Browsing by Author "Kivrak, Seyda"

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    Chemometric Differentiation of Organic Honeys From Southeastern Türkiye Based on Free Amino Acid and Phenolic Profiles
    (MDPI, 2025) Gurbuz, Semra; Kivrak, Seyda
    Verifying the geographical origin of honey is crucial for its market value and for preventing fraudulent practices. This study aimed to characterize the chemical profiles of organic honeys from three distinct regions in Southeastern T & uuml;rkiye-& Scedil;& imath;rnak Fara & scedil;in, Siirt Merkez, and Siirt Pervari-to establish a robust method for geographical authentication. A total of 51 multifloral honey samples were analyzed. The concentrations of 20 free amino acids (FAAs) and 16 phenolic compounds were quantified using (UPLC-ESI-MS/MS). The resulting data were subjected to both an unsupervised (PCA, CA) and supervised (PLS-DA, RF, SVM) chemometric analysis to identify biochemical markers for each region. The results revealed a distinct chemical fingerprint for each region. Based on the FAA profiles, the PLS-DA method provided the best overall classification, achieving an excellent discrimination with a total accuracy of 94.1% in the & Scedil;& imath;rnak Fara & scedil;in honeys. For the phenolic compound profiles, the RF method achieved the highest correct classification rate for & Scedil;& imath;rnak Fara & scedil;in honeys at 88.2%. This study demonstrates that an integrated approach, combining FAA and phenolic profiles with supervised chemometric methods, provides a successful and reliable model for determining the geographical origin of these multifloral honeys.
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    Comparative Evaluation of Machine Learning Models for Discriminating Honey Geographic Origin Based on Altitude-Dependent Mineral Profiles
    (MDPI, 2025) Gurbuz, Semra; Kivrak, Seyda
    Authenticating the geographical origin of honey is crucial for ensuring its quality and preventing fraudulent labeling. This study investigates the influence of altitude on the mineral composition of honey and comparatively evaluates the performance of chemometric and machine learning models for its geographic discrimination. Honey samples from three distinct altitude regions in T & uuml;rkiye were analyzed for their mineral content using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Results revealed that Calcium (Ca), Potassium (K), and Sodium (Na) were the predominant minerals. A significant moderate negative correlation was found between altitude and Ca concentration (r = -0.483), alongside a weak negative correlation with Copper (Cu) (r = -0.371). Among the five supervised models tested (Partial Least Squares-Discriminant Analysis (PLS-DA), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)), PLS-DA achieved the highest classification accuracy (94.9%). Variable importance analysis consistently identified Ca as the most influential discriminator across all models, followed by Barium (Ba) and Cu. These minerals, therefore, represent key markers for differentiating honey by geographical origin. This research demonstrates that an integrated model utilizing mineral profiles provides a robust, practical, and reliable method for the geographical authentication of honey.