Early Detection Of Fungal Infection Research Articles

Serum GSDMD for Early Diagnosis of Bloodstream Infection and Differentiating Bacterial from Fungal Infections.

The role of Gasdermin D (GSDMD) in bloodstream infection (BSI) diagnosis is unknown. Serum GSDMD levels were measured in BSI patients. Endothelial cells and PBMCs were isolated, infected with bacteria/fungi, and intracellular/extracellular GSDMD concentrations were measured. An animal model was established to investigate the association between serum GSDMD levels and BSI incidence/progression. ROC curve analysis indicated that GSDMD could be a potential early diagnostic biomarker for BSI (AUC = 0.9885). Combining GSDMD with procalcitonin (PCT) improved the differential diagnosis of Gram-positive and Gram-negative bacteria (AUC = 0.6699, 66.15% specificity), and early diagnosis of Gram-positive bacteria (98.46% sensitivity), while PCT was not significantly elevated. The combined GSDMD and G-test had higher sensitivity (AUC = 0.7174) for differential diagnosis of bacterial and fungal infections, and early detection of fungal infections (98.44% sensitivity). In vitro and in vivo experiments confirmed that GSDMD levels increased significantly within 2 hours, peaked at 16 hours, and exhibited a time-dependent upward trend. Serum GSDMD, alone or combined with other biomarkers, has potential for early diagnosis and differential diagnosis of BSI caused by various pathogens. This finding offers a new strategy for early detection and treatment of BSI.

MeFunX: A novel meta-learning-based deep learning architecture to detect fungal infection directly from microscopic images

Fungal infections are a growing threat to human health. They can lead to a range of health problems and can be life-threatening. There are many impediments to the traditional diagnosis of fungal infections, such as a diminishing number of clinical mycologists, expensive procedures, high time consumption, and requirements for sensitivity and specificity. Therefore, early diagnosis of fungal infection is critical to effective treatment. In this paper, a novel meta-learning-based deep learning architecture, termed MeFunX, is proposed for the early detection of fungal infections from microscopic images. MeFunX architecture consisted of two convolutional neural network-based models as base learners and XGBoost as the meta-learner. To assess the proposed approach, standard metrics namely, accuracy, precision, recall and f1-score, were used. The fungal disease identification performance of MeFunX was compared with state-of-the-art architectures like VGG16, InceptionV3, ResNet, AlexNet, DenseNet, and EfficientNet. In addition, MeFunX was also benchmarked against its base learners and other meta-learning model with EfficientNet and ResNet as the base learners to demonstrate the effectiveness of the meta-learning architecture. Rigorous experimentation clearly signifies the superior performance of MeFunX, which achieved an overall accuracy of 92.49 % for the early diagnoses of fungal infections in microscopic images.

A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation.

The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings to the early detection of fungal infection in major agro-industrial crops and products. So, we propose a prototype method, the key feature of which is a procedure analyzing the lineshape of a spectrum. The shape of the absorption spectrum corresponding to this transition strongly depends on the immediate environment of a pigment and can serve as a marker to detect the presence of a particular pigment molecule in a sample. Considering carotenoids as an object of study, we demonstrate that the combined operation of the differential evolution algorithm and semiclassical quantum modeling of the optical response based on a generalized spectral density (the number of vibronic modes is arbitrary) allows us to distinguish quantum models of the pigment for different solvents. Moreover, it is determined that to predict the optical properties of monomeric pigments in protein, it is necessary to create a database containing, for each pigment, in addition to the absorption spectra measured in a predefined set of solvents, the parameters of the quantum model found using differential evolution.

Soluble urokinase plasminogen activator receptor (suPAR) in bronchoalveolar fluid and blood in critically ill patients—a prospective cohort study

