Identification of fungal phytopathogens using Fourier transform infrared-attenuated total reflection spectroscopy and advanced statistical methods

The Membrane-Targeting Synergistic Antifungal Effects of Walnut-Derived Peptide and Salicylic Acid on Prickly Pear Spoilage Fungus

Fermented walnut (FW) meal exhibits antifungal activity against Penicillium victoriae (the fungus responsible for prickly pear spoilage), which is mainly attributed to the synergistic effect of antimicrobial peptides and salicylic acid (SA). This study aimed to investigate the synergistic mechanism between YVVPW (YW-5, the peptide with the highest antifungal activity) and SA against the cell membrane of P. victoriae. Treatment enhanced prickly pear's rot rate, polyphenol concentration, and superoxide dismutase (SOD) activity by 38.11%, 8.11%, and 48.53%, respectively, while reducing the microbial count by 19.17%. Structural analyses revealed β-sheets as YW-5's predominant structure (41.18%), which increased to 49.0% during SA interaction. Molecular docking demonstrated YW-5's stronger binding to β-(1,3)-glucan synthase and membrane protein amino acids via hydrogen bonds, hydrophobic forces, and π-π conjugate interactions. Spectroscopic analyses demonstrated SA's major role in YW-5 synergy at the interface and polar head region of phospholipids, enhancing lipid chain disorder and the leakage of cell components. Malondialdehyde and SOD levels increased nearly two-fold and six-fold when treated with YW-5/SA, and YW-5 showed a more pronounced effect. Scanning electron and transmission electron microscopy confirmed that SA caused greater damage to spore morphology and cell ultrastructure. These findings support this formulation's functions as an efficient antifungal substance in fruit storage.

Prediction of melanin content of Fonsecaea pedrosoi using Fourier transform infrared spectroscopy (FTIR) and chemometrics

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Genomic Characteristics and Comparative Genomics Analysis of the Endophytic Fungus Paraphoma chrysanthemicola DS-84 Isolated from Codonopsis pilosula Root

Paraphoma chrysanthemicola is a newly identified endophytic fungus. The focus of most studies on P. chrysanthemicola has been on its isolation, identification and effects on plants. However, the limited genomic information is a barrier to further research. Therefore, in addition to studying the morphological and physiological characteristics of P. chrysanthemicola, we sequenced its genome and compared it with that of Paraphoma sp. The results showed that sucrose, peptone and calcium phosphate were suitable sources of carbon, nitrogen and phosphorus for this strain. The activities of amylase, cellulase, chitosanase, lipase and alkaline protease were also detected. Sequencing analysis revealed that the genome of P. chrysanthemicola was 44.1 Mb, with a scaffold N50 of 36.1 Mb and 37,077 protein-coding genes. Gene Ontology (GO) annotation showed that mannose-modified glycosylation was predominant in monosaccharide utilisation. The percentage of glycoside hydrolase (GH) modules was the highest in the carbohydrate-active enzymes database (CAZy) analysis. Secondary metabolite-associated gene cluster analysis identified melanin, dimethylcoprogen and phyllostictine A biosynthetic gene clusters (>60% similarity). The results indicated that P. chrysanthemicola had a mannose preference in monosaccharide utilisation and that melanin, dimethylcoprogen and phyllostictine A were important secondary metabolites for P. chrysanthemicola as an endophytic fungus.

Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning

Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.

Congruence Concept for Comparison of Spectra

This paper introduces an alternative, easy-to-implement spectrum comparison concept. The evaluation procedure is illustrated by artificial and attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra, which it can also be extended to other spectrometries (e.g., ultraviolet-visible or UV-Vis and Raman). The evaluation for the comparison of two spectra is divided into four phases: (i) spectrum pre-treatment (e.g., smoothing and background correction), (ii) standard normal variate (SNV) transformation, (iii) regression analysis of SNV spectra, and (iv) calculation of the quantification index (FG). The FG is derived from the formula of R². It characterizes and quantifies the identity and/or similarity of the compared spectra.

