inner cuticle region, close to the epidermal cell where flavonoid impregnation started during ripening. In the first ripening stage, chalconaringenin was observed, while methoxylated chalcones were chosen by the algorithm to fit the mature cuticle spectra. The colocation of carbohydrates, esterified p-coumaric acid, and methoxylated chalconaringenin suggests that the latter two link polysaccharide and cutin domains. Elucidating the different distribution of aromatics within the cuticle, suggests important functions: (1) overall impregnation conferring mechanical and thermal functions (2) the outermost phenolic acid layer displaying UV-B protection of the plant tissue.

Confirmatory detection and identification of biotic and abiotic stresses in wheat using Raman spectroscopy

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.

Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra

The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels-under ripe, ripe, and over ripe-were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets.

Non-Invasive and Confirmatory Differentiation of Hermaphrodite from Both Male and Female Cannabis Plants Using a Hand-Held Raman Spectrometer

Cannabis (Cannabis sativa L.) is a dioecious plant that produces both male and female inflorescences. In nature, male and female plants can be found with nearly equal frequency, which determines species out-crossing. In cannabis farming, only female plants are preferred due to their high yield of cannabinoids. In addition to unfavorable male plants, commercial production of cannabis faces the appearance of hermaphroditic inflorescences, species displaying both pistillate flowers and anthers. Such plants can out-cross female plants, simultaneously producing undesired seeds. The problem of hermaphroditic cannabis triggered a search for analytical tools that can be used for their rapid detection and identification. In this study, we investigate the potential of Raman spectroscopy (RS), an emerging sensing technique that can be used to probe plant biochemistry. Our results show that the biochemistry of male, female and hermaphroditic cannabis plants is drastically different which allows for their confirmatory identification using a hand-held Raman spectrometer. Furthermore, the coupling of machine learning approaches enables the identification of hermaphrodites with 98.7% accuracy, whereas both male and female plants can be identified with 100% accuracy. Considering the label-free, non-invasive and non-destructive nature of RS, the developed optical sensing approach can transform cannabis farming in the U.S. and overseas.

Rapid In Situ Detection of THC and CBD in Cannabis sativa L. by 1064 nm Raman Spectroscopy

The need to find a rapid and worthwhile technique for the in situ detection of the content of delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in Cannabis sativa L. is an ever-increasing problem in the forensic field. Among all the techniques for the detection of cannabinoids, Raman spectroscopy can be identified as the most cost-effective, fast, noninvasive, and nondestructive. In this study, 42 different samples were analyzed using Raman spectroscopy with 1064 nm excitation wavelength. The use of an IR wavelength laser showed the possibility to clearly identify THC and CBD in fresh samples, without any further processing, knocking out the contribution of the fluorescence generated by visible and near-IR sources. The results allow assigning all the Raman features in THC- and CBD-rich natural samples. The multivariate analysis underlines the high reproducibility of the spectra and the possibility to distinguish immediately the Raman spectra of the two cannabinoid species. Furthermore, the ratio between the Raman bands at 1295/1440 and 1623/1663 cm^–1 is identified as an immediate test parameter to evaluate the THC content in the samples.

Raman Spectroscopy Enables Confirmatory Diagnostics of Fusarium Wilt in Asymptomatic Banana

Fusarium oxysporum f. sp. cubense (FOC) causes Fusarium wilt, one of the most concerning diseases in banana (Musa spp.), compromising global banana production. There are limited curative management options after FOC infections, and early Fusarium wilt symptoms are similar with other abiotic stress factors such as drought. Therefore, finding a reliable and timely form of early detection and proper diagnostics is critical for disease management for FOC. In this study, Portable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana. Banana plantlets were inoculated with FOC; uninoculated plants exposed to a drier condition were also prepared compared to well-watered uninoculated control plants. Subsequent Raman readings from the plant leaves, without damaging or destroying them, were performed weekly. The conditions of biotic and abiotic stresses on banana were modeled to examine and identify specific Raman spectra suitable for diagnosing FOC infection. Our results showed that Raman spectroscopy could be used to make highly accurate diagnostics of FOC at the asymptomatic stage. Based on specific Raman spectra at vibrational bands 1,155, 1,184, and 1,525 cm-1, Raman spectroscopy demonstrated nearly 100% accuracy of FOC diagnosis at 40 days after inoculation, differentiating FOC-infected plants from uninoculated plants that were well-watered or exposed to water deficit condition. This study first reported that Raman spectroscopy can be used as a rapid and non-destructive tool for banana Fusarium wilt diagnostics.

