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Optical Multisensor Systems for Chemical Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3367

Special Issue Editors


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Guest Editor
Department of Analytical and Physical Chemistry, Faculty of Chemical Engineering, Samara State Technical University, 244 Molodogvardeyskaya Str., 443100 Samara, Russia
Interests: optical multisensor system development; fibre-based spectroscopy; in-line process analysis; monitoring of biotechnological processes; milk quality analysis; chemometrics

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Guest Editor
Department of Analytical and Physical Chemistry, Faculty of Chemical Engineering, Samara State Technical University, 244 Molodogvardeyskaya Str., 443100 Samara, Russia
Interests: data analysis computer simulation of spectral data; optimization of multiparametric systems; modelling of phase diagrams “composition-property”

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Guest Editor
Department of Analytical and Physical Chemistry, Faculty of Chemical Engineering, Samara State Technical University, 244 Molodogvardeyskaya Str., 443100 Samara, Russia
Interests: analytical chemistry; optical spectroscopy; spectral analysis of milk and dairy products; chemometrics; optical multisensor systems

Special Issue Information

Dear Colleagues,

Optical sensing is starting to compete with the traditional lab spectroscopic methods in the chemical analysis of industrial, environmental, medical, and other complex samples. There is a growing demand for inexpensive compact analysers for the field, in-line, and express analysis of various objects. Such devices are carefully optimized for a particular application by including a few optical channels sensitive to different (and possibly interfering) component signals in a mixture, hence the name of this class of analysers—optical multisensor systems. The sensory channels may belong to different spectral regions and even be based on different physical principles, but their data are analysed jointly using the methods of chemometrics. This Special Issue is intended to be a collection of scientific works aimed at the development and testing of novel optical analysers using the concept of multisensory measurement.

This topic is conceptually focused on the development of modern optical sensing devices, as an alternative to the traditional lab spectroscopy, and therefore ideally fits within the scope of

Prof. Dr. Andrey Bogomolov
Dr. Elena Moshchenskaya
Dr. Anastasiia Surkova
Guest Editors

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Keywords

  • chemical analysis
  • optical multisensor system
  • spectroscopy
  • chemometrics
  • mixture analysis

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Published Papers (2 papers)

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Research

14 pages, 2402 KiB  
Article
A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils
by Arseniy A. Pypin, Anna V. Shik, Irina A. Stepanova, Irina A. Doroshenko, Tatyana A. Podrugina and Mikhail K. Beklemishev
Sensors 2023, 23(18), 7682; https://doi.org/10.3390/s23187682 - 6 Sep 2023
Cited by 2 | Viewed by 959
Abstract
Optical “fingerprints” are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining “static” spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this [...] Read more.
Optical “fingerprints” are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining “static” spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20–55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy. Full article
(This article belongs to the Special Issue Optical Multisensor Systems for Chemical Analysis)
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13 pages, 3991 KiB  
Article
LED-Based Desktop Analyzer for Fat Content Determination in Milk
by Anastasiia Surkova, Yana Shmakova, Marina Salukova, Natalya Samokhina, Julia Kostyuchenko, Alina Parshina, Ildar Ibatullin, Viacheslav Artyushenko and Andrey Bogomolov
Sensors 2023, 23(15), 6861; https://doi.org/10.3390/s23156861 - 1 Aug 2023
Cited by 1 | Viewed by 1746
Abstract
In dairy, there is a growing request for laboratory analysis of the main nutrients in milk. High throughput of analysis, low cost, and portability are becoming critical factors to provide the necessary level of control in milk collection, processing, and sale. A portable [...] Read more.
In dairy, there is a growing request for laboratory analysis of the main nutrients in milk. High throughput of analysis, low cost, and portability are becoming critical factors to provide the necessary level of control in milk collection, processing, and sale. A portable desktop analyzer, including three light-emitting diodes (LEDs) in the visible light region, has been constructed and tested for the determination of fat content in homogenized and raw cow’s milk. The method is based on the concentration dependencies of light scattering by milk fat globules at three different wavelengths. Univariate and multivariate models were built and compared. The red channel has shown the best performance in prediction. However, the joint use of all three LED signals led to an improvement in the calibration model. The obtained preliminary results have shown that the developed LED-based technique can be sufficiently accurate for the analysis of milk fat content. The ways of its further development and improvement have been discussed. Full article
(This article belongs to the Special Issue Optical Multisensor Systems for Chemical Analysis)
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