IntroductionSoluble urokinase plasminogen activator receptor (suPAR) is a biologically active protein and increased levels are associated with worse outcomes in critically ill patients. suPAR in bronchoalveolar fluid (BALF) may be helpful to differentiate between types of acute respiratory distress syndrome (ARDS) and may have potential for early detection of fungal infection.MethodsWe prospectively investigated levels of suPAR in BALF and serum in critically ill patients who underwent bronchoscopy for any reason at the ICU of the Department of Internal Medicine, Medical University of Graz, Graz, Austria.ResultsSeventy-five patients were available for analyses. Median age was 60 [25th–75th percentile: 50–69] years, 27% were female, and median SOFA score was 12 [11–14] points. Serum suPAR levels were significantly associated with ICU mortality in univariable logistic regression analysis. There was no correlation between BALF and serum suPAR. Serum suPAR was higher in ARDS patients at 11.2 [8.0–17.2] ng/mL compared to those without ARDS at 7.1 [3.7–10.1] (p < 0.001). BALF-suPAR was significantly higher in patients with evidence of fungal lung infection compared to patients without fungal infection both in the general cohort (7.6 [3.2–9.4] vs 2.5 [1.1–5.3], p = 0.013) and in the subgroup of ARDS (7.2 [3.1–39.2] vs 2.5 [1.0–5.2], p = 0.022). All patients were classified as putative/probable invasive aspergillosis.ConclusionWe found significant higher levels of serum suPAR in ARDS patients compared to those not fulfilling ARDS criteria. Serum and BALF-suPAR were significantly higher in those patients with evidence for invasive pulmonary aspergillosis. These findings may suggest testing this biomarker for early diagnosis of fungal infection in a greater cohort.

Detection of the Early Fungal Infection of Citrus by Fourier Transform Near-Infrared Spectra

Early fungal infection of citrus is one of the common diseases found during the storage period of citrus, and fungus that infects citrus will spread to the entire batch of citrus as the degree of infection deepens, causing enormous economic losses. Therefore, early detection of fungal infection of citrus is fundamental. The purpose of this study is to explore the qualitative identification of early fungal infections in citrus by using Fourier transform near-infrared (FT-NIR) combined with a variety of chemometric methods. First, discrete wavelet transform (DWT) is used to filter the noise of the spectral signal, then combined with a PLS-DA model, that helps discriminate healthy from infected Citrus. Subsequently, four different feature variable selection methods were introduced, Then, the linear discriminant analysis (LDA) and support vector machine (SVM) two classifiers were combined to establish a qualitative model for the degree of fungal infection. The modeling results show that the SVM modeling effect is better than LDA, and the DWT-CARS-SVM based on the RBF kernel function has the best result, the accuracy rates of the training set and test set are 100% and 97%. The results indicate that FT-NIR spectroscopy, combined with chemometric methods, is able to distinguish early fungal infections in citrus.

Vaccines against candidiasis: Status, challenges and emerging opportunity.

Candidiasis is a mycosis caused by opportunistic Candida species. The occurrence of fungal infections has considerably increased in the last few years primarily due to an increase in the number of immune-suppressed individuals. Alarming bloodstream infections due to Candida sp. are associated with a higher rate of morbidity and mortality, and are emerged as major healthcare concerns worldwide. Currently, chemotherapy is the sole available option for combating fungal diseases. Moreover, the emergence of resistance to these limited available anti-fungal drugs has further accentuated the concern and highlighted the need for early detection of fungal infections, identification of novel antifungal drug targets, and development of effective therapeutics and prophylactics. Thus, there is an increasing interest in developing safe and potent immune-based therapeutics to tackle fungal diseases. In this context, vaccine design and its development have a priority. Nonetheless, despite significant advances in immune and vaccine biology over time, a viable commercialized vaccine remains awaited against fungal infections. In this minireview, we enumerate various concerted efforts made till date towards the development of anti-Candida vaccines, an option with pan-fugal vaccine, vaccines in the clinical trial, challenges, and future opportunities.

A Clinical Significance of Fungal Infections in Diabetic Foot Ulcers.

Background: Diabetic foot ulcers (DFUs) are the most common and serious complications in uncontrolled diabetes. Infections are predominantly polymicrobial, with aerobic Gram-positive, anerobic, and fungal infections. Early detection of fungal infection and initiation of appropriate treatment in DFUs may lead to better healing and avoid amputations. The primary objective was to find out the prevalence of DFUs getting infected with fungus and the secondary objective was to identify the appropriate methodology for the detection of the fungus in DFUs.Materials and methods: This was a cross-sectional observational study carried out in a tertiary care hospital with a sample size of 60 DFUs. Microbiological analysis was done by swab culture and deep tissue culture. Observational data were collected and the significance level was statistically analyzed.Results: In the present study, the prevalence of fungal infections in DFUs was 31.7%. Only fungal tissue was positive in 15%, the fungal swab was positive in 8.33%, and both tissue and swab were positive in 8.33%. All these patients were treated with antifungal treatment as per the culture report in addition to appropriate antimicrobial therapy.Conclusion: A fungal culture should be done in all patients with non-healing DFUs. Both fungal swab and tissue culture testing should be advocated in patients with DFUs for better mycological evaluation. The addition of antifungal medications may provide better outcomes in selected cases.