Raman Spectroscopy Applications in Grapevine: Metabolic Analysis of Plants Infected by Two Different Viruses

Grapevine is one of the most cultivated fruit plant among economically relevant species in the world. It is vegetatively propagated and can be attacked by more than 80 viruses with possible detrimental effects on crop yield and wine quality. Preventive measures relying on extensive and robust diagnosis are fundamental to guarantee the use of virus-free grapevine plants and to manage its diseases. New phenotyping techniques for non-invasive identification of biochemical changes occurring during virus infection can be used for rapid diagnostic purposes. Here, we have investigated the potential of Raman spectroscopy (RS) to identify the presence of two different viruses, grapevine fan leaf virus (GFLV) and grapevine rupestris stem pitting-associated virus (GRSPaV) in Vitis vinifera cv. Chardonnay. We showed that RS can discriminate healthy plants from those infected by each of the two viruses, even in the absence of visible symptoms, with accuracy up to 100% and 80% for GFLV and GRSPaV, respectively. Chemometric analyses of the Raman spectra followed by chemical measurements showed that RS could probe a decrease in the carotenoid content in infected leaves, more profoundly altered by GFLV infection. Transcriptional analysis of genes involved in the carotenoid pathway confirmed that this biosynthetic process is altered during infection. These results indicate that RS is a cutting-edge alternative for a real-time dynamic monitoring of pathogens in grapevine plants and can be useful for studying the metabolic changes ensuing from plant stresses.

Bio-Herbicidal Potential of Nanoemulsions with Peppermint Oil on Barnyard Grass and Maize

Bio-based nanoemulsions are part of green pest management for sustainable agriculture. This study assessed the physicochemical properties and the herbicidal activities of the peppermint essential oil nanoemulsions (PNs) in concentrations 1.0–10% stabilized by Eco-Polysorbate 80 on germinating seeds and young plants of maize and barnyard grass. Based on the design of experiment (DOE) results, the final nanoemulsion formulations were obtained with 1, 1.5, 2, and 5% of essential oil concentration. Biological analyses were conducted to select the most promising sample for selective control of barnyard grass in maize. Seedlings growing in the presence of PNs displayed an overall inhibition of metabolism, as expressed by the calorimetric analyses, which could result from significant differences in both content and composition of carbohydrates. Concentration–response sub estimation showed that leaf-sprayed concentration of PN causing 10% of maize damage is equal to 2.2%, whereas doses causing 50% and 90% of barnyard grass damage are 1.1% and 1.7%, respectively. Plants sprayed with PN at 5% or 10% concentration caused significant drops in relative water content in leaves and Chlorophyll a fluorescence 72 h after spraying. In summary, peppermint nanoemulsion with Eco-Polysorbate 80 at 2% concentration is a perspective preparation for selective control of barnyard grass in maize. It should be analyzed further in controlled and field conditions.

Raman Spectroscopy Enables Non-invasive and Confirmatory Diagnostics of Aluminum and Iron Toxicities in Rice

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.

