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Molecules
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Vibrational Spectroscopy and Imaging for Chemical Application
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Vibrational Spectroscopy and Imaging for Chemical Application

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1540

Special Issue Editors

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: infrared spectroscopy and imaging; spectral library matching; forensic automotive paint analysis; chemometrics; bioinformatics; swellable pH sensitive polymers for optical sensing; chemical data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Karl Booksh

E-Mail Website
Guest Editor
Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, USA
Interests: in situ chemical sensors for environmental, biomedical, and industrial process monitoring; Raman; SPR; fiber optics; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will explore the techniques of vibrational (Raman and infrared) spectroscopy and imaging, which have the potential to acquire molecular information with little or no sample preparation. Raman and infrared spectroscopy are rapid, non-destructive, and often yield information-rich data on the chemical and physical properties of a sample. This Special Issue will highlight new technological developments in this area, including data analytics and novel applications of these methods to address questions about the qualitative and quantitative composition of samples and the location and distribution of chemical species in samples. In this issue, topics of interest within potential applications include disease detection, polymers, food adulteration, forensics, geochemistry, environmental analysis, and quality assurance in materials and pharmaceuticals. We are soliciting both original research papers and review articles that cover instrumentation, methodology, data analysis, and cutting-edge applications, a some combination of these, with impacts on the current state of analytical problem solving.

Prof. Dr. Barry K. Lavine
Prof. Dr. Karl Booksh
Guest Editors

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

  • Raman spectroscopy
  • infrared spectroscopy
  • Raman image analysis
  • infrared image analysis
  • chemometrics
  • multiway analysis
  • multivariate curve resolution
  • alternating least squares
  • multivariate image analysis
  • classification
  • machine learning

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Published Papers (3 papers)

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Research

14 pages, 2787 KiB  
Article
A Rapid Intelligent Screening of a Three-Band Index for Estimating Soil Copper Content
by Shiyao Liu, Shichao Cui, Rengui Wang, Minming Han and Jingtao Kou
Molecules 2025, 30(15), 3215; https://doi.org/10.3390/molecules30153215 - 31 Jul 2025
Viewed by 227
Abstract
Research has widely validated three-band spectral index as a simple, valid, and highly accurate method of estimating the copper content of soil. However, selecting the best band combination from hundreds of thousands, even millions of candidate combinations in hyperspectral data, is a very [...] Read more.
Research has widely validated three-band spectral index as a simple, valid, and highly accurate method of estimating the copper content of soil. However, selecting the best band combination from hundreds of thousands, even millions of candidate combinations in hyperspectral data, is a very complicated problem. To address this issue, this study collected a total of 170 soil samples from the Aktas copper-gold mining area in Fuyun County, Xinjiang, China. Then, two algorithms including Competitive Weighted Resampling (CARS) and Stepwise Regression Analysis (STE) were applied to pick the bands from the original and first-order derivative spectra, respectively. A three-band index model was developed using the selected feature bands to estimate soil copper content. Results showed the first-order derivative spectrum transforms the spectral curve into a sharper one, with more peaks and valleys, which is beneficial for increasing the correlation between bands and copper content compared with the original spectrum. Moreover, integrating first-order derivative spectroscopy with CARS makes it possible to precisely identify key spectral bands and outperforms the dimensionality-reduction capabilities compared with the integration of STE. This strategy drastically reduces the time spent screening and is proven to have similar model accuracy, as compared to the individual group lifting method. Specifically, it reduces the duration of an 8 h task down to a mere 2 s. An intelligent screening of three-band indices is proposed in this study as a method of rapidly estimating copper content in soil. Full article
(This article belongs to the Special Issue Vibrational Spectroscopy and Imaging for Chemical Application)
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21 pages, 1691 KiB  
Article
Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy
by Ebrahim Taghinezhad, Antoni Szumny, Adam Figiel, Ehsan Sheidaee, Sylwester Mazurek, Meysam Latifi-Amoghin, Hossein Bagherpour, Natalia Pachura and Jose Blasco
Molecules 2025, 30(14), 2938; https://doi.org/10.3390/molecules30142938 - 11 Jul 2025
Viewed by 323
Abstract
Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different [...] Read more.
Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R2 values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R2 values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R2 values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R2 ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R2 < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T2 analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article
(This article belongs to the Special Issue Vibrational Spectroscopy and Imaging for Chemical Application)
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20 pages, 2562 KiB  
Article
Application of Fourier Transform Near-Infrared Spectroscopy and Chemometrics for Quantitative Analysis of Milk of Lime (MOL) Used in the Sugar Industry
by Radosław Michał Gruska, Alina Kunicka-Styczyńska and Magdalena Molska
Molecules 2025, 30(11), 2308; https://doi.org/10.3390/molecules30112308 - 24 May 2025
Viewed by 723
Abstract
Milk of lime (MOL), a suspension of calcium oxide and calcium hydroxide, is vital in the purification of sugar beet and cane juices. This study evaluates the application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with chemometric models—Partial Least Squares (PLS) and Principal [...] Read more.
Milk of lime (MOL), a suspension of calcium oxide and calcium hydroxide, is vital in the purification of sugar beet and cane juices. This study evaluates the application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with chemometric models—Partial Least Squares (PLS) and Principal Component Regression (PCR)—for rapid, non-destructive assessment of key MOL parameters: density, total lime content, calcium oxide availability, and sucrose content. Ninety samples were analyzed using both wet chemistry and FT-NIR. The predictive performance was assessed using the coefficient of determination (R2). High predictive accuracy was observed for density (PLS: R2 = 0.8274; PCR: R2 = 0.8795) and calcium oxide availability (PLS: R2 = 0.9035; PCR: R2 = 0.9115). Total lime content showed moderate accuracy (PLS: R2 = 0.7748; PCR: R2 = 0.7983), while sucrose content exhibited low predictive power (PLS: R2 = 0.2312; PCR: R2 = 0.3747). The weak performance was noted for %CaO (PLS: R2 = 0.4893; PCR: R2 = 0.2409), likely due to spectral overlap and matrix complexity. Despite these challenges, FT-NIR remains a viable, reagent-free method for monitoring MOL, with the potential to enhance process control in the sugar industry. Future work should focus on refining calibration strategies and addressing spectral interferences to improve predictive accuracy for complex matrices. Full article
(This article belongs to the Special Issue Vibrational Spectroscopy and Imaging for Chemical Application)
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