K. Subki, A. Putri, A. Qusthalani, N.H.P. Sari, M. Subianto, Rajibussalim, K. Sadami, M. Ramli, K. Lahna, N. Idris
The increasing demand for organic chicken eggs has led to cases of counterfeit organic eggs being sold, often produced using commercially-fed chickens. A reliable and accurate method is therefore required to verify the authenticity of organic eggs. This study aims to determine the authenticity of organic eggs by analyzing emission spectra from eggshells using Laser Induced Breakdown Spectroscopy (LIBS) combined with Artificial Neural Networks (ANN). LIBS was chosen for its modern analytical capability with minimal sample preparation, while ANN provides robust classification support. The samples consisted of organic and non-organic eggshells, collected from five groups with controlled feeding: one entirely organic-fed group (100% organic), a completely non-organic group (0% organic), and three other groups with combined feed. A neodymium-doped yttrium aluminum garnet (Nd-YAG) laser was applied to the samples at 1 ATM pressure, producing plasma in an atmospheric air environment. Emission spectra from the plasma were captured by an Optical Multichannel Analyzer (OMA) for elemental analysis. The ANN model was then built to classify the samples based on the spectral data, successfully demonstrating a good classification ability in distinguishing between sample groups. The elemental composition of eggshell samples included Ca, C, O, P, S, Mg, V, Pb, Cr, Hg, Na, Zn, Cu, N, K, and Sr, with Ca showing the highest intensity due to its abundance. These findings demonstrate the potential of LIBS integrated with ANN for organic egg authenticity verification and suggest its applicability in detecting compositional variations at different authenticity levels. © Published under licence by IOP Publishing Ltd.
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
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