Portable Raman leaf-clip sensor for rapid detection of plant stress

Terahertz Spectra of Mannitol and Erythritol: A Joint Experimental and Computational Study

Sugar substitutes, which generally refer to a class of food additives, mostly have vibration frequencies within the terahertz (THz) band. Therefore, THz technology can be used to analyze their molecular properties. To understand the characteristics of sugar substitutes, this study selected mannitol and erythritol as representatives. Firstly, PXRD and Raman techniques were used to determine the crystal structure and purity of mannitol and erythritol. Then, the THz time-domain spectroscopy (THz-TDS) system was employed to measure the spectral properties of the two sugar substitutes. Additionally, density functional theory (DFT) was utilized to simulate the crystal configurations of mannitol and erythritol. The experimental results showed good agreement with the simulation results. Finally, microfluidic chip technology was used to measure the THz spectroscopic properties of the two sugar substitutes in solution. A comparison was made between their solid state and aqueous solution state, revealing a strong correlation between the THz spectra of the two sugar substitutes in both states. Additionally, it was found that the THz spectrum of a substance in solution is related to its concentration. This study provides a reference for the analysis of sugar substitutes.

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.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (F e S O 4 ), zinc (II) sulphate (Z n S O 4 ), and copper (II) sulphate (C u S O 4 ), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Identification and quantification of pigments in plant leaves using thin layer chromatography-Raman spectroscopy (TLC-Raman)

Carotenoids are yellow, orange, and red pigments commonly found in plants. In leaves, these molecules are essential for photosynthesis, but they also play a major role in plant growth and development. Efficiently monitoring concentrations of specific carotenoids in plant tissues could help to explain plant responses to environmental stressors, infection and disease, fertilization, and other conditions. Previously, Raman methods have been used to demonstrate a correlation between plant fitness and the carotenoid content of leaves. Due to solvatochromatic effects and structural similarities within the carotenoid family, current Raman spectroscopy techniques struggle to assign signals to specific carotenoids with certainty, complicating the determination of amounts of individual carotenoids present in a sample. In this work, we use thin layer chromatography-Raman spectroscopy, or TLC-Raman, to identify and quantify carotenoids extracted from tomato leaves. These quick and accurate methods could be applied to study the relationship between pigment content and a number of factors affecting plant health.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

Next-generation methods for early disease detection in crops

Plant pathogens are commonly identified in the field by the typical disease symptoms that they can cause. The efficient early detection and identification of pathogens are essential procedures to adopt effective management practices that reduce or prevent their spread in order to mitigate the negative impacts of the disease. In this review, the traditional and innovative methods for early detection of the plant pathogens highlighting their major advantages and limitations are presented and discussed. Traditional techniques of diagnosis used for plant pathogen identification are focused typically on the DNA, RNA (when molecular methods), and proteins or peptides (when serological methods) of the pathogens. Serological methods based on mainly enzyme-linked immunosorbent assay (ELISA) are the most common method used for pathogen detection due to their high-throughput potential and low cost. This technique is not particularly reliable and sufficiently sensitive for many pathogens detection during the asymptomatic stage of infection. For non-cultivable pathogens in the laboratory, nucleic acid-based technology is the best choice for consistent pathogen detection or identification. Lateral flow systems are innovative tools that allow fast and accurate results even in field conditions, but they have sensitivity issues to be overcome. PCR assays performed on last-generation portable thermocyclers may provide rapid detection results in situ. The advent of portable instruments can speed pathogen detection, reduce commercial costs, and potentially revolutionize plant pathology. This review provides information on current methodologies and procedures for the effective detection of different plant pathogens. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Identifying cadmium and lead co-accumulation from living rice blade spectrum

Neither cadmium (Cd) nor lead (Pb) is necessary for crop growth, but they both can accumulate in soil and crop tissues, resulting in land degradation and crop reduction. Few researchers have explored how to detect Cd-Pb co-accumulation in leaves using proximal sensing techniques, especially by low-cost, easy-to-use leaf clips that capture hyperspectral reflections at suitable foliar positions. In this study, a hyperspectral imager was employed to collect images of the rice canopy from a designed greenhouse experiment that included 16 pretreatments of Cd-Pb co-accumulation, followed by spectral extractions from 3 foliar positions: the blade root, the middle of the leaf, and the leaf apex. A support vector machine with leave-one-out cross-validation was performed to diagnose the contaminative levels based on the feature wavelengths selected by an improved successive projection algorithm. Partial least squares regression was used to predict Cd-Pb concentrations in rice blades. The results indicated that diagnostic accuracies were varied using spectra of different foliar positions. The blade root and leaf apex of rice blades were the optimal foliar position for detecting Cd and Pb contamination, respectively. At the optimal foliar positions, diagnostic accuracies exceeded 0.80 for distinguishing whether the rice is subject to Cd-Pb contamination. The Cd prediction performed 'very good' with a residual prediction deviation (RPD) of 2.21, a R2 of 0.79, and a root mean square error (RMSE)of 6.14, while that of Pb was 1.62, 0.61, and 186.54. Important wavelengths were identified at 659-694 nm and 667-694 nm to detect Cd and Pb contamination. In summary, our results verified the feasibility and clarified the optimal foliar positions of rice blades to detect Cd-Pb contamination. The wavelengths selecting have the great potential in the design of future leaf clips, and the optimal foliar position can provide suggestions to improve diagnostic performances in field 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.