Multiple characteristic structure fragments scans coupled to high-resolution mass spectrometry strategy for discovering unknown structural analogs of mycotoxins in food

The cereals and their products, which have been easily infected by fungi and contaminated with mycotoxins, are serious threat to both human and animals alike. And yet, detection of these unknown fungal infection and mycotoxins contaminates remains a great challenge. In this work, a holistic approach based on multiple characteristic structure fragments scans and high-resolution mass spectrometry (HRMS) was proposed for discovering unknown structural analogues of mycotoxins. The structural similarity of the same class of compounds provides a direction for the discovery and identification of unknown structural analogues of mycotoxins. The following steps were carried out: the fragmentation pathways of four types of mycotoxins were elucidated through comprehensive fragment analysis. By the combination of fragmentation pathways, the multiple characteristic structure fragments were screened out, with the common fragments were obtained by Veen diagram. Finally multiple characteristic structure fragments scans were carried out to find the unknown structural analogues of mycotoxins. The approach, first proposed by us, was proved to be effective in discovering and identifying 5 structural analogues of mycotoxins in real samples. It was proved to be a simple, fast and accurate method for early detection of fungal infection and mycotoxin contaminants, even for trace amounts of chemicals in complex matrix, and is of great significance to prevent hazardous substances infection from the food supply chains worldwide.

Aflatoxin Contamination, Its Impact and Management Strategies: An Updated Review.

Aflatoxin, a type of mycotoxin, is mostly produced by Aspergillus flavus and Aspergillus parasiticus. It is responsible for the loss of billions of dollars to the world economy, by contaminating different crops such as cotton, groundnut, maize, and chilies, and causing immense effects on the health of humans and animals. More than eighteen different types of aflatoxins have been reported to date, and among them, aflatoxins B1, B2, G1, and G2 are the most prevalent and lethal. Early detection of fungal infection plays a key role in the control of aflatoxin contamination. Therefore, different methods, including culture, chromatographic techniques, and molecular assays, are used to determine aflatoxin contamination in crops and food products. Many countries have set a maximum limit of aflatoxin contamination (2–20 ppb) in their food and agriculture commodities for human or animal consumption, and the use of different methods to combat this menace is essential. Fungal infection mostly takes place during the pre- and post-harvest stage of crops, and most of the methods to control aflatoxin are employed for the latter phase. Studies have shown that if correct measures are adopted during the crop development phase, aflatoxin contamination can be reduced by a significant level. Currently, the use of bio-pesticides is the intervention employed in many countries, whereby atoxigenic strains competitively reduce the burden of toxigenic strains in the field, thereby helping to mitigate this problem. This updated review on aflatoxins sheds light on the sources of contamination, and the on occurrence, impact, detection techniques, and management strategies, with a special emphasis on bio-pesticides to control aflatoxins.

Untargeted rapid differentiation and targeted growth tracking of fungal contamination in rice grains based on headspace-gas chromatography-ion mobility spectrometry.

Milled rice are prone to be contaminated with spoilage or toxigenic fungi during storage, which may pose a real threat to human health. Most traditional methods require long periods of time for enumeration and quantification. However, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) technology could characterize the complex volatile organic compounds (VOCs) released from samples in a non-destructive and environmentally friendly manner. Thus, this study described an innovative HS-GC-IMS strategy for analyzing VOC profiles to detect fungal contamination in milled rice. A total of 24 typical target compounds were identified. Analysis of variance-partial least squares regression (APLSR) showed significant correlations between the target compounds and colony counts of fungi. While the changes of selected volatile components (acetic acid, 3-hydroxy-2-butanone and oct-en-3-ol) in fungi-inoculated rice had sufficiently high positive correlations with the colony counts, the logistic model could effectively be used to monitor the growth of individual fungus (R2 =0.902-0.980). PLSR could effectively be used to predict fungal colony counts in rice samples (R2 =0.831-0.953), and the different fungi-inoculated rice samples at 24 h could be successfully distinguished by support vector machine (SVM) (94.6%). The ability of HS-GC-IMS to monitor fungal infection would help to prevent contaminated rice grains from entering the food chain. This result indicated that HS-GC-IMS three-dimensional fingerprints may be appropriate for the early detection of fungal infection in rice grains. © 2021 Society of Chemical Industry.