Novel development of Lecanicillium lecanii-based granules as a platform against malarial vector Anopheles culicifacies

Mosquitoes are infectious vectors for a wide range of pathogens and parasites thereby transmitting several diseases including malaria, dengue, Zika, Japanese encephalitis and chikungunya which pose a major public health concern. Mostly synthetic insecticides are usually applied as a primary control strategy to manage vector-borne diseases. However excessive and non-judicious usage of such chemically derived insecticides has led to serious environmental and health issues owing to their biomagnification ability and increased toxicity towards non-target organisms. In this context, many such bioactive compounds originating from entomopathogenic microbes serve as an alternative strategy and environmentally benign tool for vector control. In the present paper, the entomopathogenic fungus, Lecanicillium lecanii (LL) was processed to make the granules. Developed 4% LL granules have been characterized using the technique of Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM). The developed formulation was also subjected to an accelerated temperature study at 40 °C and was found to be stable for 3 months. Further, GCMS of the L. lecanii was also performed to screen the potential biomolecules present. The developed formulation was found to be lethal against Anopheles culicifacies with an LC₅₀ value of 11.836 µg/mL. The findings from SEM and histopathology also substantiated the mortality effects. Further, the SEM EDX (energy dispersive X-ray) studies revealed that the treated larvae have lower nitrogen content which is correlated to a lower level of chitin whereas the control ones has higher chitin content and healthy membranes. The developed LL granule formulation exhibited high toxicity against Anopheles mosquitoes. The granule formulations can be used as an effective biocontrol strategy against malaria-causing mosquitoes.

Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest

Bacterial infections cause serious illnesses that are treated with antibiotics. Currently used methods for detecting bacterial antibiotic susceptibility consume 48-72 h, leading to overuse of antibiotics. Thus, many bacterial species have acquired resistance to a broad range of available antibiotics. There is an urgent need to develop efficient methods for rapid determination of bacterial susceptibility to antibiotics. The combination of machine learning and Fourier-transform infrared (FTIR) spectroscopy has generated a promising diagnostic approach in medicine and biology. Our main goal is to examine the potential of FTIR spectroscopy to determine the susceptibility of urinary tract infection-Proteus mirabilis to a specific range of antibiotics, within about 20 min after 24 h culture and identification. We measured the infrared spectra of 489 different P. mirabilis isolates and used random forest to analyze this spectral database. A classification success rate of ~84% was achieved in differentiating between the resistant and sensitive isolates based on their susceptibility to ceftazidime, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, ciprofloxacin, gentamicin, and sulfamethoxazole antibiotics in a time span of 24 h instead of 48 h.

Determination of Klebsiella pneumoniae Susceptibility to Antibiotics Using Infrared Microscopy

Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria associated with human infections, resulting in high mortality and morbidity. We obtained 1190 K. pneumoniae isolates from different patients with urinary tract infections. The isolates were measured to determine their susceptibility regarding nine specific antibiotics. This study's primary goal is to evaluate the potential of infrared spectroscopy in tandem with machine learning to assess the susceptibility of K. pneumoniae within approximately 20 min following the first culture. Our results confirm that it was possible to classify the isolates into sensitive and resistant with a success rate higher than 80% for the tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful method for a K. pneumoniae susceptibility test.

Identification and characterization of Aspergillus species of fruit rot fungi using microscopy, FT-IR, Raman and UV-Vis spectroscopy

During the investigation of fungal isolation from fruit, the major genera were Aspergillus, Penicillium, cladosporium, Alternaria, fusarium, Colletotrichum were found. Among them Aspergillus (15 species) was found major dominant on different fruits. Fifteen different Aspergillus species viz. Aspergillus brasiliensis, Aspergillus phoenicis, Aspergillus carbonarius, four Aspergillus flavus, Aspergillus acidus, two Aspergillus awamori, Aspergillus aculeatus, Aspergillus eucalypticola, Aspergillus oryzae and two Aspergillus Spp. have been differentiate and identify using morphology (microscopic technique), Fourier Transforms Infrared spectroscopy (FTIR), Raman Spectroscopy (RS) and UV-visible spectrophotometry (UV-vis). The fungal mass in powder form was used in present study. In FTIR the finger print region is important for the characterization of Aspergillus because this region is unique and contains peaks indicating the presence of DNA. From the results were found Fourier transform infrared (FTIR) technique and Raman spectroscopy a useful tool, sensitive, fast, economical, accurate, not require sample preparation and successfully used to identify fungi.