Raman spectroscopy enables highly accurate differentiation between young male and female hemp plants

Hand-held Raman spectroscopy can be used for highly accurate differentiation between young male and female hemp plants. This differentiation is based on significantly different concentration of lutein in these plants. Last year, a global market of only industrial hemp attained the value of USD 4.7 billion. It is by far the fastest growing market with projected growth of 22.5% between 2021 and 2026. Hemp (Cannabis sativa L.) is a dioecious species that has separate male and female plants. In hemp farming, female plants are strongly preferred because male plants do not produce sufficient amount of cannabinoids. Male plants are also eliminated to minimize a possibility of uncontrolled cross-fertilization of plants. Silver treatments can induce development of male flowers on genetically female plants in order to produce feminized seed. Resulting cannabinoid hemp production fields should contain 100% female plants. However, any unintended pollination from male plants can produce unwanted males in production fields. Therefore, there is a growing demand for a label-free, non-invasive, and confirmatory approach that can be used to differentiate between male and female plants before flowering. In this study, we examined the extent to which Raman spectroscopy, an emerging optical technique, can be used for the accurate differentiation between young male and female hemp plants. Our findings show that Raman spectroscopy enables differentiation between male and female plants with 90% and 94% accuracy on the level of young and mature plants, respectively. Such analysis is entirely non-invasive and non-destructive to plants and can be performed in seconds using a hand-held spectrometer. High-performance liquid chromatography (HPLC) analysis and collected Raman spectra demonstrate that this spectroscopic differentiation is based on significantly different concentrations of carotenoids in male vs female plants. These findings open up a new avenue for quality control of plants grown in both field and a greenhouse.

A Proof-of-Principle Study of Non-invasive Identification of Peanut Genotypes and Nematode Resistance Using Raman Spectroscopy

Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.

Raman Spectroscopy Detects Changes in Carotenoids on the Surface of Watermelon Fruits During Maturation

A non-invasive and non-destructive technique, Raman spectroscopy, was explored to distinguish different maturity stages (20, 30, 40, and 50 days after anthesis) of watermelon (Citrullus lanatus) fruits from four cultivars: Fascination, Orange Crisp, Amarillo and Crimson Sweet. Spectral acquisition from the fruit surface was carried out at the wavelength range of 400-2,000 cm^-1 using a handheld Raman spectrometer equipped with 830 nm laser excitation source. The spectra were normalized at 1,438 cm^-1 which was assigned to CH₂ and CH₃ vibration. Detecting changes in the spectral features of carotenoids on the surface of watermelon fruits can be used as a marker to monitor the maturity of the fruit. The spectral analysis confirmed the presence of two major carotenoids, lutein and β-carotene, and their intensity decreased upon maturity on the fruit surface. Identification of these pigments was further confirmed by resonance Raman spectra and high-performance liquid chromatography analysis. Results of partial least square discriminant analysis of pre-processed spectra have demonstrated that the method can successfully predict the maturity of watermelon samples with more than 85% accuracy. Analysis of Variance of individual Raman bands has revealed a significant difference among the stages as the level of carotenoids was declined during the ripening of the fruits. Thus, Raman spectral signatures can be used as a versatile tool for the non-invasive determination of carotenoid changes on the watermelon fruits' surface during ripening, thereby enabling effective monitoring of nutritional quality and maturity indices before harvesting the watermelon.

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-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.

Raman spectroscopy-based diagnostics of water deficit and salinity stresses in two accessions of peanut

Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)-based imaging and satellite-based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses on two greenhouse-grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240-mM salt water per day for the duration of 5-day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand-held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman-based surveillance in commercial-scale agriculture and digital farming.

Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition

Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

Non-glandular trichomes of sunflower are important in the absorption and translocation of foliar-applied Zn

Trichomes are potentially important for absorption of foliar fertilizers. A study has shown that the non-glandular trichromes (NGTs) of sunflower (Helianthus annuus) accumulated high concentrations of foliar-applied zinc (Zn); however, the mechanisms of Zn accumulation in the NGTs and the fate of this Zn are unclear. Here we investigated how foliar-applied Zn accumulates in the NGTs and the subsequent translocation of this Zn. Time-resolved synchrotron-based X-ray fluorescence microscopy and transcriptional analyses were used to probe the movement of Zn in the NGTs, with the cuticle composition of the NGTs examined using confocal Raman microscopy. The accumulation of Zn in the NGTs is both an initial preferential absorption process and a subsequent translocation process. This preferred absorption is likely because the NGT base has a higher hydrophilicity, whilst the subsequent translocation is due to the presence of plasmodesmata, Zn-chelating ligands, and Zn transporters in the NGTs. Furthermore, the Zn sequestered in the NGTs was eventually translocated out of the trichome once the leaf Zn concentration had decreased, suggesting that the NGTs are also important in maintaining leaf Zn homeostasis. This study demonstrates for the first time that trichomes have a key structural and functional role in the absorption and translocation of foliar-applied Zn.

