Low-resolution Raman Spectroscopy Research Articles

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution.

Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is "backwards-compatible" with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.

Low-Resolution Raman Spectroscopy for the detection of contaminant species in algal bioreactors

Fouling of aquatic systems by harmful microalgal and cyanobacterial species is an environmental and public health concern. Microalgal bioreactors are engineered ecosystems for the cultivation of algal biomass to meet the increasing demand for alternative protein sources and algae-derived products. Such bioreactors are often open or semi-open ponds or raceways that are prone to contamination by contaminant photosynthetic microorganisms, including harmful cyanobacterial species (HCBs). HCBs affect the quality of products through the accumulation of off-flavours, reducing their acceptance by consumers, and through the production of several different toxins collectively known as cyanotoxins. The density of cultured species within the bioreactor environment creates difficulty in detecting low concentrations of contaminant cells, and there is currently no technology enabling rapid monitoring of contaminations. The present study demonstrates the potential of Low-Resolution Raman Spectroscopy (LRRS) as a tool for rapid detection of low concentrations of HCBs within dense populations of the spirulina (Arthrospira platensis) cultures. An LRRS system adapted for the direct measurement of raw biomass samples was used to assemble a database of Raman spectral signatures, from eight algal and cyanobacterial strains. This dataset was used to develop both quantitative and discriminative chemometric models. The results obtained from the chemometric analyses demonstrate the ability of the LRRS to detect and quantify algal and cyanobacterial species at concentrations as low as 103 cells/mL and to robustly discriminate between species at concentrations of 104 cells/mL. The LRRS and chemometric analyses were further able to detect the presence of low concentrations (103cells/mL) of contaminating species, including the toxic cyanobacterium Microcystis aeruginosa, within dense (>107 cells/mL) spirulina cultures. The results presented provide a first demonstration of the potential of LRRS technology for real-time detection of contaminant species within microalgal bioreactors, and possibly for early detection of developing harmful algal blooms in other aquatic ecosystems.

Rapid material identification via low-resolution Raman spectroscopy and deep convolutional neural network

Raman spectroscopy is a vital technique being able to detect and identify molecular information with advantages of being fast and non-invasive. This technique also enables numbers of potential applications, including forensic drugs detector, explosive detection, and biomedical analysis. In this work, we investigated the identification performance of a custom-made low-resolution Raman system equipped with machine learning capability to classify various types of materials. Here, a relatively broadband laser diode with center wavelength of 808 nm was used for Raman excitation. An off-axis parabolic mirror with through hole was used in place of a beamspiltter for sample excitation, as well as collection, and collimation of scattered light from long working distance of 50 mm. The signal was filtered and delivered to a cooled spectrometer via an optical fiber for spectra measurements. Raman spectra of test samples were on the range of 100-2000 cm−1 with 7.65 cm−1 data steps. For spectral analysis, a convolutional neural network (CNN) was implemented as classification algorithm with feature extraction from multiple layers together with error-back propagation, which displayed the performance in term of accuracy. It was found that with only three sets of convolution layers up to 96.7% testing performance can be achieved even with low spectral resolution input.

Improving Raman spectra of pure silicon using super-resolved method

In this paper, we demonstrated a method to super-resolved Raman spectroscopy, which improves the resolution of pure silicon spectra. Experimental investigations were carried out on the spectra of pure silicon. A tunable Fabry–Perot (F-P) filter was added to the classical Raman setup in which measuring the spectra for different states of the F-P filter coupled with decoded experimental results yielded a spectrum of higher resolution having a reduced linewidth in the pure silicon spectrum. Therefore, this method can be very helpful for improving the detection of silicon material in nanostructures using a low resolution Raman spectroscopy system.

