Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Gao, Chi; Zhao, Peng; Fan, Qi; Jing, Haonan; Dang, Ruochen; Sun, Wei; Feng, Yutao; Hu, Bingliang; Wang, Quan

doi:10.1016/j.saa.2023.123086

Cited by 14 publications

(1 citation statement)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wavelet requires artificial selection of the optimal basis function, number of layers, and threshold, otherwise it can lead to distortion of the corrected spectrum. 25 Moving-window averaging usually overestimates the baseline in the peak region and performs poorly if spectral peaks overlap. Robust baseline estimation requires manual specification of bandwidth and tuning parameters, and applies only to smooth, slowly varying baselines.…”

Section: Introductionmentioning

confidence: 99%

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

Li,

Tang,

Wang

et al. 2023

Anal. Methods

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

Li,

Tang,

Wang

et al. 2023

Anal. Methods

View full text Add to dashboard Cite

show abstract

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

View full text Add to dashboard Cite

Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

show abstract

Intracellular Calcium Dynamics in Primary Human Adrenocortical Cells Deciphered with a Novel Pipeline

Ajjour,

Pallafacchina,

Lenzini

et al. 2024

High Blood Press Cardiovasc Prev

View full text Add to dashboard Cite

Introduction The fluctuations of the intracellular Ca 2+ concentration ([Ca 2+ ] i ) are key physiological signals for cell function under normal conditions and can undergo profound alterations in disease states, as high blood pressure due to endocrine disorders like primary aldosteronism (PA). However, when assessing such fluctuations several parameters in the Ca 2+ signal dynamics need to be considered, which renders their assessment challenging. Aim Aim to develop an observer-independent custom-made pipeline to analyze Ca 2 + dynamics in terms of frequency and peak parameters, as amplitude, full width at half maximum (FWHM) and area under the curve (AUC). Methods We applied a custom-made methodology to aldosterone-producing adenoma (APA) and APA adjacent cells (AAC) and found this pipeline to be suitable for monitoring and processing a wide-range of [Ca 2+ ] i events in these cell types delivering reproducible results. Conclusion The designed pipeline can provide a useful tool for [Ca 2+ ] i signal analysis that allows comparisons of Ca 2+ dynamics not only in PA, but in other cell phenotypes that are relevant for the regulation of blood pressure. Supplementary Information The online version contains supplementary material available at 10.1007/s40292-024-00641-5.

show abstract

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Cited by 14 publications

References 84 publications

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Intracellular Calcium Dynamics in Primary Human Adrenocortical Cells Deciphered with a Novel Pipeline

Contact Info

Product

Resources

About