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Novel Raman-Based Spectroscopic Techniques for Bio and Chemical Sensing Application

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Dear Colleagues,

Sir Chandrasekhara Venkata Raman discovered Raman scattering in 1928. Since then, the possibility of studying the chemical composition of a sample simply by probing it with a light illumination has attracted massive interest in multiple fields of applications.

Raman spectroscopy is now widely employed in semiconductor manufactory, gemmology, and the pharmaceutical industry; furthermore, its application in biomedicine, food safety, and environmental protection is gaining importance.

The focus on identifying new applications for Raman spectroscopy also drove new methods' development. As a result, Raman-based techniques such as Surface Enhanced Raman Spectroscopy (SERS), aimed at improving the sensitivity, or Spatially offset Raman spectroscopy (SORS), able to collect signals below the surface of the sample, are now available.

This Special Issue intends to cover the latest developments in using Raman spectroscopy to explore samples' chemical composition or detect elusive analytes by looking at new applications and new technologies.

Potential topics of interest for this Special Issue include, but are not limited to:

Raman spectroscopy in bio-medicine;
Raman spectroscopy in food safety;
Raman spectroscopy in the pharmaceutical industry;
Raman spectroscopy for the analysis of carbon materials (graphene and others);
Surface Enhanced Raman Spectroscopy (SERS);
SERS sensors;
New materials for SERS analysis;
SERS detection of diseases.

Dr. Carlo Morasso
Prof. Dr. Chandrabhas Narayana
Guest Editors

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Research

11 pages, 921 KiB

Open AccessArticle

Resonant Raman Spectroscopy of Carotenoids in Aging of Extra Virgin Olive Oil

by Edward Curran Eggertson and Francesca Venturini

Sensors 2023, 23(17), 7621; https://doi.org/10.3390/s23177621 - 2 Sep 2023

Viewed by 1182

Abstract

This work uses resonant Raman spectroscopy (RRS) to investigate changes in carotenoid concentration in extra virgin olive oil (EVOO) as it oxidizes under accelerated thermal aging. Carotenoids are nutritious antioxidants and biomarkers that represent the general quality of olive oil. HPLC is the conventional method used to determine the concentration of carotenoids, but it is expensive, time-consuming, and requires sample handling. A simple optical technique for estimating carotenoid concentration in extra virgin olive oil is, therefore, desirable. This work shows that the normally weak carotenoid signal is strongly amplified when using the resonant Raman technique. The aging and oxidation of EVOO decreases the Raman intensities associated with carotenoids and increases the fluorescence and Raman intensities associated with fatty acids. From these quantities, two Raman intensity ratios are presented as indicators of the effects of aging. Full article

(This article belongs to the Special Issue Novel Raman-Based Spectroscopic Techniques for Bio and Chemical Sensing Application)

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12 pages, 2391 KiB

Open AccessArticle

Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp

by Zhenfang Liu, Yu Yang, Min Huang and Qibing Zhu

Sensors 2023, 23(5), 2827; https://doi.org/10.3390/s23052827 - 5 Mar 2023

Cited by 7 | Viewed by 2583

Abstract

Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R², RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp. Full article

(This article belongs to the Special Issue Novel Raman-Based Spectroscopic Techniques for Bio and Chemical Sensing Application)

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