Prediction of itraconazole minimum inhibitory concentration for Fonsecaea pedrosoi using Fourier Transform Infrared Spectroscopy (FTIR) and chemometrics

Fonsecaea pedrosoi is one of the main agents of chromoblastomycosis, a chronic subcutaneous mycosis. Itraconazole (ITC) is the most used antifungal in its treatment, however, in vitro antifungal susceptibility tests are important to define the best therapy. These tests are standardized by the Clinical and Laboratory Standards Institute (CLSI), but these protocols have limitations such as the high complexity, cost and time to conduct. An alternative to in vitro susceptibility test, which overcomes these limitations, is FTIR. This study determined the minimum inhibitory concentration (MIC) of itraconazole for F. pedrosoi, using FTIR and chemometrics. The susceptibility to ITC of 36 strains of F. pedrosoi was determined according to CLSI and with the addition of tricyclazole (TCZ), to inhibit 1,8-dihydroxynaphthalene (DHN)-melanin biosynthesis. Strains were grown in Sabouraud agar and prepared for Attenuated Total Reflection (ATR)/FTIR. Partial least squares (PLS) regression was performed using leave-one-out cross-validation (by steps of quintuplicates), then tested on an external validation set. A coefficient of determination (R²) higher than 0.99 was obtained for both the MIC-ITC and MIC-ITC+TCZ ATR/PLS models, confirming a high correlation of the reference values with the ones predicted using the FTIR spectra. This is the first study to propose the use of FTIR and chemometric analyses according to the M38-A2 CLSI protocol to predict ITC MICs of F. pedrosoi. Considering the limitations of the conventional methods to test in vitro susceptibility, this is a promising methodology to be used for other microorganisms and drugs.

Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis

Pectobacterium and Dickeya spp. are soft rot Pectobacteriaceae that cause aggressive diseases on agricultural crops leading to substantial economic losses. The accurate, rapid and low-cost detection of these pathogenic bacteria are very important for controlling their spread, reducing the consequent financial loss and for producing uninfected potato seed tubers for future generations. Currently used methods for the identification of these bacterial pathogens at the strain level are based mainly on molecular techniques, which are expensive. We used an alternative method, infrared spectroscopy, to measure 24 strains of five species of Pectobacterium and Dickeya. Measurements were then analyzed using machine learning methods to differentiate among them at the genus, species and strain levels. Our results show that it is possible to differentiate among different bacterial pathogens with a success rate of ~99% at the genus and species levels and with a success rate of over 94% at the strain level.

ATR-FTIR spectroscopy non-destructively detects damage-induced sour rot infection in whole tomato fruit

ATR-FTIR spectroscopy with subsequent multivariate analysis non-destructively identifies plant-pathogen interactions during disease progression, both directly and indirectly, through alterations in the spectral fingerprint. Plant-environment interactions are essential to understanding crop biology, optimizing crop use, and minimizing loss to ensure food security. Damage-induced pathogen infection of delicate fruit crops such as tomato (Solanum lycopersicum) are therefore important processes related to crop biology and modern horticulture. Fruit epidermis as a first barrier at the plant-environment interface, is specifically involved in environmental interactions and often shows substantial structural and functional changes in response to unfavourable conditions. Methods available to investigate such systems in their native form, however, are limited by often required and destructive sample preparation, or scarce amounts of molecular level information. To explore biochemical changes and evaluate diagnostic potential for damage-induced pathogen infection of cherry tomato (cv. Piccolo) both directly and indirectly, mid-infrared (MIR) spectroscopy was applied in combination with exploratory multivariate analysis. ATR-FTIR fingerprint spectra (1800-900 cm^-1) of healthy, damaged or sour rot-infected tomato fruit were acquired and distinguished using principal component analysis and linear discriminant analysis (PCA-LDA). Main biochemical constituents of healthy tomato fruit epidermis are characterized while multivariate analysis discriminated subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato indirectly based solely on changes in the fruit epidermis. Sour rot causing agent Geotrichum candidum was detected directly in vivo and characterized based on spectral features distinct from tomato fruit. Diagnostic potential for indirect pathogen detection based on tomato fruit skin was evaluated using the linear discriminant classifier (PCA-LDC). Exploratory and diagnostic analysis of ATR-FTIR spectra offers biological insights and detection potential for intact plant-pathogen systems as they are found in horticultural industries.

