Application of Fluorescence Spectroscopy in Wheat Crop: Early Disease Detection and Associated Molecular Changes

Modulation of secondary metabolism and redox regulation by exogenously applied glutathione improves the shelf life of Capsicum annuum L. fruit

Role of redox homeostasis in fruit ripening of Capsicum annuum L. with oxidative metabolism was studied. The research aims the ability to reduce agents during postharvest storage on fruit for delayed ripening with the regulation of oxidative stress. Thus, we applied 10 mM reduced glutathione (GSH) to fruit as pretreatment followed by 1 mM hydrogen peroxide (H2O2) as ripening-inducing treatment and observed during 7 days of storage at 25 °C. A decrease in total soluble solid and firmness under H2O2, was increased while dehydration in tissue was decreased by GSH pretreatment. Glutathione regulated the turnover of organic acids to reducing sugars with higher activity of NADP malic enzyme that sustained the fruit coat photosynthesis through chlorophyll fluorescence, pigment composition, and photosystem II activity. Malondialdehyde accumulation was inversely correlated with GSH content and antioxidative enzyme activity that reduced loss of cell viability. Conclusively, regulation of oxidative stress with GSH may be effective in the extension of shelf life under postharvest storage.

The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques

Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.

Metabolomic Reconfiguration in Primed Barley (Hordeum vulgare) Plants in Response to Pyrenophora teres f. teres Infection

Necrotrophic fungi affect a wide range of plants and cause significant crop losses. For the activation of multi-layered innate immune defences, plants can be primed or pre-conditioned to rapidly and more efficiently counteract this pathogen. Untargeted and targeted metabolomics analyses were applied to elucidate the biochemical processes involved in the response of 3,5-dichloroanthranilic acid (3,5-DCAA) primed barley plants to Pyrenophora teres f. teres (Ptt). A susceptible barley cultivar ('Hessekwa') at the third leaf growth stage was treated with 3,5-DCAA 24 h prior to infection using a Ptt conidia suspension. The infection was monitored over 2, 4, and 6 days post-inoculation. For untargeted studies, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) was used to analyse methanolic plant extracts. Acquired data were processed to generate the data matrices utilised in chemometric modelling and multi-dimensional data mining. For targeted studies, selected metabolites from the amino acids, phenolic acids, and alkaloids classes were quantified using multiple reaction monitoring (MRM) mass spectrometry. 3,5-DCAA was effective as a priming agent in delaying the onset and intensity of symptoms but could not prevent the progression of the disease. Unsupervised learning methods revealed clear differences between the sample extracts from the control plants and the infected plants. Both orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and 'shared and unique structures' (SUS) plots allowed for the extraction of potential markers of the primed and naïve plant responses to Ptt. These include classes of organic acids, fatty acids, amino acids, phenolic acids, and derivatives and flavonoids. Among these, 5-oxo-proline and citric acid were notable as priming response-related metabolites. Metabolites from the tricarboxylic acid pathway were only discriminant in the primed plant infected with Ptt. Furthermore, the quantification of targeted metabolites revealed that hydroxycinnamic acids were significantly more prominent in the primed infected plants, especially at 2 d.p.i. Our research advances efforts to better understand regulated and reprogrammed metabolic responses that constitute defence priming in barley against Ptt.

Early detection of stripe rust infection in wheat using light-induced fluorescence spectroscopy

In the current study, the application of fluorescence spectroscopy along with the advanced statistical technique and confocal microscopy was investigated for the early detection of stripe rust infection in wheat grown under field conditions. The indigenously developed Fluorosensor fitted with LED, emitting monochromatic light was used that covered comparatively larger leaf area for recording fluorescence data thus presenting more reliable current status of the leaf. The examined leaf samples covered the entire range of stripe rust disease infection from no visible symptoms to the complete disease prevalence. The molecular changes were also assessed in the leaves as the disease progresses. The emission spectra mainly produce two fluorescence emission classes, namely the blue-green fluorescence (400-600 nm range) and chlorophyll fluorescence (650-800 nm range). The chlorophyll fluorescence region showed lower chlorophyll bands both at 685 and 735 nm in the asymptomatic (early diseased) and symptomatic (diseased) leaf samples than the healthy ones as a result of partial deactivation of PSII reaction centers. The 735 nm chlorophyll fluorescence band was either slight or completely absent in the leaf samples with lower to higher disease incidence and thus differentiate between the healthy and the infected leaf samples. The Hydroxycinnamic acids (caffeic and sinapic acids) showed decreasing trend, whereas the ferulic acid increased with the rise in disease infection. Peak broadening/shifting has been observed in case of ferulic acid and carotenes/carotenoids, with the increase in the disease intensity. While using the LEDs (365 nm), the peak broadening and the decline in the chlorophyll fluorescence bands could be used for the early prediction of stripe rust disease in wheat crop. The PLSR statistical techniques discriminated well between the healthy and the diseased samples, thus showed promise in early disease detection. Confocal microscopy confirmed the early prevalence of stripe rust disease infection in a susceptible variety at a stage when the disease is not detectable visually. It is inferred that fluorescence emission spectroscopy along with the chemometrics aided in the effective and timely diagnosis of plant diseases and the detected signatures provide the basis for remote sensing.

