Cost-Effective Multispectral Sensor and Artificial Neural Networks for the Detection of Starch Adulteration in Raw Milk
Yazarlar (2)
Doç. Dr. Yeliz DURGUN
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Mahmut DURGUN
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | APPLIED SCIENCES-BASEL |
| Dergi ISSN | 2076-3417 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | Türkçe |
| Basım Tarihi | 10-2024 |
| Cilt No | 14 |
| Sayı | 21 |
| DOI Numarası | 10.3390/app14219800 |
| Makale Linki | https://doi.org/10.3390/app14219800 |
| Özet |
| This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets … |
| Anahtar Kelimeler |
| dairy product integrity | advanced detection techniques | machine learning applications | food safety | milk quality analysis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 2 |