Early diagnosis of Alzheimer's disease using infrared spectroscopy of isolated blood samples followed by multivariate analyses

Alzheimer's disease (AD) is the most common cause of dementia, particularly in the elderly. The disease is characterized by cognitive decline that typically starts with insidious memory loss and progresses relentlessly to produce global impairment of all higher cortical functions. Due to better living conditions and health facilities in developed countries, which result in higher overall life spans, these countries report upward trends of AD among their populations. There are, however, no specific diagnostic tests for AD and clinical diagnosis is especially difficult in the earliest stages of the disease. Early diagnosis of AD is frequently subjective and is determined by physicians (generally neurologists, geriatricians, and psychiatrists) depending on their experience. Diagnosing AD requires both medical history and mental status testing. Having trouble with memory does not mean you have AD. AD has no current cure, but treatments for symptoms are available and research continues. In this study, we investigated the potential of infrared microscopy to differentiate between AD patients and controls, using Fourier transform infrared (FTIR) spectroscopy of isolated blood components. FTIR is known as a quick, safe, and minimally invasive method to investigate biological samples. For this goal, we measured infrared spectra from white blood cells (WBCs) and plasma taken from AD patients and controls, with the consent of the patients or their guardians. Applying multivariate analysis, principal component analysis (PCA) followed by linear discriminant analysis (LDA), it was possible to differentiate among the different types of mild, moderate, and severe AD, and the controls, with 85% accuracy when using the WBC spectra and about 77% when using the plasma spectra. When only the moderate and severe stages were included, an 83% accuracy was obtained using the WBC spectra and about 89% when using the plasma spectra.

Differentiation of mixed soil-borne fungi in the genus level using infrared spectroscopy and multivariate analysis

Early detection of soil-borne pathogens, which have a negative effect on almost all agricultural crops, is crucial for effective targeting with the most suitable antifungal agents and thus preventing and/or reducing their severity. They are responsible for severe diseases in various plants, leading in many cases to substantial economic losses. In this study, infrared (IR) spectroscopic method, which is known as sensitive, accurate and rapid, was used to discriminate between different fungi in a mixture was evaluated. Mixed and pure samples of Colletotrichum, Verticillium, Rhizoctonia, and Fusarium genera were measured using IR microscopy. Our spectral results showed that the best differentiation between pure and mixed fungi was obtained in the 675-1800 cm^-1 wavenumber region. Principal components analysis (PCA), followed by linear discriminant analysis (LDA) as a linear classifier, was performed on the spectra of the measured classes. Our results showed that it is possible to differentiate between mixed-calculated categories of phytopathogens with high success rates (~100%) when the mixing percentage range is narrow (40-60) in the genus level; when the mixing percentage range is wide (10-90), the success rate exceeded 85%. Also, in the measured mixed categories of phytopathogens it is possible to differentiate between the different categories with ~100% success rate.

Tracing overlapping biological signals in mid-infrared using colonic tissues as a model system

AIM

To understand the interference of carbohydrates absorbance in nucleic acids signals during diagnosis of malignancy using Fourier transform infrared (FTIR) spectroscopy.

