Quantum Mechanical Simulation of Near‐Infrared Spectra: Applications in Physical and Analytical Chemistry

Beć, Krzysztof B.; Grabska, Justyna; Huck, Christian W.; Ozaki, Yukihiro

doi:10.1002/9783527814596.ch13

Cited by 21 publications

(9 citation statements)

References 65 publications

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“…Chapter 5 by Krzysztof B Beć, Justyna Grabska and Thomas S Hofer presents the background to the anharmonic quantum chemical approaches that are indispensable for that purpose. 6 The chapter gives the reader a good understanding of the underlying principles, beginning with the general framework of quantum chemistry for the description of molecular vibrations, the harmonic approximation and its role as the preliminary step for various anharmonic approaches. Various available routes to anharmonic treatment of molecular vibrations are presented, and their applicability to NIR spectroscopy is evaluated based on several examples.…”

Section: Overview Of the Book’s Contentmentioning

confidence: 99%

The comprehensive sourcebook for modern NIR spectroscopy: A commentary on “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications”

Beć¹,

Grabska²,

Huck³

2021

NIR news

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The instrumentation, methods and applications of near-infrared spectroscopy has advanced remarkably in the last decade, in which near-infrared spectroscopy has successfully progressed at multiple directions and faced new challenges. Thus, gaps inevitably appeared in the coverage provided by renowned and handy cornerstone textbooks focused on near-infrared spectroscopy that were published in the past. A demand grew in near-infrared spectroscopy community for a new state-of-the-art textbook. With aim to satisfy such need, a go-to-book for background theory, applications and tutorial “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications” was prepared. That full-scale project, edited by Yukihiro Ozaki, Christian Huck, Satoru Tsuchikawa and Søren B. Engelsen, comprises of 23 chapters contributed by scholars and practitioners pushing the frontier of near-infrared spectroscopy. The chapters scope on newly opened pathways, major breakthroughs in basic science and applications as well as revisit several other topics. The sourcebook is intended for a wide range of readers from graduate students to scientists and engineers in both academia and industry. In this article, we sketch the main features of the newly released sourcebook with aim to help the community members in deciding whether this book should find its place in their library.

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Section: Overview Of the Book’s Contentmentioning

confidence: 99%

The comprehensive sourcebook for modern NIR spectroscopy: A commentary on “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications”

Beć¹,

Grabska²,

Huck³

2021

NIR news

Self Cite

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“…In comparison, short-wave infrared (SWIR) spectroscopy achieves 0.5–5 mm penetration depth while tolerating the presence of water with the analyte . In fact, shifts in the water absorption band can be used as a molecular mirror that reflects the concentration of other solutes dissolved in the water hydrogen bond matrix. − This alleviates the need for both a submerged probe and sample processing prior to analysis. Both Raman and SWIR spectroscopy display highly nonlinear chemical vibrations that are difficult to simulate .…”

Section: Introductionmentioning

confidence: 99%

“…In fact, shifts in the water absorption band can be used as a molecular mirror that reflects the concentration of other solutes dissolved in the water hydrogen bond matrix. − This alleviates the need for both a submerged probe and sample processing prior to analysis. Both Raman and SWIR spectroscopy display highly nonlinear chemical vibrations that are difficult to simulate . These weak, nonlinear signals are challenging to analyze and quantify without the use of chemometric machine learning models. ,,, The use of fiber-optic probes limits dataset size to a few thousand samples, so only a handful of nonlinear chemometric analysis techniques have been applied to unravel these complex signals for metabolite regression.…”

Section: Introductionmentioning

confidence: 99%

Fast-Training Deep Learning Algorithm for Multiplex Quantification of Mammalian Bioproduction Metabolites via Contactless Short-Wave Infrared Hyperspectral Sensing

et al. 2023

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Within the biopharmaceutical sector, there exists the need for a contactless multiplex sensor, which can accurately detect metabolite levels in real time for precise feedback control of a bioreactor environment. Reported spectral sensors in the literature only work when fully submerged in the bioreactor and are subject to probe fouling due to a cell debris buildup. The use of a short-wave infrared (SWIR) hyperspectral (HS) cam era allows for efficient, fully contactless collection of large spectral datasets for metabolite quantification. Here, we report the development of an interpretable deep learning system, a convolution metabolite regression (CMR) approach that detects glucose and lactate concentrations using label-free contactless HS images of cell-free spent media samples from Chinese hamster ovary (CHO) cell growth flasks. Using a dataset of <500 HS images, these CMR algorithms achieved a competitive test root-mean-square error (RMSE) performance of glucose quantification within 27 mg/dL and lactate quantification within 20 mg/dL. Conventional Raman spectroscopy probes report a validation performance of 26 and 18 mg/dL for glucose and lactate, respectively. The CMR system trains within 10 epochs and uses a convolution encoder with a sparse bottleneck regression layer to pick the best-performing filters learned by CMR. Each of these filters is combined with existing interpretable models to produce a metabolite sensing system that automatically removes spurious predictions. Collectively, this work will advance the safe and efficient adoption of contactless deep learning sensing systems for fine control of a variety of bioreactor environments.

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“…In this respect, theoretical simulations are particularly adapted to such a purpose. 15,16 Two distinct strategies exist for computing IR spectra, namely, the normal mode analysis (NMA), 17,18 and the Fourier transform of time-dependent (TD) dipole autocorrelation functions extracted from molecular dynamics. 19,20 Both methods have their strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Infrared spectroscopy from electrostatic embedding QM/MM: local normal mode analysis of infrared spectra of arabidopsis thaliana plant cryptochrome

Huix‐Rotllant

Schwinn

Ferré

2021

Phys. Chem. Chem. Phys.

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Quantum Mechanical Simulation of Near‐Infrared Spectra: Applications in Physical and Analytical Chemistry

Cited by 21 publications

References 65 publications

The comprehensive sourcebook for modern NIR spectroscopy: A commentary on “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications”

The comprehensive sourcebook for modern NIR spectroscopy: A commentary on “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications”

Fast-Training Deep Learning Algorithm for Multiplex Quantification of Mammalian Bioproduction Metabolites via Contactless Short-Wave Infrared Hyperspectral Sensing

Infrared spectroscopy from electrostatic embedding QM/MM: local normal mode analysis of infrared spectra of arabidopsis thaliana plant cryptochrome

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