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Molecules
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New Insights into Vibrational Spectroscopy and Imaging
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New Insights into Vibrational Spectroscopy and Imaging

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (2 February 2022) | Viewed by 8193

Special Issue Editor

Prof. Dr. Barry K. Lavine

E-Mail Website
Guest Editor
Department of Chemistry, College of Arts and Sciences, Oklahoma State University, Stillwater, OK 74078, USA
Interests: swellable polymers for optical pH sensing; vibrational spectroscopy; infrared imaging; forensic automotive paint analysis; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

This special issue will explore new developments and novel applications of infrared and Raman spectroscopy and spectroscopic imaging (including data analysis) to address questions that arise about the location and distribution of chemical species in a heterogamous sample as well as questions about the qualitative and quantitative composition of the sample. New insights about samples that are achieved and the multiplicity of applications for this methodology are the focus of this special issue. Specific application areas highlighted include but are not limited to quality assurance (e.g., pharmaceuticals), polymers, forensics, disease detection, food, and agriculture to name a few. Papers for this special issue of Molecules discussing recent methodological developments in these application areas including post processing of the spectroscopic and imaging data are also welcomed. The coupling of recent methodological developments in infrared and Raman including advances in data analysis with the breath of applications of infrared and Raman imaging encompasses an active area of research in modern analytical chemistry.     

Prof. Barry K. Lavine
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vibrational spectroscopy
  • infrared microscopic imaging
  • Raman microscopic imaging
  • microspectroscopy
  • chemometrics
  • machine learning
  • spectroscopy for disease detection

Published Papers (3 papers)

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Research

20 pages, 3424 KiB  
Article
Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models
by William E. Gilbraith, J. Chance Carter, Kristl L. Adams, Karl S. Booksh and Joshua M. Ottaway
Molecules 2021, 26(23), 7281; https://doi.org/10.3390/molecules26237281 - 30 Nov 2021
Cited by 9 | Viewed by 2624
Abstract
We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration [...] Read more.
We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils. Full article
(This article belongs to the Special Issue New Insights into Vibrational Spectroscopy and Imaging)
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19 pages, 5440 KiB  
Article
Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging
by Jun-Li Xu, Ana Herrero-Langreo, Sakshi Lamba, Mariateresa Ferone, Amalia G. M. Scannell, Vicky Caponigro and Aoife A. Gowen
Molecules 2021, 26(20), 6318; https://doi.org/10.3390/molecules26206318 - 19 Oct 2021
Cited by 5 | Viewed by 2398
Abstract
This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density [...] Read more.
This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing. Full article
(This article belongs to the Special Issue New Insights into Vibrational Spectroscopy and Imaging)
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16 pages, 2704 KiB  
Article
Possibility of Human Gender Recognition Using Raman Spectra of Teeth
by Ozren Gamulin, Marko Škrabić, Kristina Serec, Matej Par, Marija Baković, Maria Krajačić, Sanja Dolanski Babić, Nikola Šegedin, Aziz Osmani and Marin Vodanović
Molecules 2021, 26(13), 3983; https://doi.org/10.3390/molecules26133983 - 29 Jun 2021
Cited by 11 | Viewed by 2553
Abstract
Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and [...] Read more.
Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events. Full article
(This article belongs to the Special Issue New Insights into Vibrational Spectroscopy and Imaging)
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