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Prediction of moisture content of wet and dried nixtamal after alkaline cooking process by using artificial neural network

Journal of Food Measurement and Characterization · Ağustos 2022

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YÖKSİS Kayıtları
Prediction of moisture content of wet and dried nixtamal after alkaline cooking process by using artificial neural network
Journal of Food Measurement and Characterization · 2022 SCI-Expanded
Doç. Dr. MUSTAFA ŞAMİL ARGUN →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

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Prediction of moisture content of wet and dried nixtamal after alkaline cooking process by using artificial neural network
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Makale Bilgileri

Dergi Journal of Food Measurement and Characterization
ISSN21934126
Yayın TarihiAğustos 2022
Cilt / Sayfa16 · 2922-2928
DOI10.1007/s11694-022-01364-8
Scopus ID2-s2.0-85128695976
Özet In this study, a versatile approach was presented by using a feedforward multi-layer perceptron (MLP) neural network utilizing Bayesian Regularization and Levenberg-Marquardt training algorithms with the aim of determining the moisture content of wet and dried nixtamal after the application of alkaline cooking. Two different corn varieties were dehydrated at different Ca(OH)2 concentrations, cooking and steeping periods. The corn variety and processing conditions were accepted as inputs of an artificial neural network. In predicting the moisture content of the wet nixtamal, it was discovered that the network where Bayesian Regularization training algorithm was used and which had been designed to contain 20 neurons each in its first and second hidden layers, fitted best with the experimental data. In predicting the moisture content of dried nitamal, the network where the Bayesian Regularization training algorithm was used and which had been designed to contain 40 neurons each in first and second hidden layers, fit best to the experimental data. These configurations can predict the moisture content of wet and dried nixtamal with a regression coefficient of 0.99. Additionally, statistical analysis showed that the most effective two input variables related to the moisture content of corn were corn type and the cooking period.

Yazarlar (1)

1
Mustafa Samil Argun

Anahtar Kelimeler

Alkaline cooking Artificial neural network Corn processing Nixtamal Prediction of moisture content

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Food Measurement and Characterization
Q2
SJR Skoru0,620
H-Index66
YayıncıSpringer Science + Business Media
ÜlkeUnited States
Chemical Engineering (miscellaneous) (Q2)
Food Science (Q2)
Industrial and Manufacturing Engineering (Q2)
Safety, Risk, Reliability and Quality (Q2)
Dergi sayfasına git

Metrikler

2
Atıf
1
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

MA
MUSTAFA ŞAMİL ARGUN
Doç. Dr.

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