Gazi Üniversitesi BAP
06/2018-12
Finansal desteklerinden dolayı Gazi Üniversitesi Bilimsel Araştırma Projesi Birimi'ne teşekkürler.
In this study, unsupervised and supervised machine learning techniques, principal component analysis and classification tree modelling which could be improved with additional input variables were applied on iodine oxidation voltammetric data in order to determine routes and extract information about the electrochemical conditions leading to different damaged starch ratios in flour. For this purpose a database of 3542 observations which was normalized and filtered from outliers was used. It was seen that although it was almost impossible to generalize information or determine correlations from voltammetric data at different conditions, principal component analysis indicate that on platinum electrode UCD values of 16.5 mostly seen at high potentials, optimized decision tree indicate that the impact of variables on UCD values can be ordered as current density > potential > electrode type > KI concentration and give routes to UCD values with high class membership leaf nodes. Therefore machine learning with decision tree modelling could open perspectives for practical and fast prediction of damaged starch ratio which would help food industry to speed up and economize costs for analysis in flour.
Decision Tree Damaged Starch Electrochemical Machine Learning
06/2018-12
Birincil Dil | İngilizce |
---|---|
Bölüm | Kimya Mühendisliği |
Yazarlar | |
Proje Numarası | 06/2018-12 |
Yayımlanma Tarihi | 30 Aralık 2021 |
Gönderilme Tarihi | 17 Eylül 2021 |
Yayımlandığı Sayı | Yıl 2021 Cilt: 8 Sayı: 4 |