Automatic classification of Candida species using Raman spectroscopy and machine learning

One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.This work was supported by the R + D projects INNVAL19/17 (funded by Instituto de Investigación Valdecilla-IDIVAL), PID2019-107270RB-C21 (funded by MCIN/ AEI /10.13039/501100011033) and by Plan Nacional de I + D + and Instituto de Salud Carlos III (ISCIII), Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades, Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0007), CIBERINFEC (CB21/13/00068), CIBER-BBN (BBNGC1601), cofinanced by European Development Regional Fund “A way to achieve Europe”. A. A. O.-S was financially supported by the Miguel Servet II program (ISCIII-CPII17-00011)

Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wavelength under controlled laboratory conditions. Results demonstrated the capability of Raman spectroscopy to identify an unknown substance to be semen, blood or saliva with high confidence

Rapid determination of pork sensory quality using Raman spectroscopy

Currently existing objective methods to evaluate tenderness and chewiness of pork in general do not yield satisfactory correlation to sensory panel evaluations, and their applications in meat industry are hence limited. In this study, a Raman spectrosensing method was developed to evaluate and predict tenderness and chewiness of pork loins. Raman spectroscopic binary barcodes for pork loins from 169 pigs were created based on their spectroscopic characteristics, and multivariate statistical discriminant model was developed based on the Raman barcodes to differentiate and classify pork loins into tenderness grades. Good agreement (\u3e 82% correct predictions) with sensory panel results were obtained especially for pork loins that were at the extreme ends of tenderness (tenderness score \u3c 8 or \u3e 11) and chewiness (chewiness score \u3c 2 and \u3e 4) spectrum. The method developed in this report has the potential to become a rapid objective assay for tenderness and chewiness of pork products that may find practical applications in pork industry

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available

A multiplex self-referencing detection of pathogens using surface enhanced raman scattering nanoprobes with a nano-DEP microfluidic concentrator

In this dissertation, I successfully developed the multiplex self-referencing SERS pathogen (E.coli O157: H7) detection biosensor platform which offers high sensitivity (10^1 CFU/mL) and strain level discrimination by measuring the superimposed SERS signatures with multiple characteristic peaks. To harvest the effective Raman molecular probes, I developed methods to fabricate anisotropic metallic nanoparticles to serve as SERS enhancers, and more importantly, I developed surface modification methodology to add functionality to the SERS enhancers so that they can bind specifically to their pathogen targets for highly accurate and sensitive detection. Gold nanorods (GNRs) and gold/silver nanocages are successfully fabricated with high particle yield. Three highly effective linker molecules (4-Aminothiophenol (4-ATP), 3-Amino-1,2,4-triazole-5-thiol (ATT), and 3-Mercaptopropionic acid (3-MPA)) are identified and designed to conjugate on gold nanostructures, and then different monoclonal antibody molecules are designed to bond to the different linkers through diazo-histine binding (4-ATP and ATT) and EDC/NHS bonding (3-MPA-antibody). In addition, this platform demonstrated excellent separation and concentration capacities by using DEP microfluidic devices and further improves the sensitivity to 10^0 CFU/mL. The integration of microfluidic devices with SERS detection has yielded simple and miniaturized instrumentation that is suitable for the detection and characterization of small volume of chemical and biological analytes with high sensitivity and specificity. For data analysis, various preprocessing methods are used for spectral background removal, baseline correction, smoothing, and normalization. Principle Component Analysis (PCA) is applied to reduce the variable dimensions. A Support Vector Machine (SVM) discriminant analysis model based on statistical multivariate model is being developed for superimposed spectra classification. The validation of spectral classification model (target binding VS no target binding) is evaluated by the accuracy percentage, which is above 95%

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination

Soft sensor based on Raman spectroscopy for the in-line monitoring of metabolites and polymer quality in the biomanufacturing of polyhydroxyalkanoates

Polyhydroxyalkanoates (PHA) are among the most promising bio-based alternatives to conventional petroleum-based plastics. These biodegradable polyesters can in fact be produced by fermentation from bacteria like Cupriavidus necator, thus reducing the environmental footprint of the manufacturing process. However, ensuring consistent product quality attributes is a major challenge of biomanufacturing. To address this issue, the implementation of real-time monitoring tools is essential to increase process understanding, enable a prompt response to possible process deviations and realize on-line process optimization. In this work, a soft sensor based on in situ Raman spectroscopy was developed and applied to the in-line monitoring of PHA biomanufacturing. This strategy allows the collection of quantitative information directly from the culture broth, without the need for sampling, and at high frequency. In fact, through an optimized multivariate data analysis pipeline, this soft sensor allows monitoring cell dry weight, as well as carbon and nitrogen source concentrations with root mean squared errors (RMSE) equal to 3.71, 7 and 0.03 g/L, respectively. In addition, this tool allows the in-line monitoring of intracellular PHA accumulation, with an RMSE of 14 gPHA/gCells. For the first time, also the number and weight average molecular weights of the polymer produced could be monitored, with RMSE of 8.7E4 and 11.6E4 g/mol, respectively. Overall, this work demonstrates the potential of Raman spectroscopy in the in -line monitoring of biotechnology processes, leading to the simultaneous measurement of several process variables in real time without the need of sampling and labor-intensive sample preparations