Non-Invasive Identification of Nutrient Components in Grain

Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.

Nutraceutical properties of tamarillo fruits: A vibrational study

Tamarillo (tree tomato) is a subtropical tree that produces edible fruits with health-beneficial properties, since it contains several components with potential therapeutic and chemoprotective activity. The present work reports the first complete vibrational study (Raman and infrared) of this edible fruit. This approach allowed us to determine where the most relevant nutraceutical compounds are located in the fruit, as well as their relative amounts. Particular vibrational signatures were obtained for each part of the fruit, reporting different components for the epicarp (outer and inner sections), the pulp and the seeds, especially regarding the content in phenolic compounds, unsaturated fatty acids/esters and polymeric chains from the cuticular wax. Valuable information, at the molecular level was gathered regarding the nutricional value of tamarillo's fruit, for its different fractions. This is expected to pave the way for its introduction as a promising nutraceutical, based on the potential therapeutic properties of its main components.

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.

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.

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.

Poison ivy (Toxicodendron radicans) is a noxious weed that grows throughout North America and induces terrible rashes on contact. Using a portable Raman device, we identified these plants and differentiated them from other species with high accuracy.

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.

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.

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

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 (Sulč), 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.

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.

Non-Invasive Characterization of Single-, Double- and Triple-Viral Diseases of Wheat With a Hand-Held Raman Spectrometer

Plant diseases can reduce crop yield by up to 100%. Therefore, timely and confirmatory diagnosis of plant diseases is strongly desired. Typical pathogen assaying methods include polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). These approaches are quite useful but are also time-consuming and destructive to the sample. Raman spectroscopy (RS) is a modern analytical technique that enables non-invasive plant disease detection. In this study, we report on Raman-based detection of wheat diseases caused by wheat streak mosaic virus (WSMV) and barley yellow dwarf virus (BYDV). Our results show that RS can be used to differentiate between healthy wheat and wheat infected by these two viruses. We also show that RS can be used to identify whether wheat is infected by these individual viruses or by a combination of WSMV and BYDV, as well as WSMV, BYDV, and Triticum mosaic virus (TriMV). We found that wheat spectra showed non-linear spectroscopic responses to coinfection by different viruses. These results suggest that RS can be used to probe pathogen-specific changes in plant metabolism. The portable nature of this approach opens the possibility of RS directly in the field for confirmatory diagnostics of viral diseases.

Structure determination of amyrin isomers in cuticular waxes: a combined DFT/vibrational spectroscopy methodology

We present a new methodology for the structural characterization of amyrins, a class of triterpenoids found within the fruit and leaf cuticles of higher plants. Two amyrin isomers (α and β) have been studied taking into consideration a hydrophobic molecular scenario that mimics the cuticle matrix. DFT calculations have been employed in combination with experimental data from Raman vibrational spectroscopy and X-ray diffraction.

A new methodology for the structural characterization of amyrins based on DFT calculations and Raman vibrational spectroscopy.

Raman spectroscopy as an early detection tool for rose rosette infection

Hand-held Raman spectroscopy is a potential tool for a confirmatory, non-invasive, and non-destructive detection and identification of rose rosette disease. Using this spectroscopic approach, structural changes in roses that are associated with this viral infection can be revealed. The commercial rose shrub industry in the United States is one of the largest of its kind. All commercial rose varieties are susceptible to rose rosette disease (RRD), a deadly viral disease vectored by eriophyid mites. This disease is typically diagnosed visually and/or by PCR-based detection assays. The present work demonstrates that Raman spectroscopy can detect RRD in intact leaf tissue. It is shown that chemometric analysis can distinguish between spectra collected from symptomatic and asymptomatic tissue, as well as between healthy and asymptomatic tissue. This method will be useful as an initial screen for RRD prior to PCR analysis to help conserve reagents and save time.

