Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Rzecki, Krzysztof; Sośnicki, Tomasz; Baran, Mateusz; Niedźwiecki, Michał; Król, Małgorzata; Łojewski, Tomasz; Acharya, U. Rajendra; Yıldırım, Özal; Pławiak, Paweł

doi:10.3390/s18113670

Cited by 38 publications

(13 citation statements)

References 45 publications

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“…Laser‐induced breakdown spectroscopy (LIBS) and Raman spectroscopy are two famous spectral techniques because of their advantages in rapid, online, and in‐site detection. [ 1,2 ] As a new elemental detection method, LIBS has been used in identification of many kinds of substance with chemometrics, for instance, biomedical samples, [ 3–5 ] forensic evidences, [ 6–9 ] explosives, [ 10 ] plants, [ 11 ] alloy, [ 12 ] ore, [ 13 ] nutrient food, [ 14–16 ] and pathogenic microorganisms. [ 17 ] However, for substances with the same elemental composition, the feature line wavelengths are always similar.…”

Section: Introductionmentioning

confidence: 99%

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

Teng

Wang

Cui

et al. 2021

J Raman Spectroscopy

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In clinical field, the diagnosis of many diseases and their development stages depend on the detection of the corresponding bacteria. Raman spectroscopy and laser‐induced breakdown spectroscopy (LIBS) are two novel spectral diagnostic technologies for clinical bacteria identification. Both of them have been used in clinical detection combined with optimized support vector machine (SVM). In this paper, two feature‐level fusion methods (before feature selection fusion [BFSF] and after feature selection fusion [AFSF]) were proposed to improve the performance of SVM classifier and reduce the analyzing time (including the parameter tuning, model training, and testing time) simultaneously by combining data of LIBS and Raman spectroscopy. Using the most important 10 feature lines as the inputs of the optimized classifier, the analyzing time could be reduced to 1 to 2 min and the correct classification rate (CCR) achieved 95.67%. Without optimizing SVM parameters, the two proposed methods could achieve rapid and accurate classification of pathogenic bacteria further. The AFSF method showed better results with less fusion features while the BFSF method achieved higher CCR at 100% with more features. Both two methods costed around 0.2 s for analysis. These indicate that the proposed feature‐level fusion methods can improve the performance of SVM for bacteria detection. Maintaining the highest diagnostic accuracy, they can achieve the minimum analyzing time.

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Section: Introductionmentioning

confidence: 99%

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

Teng

Wang

Cui

et al. 2021

J Raman Spectroscopy

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“…Similar conclusions were reached by Trejos et al, who reported discrimination by LIBS among black gel inks and ballpoint pen inks between 96% and 99% [ 8 ]. Other research papers published about pen ink analysis by using LIBS [ 26 , 27 , 28 , 29 ] reported that the discrimination power depends on the experimental conditions and the data analysis procedure. LIBS analysis of printing inks also showed a high classification rate, which was 100% for black toners and 97.8% for black inks from inkjet printers [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Forensic Analysis of Commercial Inks by Laser-Induced Breakdown Spectroscopy (LIBS)

Cicconi

Lazic

Palucci

et al. 2020

Sensors

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Laser-induced breakdown spectroscopy (LIBS) was tested for all of the relevant issues in forensic examinations of commercial inks, including classification of pen inks on one paper type and on different paper types, determination of the deposition order of layered inks, and analysis of signatures and toners on one questioned document. The scope of this work was to determine the potential of a single LIBS setup that is compatible with portable instruments for different types of ink analysis, rather than building a very large database for inks and papers. We identified up to seven metals characteristic for the examined inks, which allowed to fully discriminate all eight black inks on one type of printing paper. When the inks were tested on ten different papers, the correct classification rates for some of them were reduced for reasons thoroughly studied and explained. The replicated tests on three crossing points, each one involving a pair of blue or black inks, were successful in five cases out of six. In the test simulating documents of forensic interest (questioned documents), LIBS was able to correctly identify the differences in three inks used for signatures on one of the three pages and the use of different printing inks on each page of the document.

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“…Ksiazec et al [21] used a machine learning approach to detect Hepatocellular Carcinoma using physiological features. Rzecki et al [22,23] proposed the computational intelligence methods for person recognition using biometric features and used the same method for the automated identification of paper-ink samples through laser-induced breakdown spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study

Bitkina

Kim

Park

et al. 2019

Sensors

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Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.

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Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Cited by 38 publications

References 45 publications

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

Forensic Analysis of Commercial Inks by Laser-Induced Breakdown Spectroscopy (LIBS)

Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study

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