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

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

Unmasking Spatial Heterogeneity in Phytotoxicology Mechanisms Induced by Carbamazepine by Mass Spectrometry Imaging and Multiomics Analyses

Previous studies have highlighted the toxicity of pharmaceuticals and personal care products (PPCPs) in plants, yet understanding their spatial distribution within plant tissues and specific toxic effects remains limited. This study investigates the spatial-specific toxic effects of carbamazepine (CBZ), a prevalent PPCP, in plants. Utilizing desorption electrospray ionization mass spectrometry imaging (DESI-MSI), CBZ and its transformation products were observed predominantly at the leaf edges, with 2.3-fold higher concentrations than inner regions, which was confirmed by LC-MS. Transcriptomic and metabolic analyses revealed significant differences in gene expression and metabolite levels between the inner and outer leaf regions, emphasizing the spatial location's role in CBZ response. Notably, photosynthesis-related genes were markedly downregulated, and photosynthetic efficiency was reduced at leaf edges. Additionally, elevated oxidative stress at leaf edges was indicated by higher antioxidant enzyme activity, cell membrane impairment, and increased free fatty acids. Given the increased oxidative stress at the leaf margins, the study suggests using in situ Raman spectroscopy for early detection of CBZ-induced damage by monitoring reactive oxygen species levels. These findings provide crucial insights into the spatial toxicological mechanisms of CBZ in plants, forming a basis for future spatial toxicology research of PPCPs.

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.

First Chemical Profile Analysis of Acacia Pods

This study intended to evaluate the potential industrial applications of various Acacia species (Acacia melanoxylon, Acacia longifolia, Acacia cyclops, Acacia retinodes, Acacia pycnantha, Acacia mearnsii, and Acacia dealbata) by examining their chemical composition, antioxidant, and antimicrobial properties. Using high-resolution mass spectrometry, a comprehensive analysis successfully identified targeted compounds, including flavonoids (flavonols/flavones) and phenolic acids, such as 4-hydroxybenzoic acid, p-coumaric acid, and ellagic acid. Additionally, p-coumaric acid was specifically identified and quantified within the hydroxycinnamic aldehydes. This comprehensive characterization provides valuable insights into the chemical profiles of the studied species. Among the studied species, A. pycnantha exhibited a higher concentration of total phenolic compounds, including catechin, myricetin, quercetin, and coniferaldehyde. Furthermore, A. pycnantha displayed notable antibacterial activity against K. pneumoniae, E. coli, S. Typhimurium, and B. cereus. The identified compounds in Acacia pods and their shown antibacterial activities exhibit promising potential for future applications. Moreover, vibrational spectroscopy was a reliable method for distinguishing between species. These significant findings enhance our understanding of Acacia species and their potential for various industrial applications.

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.

Proximal hyperspectral sensing of abiotic stresses in plants

Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.

Differentiation of advanced generation mutant wheat lines: Conventional techniques versus Raman spectroscopy

This research aimed to assess the feasibility of utilizing Raman spectroscopy in plant breeding programs. For this purpose, the evaluation of the mutant populations set up the application of 4 mM NaN₃ to the somatic embryos obtained from mature wheat (Triticum aestivum L. Adana-99 cv.) embryos. Advanced wheat mutant lines, which were brought up to the seventh generation with salt stress tolerance by following in vitro and in vivo environments constructed by mutated populations, were evaluated using conventional techniques [measurement of antioxidant enzyme activities (SOD, CAT, and POX), total chlorophyll, TBARS, and proline contents; measurement of the concentration of Na⁺ and K⁺ ions; and evaluation of gene expression by qPCR (TaHKT2;1, TaHKT1;5, TaSOS1, TaNa⁺/H⁺ vacuolar antiporter, TaV-PPase, TaV-ATPase, and TaP5CS)] and Raman spectroscopy. In this research, no significant difference was found in the increase of SOD, CAT, and POX antioxidant enzyme activities between the salt-treated and untreated experimental groups of the commercial cultivar, while there was a statistically significant increase in salt-treated advanced generation mutant lines as compared to control and the salt-treated commercial cultivar. Proline showed a statistically significant increase in all experimental groups compared to the untreated commercial cultivar. The degradation in the amount of chlorophyll was lower in the salt-treated advanced generation mutant lines than in the salt-treated commercial cultivar. According to gene expression studies, there were statistical differences at various levels in terms of Na⁺ and/or K⁺ uptake from soil to plant (TaHKT2;1, TaHKT1;5, and TaSOS1), and Na⁺ compartmentalizes into the cell vacuole (TaNa⁺/H⁺ vacuolar antiporter, Ta vacuolar pyrophosphatase, and Ta vacuolar H⁺-ATPase). The expression activity of TaP5CS, which is responsible for the transcription of proline, is similar to the content of proline in the current study. As a result of Raman spectroscopy, the differences in peaks represent the protein-related bands in mutant lines having a general decreasing trend in intensity when compared to the commercial cultivar. Amide-I (1,630 and 1,668 cm^-1), Histidine, Lysine, Arginine, and Leucine bands (823, 849, 1,241, 1,443, and 1,582 cm^-1) showed decreasing wavenumbers. Beta-carotene peaks at 1,153 and 1,519 cm^-1 showed increasing trends when the normalized Raman intensities of the mutant lines were compared.