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.

Waterproofing in Arabidopsis: Following Phenolics and Lipids In situ by Confocal Raman Microscopy

Waterproofing of the aerial organs of plants imposed a big evolutionary step during the colonization of the terrestrial environment. The main plant polymers responsible of water repelling are lipids and lignin, which play also important roles in the protection against biotic/abiotic stresses, regulation of flux of gases and solutes, and mechanical stability against negative pressure, among others. While the lipids, non-polymerized cuticular waxes together with the polymerized cutin, protect the outer surface, lignin is confined to the secondary cell wall within mechanical important tissues. In the present work a micro cross-section of the stem of Arabidopsis thaliana was used to track in situ the distribution of these non-carbohydrate polymers by Confocal Raman Microscopy. Raman hyperspectral imaging gives a molecular fingerprint of the native waterproofing tissues and cells with diffraction limited spatial resolution (~300 nm) at relatively high speed and without any tedious sample preparation. Lipids and lignified tissues as well as their effect on water content was directly visualized by integrating the 1299, 1600, and 3400 cm(-1) band, respectively. For detailed insights into compositional changes of these polymers vertex component analysis was performed on selected sample positions. Changes have been elucidated in the composition of lignin within the lignified tissues and between interfascicular fibers and xylem vessels. Hydrophobizing changes were revealed from the epidermal layer to the cuticle as well as a change in the aromatic composition within the cuticle of trichomes. To verify Raman signatures of different waterproofing polymers additionally Raman spectra of the cuticle and cutin monomer from tomato (Solanum lycopersicum) as well as aromatic model polymers (milled wood lignin and dehydrogenation polymer of coniferyl alcohol) and phenolic acids were acquired.

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

A focus on the biosynthesis and composition of cuticle in fruits

Cuticles are plant structures, composed mostly by lipidic layers, synthesized by nonwoody aerial plant organs and deposited on the surface of outer epidermal cell walls. Although its significance has been often disregarded, cuticle deposition modifies organ chemistry, influences mechanical properties, and plays a central role in sensing and interacting with the surrounding environment. Even though some research has been undertaken addressing cuticle biosynthesis and composition in vegetative plant tissues, comparatively less information is available regarding cuticle composition in the epidermis of fruits. However, recent work points to a role for cuticles in the modulation of fruit quality and postharvest performance, indicating that current models for the investigation of fruit development, metabolism, and quality need to integrate a comprehensive knowledge of the cuticle layer. This paper provides an overview of recent findings and observations regarding cuticle biosynthesis and composition in fruits from species of agronomic and economic relevance. Important, but often neglected differences in cuticle composition and biosynthesis patterns among diverse fruit species are described herein to generate an atlas of what is currently known about fruit cuticles and to highlight what remains to be explored. Emphasis is placed on the need to investigate each genetic background considering its own specificities, to permit correlations with the particular physiology of each species considered. Both specific composition and changes during maturation and ripening are reviewed.

Triterpene acids from apple peel inhibit lepidopteran larval midgut lipases and larval growth

Fruit extracts from apple, kiwifruit, feijoa, boysenberry, and blueberry were screened for the presence of lipase inhibitory compounds against lepidopteran larval midgut crude extracts. From 120 extracts, six showed significant inhibition with an extract from the peel of Malus × domestica cv. "Big Red" showing highest levels of inhibition. Because this sample was the only apple peel sample in the initial screen, a survey of peels from seven apple cultivars was undertaken and showed that, despite considerable variation, all had inhibitory activity. Successive solvent fractionation and LC-MS of cv. "Big Red" apple peel extract identified triterpene acids as the most important inhibitory compounds, of which ursolic acid and oleanolic acid were the major components and oxo- and hydroxyl-triterpene acids were minor components. When ursolic acid was incorporated into artificial diet and fed to Epiphyas postvittana Walker (Tortricidae: Lepidoptera) larvae at 0.16% w/v, a significant decrease in larval weight was observed after 21 days. This concentration of ursolic acid is less than half the concentration reported in the skin of some apple cultivars.

