scholarly journals Benchmarking feature quality assurance strategies for non-targeted metabolomics

2021 ◽  
Author(s):  
Yasin El Abiead ◽  
Maximilian Milford ◽  
Harald Schoeny ◽  
Mate Rusz ◽  
Reza M Salek ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Liquid Chromatography ◽  
Deep Learning ◽  
Performance Metrics ◽  
Targeted Metabolomics ◽  
Mass Spec ◽  
Chromatographic Peaks ◽  
Resolution Mass

Automated data pre-processing (DPP) forms the basis of any liquid chromatography-high resolution mass spec-trometry-driven non-targeted metabolomics experiment. However, current strategies for quality control of this im-portant step have rarely been investigated or even discussed. We exemplified how reliable benchmark peak lists could be generated for eleven publicly available datasets acquired across different instrumental platforms. Moreover, we demonstrated how these benchmarks can be utilized to derive performance metrics for DPP and tested whether these metrics can be generalized for entire datasets. Relying on this principle, we cross-validated different strategies for quality assurance of DPP, including manual parameter adjustment, variance of replicate injection-based metrics, unsupervised clustering performance, automated parameter optimization, and deep learning-based classification of chromatographic peaks. Overall, we want to highlight the importance of assessing DPP performance on a regular basis.

scholarly journals L-TARTARIC ACID: PRODUCTION TECHNOLOGY, ECONOMIC GROWTH AND QUALITY CONTROL

2018 ◽  
Vol 15 (30) ◽  
pp. 12-18
Author(s):  
G. D. LEIROSE ◽  
M-F GRENIER-LOUS TALOT ◽  
A. H. OLIVEIRA
Keyword(s):  
Economic Growth ◽  
Hydrogen Peroxide ◽  
Quality Control ◽  
Quality Assurance ◽  
Tartaric Acid ◽  
Molecular Formula ◽  
Natural Substances ◽  
By Products ◽  
Final Consumer

Natural substances are the basis of many types of industries and represent a growing market. The study of these products and the development of analytical methods should accompany this growth to ensure quality and provenance to consumers. An example to be discussed is the L(+)-Tartaric acid, an organic compound of molecular formula C4H6O6. This organic acid is widely applied in wine, food and pharmaceutical industry. It is obtained naturally through the fermentation of fruits, especially grape and tamarind. Synthetically, there are two ways of obtaining L(+)-tartaric acid on industrial scale. It can be synthesized by the reaction of maleic anhydride with hydrogen peroxide, which is derived from petroleum by-products. And by biotechnological synthesis, in which cis-epoxy succinic acid, also derived from petroleum, is converted into L(+)-tartaric acid by hydrolase enzyme. The market for tartaric acid is growing and is considered promising. Currently, there is a lack of legislation and specific rules that allow classification of tartaric acid according to its origin. This legal vacuum precludes quality assurance for the consumer. This lack of safety is a matter of great concern as applications of tartaric acid come directly to final consumer.

scholarly journals Non-Targeted Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) Fingerprints for the Chemometric Characterization and Classification of Turmeric and Curry Samples

Separations ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Nerea Núñez ◽  
Oscar Vidal-Casanella ◽  
Sonia Sentellas ◽  
Javier Saurina ◽  
Oscar Núñez
Keyword(s):  
Mass Spectrometry ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Resolution ◽  
High Performance ◽  
High Resolution Mass Spectrometry ◽  
Curcuma Longa ◽  
Chemical Descriptors ◽  
Resolution Mass

In this work, non-targeted ultra-high performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) fingerprints obtained by C18 reversed-phase chromatography were proposed as sample chemical descriptors for the characterization and classification of turmeric and curry samples. A total of 21 turmeric and 9 curry commercially available samples were analyzed in triplicate after extraction with dimethyl sulfoxide (DMSO). The results demonstrated the feasibility of non-targeted UHPLC-HRMS fingerprints for sample classification, showing very good classification capabilities by partial least squares regression-discriminant analysis (PLS-DA), with 100% classification rates being obtained by PLS-DA when randomly selected samples were processed as “unknown” ones. Besides, turmeric curcuma species (Curcuma longa vs. Curcuma zedoaria) and turmeric Curcuma longa varieties (Madras, Erodes, and Alleppey) discrimination was also observed by PLS-DA when using the proposed fingerprints as chemical descriptors. As a conclusion, non-targeted UHPLC-HRMS fingerprinting is a suitable methodology for the characterization, classification, and authentication of turmeric and curry samples, without the requirement of using commercially available standards for quantification nor the necessity of metabolite identification.