Early detection and classification of fungal infection in garlic (A. sativum) using electronic nose

Garlic (Allium sativum) is an important crop with numerous benefits and uses. This plant is highly exposed to various pathogenic factors, including fungi. Fungi are the most distinct group responsible for planting diseases the loss involved. In this respect, detection of fungal pathogens infection in the early stages is a major challenge in food security to minimize losses as much as possible. Aroma investigation to detect fungal pathogens infection has been widely welcomed in this regard. In the present study, an electronic nose (E-nose) is utilized as a non-destructive and fast method for early detection of fungal infection on garlic that was synthetically infected with Fusariumoxysporum f. sp. Cepae (FU), Alternariaembellisia (syn. Embellisiaallii) (AL), and Botrytis allii (BO). Statistical analyses including ANOVA, PCA, LDA, SVM, and BPNN were employed to evaluate the aroma profile obtained by the E-nose. According to the obtained results, degradation occurs more quickly in the presence of infection. Due to the different destructive effects of each type of infection, the response changes of each sensor toward the aroma of various infection treatments were not the same. Thus, the E-nose can be used as a practical and beneficial tool to detect fungal infection on garlic in the early stages.

Selection and characterization of two monoclonal antibodies specific for the Aspergillus flavus major antigenic cell wall protein Aflmp1

Aspergillus flavus is a major fungal pathogen of plants and an opportunistic pathogen of humans. In addition to the direct impact of infection, it produces immunosuppressive and carcinogenic aflatoxins. The early detection of A. flavus is therefore necessary to diagnose and monitor fungal infection, to prevent aflatoxin contamination of food and feed, and for effective antifungal therapy. Aspergillus-specific monoclonal antibodies (mAbs) are promising as diagnostic and therapeutic reagents for the tracking and treatment of Aspergillus infections, respectively. However, A. flavus has a complex cell wall composition and dynamic morphology, hindering the discovery of mAbs with well-characterized targets. Here we describe the generation and detailed characterization of mAb5.52 (IgG2aκ) and mAb17.15 (IgG1κ), which bind specifically to the highly immunogenic cell wall antigen A. flavus mannoprotein 1 (Aflmp1). Both mAbs were generated using hybridoma technology following the immunization of mice with a recombinant truncated version of Aflmp1 (ExD, including the homologous CR4 domain) produced in bacteria. We show that mAb5.52 and mAb17.15 bind specifically to A. flavus and A. parasiticus cell wall fragments (CWFs), with no cross-reaction to CWFs from other fungal pathogens. Immunofluorescence microscopy revealed that both mAbs bind to the surface of Aspergillus hyphae and that mAb17.15 also binds to spores. The epitope for both mAbs is localized within the CR4 region of the Aflmp1 protein. These Aspergillus-specific mAbs may be useful for the early detection of fungal infection in food/feed crops, for serodiagnosis in patients with invasive aspergillosis caused by A. flavus infection and for the development of antibody-expressing disease-resistant crops.

An Automatic Detector for Fungal Spores in Microscopic Images Based on Deep Learning

HighlightsA fungal spore dataset with microscopic images was established.A fungal spore detector, FSNet, based on deep learning was proposed to automatically recognize and count fungal spores in microscopic images.A framework based on a CNN for feature extraction was designed to enrich feature maps.A loss was formulated to decrease the number of missed detections due to clustered spores.Abstract. The most common cause of spoiled grain in storage is the growth of fungi on the grain. In China, the level of fungal infection is indicated by the quantity and the categories of fungal spores in grain samples. In most Chinese grain depots, the recognition and counting of fungal spores in grain samples are conducted with microscopes, which are mainly operated manually by professionals. This method is time-consuming, laborious and error-prone. In this article, a fungal spore detector, named FSNet, was proposed to automatically recognize and count fungal spores in microscopic images. To enrich the features in feature maps, a multi-layer fusion structure based on convolutional neural network for feature extraction was designed using deep, intermediate and shallow layers. Moreover, the detection performance of FSNet was further improved by a designed loss for clustered spores, an anchor optimization and region sampling strategy. Our method achieved 91.6% mean average precision [intersection-over-union (IoU) &amp;gt; 0.6]. The experimental results indicated that our method was capable of recognizing and counting fungal spores in microscopic images, and it is very effective for the early detection of fungal infection. Keywords: Fungal spore detection, Deep learning, Microscopic images.