Raman spectroscopy in crop quality assessment: focusing on sensing secondary metabolites: a review

As a crop quality sensor, Raman spectroscopy has been consistently proposed as one of the most promising and non-destructive methods for qualitative and quantitative analysis of plant substances, because it can measure molecular structures in a short time without requiring pretreatment along with simple usage. The sensitivity of the Raman spectrum to target chemicals depends largely on the wavelength, intensity of the laser power, and exposure time. Especially for plant samples, it is very likely that the peak of the target material is covered by strong fluorescence effects. Therefore, methods using lasers with low energy causing less fluorescence, such as 785 nm or near-infrared, are vigorously discussed. Furthermore, advanced techniques for obtaining more sensitive and clear spectra, like surface-enhanced Raman spectroscopy, time-gated Raman spectroscopy or combination with thin-layer chromatography, are being investigated. Numerous interpretations of plant quality can be represented not only by the measurement conditions but also by the spectral analysis methods. Up to date, there have been attempted to optimize and generalize analysis methods. This review summarizes the state of the art of micro-Raman spectroscopy in crop quality assessment focusing on secondary metabolites, from in vitro to in vivo and even in situ, and suggests future research to achieve universal application.

Integrating biopolymer design with physical unclonable functions for anticounterfeiting and product traceability in agriculture

Smallholder farmers and manufacturers in the Agri-Food sector face substantial challenges because of increasing circulation of counterfeit products (e.g., seeds), for which current countermeasures are implemented mainly at the secondary packaging level, and are generally vulnerable because of limited security guarantees. Here, by integrating biopolymer design with physical unclonable functions (PUFs), we propose a cryptographic protocol for seed authentication using biodegradable and miniaturized PUF tags made of silk microparticles. By simply drop casting a mixture of variant silk microparticles on a seed surface, tamper-evident PUF tags can be seamlessly fabricated on a variety of seeds, where the unclonability comes from the stochastic assembly of spectrally and visually distinct silk microparticles in the tag. Unique, reproducible, and unpredictable PUF codes are generated from both Raman mapping and microscopy imaging of the silk tags. Together, the proposed technology offers a highly secure solution for anticounterfeiting and product traceability in agriculture.

In vivo surface-enhanced Raman scattering nanosensor for the real-time monitoring of multiple stress signalling molecules in plants

When under stress, plants release molecules to activate their defense system. Detecting these stress-related molecules offers the possibility to address stress conditions and prevent the development of diseases. However, detecting endogenous signalling molecules in living plants remains challenging due to low concentrations of these analytes and interference with other compounds; additionally, many methods currently used are invasive and labour-intensive. Here we show a non-destructive surface-enhanced Raman scattering (SERS)-based nanoprobe for the real-time detection of multiple stress-related endogenous molecules in living plants. The nanoprobe, which is placed in the intercellular space, is optically active in the near-infrared region (785 nm) to avoid interferences from plant autofluorescence. It consists of a Si nanosphere surrounded by a corrugated Ag shell modified by a water-soluble cationic polymer poly(diallyldimethylammonium chloride), which can interact with multiple plant signalling molecules. We measure a SERS enhancement factor of 2.9 × 10⁷ and a signal-to-noise ratio of up to 64 with an acquisition time of ~100 ms. To show quantitative multiplex detection, we adopted a binding model to interpret the SERS intensities of two different analytes bound to the SERS hot spot of the nanoprobe. Under either abiotic or biotic stress, our optical nanosensors can successfully monitor salicylic acid, extracellular adenosine triphosphate, cruciferous phytoalexin and glutathione in Nasturtium officinale, Triticum aestivum L. and Hordeum vulgare L.-all stress-related molecules indicating the possible onset of a plant disease. We believe that plasmonic nanosensor platforms can enable the early diagnosis of stress, contributing to a timely disease management of plants.

Optical Sensing Technologies to Elucidate the Interplay between Plant and Microbes

Plant-microbe interactions are critical for ecosystem functioning and driving rhizosphere processes. To fully understand the communication pathways between plants and rhizosphere microbes, it is crucial to measure the numerous processes that occur in the plant and the rhizosphere. The present review first provides an overview of how plants interact with their surrounding microbial communities, and in turn, are affected by them. Next, different optical biosensing technologies that elucidate the plant-microbe interactions and provide pathogenic detection are summarized. Currently, most of the biosensors used for detecting plant parameters or microbial communities in soil are centered around genetically encoded optical and electrochemical biosensors that are often not suitable for field applications. Such sensors require substantial effort and cost to develop and have their limitations. With a particular focus on the detection of root exudates and phytohormones under biotic and abiotic stress conditions, novel low-cost and in-situ biosensors must become available to plant scientists.