Methods of Classification of the Genera and Species of Bacteria Using Decision Tree, Journal of Telecommunications and Information Technology, 2019, nr 4

This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristic

Sensor based pre-symptomatic detection of pests and pathogens for precision scheduling of crop protection products

Providing global food security requires a better understanding of how plants function and how their products, including important crops are influenced by environmental factors. Prominent biological factors influencing food security are pests and pathogens of plants and crops. Traditional pest control, however, has involved chemicals that are harmful to the environment and human health, leading to a focus on sustainability and prevention with regards to modern crop protection. A variety of physical and chemical analytical tools is available to study the structure and function of plants at the whole-plant, organ, tissue, cellular, and biochemical levels, while acting as sensors for decision making in the applied crop sciences. Vibrational spectroscopy, among them mid-infrared and Raman spectroscopy in biology, known as biospectroscopy are well-established label-free, nondestructive, and environmentally friendly analytical methods that generate a spectral “signature” of samples using mid-infrared radiation. The generated wavenumber spectrum containing hundreds of variables as unique as a biochemical “fingerprint”, and represents biomolecules (proteins, lipids, carbohydrates, nucleic acids) within biological samples. Spectral “biomarkers” generated by biospectroscopy is useful for the discrimination of distinct as well as closely related biomaterials, for various applications. Applications within the plant and crop sciences has been limited to date, especially for the investigation of dynamic biological processes in intact plant tissues. Even more scarce is the application of biospectroscopy to plant interactions with pests and pathogens. To adequately probe in vivo plant-environment interactions, surface structures of intact plant tissues such as leaves, and fruit need to be characterized. Infrared light energy can measure plant epidermal structures including the cuticle and cell wall for chemical profiling of different varieties and cultivars, as well as physiological applications such as plant health monitoring and disease detection. A review of the application of biospectroscopy to study plant and crop biology reveals the potential of biospectroscopy as a prominent technology for fundamental plant research and applied crop science. The application of biospectroscopy for in vivo plant analysis, to elucidate spectral alterations indicative of pest and pathogen effects, may therefore be highly beneficial to crop protection. Highlighting the in vivo analysis capability and portability of modern biospectroscopy, ATR-FTIR provided an invaluable tool for a thorough spectrochemical investigation of intact tomato fruit during development and ripening. This contributes novel spectral biomarkers, distinct for each development and ripening stage to indicate healthy development. Concurrently, this approach demonstrates the effectiveness of using spectral data for machine learning, indicated by classifier results, which may be applied to crop biology. Complementary to monitoring healthy growth and development of plants and crops, is the detection of threats to plant products that compromise yield or quality. This includes physical damage and accelerated decay caused by pests and pathogens. Biochemical changes detected by ATR-FTIR using principal component analysis and linear discriminant analysis (PCA–LDA), for damage-induced pathogen infection of cherry tomato (cv. Piccolo), showed subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato. Sour rot fungus Geotrichum candidum was detected in vivo and characterized based on spectral features distinct from tomato fruit providing biochemical insight and detection potential for intact plant–pathogen systems. Pre-harvest detection of pests and pathogens in growing plants is paramount for crop protection and for effective use of crop protection products. Established previously as an exceptionally versatile bioanalytical sensor, for post-harvest applications, biospectroscopy was applied for the pre-harvest detection of microscopic pathogen Botrytis cinerea fungus infecting developing tomato plants. Compact MIR spectroscopy using ATR mode was adapted for the biochemical investigation of the plant-microbe interaction S. lycopersicum and B. cinerea, on the whole-plant level. Chemometric modeling including principal component analysis, and linear discriminant analysis were applied. Fingerprint spectra (1800-900 cm-1) were excellent discriminators of plant disease in pre-symptomatic as well as symptomatic plants. Spectral alterations in leaf tissue caused by infection are discussed. Potential for automatic decision-making is shown by high accuracy rates of 100% for detecting plant disease at various stages of progression. Similar accuracy rates using similar chemometric models are obtained for fruit development and ripening also. Overall, this research showcases the biospectroscopy potential for development monitoring and ripening of fruit crops, damage and infection induced decay of fruit in horticultural systems post-harvest, complemented by pre-harvest detection of microscopic pathogens. Based on the results from experiments performed under semi-controlled conditions, biospectroscopy is ready for field applications directed at pest and pathogen detection for improved crop production through the mitigation of crop loss