A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations

2021 ◽  
Author(s):  
Jinxiao Zhang ◽  
Sheng Ye ◽  
Kai Zhong ◽  
Yaolong Zhang ◽  
Yuanyuan Chong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Absorption Spectroscopy ◽  
Protein Backbone ◽  
Environmental Fluctuations

scholarly journals Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1241
Author(s):  
Nikolaos Gyftokostas ◽  
Eleni Nanou ◽  
Dimitrios Stefas ◽  
Vasileios Kokkinos ◽  
Christos Bouras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Olive Oil ◽  
Absorption Spectra ◽  
Absorption Spectroscopy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spectroscopic Techniques ◽  
Breakdown Spectroscopy ◽  
Olive Oils ◽  
Laser Induced Breakdown

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

scholarly journals Protein backbone angle prediction with machine learning approaches

2004 ◽  
Vol 20 (10) ◽  
pp. 1612-1621 ◽  
Author(s):  
R. Kuang ◽  
C. S. Leslie ◽  
A.-S. Yang
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Protein Backbone

scholarly journals Application of Ultraviolet-Visible Absorption Spectroscopy with Machine Learning Techniques for the Classification of Cretan Wines

Foods ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Aggelos Philippidis ◽  
Emmanouil Poulakis ◽  
Renate Kontzedaki ◽  
Emmanouil Orfanakis ◽  
Aikaterini Symianaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Absorption Spectroscopy ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Visible Absorption ◽  
Wine Analysis ◽  
Aging Period ◽  
Wine Aging ◽  
Container Type ◽  
Visible Absorption Spectroscopy

The present study was aimed at the identification, differentiation and characterization of red and white Cretan wines, which are described with Protected Geographical Indication (PGI), using ultraviolet–visible absorption spectroscopy. Specifically, the grape variety, the wine aging process and the role of barrel/container type were investigated. The combination of spectroscopic results with machine learning-based modelling demonstrated the use of absorption spectroscopy as a facile and low-cost technique in wine analysis. In this study, a clear discrimination among grape varieties was revealed. Moreover, a grouping of samples according to aging period and container type of maturation was accomplished, for the first time.

Probing Active Sites in CuxPdy Cluster Catalysts by Machine-Learning-Assisted X-ray Absorption Spectroscopy

2021 ◽  
Author(s):  
Yang Liu ◽  
Avik Halder ◽  
Soenke Seifert ◽  
Nicholas Marcella ◽  
Stefan Vajda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Absorption Spectroscopy ◽  
Active Sites ◽  
X Ray ◽  
X Ray Absorption

scholarly journals A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements

2020 ◽  
Vol 13 (10) ◽  
pp. 5537-5550
Author(s):  
Yun Dong ◽  
Elena Spinei ◽  
Anuj Karpatne
Keyword(s):  
Machine Learning ◽  
Absorption Spectroscopy ◽  
Extinction Coefficient ◽  
Short Term Memory ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Asymmetry Factor ◽  
Optical Absorption Spectroscopy ◽  
Aerosol Extinction ◽  
Aerosol Extinction Coefficient

Abstract. In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single-scattering albedo and asymmetry parameter at 360 nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan. Our method relies on a multi-output sequence-to-sequence model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for profile prediction. The model was trained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) v2.7, which contains 1 459 200 unique mappings. From the simulations, 75 % were randomly selected for training and the remaining 25 % for validation. The overall error of estimated aerosol properties (1) for total aerosol optical depth (AOD) is -1.4±10.1 %, (2) for the single-scattering albedo is 0.1±3.6 %, and (3) for the asymmetry factor is -0.1±2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

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