Comprehensive Chemometrics

Guo, Shuxia; Ryabchykov, Oleg; Ali, Nairveen; Houhou, Rola; Bocklitz, Thomas

doi:10.1016/b978-0-12-409547-2.14600-1

Cited by 8 publications

(4 citation statements)

References 99 publications

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“…clinical samples, maybe because complex models are often required and the spectra alone usually cannot be directly interpreted [ 75 ]. Further reasons may include different data pretreatment methods or the fact that chemometric methods are required in the first place to convert the spectral data into easily interpretable information [ 76 ]. In this regard, projects like RAMANMETRIX [ 75 ] are interesting, where users can apply relatively simple chemometric methods to their Raman data, and even some data sets are freely available.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

Tewes,

Kerst,

Pavlov

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

Tewes,

Kerst,

Pavlov

et al. 2024

Heliyon

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“…Although the general study workflow can be followed for other studies, all parts of the study such as the type of the sample and Raman spectroscopic alterations because of the external factor will influence the spectral data. To tackle this proper calibration, model transfer methods or fine-tuning of the DCNN needs to be performed . Overall, utilizing Raman spectroscopy along with DCNNs for characterizing inflammation stages in UC can provide not only a minimal-risk diagnostic procedure but also a real-time decision-making system.…”

Section: Discussionmentioning

confidence: 99%

“…To tackle this proper calibration, model transfer methods or fine-tuning of the DCNN needs to be performed. 34 Overall, utilizing Raman spectroscopy along with DCNNs for characterizing inflammation stages in UC can provide not only a minimal-risk diagnostic procedure but also a real-time decision-making system.…”

Section: ■ Conclusionmentioning

confidence: 99%

Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy

et al. 2020

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Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.

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“…It converts raw spectral data into useful information to achieve aims including quantitative analysis, pattern recognition, and data interpretation, in addition to accurate molecular identification. Raman spectroscopic analysis protocols and pipelines are explained comprehensively in references [ 259 , 260 , 261 , 262 ].…”

Section: Statistical Evaluation and Data Modelingmentioning

confidence: 99%

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Frempong,

Salbreiter,

Mostafapour

et al. 2024

Molecules

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Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

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Comprehensive Chemometrics

Cited by 8 publications

References 99 publications

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

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