Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy
PeerJ Computer Science · Ocak 2023
YÖKSİS Kayıtları
Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy
PeerJ Computer Science · 2023 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ ÖZCAN ÇATALTAŞ →
Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy
PeerJ Computer Science · 2023 SCI-Expanded
DOÇENT KEMAL TÜTÜNCÜ →
Makale Bilgileri
DergiPeerJ Computer Science
Yayın TarihiOcak 2023
Cilt / Sayfa9 · 1-29
Scopus ID2-s2.0-85151804275
Erişim🔓 Açık Erişim
Özet
Background. Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area. Methods. This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirty-two latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models. Results. R2, RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.
Yazarlar (2)
1
Ozcan Cataltas
2
Kemal Tutuncu
Anahtar Kelimeler
Cereal analysis
Chemometrics
Convolutional autoencoder
Multiple linear regression
Near-infrared spectroscopy
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
8
Atıf
2
Yazar
5
Anahtar Kelime