01792nas a2200205   4500008004100000245009000041210006900131520101500200100002101215700002601236700002601262700001701288700002401305700002301329700002301352700002401375700002101399700002001420856014601440       2022                        eng d00aHoney discrimination based on the bee feeding by Laser Induced Breakdown Spectroscopy0 aHoney discrimination based on the bee feeding by Laser Induced B3 aIn 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.1 aStefas, Dimitris1 aGyftokostas, Nikolaos1 aKourelias, Panagiotis1 aNanou, Eleni1 aTananaki, Chrysoula1 aKanelis, Dimitrios1 aLiolios, Vasileios1 aKokkinos, Vasileios1 aBouras, Christos1 aCouris, Stelios  uhttps://telematics.upatras.gr/telematics/publications/honey-discrimination-based-bee-feeding-laser-induced-breakdown-spectroscopy?language=el01788nas a2200169   4500008004100000245015200041210006900193490000700262520105700269100002601326700001701352700002201369700002401391700002101415700002001436856016201456       2021                        eng d00aClassification of Greek olive from different regions by machine learning - aided Laser - Induced Breakdown Spectroscopy and Absorption Spectroscopy0 aClassification of Greek olive from different regions by machine 0 v263 aIn 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.1 aGyftokostas, Nikolaos1 aNanou, Eleni1 aStefas, Dimitrios1 aKokkinos, Vasileios1 aBouras, Christos1 aCouris, Stelios  uhttps://telematics.upatras.gr/telematics/publications/classification-greek-olive-different-regions-machine-learning-aided-laser-induced-breakdown?language=el01856nas a2200193   4500008004100000245015500041210006900196300000800265490000800273520105400281100002101335700002201356700002601378700002601404700001701430700002401447700002001471856017101491       2021                        eng d00aDiscrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data0 aDiscrimination of olive oils based on the olive cultivar origin   a1-80 v1303 aIn 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.1 aBouras, Christos1 aStefas, Dimitrios1 aGyftokostas, Nikolaos1 aKourelias, Panagiotis1 aNanou, Eleni1 aKokkinos, Vasileios1 aCouris, Stelios  uhttps://telematics.upatras.gr/telematics/publications/discrimination-olive-oils-based-olive-cultivar-origin-machine-learning-employing-fusion-emission-and?language=el01239nas a2200169   4500008004100000245006700041210006500108520061400173100002200787700002600809700002600835700001700861700002400878700002100902700002000923856012600943       2021                        eng d00aA Laser‐Based Method for the Detection of Honey Adulteration0 aLaser‐Based Method for the Detection of Honey Adulteration3 aIn 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.1 aStefas, Dimitrios1 aGyftokostas, Nikolaos1 aKourelias, Panagiotis1 aNanou, Eleni1 aKokkinos, Vasileios1 aBouras, Christos1 aCouris, Stelios  uhttps://telematics.upatras.gr/telematics/publications/laser%E2%80%90based-method-detection-honey-adulteration?language=el