Good vibrations: Raman spectroscopy enables insights into plant biochemical composition

Non-invasive techniques are needed to enable an integrated understanding of plant metabolic responses to environmental stresses. Raman spectroscopy is one such technique, allowing non-destructive chemical characterisation of samples in situ and in vivo and resolving the chemical composition of plant material at scales from microns to metres. Here, we review Raman band assignments of pigments, structural and non-structural carbohydrates, lipids, proteins and secondary metabolites in plant material and consider opportunities this technology raises for studies in vascular plant physiology.

Non-destructive insights into photosynthetic and photoprotective mechanisms in Arabidopsis thaliana grown under two light regimes

Probing insights into understanding photosynthetic processes via non-invasive means has an added advantage when used in phenotyping or precision agriculture. We employed Raman spectroscopy and fluorescence-based methods to investigate both the changes in the photosynthetic processes and the underlying protective mechanisms on Arabidopsis thaliana wild-type (WT), and ros1, which is a mutant of a repressor of transcriptional gene silencing, both grown under low light (LL: 100 μmol m^-2s^-1) and high light (HL: 400 μmol m^-2s^-1) regimes. Raman imaging detected a lower carotenoid intensity after two weeks in those plants grown under HL, compared to those grown under the LL regime; we interpret this as the result of oxidative damage of β-carotene molecules. Further, the data revealed a significant depletion in carotenoids with enhanced phenolics around the midrib and tip of the WT leaves, but not in the ros1. On the contrary, small necrotic zones appeared after two weeks of HL in the ros1 mutant, pointing to the starting oxidative damage. The lower maximum quantum yield of the photochemistry (F_v/F_m) in the WT as well as in the ros1 mutant grown in HL (compared to those in the LL two weeks post-exposure), indicates the HL partially inactivated photosystems. Chlorophyll a fluorescence imaging further showed high non-photochemical quenching (NPQ) in the plants grown under the HL regime for both the WT and the ros1 mutant, but the spatial heterogeneity of NPQ images was much higher in the HL-grown ros1 mutant. Fluorescence screening methods revealed significantly high values of chlorophyll proxies in the WT as well as in the ros1 mutant two weeks after in the HL compared to those under LL. The data generally revealed an increased accumulation of phenolics under HL in both the WT and ros1 mutant plants, but the proxies of anthocyanin and flavonols were significantly lower in the ros1 mutant than in the WT. The comparatively low accumulation of anthocyanin in the ros1 mutant compared to the WT supports the Raman data. We conclude that integrated use of these techniques can be efficiently applied for a better understanding of insights into photosynthetic mechanisms.

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.

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

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 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-based identification of tick species (Ixodidae) by spectroscopic analysis of their feces

Ticks are blood-feeding parasites that vector a large number of pathogens of medical and veterinary importance. There are strong connections between tick and pathogen species. Timely detection of certain tick species on cattle can cease the spread of numerous devastating diseases such as Bovine babiesiosis and anaplasmosis. Detection of ticks is currently performed by slow and laborious scout-based inspection of cattle. In this study, we investigated the possibility of identification of tick species (Ixodidae) based on spectroscopic signatures of their feces. We collected Raman spectra from individual grains of feces of seven different species of ticks. Our results show that Raman spectroscopy (RS) allows for highly accurate (above 90%) differentiation between tick species. Furthermore, RS can be used to predict the tick developmental stage and differentiate between nymphs, meta-nymphs and adult ticks. We have also demonstrated that diagnostics of tick species present on cattle can be achieved using a hand-held Raman spectrometer. These findings show that RS can be used for non-invasive, non-destructive and confirmatory on-site analysis of tick species present on cattle.

Sensor-based phenotyping of above-ground plant-pathogen interactions

Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.

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.

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

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.

Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming

A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The power of RS in such cases to a large extent depends on chemometric analyses of spectra. In this work, we critically discuss three major approaches that can be used for advanced analyses of spectroscopic data: summary statistics, statistical testing and chemometric classification. On the example of Raman spectra collected from roses, we demonstrate the outcomes and the potential of all three types of spectral analyses. We anticipate that our findings will help to design the most optimal spectral processing and preprocessing that is required to achieved the desired results. We also expect that reported collection of results will be useful to all researchers who work on spectroscopic analyses of plant specimens.

Insight into Details of the Photosynthetic Light Reactions and Selected Metabolic Changes in Tomato Seedlings Growing under Various Light Spectra

The objective of our study was to characterise the growth of tomato seedlings under various light spectra, but special attention has been paid to gaining a deeper insight into the details of photosynthetic light reactions. The following light combinations (generated by LEDs, constant light intensity at 300 μmol m⁻² s⁻¹) were used: blue/red light; blue/red light + far red; blue/red light + UV; white light that was supplemented with green, and white light that was supplemented with blue. Moreover, two combinations of white light for which the light intensity was changed by imitating the sunrise, sunset, and moon were also tested. The reference point was also light generated by high pressure sodium lamps (HPS). Plant growth/morphological parameters under various light conditions were only partly correlated with the photosynthetic efficiency of PSI and PSII. Illumination with blue/red as the main components had a negative effect on the functioning of PSII compared to the white light and HPS-generated light. On the other hand, the functioning of PSI was especially negatively affected under the blue/red light that was supplemented with FR. The FT-Raman studies showed that the general metabolic profile of the leaves (especially proteins and β-carotene) was similar in the plants that were grown under the HPS and under the LED-generated white light for which the light intensity changed during a day. The effect of various light conditions on the leaf hormonal balance (auxins, brassinosteroids) is also discussed.

Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy

We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.

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.

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.