METHODS

We used formalin fixed paraffin embedded colonic tissues to obtain infrared (IR) spectra in the mid IR region using a bruker II IR microscope with a facility for varying the measurement area by varying the aperture available. Following this procedure we could measure different regions of the crypt circles containing different biochemicals. Crypts from 18 patients were measured. Circular crypts with a maximum diameter of 120 μm and a lumen of about 30 μm were selected for uniformity. The spectral data was analyzed using conventional and advanced computational methods.

RESULTS

Among the various components that are observed to contribute to the diagnostic capabilities of FTIR, the carbohydrates and nucleic acids are prominent. However there are intrinsic difficulties in the diagnostic capabilities due to the overlap of major absorbance bands of nucleic acids, carbohydrates and phospholipids in the mid-IR region. The result demonstrates colonic tissues as a biological system suitable for studying interference of carbohydrates and nucleic acids under ex vivo conditions. Among the diagnostic parameters that are affected by the absorbance from nucleic acids is the RNA/DNA ratio, dependent on absorbance at 1121 cm^-1 and 1020 cm^-1 that is used to classify the normal and cancerous tissues especially during FTIR based diagnosis of colonic malignancies. The signals of the nucleic acids and the ratio (RNA/DNA) are likely increased due to disappearance of interfering components like carbohydrates and phosphates along with an increase in amount of RNA.

CONCLUSION

The present work, proposes one mechanism for the observed changes in the nucleic acid absorbance in mid-IR during disease progression (carcinogenesis).

Detection of Vero Cells Infected with Herpes Simplex Types 1 and 2 and Varicella Zoster Viruses Using Raman Spectroscopy and Advanced Statistical Methods

Of the eight members of the herpes family of viruses, HSV1, HSV2, and varicella zoster are the most common and are mainly involved in cutaneous disorders. These viruses usually are not life-threatening, but in some cases they might cause serious infections to the eyes and the brain that can lead to blindness and possibly death. An effective drug (acyclovir and its derivatives) is available against these viruses. Therefore, early detection and identification of these viral infections is highly important for an effective treatment. Raman spectroscopy, which has been widely used in the past years in medicine and biology, was used as a powerful spectroscopic tool for the detection and identification of these viral infections in cell culture, due to its sensitivity, rapidity and reliability. Our results showed that it was possible to differentiate, with a 97% identification success rate, the uninfected Vero cells that served as a control, from the Vero cells that were infected with HSV-1, HSV-2, and VZV. For that, linear discriminant analysis (LDA) was performed on the Raman spectra after principal component analysis (PCA) with a leave one out (LOO) approach. Raman spectroscopy in tandem with PCA and LDA enable to differentiate among the different herpes viral infections of Vero cells in time span of few minutes with high accuracy rate. Understanding cell molecular changes due to herpes viral infections using Raman spectroscopy may help in early detection and effective treatment.

Distinction of fungal polysaccharides by N/C ratio and mid infrared spectroscopy

A set of fungal polysaccharide samples was characterised by elemental analysis and FTIR spectroscopy and compared with reference chitins, chitosans and β-D-glucans. The nitrogen to carbon (N/C) values and FTIR spectra were used to compare the samples based on their composition. It was found that the N/C ratio correlates well with deacetylation degree (DD) of chitosans and chitin/glucan ratio R(chit) of fungal chitin – β-D-glucan complexes with the exception of some samples having significant nitrogen and/or carbon admixtures. FTIR spectroscopy was indicative for the N-acetylation of chitins (chitosans) as well as for the chitin (chitosan) contribution to fungal polysaccharide preparations. Multivariate analyses of the FTIR data (HCA, PCA) discriminated samples and reference materials into several clusters depending on their similarity. Chitosan lactates, chitosan – β-D-glucans and chitin – β-D-glucans of high and low amounts of chitin were successfully discriminated from the reference polysaccharides and from each other. The proposed procedures based on the N/C ratio and multivariate analyses of FTIR spectra may be used in screening fungal polysaccharide preparations.

Assignment of Colletotrichum coccodes isolates into vegetative compatibility groups using infrared spectroscopy: a step towards practical application

FTIR spectroscopy may provide a specific, rapid, and inexpensive method for the successful classification of Colletotrichum coccodes isolates into vegetative compatibility groups.

