scholarly journals Portable Spectroscopy

2018 ◽  
Vol 72 (12) ◽  
pp. 1701-1751 ◽  
Author(s):  
Richard A. Crocombe
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Medical Diagnostics ◽  
Spectroscopic Techniques ◽  
The Public ◽  
Qualitative And Quantitative ◽  
Breakdown Spectroscopy ◽  
Crowdsourced Data ◽  
Laser Induced Breakdown ◽  
Consumer Devices

Until very recently, handheld spectrometers were the domain of major analytical and security instrument companies, with turnkey analyzers using spectroscopic techniques from X-ray fluorescence (XRF) for elemental analysis (metals), to Raman, mid-infrared, and near-infrared (NIR) for molecular analysis (mostly organics). However, the past few years have seen rapid changes in this landscape with the introduction of handheld laser-induced breakdown spectroscopy (LIBS), smartphone spectroscopy focusing on medical diagnostics for low-resource areas, commercial engines that a variety of companies can build up into products, hyphenated or dual technology instruments, low-cost visible-shortwave NIR instruments selling directly to the public, and, most recently, portable hyperspectral imaging instruments. Successful handheld instruments are designed to give answers to non-scientist operators; therefore, their developers have put extensive resources into reliable identification algorithms, spectroscopic libraries or databases, and qualitative and quantitative calibrations. As spectroscopic instruments become smaller and lower cost, “engines” have emerged, leading to the possibility of being incorporated in consumer devices and smart appliances, part of the Internet of Things (IOT). This review outlines the technologies used in portable spectroscopy, discusses their applications, both qualitative and quantitative, and how instrument developers and vendors have approached giving actionable answers to non-scientists. It outlines concerns on crowdsourced data, especially for heterogeneous samples, and finally looks towards the future in areas like IOT, emerging technologies for instruments, and portable hyphenated and hyperspectral instruments.

scholarly journals Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1241
Author(s):  
Nikolaos Gyftokostas ◽  
Eleni Nanou ◽  
Dimitrios Stefas ◽  
Vasileios Kokkinos ◽  
Christos Bouras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Olive Oil ◽  
Absorption Spectra ◽  
Absorption Spectroscopy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spectroscopic Techniques ◽  
Breakdown Spectroscopy ◽  
Olive Oils ◽  
Laser Induced Breakdown

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

Clinical applications of spectroscopic techniques in conjunction with multivariate analysis in virus diagnosis

2021 ◽  
pp. 1-27
Author(s):  
Marfran C. D. Santos ◽  
João V. M. Mariz ◽  
Raissa V. O. Silva ◽  
Camilo L. M. Morais ◽  
Kássio M. G. Lima
Keyword(s):  
Multivariate Analysis ◽  
Near Infrared ◽  
Low Cost ◽  
Diagnostic Techniques ◽  
Clinical Samples ◽  
Spectroscopic Techniques ◽  
Global Pandemic ◽  
Molecular Fluorescence ◽  
Viral Diagnosis ◽  
Near Future

In view of the global pandemic that started in 2020, caused by COVID-19, the importance of the existence of fast, reliable, cheap diagnostic techniques capable of detecting the virus even in the first days of infection became evident. This review discusses studies involving the use of spectroscopic techniques in the detection of viruses in clinical samples. Techniques based on mid-infrared, near-infrared, Raman, and molecular fluorescence are explained and it was demonstrated how they can be used in conjunction with computational tools of multivariate analysis to build models capable of detecting viruses. Studies that used real clinical samples from 2011 to 2021 were analyzed. The results demonstrate the potential of the techniques in detecting viruses. Spectroscopic techniques, as well as chemometric techniques, were also explained. Viral diagnosis based on spectroscopy has interesting advantages compared to standard techniques such as: fast results, no need for reagents, non-destructiveness for the sample, no need for sample preparation, relatively low cost, among others. Several studies have corroborated the real possibility that, in the near future, we may have spectroscopic tools being successfully applied in viral diagnosis.

scholarly journals Matte photographic paper as a low-cost material for metal ion retention and elemental measurements with laser-induced breakdown spectroscopy

Talanta ◽  
2019 ◽  
Vol 205 ◽  
pp. 120167 ◽  
Author(s):  
Rodrigo Papai ◽  
Cleide da Silva Mariano ◽  
Camila Vilela Pereira ◽  
Paulo Vinicius Ferreira da Costa ◽  
Flavio de Oliveira Leme ◽  
...  
Keyword(s):  
Metal Ion ◽  
Low Cost ◽  
Laser Induced Breakdown Spectroscopy ◽  
Photographic Paper ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5419 ◽  
Author(s):  
Sara Sánchez-Esteva ◽  
Maria Knadel ◽  
Sergey Kucheryavskiy ◽  
Lis W. de Jonge ◽  
Gitte H. Rubæk ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Near Infrared ◽  
Soil Phosphorus ◽  
Laser Induced Breakdown Spectroscopy ◽  
Soil P ◽  
Breakdown Spectroscopy ◽  
Extractable P ◽  
Laser Induced Breakdown ◽  
Wet Chemical

Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques—in this case, laser-induced breakdown spectroscopy (LIBS)—and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.

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