Cell classification with low-resolution Raman spectroscopy (LRRS)

The identification of individual eukaryotic and prokaryotic cells is the backbone of clinical pathology and provides crucial information about the genesis and progression of a disease. While most commonly fluorescent-label based methods are applied, label-free methods, such as Raman spectroscopy, are elegant alternatives. A major disadvantage of Raman spectroscopy is the low signal yield resulting in long acquisition times, making it impractical for high-throughput clinical analysis. As a rule, Raman-based cell identification relies on high-resolution Raman spectra. This comes at a cost of detected Raman photons. In this letter we show that while the proper biochemical characterization of cells requires high-resolution Raman spectra, the proper classification of cells does not. By varying the slit-width between 50µm and 500µm it is possible to show that detected Raman signal from eukaryotic cells increased up to seven-fold. Raman-based cell classification was performed on three cancer cell lines: Jurkat, MiaPaca2, and Capan1, at three different resolutions 8cm-1 , 24cm-1 , and 48cm-1 . Moreover, we have simulated the resolution decrease due to low-diffraction gratings by binning neighboring pixels together. In both cases the cells were well classifiable using support vectors machine (SVM). For anyone working in the field of Raman spectroscopy this picture of Sir C.V. Raman is recognizable, even with reduced spatial resolution. Raman spectra of eukaryotic cells can also be recognized even with six fold reduced spectral resolution.

A portable Raman sensor for the rapid discrimination of olives according to fruit quality

In the real marketplace, providing high-quality olive oil is important from the perspective of both consumers and producers. Quality control should meet all requirements in the production process, from farm to packaging. The quality of olive oil can be affected by several factors, including agricultural techniques, seasonal conditions, farming systems, maturity, method and duration of storage, and process technology.The quality of oil produced also depends largely on the quality of the olives. In an enterprise aimed at producing high-quality oils, olives with defects (‘ground’; i.e., fallen to the ground) should be separated from healthy fruit (‘sound’; i.e., collected directly from the tree), because a very small portion of low-quality fruit can ruin the whole batch.The fruit falls partly because of its maturation process, but also because of pest and disease attack or weather conditions (strong wind). Fruit that has fallen to the ground can suffer a rapid deterioration in quality.Currently, the separation of fruits is based mainly on visual inspection or information provided by the farmer. These are not very reliable procedures. Methods using analytical parameters to characterize the oil, such as acidity and peroxide value, can be applied, but they require a lot of time and materials. Alternative techniques are therefore needed for the rapid and inexpensive discrimination of olives as part of a quality control strategy.The work described here aims to determine the potential of low-resolution Raman spectroscopy for the discrimination of olives before the oil processing stage in order to detect whether they have been collected directly from the tree (i.e., healthy fruit) or not. Low-resolution Raman spectroscopy was applied together with multivariate procedures to achieve this aim. PCA was used to find natural clusters in the data. Supervised classification methods were then applied: Soft Independent Modeling of Class Analogy (SIMCA), PLS Discriminate Analysis (PLS-DA) and K-nearest neighbors (KNN). The best results were obtained using the KNN method, with prediction abilities of 100% for ‘sound’ and 97% for ‘ground’ in an independent validation set.These results demonstrated the potential of a portable Raman instrument for detecting good quality olives before the oil processing stage, by developing models that could be applied before this stage, thus contributing to an overall improvement in quality control.

Application of low-resolution Raman spectroscopy for the analysis of oxidized olive oil

The aim of this study was to evaluate the potential of low-resolution Raman spectroscopy for monitoring the oxidation status of olive oil. Primary and secondary oxidation parameters such as peroxide value, K232 and K270 were studied. Low-resolution Raman spectra ranging from 200 to 2700 cm−1 in a set of 126 oxidized and virgin olive oil samples were collected directly using a probe. Partial Least Squares was used to calibrate the Raman instrument for the different targeted parameters. The performance of the models was determined by using validation sets, and the best results obtained were: R2 = 0.91, RMSEP = 2.57 for the peroxide value content; R2 = 0.88, RMSEP = 0.37 for K232; and R2 = 0.90, RMSEP = 0.08 for K270. These results demonstrated that low-resolution Raman spectroscopy could be a relevant technique for evaluating the oxidation status of olive oils because the key oxidation parameters can be determined quickly and in a non-destructive and direct way.

Fast differentiation of bacteria causing pharyngitis by low resolution Raman spectroscopy and PLS-discriminant analysis

The diagnosis of the bacteria that cause pharyngitis through classical microbiological methods is a difficult, but usually very efficient task. However, the high cost of reagents and the time required for such identifications, about four days, could generate serious consequences, mainly when the patients concerned are children, the elderly or adults with low resistance. Thus, the search for new methods allowing a fast and reagentless differentiation of these microorganisms is extremely relevant. In this work, the main microorganisms responsible for pharyngitis, S. aureus, S. pyogenes and N. gonorrhoeae were studied. For each microorganism, sixty different dispersions were prepared using physiological solution as solvent. The Raman Spectra of these dispersions were recorded using a diode laser operating in the near infrared region. The PLS-discriminant analysis method was applied to bacteria differentiation through their respective Raman Spectra. This approach enabled the correct classification of 100% of all evaluated bacteria and unknown samples coming from clinical environment, in a reduced time interval (ca. 10 h), by using a low-cost, portable Raman spectrometer, which can be easily used in Intensive Care Units (ICU) and clinical environments.

