Application of Infrared and Raman Spectroscopy for the Identification of Disease Resistant Trees

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.

Leaf spectroscopy of resistance to Ceratocystis wilt of 'Ōhi'a

Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of 'ōhi'a, caused by the fungal pathogen Ceratocystis lukuohia has been killing large numbers of the native Hawaiian tree, Metrosideros polymorpha or 'Ōhi'a, Hawaii's most common native tree and a biocultural keystone species. Here, we assessed whether resistance to C. lukuohia is detectable in leaf-level reflectance spectra (400-2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2-3-year-old M. polymorpha clones with C. lukuohia. Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of 'ōhi'a resistance screenings.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R ² of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

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.

Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review

Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.

Evaluation of the impact of buffered peptone water composition on the discrimination between Salmonella enterica and Escherichia coli by Raman spectroscopy

The detection of Salmonella spp. in food samples is regulated by the ISO 6579:2002 standard, which requires that precise procedures are followed to ensure the reliability of the detection process. This standard requires buffered peptone water as a rich medium for the enrichment of bacteria. However, the effects of different brands of buffered peptone water on the identification of microorganisms by Raman spectroscopy are unknown. In this regard, our study evaluated the discrimination between two bacterial species, Salmonella enterica and Escherichia coli, inoculated and analyzed with six of the most commonly used buffered peptone water brands. The results showed that bacterial cells behaved differently according to the brand used in terms of biomass production and the spectral fingerprint. The identification accuracy of the analyzed strains was between 85% and 100% depending on the given brand. Several batches of two brands were studied to evaluate the classification rates between the analyzed bacterial species. The chemical analysis performed on these brands showed that the nutrient content was slightly different and probably explained the observed effects. On the basis of these results, Raman spectroscopy operators are encouraged to select an adequate culture medium and continue its use throughout the identification process to guarantee optimal recognition of the microorganism of interest.

Assessment of Genetic Relationships between Streptocarpus x hybridus V. Parents and F1 Progenies Using SRAP Markers and FT-IR Spectroscopy

The genetic relationship among three Streptocarpus parents and twelve F1 hybrids was assessed using sequence-related amplified polymorphism (SRAP) molecular markers and Fourier-transform infrared (FT-IR) spectroscopy. Both methods were able to discriminate F1 hybrids and parents as revealed by cluster analysis. For hybrid identification, the type III SRAP marker was the most effective due to the presence of male-specific bands in the hybrids. Different behaviors in the biochemical variability of DNA samples have been observed by FT-IR spectral analysis, which might be attributed to the inherent nature of the genomic DNA from parents and their F1 progenies. Mantel test was also carried out to compare morphological, SRAP, and FT-IR results based on genetic distances. The highest correlation coefficient was found between morphological and SRAP marker distances (R = 0.607; p ≤ 0.022). A lower correlation was observed between the morphological and FT-IR distance matrix (R = 0.231; p ≤0.008). Moreover, a positive correlation was found between the distances generated with SRAP and FT-IR analyses (R = 0.026) but was not statistically significant. These findings show that both SRAP and FT-IR techniques combined with morphological descriptions can be used effectively for nonconventional breeding programs for Streptocarpus to obtain new and valuable varieties.

Fourier-transform infrared (FT-IR) spectroscopy analysis discriminates asymptomatic and symptomatic Norway spruce trees

Conifer trees, including Norway spruce, are threatened by fungi of the Heterobasidion annosum species complex, which severely affect timber quality and cause economic losses to forest owners. The timely detection of infected trees is complicated, as the pathogen resides within the heartwood and sapwood of infected trees. The presence of the disease and the extent of the wood decay often becomes evident only after tree felling. Fourier-transform infrared (FT-IR) spectroscopy is a potential method for non-destructive sample analysis that may be useful for identifying infected trees in this pathosystem. We performed FT-IR analysis of 18 phloem, 18 xylem, and 18 needle samples from asymptomatic and symptomatic Norway spruce trees. FT-IR spectra from 1066 - 912 cm^-1 could be used to distinguish phloem, xylem, and needle tissue extracts. FT-IR spectra collected from xylem and needle extracts could also be used to discriminate between asymptomatic and symptomatic trees using spectral bands from 1657 - 994 cm^-1 and 1104 - 994 cm^-1, respectively. A partial least squares regression model predicted the concentration of condensed tannins, a defense-related compound, in phloem of asymptomatic and symptomatic trees. This work is the first to show that FT-IR spectroscopy can be used for the identification of Norway spruce trees naturally infected with Heterobasidion spp.