Human and Organization Errors in Reliability of Offshore Structures

1997 ◽  
Vol 119 (1) ◽  
pp. 46-52 ◽  
Author(s):  
R. G. Bea
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Life Cycle ◽  
Offshore Structures ◽  
Quality Characteristics ◽  
Assessment Process ◽  
Environmental Considerations ◽  
Generic Process

This paper addresses human and organization errors (HOE) in the context of quantitative reliability analyses (QRA) that are intended to help improve the quality of offshore structures. A classification of HOE is proposed that addresses individual, organization, equipment/hardware, procedures/software, and environmental considerations. A generic process to address the life-cycle quality characteristics of offshore structures incorporating HOE is discussed. Based on these developments, a probability-based risk analysis is developed that addresses HOE in addition to the structure system aspects. Alternatives for improved management of HOE are discussed including quality assurance and quality control (QA/QC), and design of error-tolerant or “robust” structures. Application of the HOE classification, generic assessment process, QRA formulation, and QA/QC measures are illustrated.

Developments in mycotoxin analysis: an update for 2019-2020

2021 ◽  
Vol 14 (1) ◽  
pp. 3-26 ◽  
Author(s):  
S.A. Tittlemier ◽  
J. Brunkhorst ◽  
B. Cramer ◽  
M.C. DeRosa ◽  
V.M.T. Lattanzio ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Quality Assurance ◽  
High Resolution Mass Spectrometry ◽  
Tandem Mass ◽  
Mass Spectrometry Detection ◽  
Recent Developments ◽  
Mycotoxin Analysis ◽  
Instrumental Methods ◽  
Resolution Mass

This review summarises developments on the analysis of various matrices for mycotoxins published in the period from mid-2019 to mid-2020. Notable developments in all aspects of mycotoxin analysis, from sampling and quality assurance/quality control of analytical results, to the various detection and quantitation technologies ranging from single mycotoxin biosensors to comprehensive instrumental methods are presented and discussed. Aside from sampling and quality control, discussion of this past year’s developments is organised by detection and quantitation technology and covers chromatography with targeted or non-targeted high resolution mass spectrometry, tandem mass spectrometry, detection other than mass spectrometry, biosensors, as well as assays that use alternatives to antibodies. This critical review aims to briefly present the most important recent developments and trends in mycotoxin determination as well as to address limitations of the presented methodologies.

scholarly journals Efficient few-shot machine learning for classification of EBSD patterns

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin Kaufmann ◽  
Hobson Lane ◽  
Xiao Liu ◽  
Kenneth S. Vecchio
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Performance Metrics ◽  
Materials Science ◽  
Electron Backscatter Diffraction ◽  
Point Group ◽  
Phase Identification ◽  
Classification Problems

AbstractDeep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating the training of deep learning models such as convolutional neural networks is the transfer of weights from models trained on unrelated image classification problems, commonly referred to as transfer learning. The powerful feature extractors learned previously can potentially be fine-tuned for a new classification problem without hindering performance. Transfer learning can also improve the results of training a model using a small amount of data, known as few-shot learning. Herein, we test the effectiveness of a few-shot transfer learning approach for the classification of electron backscatter diffraction (EBSD) pattern images to six space groups within the $$\left( {4/m \overline {3} 2/m} \right)$$ 4 / m 3 ¯ 2 / m point group. Training history and performance metrics are compared with a model of the same architecture trained from scratch. In an effort to make this approach more explainable, visualization of filters, activation maps, and Shapley values are utilized to provide insight into the model’s operations. The applicability to real-world phase identification and differentiation is demonstrated using dual phase materials that are challenging to analyze with traditional methods.

scholarly journals Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shintaro Sukegawa ◽  
Tamamo Matsuyama ◽  
Futa Tanaka ◽  
Takeshi Hara ◽  
Kazumasa Yoshii ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Maxillofacial Surgery ◽  
Effect Sizes ◽  
Medium Effect ◽  
Third Molars ◽  
Single Task ◽  
Task Learning ◽  
Mandibular Third Molars

AbstractPell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter’s classifications for specific respective tasks.

scholarly journals Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

2021 ◽  
Vol 7 (2) ◽  
pp. 879-882
Author(s):  
Elmer Jeto Gomes Ataide ◽  
Shubham Agrawal ◽  
Aishwarya Jauhari ◽  
Axel Boese ◽  
Alfredol Illanes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Thyroid Nodules ◽  
Performance Metrics ◽  
Diagnostic System ◽  
Diagnostic Tools ◽  
Observer Variability ◽  
Dice Coefficient ◽  
Computer Aided

Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability

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