@article {4568,
	title = {Honey discrimination based on the bee feeding by Laser Induced Breakdown Spectroscopy},
	journal = {Food Control, Elsevier Science},
	year = {2022},
	abstract = {In the present work, the effects of artificial feeding of bees on the honey are investigated by employing for the first time, Laser Induced Breakdown Spectroscopy (LIBS) by analyzing the emission spectral characteristics of the plasma created on the surface of honey samples. Correlation plots indicating the importance of spectral lines of elements as e.g., Calcium (Ca), Magnesium (Mg), Sodium (Na) and Potassium (K) are constructed, while machine learning algorithms based on Linear Discriminant Analysis (LDA) and Random Forest Classifiers (RFC) are employed to classify the honey samples in terms of the bee food used. The constructed machine learning
models were validated by both cross-validation and external validation, while the obtained accuracies exceeded 90\% of correct classification, indicating the potential of LIBS technique for honey discrimination. The obtained results by LIBS were also validated by HPLC-RID, which is the standard technique used for the analysis of the main honey sugars.},
	author = {Dimitris Stefas and Nikolaos Gyftokostas and Panagiotis Kourelias and Eleni Nanou and Chrysoula Tananaki and Dimitrios Kanelis and Vasileios Liolios and Vasileios Kokkinos and Christos Bouras and Stelios Couris}
}
@article {4521,
	title = {Classification of Greek olive from different regions by machine learning - aided Laser - Induced Breakdown Spectroscopy and Absorption Spectroscopy},
	journal = {Open Access Journal, MDPI, Special Issue Characterization of Olive Products from Greece},
	volume = {26},
	year = {2021},
	abstract = {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 {\textquotedblleft}k-fold{\textquotedblright} 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.},
	author = {Nikolaos Gyftokostas and Eleni Nanou and Dimitrios Stefas and Vasileios Kokkinos and Christos Bouras and Stelios Couris}
}
@article {4538,
	title = {Discrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data},
	journal = {Food Control, Elsevier Science},
	volume = {130},
	year = {2021},
	pages = {1-8},
	abstract = {In this work Laser-Induced Breakdown Spectroscopy (LIBS) and absorption spectroscopy aided by machine learning are employed for discriminating some extra virgin Greek olive oils of different olive cultivars for the first
time. LIBS and absorption spectra of extra virgin olive oils belonging to Kolovi and Koroneiki cultivars, as well as mixtures of them, were collected, analyzed, and used to develop classification schemes employing Linear
Discriminant Analysis and Gradient Boosting, the latter allowing the determination of the most important spectral features. Both algorithms were found to provide efficient classification of the olive oil spectra with
accuracies exceeding 90\%. Furthermore, for the first time, the emission spectra of LIBS were fused with the absorption spectra to create predictive models and their accuracies were found to be significantly improved. This
work demonstrates the enhanced capabilities of LIBS and absorption spectroscopy and the potential of their combination for olive oil quality monitoring and control.},
	author = {Christos Bouras and Dimitrios Stefas and Nikolaos Gyftokostas and Panagiotis Kourelias and Eleni Nanou and Vasileios Kokkinos and Stelios Couris}
}
@article {4546,
	title = {A Laser-Based Method for the Detection of Honey Adulteration},
	journal = {Applied Sciences, MPDI, Special Issue Chemical Composition, Properties and Applications of Honey},
	year = {2021},
	abstract = {In the present work, laser-induced breakdown spectroscopy, aided by some machine learning algorithms (i.e., linear discriminant analysis (LDA) and extremely randomized trees (ERT)), is used for the detection of honey adulteration with glucose syrup. In addition, it is shown that instead of the entire LIBS spectrum, the spectral lines of inorganic ingredients of honey (i.e., calcium, sodium, and potassium) can be also used for the detection of adulteration providing efficient discrimination. The constructed predictive models attained high classification accuracies exceeding 90\% correct classification.},
	author = {Dimitrios Stefas and Nikolaos Gyftokostas and Panagiotis Kourelias and Eleni Nanou and Vasileios Kokkinos and Christos Bouras and Stelios Couris}
}