Advanced spectroscopy-based phenotyping offers a potential solution to the ash dieback epidemic

Natural and urban forests worldwide are increasingly threatened by global change resulting from human-mediated factors, including invasions by lethal exotic pathogens. Ash dieback (ADB), incited by the alien invasive fungus Hymenoscyphus fraxineus, has caused large-scale population decline of European ash (Fraxinus excelsior) across Europe, and is threatening to functionally extirpate this tree species. Genetically controlled host resistance is a key element to ensure European ash survival and to restore this keystone species where it has been decimated. We know that a low proportion of the natural population of European ash expresses heritable, quantitative resistance that is stable across environments. To exploit this resource for breeding and restoration efforts, tools that allow for effective and efficient, rapid identification and deployment of superior genotypes are now sorely needed. Here we show that Fourier-transform infrared (FT-IR) spectroscopy of phenolic extracts from uninfected bark tissue, coupled with a model based on soft independent modelling of class analogy (SIMCA), can robustly discriminate between ADB-resistant and susceptible European ash. The model was validated with populations of European ash grown across six European countries. Our work demonstrates that this approach can efficiently advance the effort to save such fundamental forest resource in Europe and elsewhere.

Evaluating Changes in Cell-Wall Components Associated with Clubroot Resistance Using Fourier Transform Infrared Spectroscopy and RT-PCR

Clubroot disease is a serious threat to canola production in western Canada and many parts of the world. Rcr1 is a clubroot resistance (CR) gene identified recently and its molecular mechanisms in mediating CR have been studied using several omics approaches. The current study aimed to characterize the biochemical changes in the cell wall of canola roots connecting to key molecular mechanisms of this CR gene identified in prior studies using Fourier transform infrared (FTIR) spectroscopy. The expression of nine genes involved in phenylpropanoid metabolism was also studied using qPCR. Between susceptible (S) and resistance (R) samples, the most notable biochemical changes were related to an increased biosynthesis of lignin and phenolics. These results were supported by the transcription data on higher expression of BrPAL1. The up-regulation of PAL is indicative of an inducible defence response conferred by Rcr1; the activation of this basal defence gene via the phenylpropanoid pathway may contribute to clubroot resistance conferred by Rcr1. The data indicate that several cell-wall components, including lignin and pectin, may play a role in defence responses against clubroot. Principal components analysis of FTIR data separated non-inoculated samples from inoculated samples, but not so much between inoculated S and inoculated R samples. It is also shown that FTIR spectroscopy can be a useful tool in studying plant-pathogen interaction at cellular levels.

Application of Raman spectroscopy for direct analysis of Carlina acanthifolia subsp. utzka root essential oil

Carlina genus plants e.g. Carlina acanthifolia subsp. utzka have been still used in folk medicine of many European countries and its biological activity is mostly associated with root essential oils. In the present paper, Raman spectroscopy (RS) was applied for the first time for evaluation of essential oil distribution in root of C. acnthifolia subsp. utzka and identification of root structures containing the essential oil. Furthermore, RS technique was applied to assess chemical stability of oil during drying of plant material or distillation process. Gas chromatography-mass spectrometry was used for qualitative and quantitative analysis of the essential oil. The identity of compounds was confirmed using Raman, ATR-IR and NMR spectroscopy. Carlina oxide was found to be the main component of the oil (98.96% ± 0.15). The spectroscopic study showed the high stability of essential oil and Raman distribution analysis indicated that the oil reservoirs were localized mostly in the structures of outer layer of the root while the inner part showed nearly no signal assigned to the oil. Raman spectroscopy technique enabled rapid, non-destructive direct analysis of plant material with minimal sample preparation and allowed straightforward, unambiguous identification of the essential oil in the sample.

Molecular recognition of emerald ash borer infestation using leaf spray mass spectrometry

RATIONALE

The introduction of the emerald ash borer (Agrilus planipennis) (EAB) from Asia to Michigan, USA, in the 1990s caused the widespread death of ash trees in two Canadian provinces and 24 US states. The three current methods for the detection of emerald ash borer infestation, visual surveys, tree girdling and artificial traps, can be unreliable, and there is clearly a need for a rapid, dependable technique for the detection of emerald ash borer infestation.

METHODS

Leaf spray, an ambient ionization method for mass spectrometry (MS), gives direct chemical information on a leaf sample by applying a high voltage to a naturally or artificially sharply pointed leaf piece causing ions to be generated directly from the leaf tip for MS analysis.

RESULTS

Leaflets from 23 healthy and EAB-infested ash trees were analyzed by leaf spray mass spectrometry in an attempt to distinguish healthy and EAB-infested ash trees. In negative ion mode, healthy ash trees showed an increased abundance of ions m/z 455.5, 471.5 and 487.5, and ash trees infested with the EAB displayed an increased abundance of ions m/z 181 and 217. The identities of the chemical discriminators ursolic acid and oleanolic acid in healthy ash trees, and six-carbon sugar alcohols in infested ash trees, were determined by tandem mass spectrometry and confirmed with standards.

CONCLUSIONS

This preliminary study suggests that leaf spray mass spectrometry of ash tree leaflets provides a potential tool for the early detection of ash tree infestation by the emerald ash borer. Copyright © 2016 John Wiley & Sons, Ltd.