A Highly Selective Hg2+ Fluorescent Chemosensor Based On Photochromic Diarylethene With Quinoline Unit

A novel diarylethene-based fluorescent chemosensor containing a quinoline unit (1o) had been designed and synthesized. 1o showed good photochromic ability and fluorescence switching properties by alternating UV/vis light irradiation. The chemosensor showed high "Turn-off" fluorescent selectivity for Hg²⁺ by competitive tests of the fluorescence reaction in the presence other ions in acetonitrile solution. The stoichiometry between the compound 1o and Hg²⁺ was 1:1 by Job's plot curve and HRMS analysis. In addition, the LOD for Hg²⁺ was calculated as 60 nM. The fluorescence emission can be back to the "Turn-on" state by adding EDTA. Based on these facts, a molecular logic gate that including four input signals (UV/vis and Hg²⁺/EDTA) and one output signal (fluorescent intensity at 491 nm) was designed.

The Role of Hydroxycinnamic Acid Amide Pathway in Plant Immunity

The compounds involved in the hydroxycinnamic acid amide (HCAA) pathway are an important class of metabolites in plants. Extensive studies have reported that a variety of plant hydroxycinnamamides exhibit pivotal roles in plant-pathogen interactions, such as p-coumaroylagmatine and ferulic acid. The aim of this review is to discuss the emerging findings on the functions of hydroxycinnamic acid amides (HCAAs) accumulation associated with plant defenses against plant pathologies, antimicrobial activity of HCAAs, and the mechanism of HCAAs involved in plant immune responses (such as reactive oxygen species (ROS), cell wall response, plant defense hormones, and stomatal immunity). However, these advances have also revealed the complexity of HCAAs participation in plant defense reactions, and many mysteries remain to be revealed. This review provides an overview of the mechanistic and conceptual insights obtained so far and highlights areas for future exploration of phytochemical defense metabolites.

Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.

Exploring the phyto- and physicochemical evaluation, fluorescence characteristics, and antioxidant activities of Acacia ferruginea Dc: an endangered medicinal plant

Background

Herbal plants are potent in curing various ailments of ancient times as they have comparatively lesser side effects. The demands for natural drugs, mostly from plant sources, are increasing over the past few decades. Because of their potent antioxidant activity, Acacia species are used to treat a variety of diseases. One of the species Acacia ferruginea, an endangered medicinal plant, is widely used in the traditional medicine system, and it is considered that standardization would be beneficial. The present study investigates the physicochemical parameters, preliminary phytochemical screening, trace metals by SEM–EDS, and fluorescence properties of various extracts (non-polar to polar) of leaf and bark parts. Standard spectrophotometric methods (UV–Vis, FT-IR, fluorescence spectroscopy) are employed to analyze the functional groups, and the DPPH and total antioxidant methods are used to assess antioxidant potential.

Results

The ethyl acetate extract of leaves and ethanol extract of the bark are found to be the highest in yield, 16.32% and 2.54%. Results reveal that the total ash percentage and moisture content are of bark and the water-soluble ash of leaves is higher (10.3 ± 0.85, 7.6 ± 0.34, 3.22 ± 0.24%). The bark polar extract contained more macro-elements such as Na, K, Mg, Ca, S, and Cl. Phytochemical analysis reveals the polar extracts of leaves and bark show saponins, flavonoids, steroids, phenolic compounds, and non-polar extracts show mild positive. The total alkaloids, phenolics, and terpenoids (1.58 ± 0.08%; 0.56 ± 0.11; 0.75 ± 0.15) are found to be higher in A. ferruginea leaves. The FT-IR result shows the presence of alkanes, alkenes, aromatic compounds, aldehydes, phenolics and does not contain any toxic substances since there is no peak observed in the region between 2220 and 2260 cm−1. The in vitro antioxidant activity of the species demonstrated that both the leaf and bark parts have prominent antioxidant properties.

Conclusions

The results obtained from the preliminary standardization of A. ferruginea are very helpful in the determination of the quality and purity of the crude drug. The refurbished findings of A. ferruginea are promising, and further research is important to identify the bioactive compounds, thereby developing nutritional supplements and medications through therapeutic compound isolation.

Graphical Abstract