scholarly journals Olive Oils Classification via Laser-Induced Breakdown Spectroscopy

2020 ◽  
Vol 10 (10) ◽  
pp. 3462
Author(s):  
Nikolaos Gyftokostas ◽  
Dimitrios Stefas ◽  
Stelios Couris
Keyword(s):  
Machine Learning ◽  
Geographic Origin ◽  
Machine Learning Algorithms ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Olive Oils ◽  
Laser Induced Breakdown ◽  
Operation Techniques ◽  
On Line

The classification of olive oils and the authentication of their geographic origin are important issues for public health and for the olive oil market and related industry. The development of fast, easy to use, suitable for on-line, in-situ and remote operation techniques for olive oils classification is of high interest. In the present work, 36 olive oils from different places in Crete, Greece, are studied using a laser-based technique, Laser-Induced Breakdown Spectroscopy (LIBS), assisted by machine learning algorithms, aiming to classify them in terms of their geographical origin. The excellent classification results obtained demonstrate the great potential of LIBS, which is further extended by the use of machine learning.

scholarly journals Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nikolaos Gyftokostas ◽  
Dimitrios Stefas ◽  
Vasileios Kokkinos ◽  
Christos Bouras ◽  
Stelios Couris
Keyword(s):  
Machine Learning ◽  
Olive Oil ◽  
Virgin Olive Oil ◽  
Geographic Origin ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
The Mediterranean

AbstractOlive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

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.

scholarly journals Laser-Induced Breakdown Spectroscopy Assisted by Machine Learning for Plastics/Polymers Identification

Atoms ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 79 ◽  
Author(s):  
Dimitrios Stefas ◽  
Nikolaos Gyftokostas ◽  
Elli Bellou ◽  
Stelios Couris
Keyword(s):  
Machine Learning ◽  
Flame Retardants ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Laser Induced Breakdown Spectroscopy ◽  
Linear Discriminant ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Algorithmic Approaches ◽  
Polymeric Samples

In the present work, Laser-Induced Breakdown Spectroscopy (LIBS) is used for the discrimination/identification of different plastic/polymeric samples having the same polymeric matrix but containing different additives (as e.g., fillers, flame retardants, etc.). For the classification of the different plastic samples, some machine learning algorithms were employed for the analysis of the LIBS spectroscopic data, such as the Principal Component Analysis (PCA) and the Linear Discriminant Analysis (LDA). The combination of LIBS technique with these machine learning algorithmic approaches, in particular the latter, provided excellent classification results, achieving identification accuracies as high as 100%. It seems that machine learning paves the way towards the application of LIBS technique for identification/discrimination issues of plastics and polymers and eventually of other classes of organic materials. Machine learning assisted LIBS can be a simple to use, efficient and powerful tool for sorting and recycling purposes.

Accurate determination of structural H2O in rocks using LIBS coupled with machine learning algorithms extensively exploring the characteristics of the Hα line

2022 ◽  
Author(s):  
Weijie Xu ◽  
Sun Chen ◽  
Yuqing Zhang ◽  
Zengqi Yue ◽  
Sahar Shabbir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elemental Analysis ◽  
Accurate Determination ◽  
Machine Learning Algorithms ◽  
Laser Induced Breakdown Spectroscopy ◽  
Geological Materials ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Property Assessment

The application of laser-induced breakdown spectroscopy (LIBS) in elemental analysis and property assessment of geological materials has been demonstrated of great importance and effectiveness. The importance of the application becomes...

scholarly journals Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

2021 ◽  
Author(s):  
Kateřina Kiss ◽  
Anna Šindelářová ◽  
Lukáš Krbal ◽  
Václav Stejskal ◽  
Kristýna Mrázová ◽  
...  
Keyword(s):  
Machine Learning ◽  
Significant Role ◽  
Skin Tumors ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Nowadays, laser-based techniques play a significant role in medicine, mainly in the ophthalmology, dermatology, and surgical fields.

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