Infrared and Raman spectroscopic features of plant cuticles: a review

The cuticle is one of the most important plant barriers. It is an external and continuous lipid membrane that covers the surface of epidermal cells and whose main function is to prevent the massive loss of water. The spectroscopic characterization of the plant cuticle and its components (cutin, cutan, waxes, polysaccharides and phenolics) by infrared and Raman spectroscopies has provided significant advances in the knowledge of the functional groups present in the cuticular matrix and on their structural role, interaction and macromolecular arrangement. Additionally, these spectroscopies have been used in the study of cuticle interaction with exogenous molecules, degradation, distribution of components within the cuticle matrix, changes during growth and development and characterization of fossil plants.

Use of portable devices and confocal Raman spectrometers at different wavelength to obtain the spectral information of the main organic components in tomato (Solanum lycopersicum) fruits

Tomato (Solanum lycopersicum) fruit samples, in two ripening stages, ripe (red) and unripe (green), collected from a cultivar in the North of Spain (Barrika, Basque Country), were analyzed directly, without any sample pretreatment, with two different Raman instruments (portable spectrometer coupled to a micro-videocamera and a confocal Raman microscope), using two different laser excitation wavelengths (514 and 785 nm, only for the confocal microscope). The combined use of these laser excitation wavelengths allows obtaining, in a short period of time, the maximum spectral information about the main organic compounds present in this fruit. The major identified components of unripe tomatoes were cutin and cuticular waxes. On the other hand, the main components on ripe tomatoes were carotenes, polyphenoles and polysaccharides. Among the carotenes, it was possible to distinguish the presence of lycopene from β-carotene with the help of both excitation wavelengths, but specially using the 514 nm one, which revealed specific overtones and combination tones of this type of carotene.

A model-free, fully automated baseline-removal method for Raman spectra

We present here a fully automated spectral baseline-removal procedure. The method uses a large-window moving average to estimate the baseline; thus, it is a model-free approach with a peak-stripping method to remove spectral peaks. After processing, the baseline-corrected spectrum should yield a flat baseline and this endpoint can be verified with the χ(2)-statistic. The approach provides for multiple passes or iterations, based on a given χ(2)-statistic for convergence. If the baseline is acceptably flat given the χ(2)-statistic after the first pass at correction, the problem is solved. If not, the non-flat baseline (i.e., after the first effort or first pass at correction) should provide an indication of where the first pass caused too much or too little baseline to be subtracted. The second pass thus permits one to compensate for the errors incurred on the first pass. Thus, one can use a very large window so as to avoid affecting spectral peaks--even if the window is so large that the baseline is inaccurately removed--because baseline-correction errors can be assessed and compensated for on subsequent passes. We start with the largest possible window and gradually reduce it until acceptable baseline correction based on the χ(2) statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.

Fuzzy approach for identifying artistic pigments with Raman spectroscopy

In this work, a fuzzy approach for automatically identifying artistic pigments from their Raman spectra is presented. The uncertainty introduced during the Raman spectrum measurement of pigments is considered in the design of the fuzzy system. The position of the Raman bands in the unknown spectrum can be subject to small displacements due to noise, misalignments in the calibration, etc. Fuzzy logic allows us to work with this uncertainty and to design a system based on the comparison between the Raman band positions in an unknown spectrum recorded from an artwork and the Raman band positions in spectra recorded from reference pigments gathered in databases. The fuzzy system provides the reference pigments whose Raman band positions match those of the unknown pigment analyzed and gives guidance to the decision-making process in the final identification.