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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2026 / Cilt: 25 Sayı: 74

Determination of Levadopa and Benserazide Active Ingredients Used in Parkinson's Treatment by Artificial Neural Networks Method

Parkinson Tedavisinde Kullanılan Levadopa ve Benserazid Etken Maddelerinin Yapay Sinir Ağları Yöntemi ile Tayini

Ahmet Hakan Aktaş(Süleyman Demirel Üniversitesi)0000-0003-2327-4031
Damla Ezgi Barikan(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0001-5504-2257
Pages
329–337
Publication Date
2023-05-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results obtained with the help of the applied chemometric method are extremely fast, simple and reliable results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the determinations of levadopa and benserazide active ingredients in the tablet drug sample were carried out by ultraviolet visible region spectroscopy without applying any separation process at the same time and the obtained data were evaluated by artificial neural networks (ANN) chemometric method.
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Key Findings & Data:At the end of the study, the least squares method (PLS) was applied to compare the method used.
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Conclusion & Impact:The results obtained with the help of the applied chemometric method are extremely fast, simple and reliable results.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:İlaç · Levadopa · Benserazid

Abstract

In this study, the determinations of levadopa and benserazide active ingredients in the tablet drug sample were carried out by ultraviolet visible region spectroscopy without applying any separation process at the same time and the obtained data were evaluated by artificial neural networks (ANN) chemometric method. At the end of the study, the least squares method (PLS) was applied to compare the method used. In the study, the pure spectra of the active ingredients were obtained and then their synthetic mixtures were prepared. The ata obtained from these mixtures were evaluated with ANN and the method was applied to the tablet sample purchased from the market. The data obtained in the study were also analyzed statistically, and the root mean square (RMS) data, which is an important data group in the ANN method, was found to be quite small. In addition, the calculated recovery values are quite high and their standard deviations are small enough. The results obtained with the help of the applied chemometric method are extremely fast, simple and reliable results.

Özet

Bu çalışmada, tablet ilaç numunesinde bulunan levadopa ve benserazid etken maddelerinin aynı anda miktar tayinlerine hiçbir ayırma işlemi uygulanmaksızın ultraviyole görünür bölge spektroskopisi ile tayinleri gerçekleştirilmiş ve elde edilen veriler yapay sinir ağları (ANN) kemometrik yöntemi ile değerlendirilmiştir. Çalışmanın sonunda kullanılan yöntemi karşılaştırmak amacıyla kısmi en küçük kareler yöntemi (PLS) uygulanmıştır. Yapılan çalışmada etken maddelerin saf haldeki spektrumları elde edilmiş takiben sentetik karışımları hazırlanmıştır. Bu karışımlardan elde edilen veriler ANN ile değerlendirilmiş ve yöntem piyasadan alınan tablet numunesine uygulanmıştır. Çalışmada elde edilen veriler istatistiksel olarak da incelenmiş, ANN yönteminde önemli bir veri grubu olan karekök ortalama hata (RMS) verileri oldukça küçük bulunmuştur. Ayrıca hesaplanan geri kazanım değerleri oldukça yüksek, standart sapmaları ise yeterince küçüktür. Uygulanan kemometrik yöntem yardımıyla elde edilen sonuçlar son derece hızlı, basit ve güvenilir sonuçlardır.

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Cite This Article

Aktaş, A. H., Barikan, D. E. (2023). Determination of Levadopa and Benserazide Active Ingredients Used in Parkinson's Treatment by Artificial Neural Networks Method. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 25 Sayı: 74, 329–337. https://doi.org/10.21205/deufmd.2023257406

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