Two-dimensional low resolution Raman spectroscopy applied to fast discrimination of microorganisms that cause pharyngitis: A whole-organism fingerprinting approach

The discrimination of the bacteria that cause pharyngitis through classical–microbiological methods is very efficient in the great majority of the cases. However, the high cost of chemicals and the time spent for such identifications, about four days, could generate serious consequences for the patients. Thus, the search for low cost spectroscopic methods which would allow a fast and reagentless discrimination of these microorganisms is extremely relevant. In this work, the main microorganisms that cause pharyngitis: S. aureus, S. pyogenes and Neisseria gonorrhoeae were studied. For each of the microorganisms 60 different dispersions were prepared using physiological solution as solvent and its Raman spectra were recorded. The 1D spectra obtained were similar, making it very difficult to differentiate the microorganisms. However, applying the 2D correlation method, it was possible to identify the microorganisms evaluated using the synchronous spectrum as “whole-organism fingerprinting” in a reduced time interval (∼10 h).

Classification of glucose concentration in diluted urine using the low-resolution Raman spectroscopy and kernel optimization methods

In order to detect minute amounts of glucose in diluted urine, we applied the Raman spectroscopy method. To simulate abnormal diluted urine in a toilet bowl, we diluted normal urine ten-fold with water and added glucose up to 8 mg dl−1. Data were collected using a low-resolution Raman spectrometer that was preprocessed with the optimizing kernel method. We also applied the neural network algorithm to classify abnormal and normal urine samples according to their glucose concentrations. The kernel optimizing method was very effective in the classification of the tested subjects as it increased the accuracy of classification by 92%. This method suggests the possibility of caring for patients by daily monitoring their urine components in a manner non-invasive to ordinary life.

Spectroscopic and X-ray diffraction study of high Tc epitaxial YBCO thin films obtained by pulsed laser deposition

We report spectroscopic characterization of epitaxial YBCO thin films grown on LaAlO 3 by pulsed laser deposition. Raman spectroscopy and spectroscopic ellipsometry were used for film characterization and the results were correlated with X-ray diffraction measurements. The mentioned techniques allowed us to analyze crystallographic, micro-structural, and morphological properties of YBCO thin films. We also demonstrated that relatively low resolution Raman spectroscopy and spectroscopic ellipsometry are reliable techniques for a rapid and non-destructive characterization of epitaxial YBCO thin films.

Two-dimensional low resolution raman spectroscopy applied to fast discrimination of clinically relevant microorganisms: a whole-organism fingerprinting approach

The discrimination of the bacteria that cause gastroenteritis through classical microbiological methods is very efficient in the great majority of the cases. However, the high cost of chemicals and the time spent for such identifications, about four days, could generate serious consequences for the patients. Thus, the search for low cost spectroscopic methods which would allow a fast and reagentless discrimination of these microorganisms is extremely relevant. In this work the main microorganisms that cause gastroenteritis: E. coli, S. chroleraesuis, S. flexneri were studied. For each of the microorganisms sixty different dispersions were prepared using physiological solution as solvent and its Raman spectra recorded. The 1D spectra obtained were similar, making it very difficult to differentiate the microorganisms. However, applying the 2D correlation method, it was possible to identify the microorganisms evaluated using the synchronous spectrum as "whole-organism fingerprinting" in a reduced time interval (~10 h).