Visualization of volatomic profiles for early detection of fungal infection on storage Jasmine brown rice using electronic nose coupled with chemometrics

Early detection of fungal contamination can prevent fungal infected rice to enter food chains. This study aimed to use electronic nose coupled with chemometrics as a rapid and nondestructive method for detection of fungal contamination on brown rice grain. Jasmine brown rice was artificially infected with Aspergillus and stored at 30 °C and 85% RH. Volatile markers of fungal infected brown rice were identified using solid phase microextraction/gas chromatography–mass spectrometry (SPME/GC–MS). The volatomic profiles of fungal infected rice were analyzed using electronic nose and explored using principal component analysis (PCA). Linear discriminant analysis (LDA) and support vector machine (SVM) were then employed to classify samples with different levels of fungal contamination as a factor of storage time. Partial least squares (PLS) regression model was developed for prediction of the fungal growth on brown rice. The electronic nose coupled with PLS regression could accurately predict the fungal growth and gave coefficient of determination, R2 = 0.969, and root mean square error, RMSE = 0.31 Log CFU/g. The results suggested that the electronic nose can be used as a rapid and nondestructive tool for early detection of fungal infection on rice grain prior to visible growth.

Early Detection of Fungal Infection in an Immunosuppressed Patient Demonstrated by Thorax CT: Suspicion is the Best Marker!

Early Detection of Fungal Infection in an Immunosuppressed Patient Demonstrated by Thorax CT: Suspicion is the Best Marker!

Application of an electronic nose with novel method for generation of smellprints for testing the suitability for consumption of wheat bread during 4-day storage

The paper presents application of an Agrinose with novel three-parameter method for generation of smellprints to test the suitability of bread for consumption during four-day storage. Gas chromatography with mass spectrometry (GC-MS) analysis of the headspace was applied to describe the volatile metabolite contents. The CIE L*a*b* colour scale was used to identify changes in the colour during storage and the puncture and penetration tests were used to describe the hardness and staling, as a reference methods for testing bread. The process of bread staling in aerobic conditions was correlated with changes in the crumb colour, hardness, and emission of volatile substances. The analysis of volatile compounds emitted from bread stored in quasi-anaerobic conditions demonstrated a relationship between the responses of resistance sensors and the quantity and quality of groups of volatile substances in the general aroma. The highest sensitivity to volatile organic compounds (VOCs) generated by spoiled bread were noted in the case of sensors for food tests, i.e. AS-MLV-P2 and TGS2603. The Agrinose coupled with three-parameters method could accurately predict the fungal grow on bread stored in quasi-anaerobic conditions and can be used as a rapid and non-destructive tool for early detection of fungal infection.

A neglected causative agent in diabetic foot infection: a retrospective evaluation of 13 patients with fungal etiology

Background/aimClinicians often neglect fungal infections and do not routinely investigate deep tissue from the wound for fungal culture and sensitivity due to insufficient information in the literature. In this study, we aimed to evaluate fungal etiology of invasive fungal diabetic foot which is rarely reported in the literature.Materials and methodsThe patients who were unresponsive to antibiotic therapy and those with positive fungal in bone or deep tissue culture were enrolled in the study. Detailed hospital records were retrieved for demographics and clinical features. Results A total of 13 patients who were diagnosed with invasive fungal diabetic foot (ten females, three males, mean age 59.8 ± 9 years) were included. All of the patients had type-2 diabetes mellitus. Eleven (84.6%) patients had mixed infection. The most common cause of fungal infections of diabetic foot ulcers was the Candida species. Ten (76.9%) patients underwent amputation, two (15.4%) patients refused amputation, and one patient died before surgery. ConclusionInvasive fungal infections may also be a causative pathogen in deep tissue infections. Therefore, fungal pathogens should be considered in patients unresponsive to long-term antibiotic therapy. Early detection of fungal infections in high-risk individuals is critical for the prevention of severe consequences such as foot amputation.