Waveband selection of reagent-free determination for thalassemia screening indicators using Fourier transform infrared spectroscopy with attenuated total reflection

A reagent-free determination method for the thalassemia screening indicators hemoglobin (Hb), mean corpuscular Hb (MCH), and mean corpuscular volume (MCV) was developed based on Fourier transform infrared spectrometers equipped with an attenuated total reflection accessory. A random and stability-dependent rigorous process of calibration, prediction, and validation was conducted. Appropriate wavebands were selected using the improved moving window partial least squares method with stability and equivalence. The obtained optimal wavebands were 1722 to 1504 cm⁻¹ for Hb, 1653 to 901 cm⁻¹ for MCH, and 1562 to 964 cm⁻¹ for MCV. A model set equivalent to the optimal model was proposed for each indicator; the public waveband of Hb equivalent wavebands was 1717 to 1510 cm⁻¹, and the public equivalent waveband for MCH and MCV was 1562 to 901 cm⁻¹. All selected wavebands were within the MIR fingerprint region and achieved high validation effects. The sensitivity and specificity were 100.0% and 96.9% for the optimal wavebands and 100.0% and 95.3% for the equivalent wavebands, respectively. Thus, the spectral prediction was highly accurate for determining negative and positive for thalassemia screening. This technique is rapid and simple in comparison with conventional methods and is a promising tool for thalassemia screening in large populations.

Detection and identification of cancerous murine fibroblasts, transformed by murine sarcoma virus in culture, using Raman spectroscopy and advanced statistical methods

BACKGROUND

Cancer is one of the leading worldwide causes of death. It may be induced by a variety of factors, including carcinogens, radiation, genetic factors, or DNA and RNA viruses. The early detection of cancer is critical for its successful therapy, which can result in complete recovery from some types of cancer.

METHODS

Raman spectroscopy has been widely used in medicine and biology. It is a noninvasive, nondestructive, and water-insensitive technique that can detect changes in cells and tissues that are caused by different disorders, such as cancer. In this study, Raman spectroscopy was used for the identification and characterization of murine fibroblast cell lines (NIH/3T3) and malignant fibroblast cells transformed by murine sarcoma virus (NIH-MuSV) cells.

RESULTS

Using principal component analysis and LDA it was possible to differentiate between the NIH/3T3 and NIH-MuSV cells with an 80-85% success rate based on their Raman shift spectra.

CONCLUSIONS

The best results for differentiation were achieved from spectra that were obtained from the rich membrane sites.

GENERAL SIGNIFICANCE

Because of its homogeneity and complete control of most factors affecting its growth, cell culture is a preferred model for the detection and identification of specific biomarkers related to cancer transformation or other cellular modifications.

Utilizing FTIR-ATR spectroscopy for classification and relative spectral similarity evaluation of different Colletotrichum coccodes isolates

Colletotrichum coccodes (C. coccodes) is a pathogenic fungus which causes anthracnose on tomatoes and black dot disease in potatoes. It is important to differentiate among these isolates and to detect the origin of newly discovered isolates, in order to treat the disease in its early stages. However, distinguishing between isolates using common biological methods is time-consuming, and not always available. We used Fourier Transform Infra-Red (FTIR)-Attenuated Total Reflectance (ATR) spectroscopy and advanced mathematical and statistical methods to distinguish between different isolates of C. coccodes. To our knowledge, this is the first time that FTIR-ATR spectroscopy was used, combined with multivariate analysis, to classify such a large number of 15 isolates belonging to the same species. We obtained a success rate of approximately 90% which was achieved using the region 800-1775 cm(-1). In addition we succeeded in determining the relative spectral similarity between different fungal isolates by developing a new algorithm. This method could be an important potential diagnostic tool in agricultural research, since it may outline the extent of the biological similarity between fungal isolates. Based on the PCA calculations, we grouped the fifteen isolates included in this study into four different degrees of similarity.