Multiscale simulation and experimental measurements of the elastic response for constructional steel

The multiscale elastic response to the macroscopic stress was simulated to reveal the multi-scale correlation of elastic properties of the medium carbon steel. Based on the multiscale correlation constitutive equations derived from this constitutive model, the effective elastic constants (EECs) of medium carbon steel are predicted. In addition, the diffraction elastic constants (DECs) of the constituents of the medium carbon steel are also evaluated. And then, the simple in-situ X-ray diffraction experiments were performed for the measurements of DECs and EECs of treated 35CrMo steel during the four-point bending. Compared with the experimental measurements and different existing models, the results demonstrated that the developed constitutive model was in good agreement with the measured values of the EECs and DECs, and that the feasibility and reliability of the constitutive model used to simulate multiscale elastic response could reveal the correlation between the material and its constitutes.

Continuous monitoring of chemical signals in plants under stress

Time is an often-neglected variable in biological research. Plants respond to biotic and abiotic stressors with a range of chemical signals, but as plants are non-equilibrium systems, single-point measurements often cannot provide sufficient temporal resolution to capture these time-dependent signals. In this article, we critically review the advances in continuous monitoring of chemical signals in living plants under stress. We discuss methods for sustained measurement of the most important chemical species, including ions, organic molecules, inorganic molecules and radicals. We examine analytical and modelling approaches currently used to identify and predict stress in plants. We also explore how the methods discussed can be used for applications beyond a research laboratory, in agricultural settings. Finally, we present the current challenges and future perspectives for the continuous monitoring of chemical signals in plants.

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.

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm^-1 and 1360 cm^-1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed.

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 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.

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.

Intracellular polyphosphate length characterization in polyphosphate accumulating microorganisms (PAOs): Implications in PAO phenotypic diversity and enhanced biological phosphorus removal performance

Polyphosphate (polyP) accumulating organisms (PAOs) are the key agent to perform enhanced biological phosphorus removal (EBPR) activity, and intracellular polyP plays a key role in this process. Potential associations between EBPR performance and the polyP structure have been suggested, but are yet to be extensively investigated, mainly due to the lack of established methods for polyP characterization in the EBPR system. In this study, we explored and demonstrated that single-cell Raman spectroscopy (SCRS) can be employed for characterizing intracellular polyPs of PAOs in complex environmental samples such as EBPR systems. The results, for the first time, revealed distinct distribution patterns of polyP length (as Raman peak position) in PAOs in lab-scale EBPR reactors that were dominated with different PAO types, as well as among different full-scale EBPR systems with varying configurations. Furthermore, SCRS revealed distinctive polyP composition/features among PAO phenotypic sub-groups, which are likely associated with phylogenetic and/or phenotypic diversity in EBPR communities, highlighting the possible resolving power of SCRS at the microdiversity level. To validate the observed polyP length variations via SCRS, we also performed and compared bulk polyP length characteristics in EBPR biomass using conventional polyacrylamide gel electrophoresis (PAGE) and solution ³¹P nuclear magnetic resonance (³¹P-NMR) methods. The results are consistent with the SCRS findings and confirmed the variations in the polyP lengths among different EBPR systems. Compared to conventional methods, SCRS exhibited advantages as compared to conventional methods, including the ability to characterize in situ the intracellular polyPs at subcellular resolution in a label-free and non-destructive way, and the capability to capture subtle and detailed biochemical fingerprints of cells for phenotypic classification. SCRS also has recognized limitations in comparison with ³¹P-NMR and PAGE, such as the inability to quantitatively detect the average polyP chain length and its distribution. The results provided initial evidence for the potential of SCRS-enabled polyP characterization as an alternative and complementary microbial community phenotyping method to facilitate the phenotype-function (performance) relationship deduction in EBPR systems.

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.

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm⁻¹ is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

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

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

Raman spectroscopy-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 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-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.

Emerging Technologies for Monitoring Plant Health in Vivo

In the coming decades, increasing agricultural productivity is all-important. As the global population is growing rapidly and putting increased demand on food supply, poor soil quality, drought, flooding, increasing temperatures, and novel plant diseases are negatively impacting yields worldwide. One method to increase yields is plant health monitoring and rapid detection of disease, nutrient deficiencies, or drought. Monitoring plant health will allow for precise application of agrichemicals, fertilizers, and water in order to maximize yields. In vivo plant sensors are an emerging technology with the potential to increase agricultural productivity. In this mini-review, we discuss three major approaches of in vivo sensors for plant health monitoring, including genetic engineering, imaging and spectroscopy, and electrical.