Rapid differentiation among bacteria that cause gastroenteritis by use of low-resolution Raman spectroscopy and PLS discriminant analysis

Use of classical microbiological methods to differentiate bacteria that cause gastroenteritis is cumbersome but usually very efficient. The high cost of reagents and the time required for such identifications, approximately four days, could have serious consequences, however, mainly when the patients are children, the elderly, or adults with low resistance. The search for new methods enabling rapid and reagentless differentiation of these microorganisms is, therefore, extremely relevant. In this work the main microorganisms responsible for gastroenteritis, Escherichia coli, Salmonella choleraesuis, and Shigella flexneri, were studied. For each microorganism sixty different dispersions were prepared in physiological solution. The Raman spectra of these dispersions were recorded using a diode laser operating in the near infrared region. Partial least-squares (PLS) discriminant analysis was used to differentiate among the bacteria by use of their respective Raman spectra. This approach enabled correct classification of 100% of the bacteria evaluated and unknown samples from the clinical environment, in less time ( approximately 10 h), by use of a low-cost, portable Raman spectrometer, which can be easily used in intensive care units and clinical environments.

Monitoring of Seeded Batch, Semi‐batch, and Second Stage Emulsion Polymerization by Low Resolution Raman Spectroscopy

Low resolution Raman spectroscopy (LRRS) offers a convenient way to monitor many different polymerization processes and reactor environments. LRRS can be used as an additional tool for process control to determine the conversion inside the reactor in real time without disruption to the reaction. Critical to polymerization monitoring, the efficiency of LRRS eliminates the lag between taking samples and calculating conversion. For each system, the phenyl ring of styrene or polystyrene provides an internal reference, which can be used to eliminate fluctuations in laser intensity. This paper demonstrates that the LRRS system can monitor seeded emulsion homopolymerizations in batch and semi‐batch as well as second stage emulsion polymerizations with some acceptable level of confidence.

Determination of the Effective Ground State Potential Energy Function of Ozone from High-Resolution Infrared Spectra

The effective ground state potential energy function of the ozone molecule near the C2v equilibrium configuration was obtained in a least-squares fit to the largest sample of experimental, high-resolution vibration–rotation data used for this purpose so far. The fitting is based on variational calculations carried out with the extended Morse Oscillator Rigid Bender Internal Dynamics model. The potential function is expanded in Morse-type functions of the stretching variables and in cosine of the bending angle. The present calculation produces results in significantly better agreement with experiment than previous determinations of the potential energy surface, and the energies predicted with the new surface are sufficiently accurate to be useful for the assignment of new high-resolution spectra. The rms (root-mean-square) deviation of the fit of rovibrational data up to J = 5 is 0.02 cm−1. For the set of all 60 band centers of the 16O3 molecule included in the Atlas of Ozone Line Parameters, the rms deviation is 0.025 cm−1, and for all band centers determined so far from high-resolution spectra, including those recently observed and assigned in Reims corresponding to highly excited stretching and bending vibrations (v1 + v2 + v3 = 6), the rms deviation is 0.1 cm−1. The “dark states” that produce resonance perturbations in the observed bands are described with experimental accuracy up to the (v1v2v3) = (080) state. Extrapolation tests demonstrate the predictive power of the potential function obtained: rotational extrapolation up to J = 10 for the 11 lowest vibrational states results in an rms deviation of 0.06cm−1. Also, vibrational energies measured by low-resolution Raman spectroscopy (which were not included in the input data for the fit) are calculated within the experimental accuracy (rms = 1.6 cm−1) of the experimental values up to the dissociation limit. The statistical analysis suggests that the accuracy of the equilibrium geometry and force constants of the molecule is considerably improved relative to previous determinations. The long-range behavior of the fitted potential at the dissociation limit O3 → O2 + O shows very good agreement with experimental data. The new potential energy surface was used to predict the band centers of the isotopomers 17O3 and 18O3.

Low-resolution Raman spectroscopy: instrumentation and applications in chemical analysis

It is shown that low-resolution Raman spectroscopy (LRRS) satisfies the need for a highly useful, low-cost spectroscopic approach to both qualitative and quantitative analyses of organic systems. Until now, the disadvantage of using Raman spectroscopy has been the cost of the instrumentation. It is shown that when the application does not require high spectral resolution for analysis, LRRS can be used and implemented with low-cost multimode lasers and miniature low-resolution spectrometers, thus lowering the size, weight and cost of the instrumentation by an order of magnitude. First the implementation of LRRS is discussed, emphasizing a small portable instrument, then four applications are presented, each representative of a wide range of applications where LRRS is applicable. These results show that the use of LRRS provides the potential to bring Raman analyses out of the laboratory to the process floor and into the field for near real-time, on-site and, in many cases, in situ measurements, monitoring and identification. Copyright © 1999 John Wiley & Sons, Ltd.