Simultaneous Spectrophotometric Determination of Phosphate and Silicate by Using Principal Component Artificial Neural Network

2007 ◽  
Vol 97 (8) ◽  
pp. 723-731 ◽  
Author(s):  
Kobra Zarei ◽  
Morteza Atabati ◽  
Mehdi Nekoei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spectrophotometric Determination ◽  
Principal Component ◽  
Artificial Neural

scholarly journals Physicochemical Changes of the Gluten-Free Rice-Buckwheat Cookies during Storage – Artificial Neural Network Model

2019 ◽  
Author(s):  
Mladenka Pestorić ◽  
Marijana Sakač ◽  
Lato Pezo ◽  
Dubravka Škrobot ◽  
Nataša Nedeljković ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Principal Component ◽  
Physicochemical Characteristics ◽  
Storage Conditions ◽  
Gluten Free ◽  
Physicochemical Changes ◽  
Artificial Neural

The influence of storage time, temperature, and packaging on some physicochemical characteristics of gluten-free rice-buckwheat cookies was studied. Shelf life markers, such as water activity (aw), hydroxymethylfurfural (HMF), firmness, and color parameters were modelled in relation to different storage conditions. Principal component analysis was applied to study the similarity among samples according to the observed parameters. The mathematical model in the form of an artificial neural network was developed to predict the physicochemical parameters of cookies during 6-month storage. The most evident differentiation among samples was observed for color coordinate a*, aw , and HMF. Regarding the methods for determination of the parameters, priority should be given to the instrumental determination of color as the most convenient method. The processing of experimental data allowed the creation of useful mathematical model to be used in predicting the behavior of physicochemical changes of cookies by different factor combinations during storage.

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