Prevalence and outcome of systemic fungal infections in renal transplant recipients - A tertiary care experience

Fungal infections are an important cause of morbidity and mortality in renal transplant recipients. These infections account for 5% of all infections in renal transplant recipients. The symptoms of systemic fungal infections are nonspecific, particularly in their early stages, and this can lead to delay in diagnosis. Retrospective analysis was conducted on all renal transplants that were performed at our center over a 20-year period from 1996-2016. Cases of invasive fungal infections (IFIs) that occurred among renal transplant recipients were identified to describe the epidmeiology of these infections. A total of 67 (9.2%) IFI cases were identified among 725 renal transplant recipients. Of the 67 patients (9.24%) with IFI, 31 (46.2%) cases were seen in deceased donor transplant recipients. Of 67 cases with IFI, 42 (62.7%) had received induction therapy. The incidence of fungal infections according to the induction agent used was, 14.3% with basiliximab, 12.3% each with daclizumab and rabbit antithymocyte globulin, and 6.3% among patients not given any induction. Invasive candidiasis was the most common IFI overall, followed by mucormycosis, invasive aspergillosis, and cryptococcosis. Median time to onset of IFI was 117.9 days. Majority of infections occurred within 180 days after transplantation. Late posttransplant (>180 days after transplantation) IFI's were predominantly caused by Candida, followed by Cryptococcus. The longest time to infection was a case of histoplasma, occurring seven years posttransplant. The overall 12-month cumulative incidence (CI) for any IFI was 9.1%. The 12-month CI of the first IFI increased from 7.3% between 1996 and 2001 to 10.5% between 2010 and 2016. The overall mortality rate was 38.8%. The use of newer and more-effective immunosuppressive agents in recent years are associated with increased rates of fungal infections in renal transplant recipients. Therefore, early detection of fungal infections and proper therapy are important in improving survival and reducing mortality.

Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging

Early stages of fungal infections in strawberry fruit are difficult to detect by the majority of commonly used manual and automatic sorting methods In this study, hyperspectral imaging was applied for detection of fungal infections causing fruit spoilage. Fruit of two cultivars: ‘Senga Sengana’ and ‘Honeoye’, were individually inoculated with pathogenic fungi of Botrytis cinerea and Collatotrichum acutatum, while non-inoculated fruit were used as a control. The reflectance spectra of fruit were acquired during four days after inoculation. Based on the second derivative of the original spectra, 19 wavelengths were selected as the most appropriate for discrimination of infections in strawberries and were used to construct supervised classification models. Among all the studied classifiers, the best prediction accuracies for discriminating fungi species were obtained by using the backpropagation neural network (BNN) model. The accuracy of distinguishing between inoculated and control fruit was higher than 97%. The method presented in this study also allowed early detection of fungal infection, starting 24 h from inoculation when the symptoms of fungal infection are invisible. Multiple linear regression was used to create total anthocyanin content (AC), soluble solid content (SSC) and total phenolic content (TPC) models. Two wavelengths (681 and 1292 nm) were selected to create the AC model and four wavelengths (705, 842, 1162 and 2239 nm) were indicated as the most suitable for the SSC model. The coefficients of determination of the created models were R2 = 0.65 and R2 = 0.85 for AC and SSC, respectively. The results demonstrated that hyperspectral imaging technique has potential for rapid and non-invasive detection of fungal infection and for predicting and visualizing AC and SSC in strawberry fruit during storage.

Early optical detection of infection with brown rust in winter wheat by chlorophyll fluorescence excitation spectra

Puccinia recondita f. sp. tritici, the causative agent of brown rust disease is the reason for major yield losses in winter wheat plants (Triticum aestivum L.). Early detection of fungal infections would allow a specific fungicide application. Aim of the current study was to develop an automated and computer based device that differentiate infected and healthy plants non-invasively in an early stage of infection. To achieve the target an optical sensor device has been designed (“MultiDetExc”), which excites chlorophyll fluorescence in discrete wavelengths and detects the induced emissions. Wheat plants were infected artificially with brown rust in a climate chamber experiment to survey the capability of the instrument. The chlorophyll fluorescence excitation spectra of whole wheat plants were measured on several days after infection. As reference methods, HPLC and qPCR analysis were included in the current study to measure the polyphenol content of the leaves and the level of infection.The recently developed sensor device is an efficient technique to differentiate the infected and not infected wheat plants as soon as four days after inoculation. The measured fluorescence quotients correlate high positive with the polyphenol contents and the relative amount of fungal DNA. An untreated healthy control was measured parallelly, in order to associate the increased synthesis of polyphenols to the fungal infection definitely.