Determination of gold purity degrees using audio features with machine learning algorithms

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dc.contributor.authorDevrim, Mehmet Osman
dc.contributor.authorKirisoglu, Serdar
dc.date.accessioned2025-10-11T20:48:37Z
dc.date.available2025-10-11T20:48:37Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractGold purity determination is a critical part of quality control in jewellery and industrial production. Traditional methods (chemical analyses, X-ray fluorescence, etc.) can be costly, time consuming and destructive. This study aims to present an audio-based non-destructive alternative for gold purity classification. Mel-Frequency Cepstral Coefficients (MFCC) features delta-delta extracted from audio signals were analysed with 10 different machine learning algorithms (Support Vector Machines (SVM), Decision Trees, Logistic Regression, Random Forest, KNearest Neighbour (KNN), Naive Bayes, Gradient Boosting, AdaBoost, XGBoost, LightGBM). The dataset was divided into training, test and validation subsets; features were normalised with StandardScaler and the generalization performance of the models was optimised with 5-fold cross-validation. In the comparison of performance metrics (accuracy, precision, recall, F1-score), it was observed that SVM (94.58%) and Logistic Regression (93.75%) models were superior to other algorithms, especially in capturing subtle differences between classes. Confusion matrices detail the success of the model in discriminating 14, 22 and 24 carat classes. This study proves that the use of audio data in gold purity analysis has the potential for a fast, repeatable and non-destructive solution in industrial applications.en_US
dc.identifier.doi10.1016/j.apacoust.2025.110887
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-105010297615en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2025.110887
dc.identifier.urihttps://hdl.handle.net/20.500.12684/22019
dc.identifier.volume240en_US
dc.identifier.wosWOS:001554458300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Acousticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectGold purity gradesen_US
dc.subjectNon-destructive testingen_US
dc.subjectAudio classificationen_US
dc.subjectMachine learningen_US
dc.titleDetermination of gold purity degrees using audio features with machine learning algorithmsen_US
dc.typeArticleen_US

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