BALSAM—An Interactive Online Platform for Breath Analysis, Visualization and Classification

Weber, Peter; Pauling, Josch; List, Markus; Baumbach, Jan

doi:10.3390/metabo10100393

Cited by 6 publications

(3 citation statements)

References 50 publications

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“…Breath AnaLysis viSualizAtion Metabolite discovery (BALSAM), is an interactive web-based tool that integrates state-of-the-art preprocessing and analysis techniques for supervised feature extraction and visualization of multi capillary column—ion mobility spectrometry (MCC-IMS) data preprocessing workflows that deals with breath analysis (Weber et al 2020 ). In addition, it supports peak detection and peak alignment as well as RT based GC–MS and LC–MS data analysis.…”

Section: Multifunctional Toolsmentioning

confidence: 99%

New software tools, databases, and resources in metabolomics: updates from 2020

Misra

2021

Metabolomics

151

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Background Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. Aim of the review In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. Key scientific concepts of review In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

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Section: Multifunctional Toolsmentioning

confidence: 99%

New software tools, databases, and resources in metabolomics: updates from 2020

Misra

2021

Metabolomics

151

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“…Elimination of the impact of components with a significantly low signal-to-noise ratio and pollution present in Tedlar bags or measurement devices can be done by applying the manual prescreening method [67]. Reducing the number of redundant features can improve model accuracy and training speed by decreasing the computational complexity of algorithms [67,68,93] and prevent overfitting, which is common problem when analyzing data from multiple gas sensor matrices because the number of features may exceed the number of breath samples taken [24,120,121].…”

Section: Features Selectionmentioning

confidence: 99%

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Paleczek

Rydosz

2022

J. Breath Res.

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Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

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“…So far, about noise removing ways, median filtering and Gaussian filtering are normally used (Jiang, Zhang, Han, Liu, & Liu, 2013). The gaussianfilter eliminates noise by using a fixed size Gaussian kernel (Weber, Pauling, List, & Baumbach, 2020). Compared with the other filtering algorithm, Gaussian filtering has better smoothing effect and flexible filtering adjustment scale(G, L, K, & Y, 2019).…”

Section: Image Acquisition and Prepossessingmentioning

confidence: 99%

A new method for lint percentage non-destructive detection based on optical penetration imaging

Geng

Zhang

et al. 2022

Emir J Food Agric

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Lint percentage of seed cotton is one of the important bases for pricing in the trading segment. Unfortunately, the conventional methods of lint percentage are manually operated, which relies on the abundant experience of experts, and restrained by personal, physical and environmental factors. Up to date, the calculation of the lint percentage of seed cotton has not fully automated. In this paper, we proposed a non-destructive detection method for automatically obtaining lint percentage of seed cotton based on optical penetration imaging and machine vision, for the first time to our knowledge. The cotton seed image was obtained by the penetration imaging setup with a LED white backlight source. To accurately identify the number of cotton seeds, the image features of the cotton seed was studied and three key features was been found, which are the circumference, area, and greyscale value, respectively. A calculation system based on the three key features was presented to process the images and then automatically calculate the lint percentage of seed cotton. The first step of the system is to segment the original image using adaptive thresholding followed by morphological operations. Afterwards, the number of cotton seed was obtained by the three key features of the cotton seed. Then, the lint percentage was achieved by a professional industry formula. The suggested lint percentage detection methods were verified by the experiments with two seed cotton varieties samples of H219 and ZHM19. The experimental results indicated that the detection average accuracy of the developed system for seed cotton varieties H219 and ZHM19 were 96.33% and 95.40%

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BALSAM—An Interactive Online Platform for Breath Analysis, Visualization and Classification

Cited by 6 publications

References 50 publications

New software tools, databases, and resources in metabolomics: updates from 2020

New software tools, databases, and resources in metabolomics: updates from 2020

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

A new method for lint percentage non-destructive detection based on optical penetration imaging

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