Effect of Hemicellulose as a Coating Material on Water Sorption Thermodynamics of the Microencapsulated Fish Oil and Artificial Neural Network (ANN) Modeling of Isotherms

Tatar, F.; Cengiz, Alime; Kahyaoğlu, Talip

doi:10.1007/s11947-014-1291-0

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…To ensure data accuracy and to more accurately reflect the relationships between the factors, the model's input and output layer parameters are normalized to the general weights so that they fall within the neuronal transfer function region 49,50 . The specific algorithm is shown in Eqn (1) 50 :

‾ X_{i} goodbreak= \frac{x_{i} - x_{\min}}{x_{\max} - x_{\min}}

where x i and

{\overset{X}{true}}_{i}

are the values of data i before and after processing, respectively; x min and x max are the maximum and minimum values of the processed group data.…”

Section: Methodsmentioning

confidence: 99%

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Formation and prediction of heterocyclic amines and N‐nitrosamines in smoked sausages using back propagation artificial neural network

Zhou,

Zhai,

Yao

et al. 2024

J Sci Food Agric

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BACKGROUNDHeterocyclic amines (HAs) and N‐nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP‐ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat‐to‐lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP‐ANN model.RESULTSWith an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP‐ANN model was 5–11–2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance.CONCLUSIONThe contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP‐ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

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‾ X_{i} goodbreak= \frac{x_{i} - x_{\min}}{x_{\max} - x_{\min}}

where x i and

{\overset{X}{true}}_{i}

are the values of data i before and after processing, respectively; x min and x max are the maximum and minimum values of the processed group data.…”

Section: Methodsmentioning

confidence: 99%

“…To ensure data accuracy and to more accurately reflect the relationships between the factors, the model's input and output layer parameters are normalized to the general weights so that they fall within the neuronal transfer function region. 49,50 The specific algorithm is shown in Eqn (1) 50 :…”

Section: Establishment Of a Prediction Model Based On Bp-annmentioning

confidence: 99%

Formation and prediction of heterocyclic amines and N‐nitrosamines in smoked sausages using back propagation artificial neural network

Zhou,

Zhai,

Yao

et al. 2024

J Sci Food Agric

View full text Add to dashboard Cite

show abstract

“…Before training the network, it is necessary to standardize the input and output data to express the correlation between them accurately. Further, the general weight value of both input and output data between [0,1] is normalized (Tatar et al, 2014;Lato et al, 2014) according to the equation…”

Section: Cooking Experimentsmentioning

confidence: 99%

Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow

Jiang

Shang

Zheng

et al. 2023

Qual. Assur. Saf. Crops Foods

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In the experiment of this article, the artificial neural network (ANN) was used to establish the sturgeon bone marrow drying model. Further, the effects of different temperatures (40, 60, and 80°C), humidities (0, 20, and 40%), and air velocities (8, 16, and 25 m/s) on the drying characteristics of sturgeon bone marrow were stud-ied. The studies had shown that with the increase of drying temperature, the acceleration of air velocity, and the decrease of humidity, the sturgeon bone marrow can be dried in the shortest period of 100 min. This study used ANN to feasibly predict dried sturgeon bone marrow moisture ratio, based on the time, temperature, humidity, and air velocity drying inputs. The results revealed that 11 hidden neurons were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R value 0.996. This model correctly predicted the optimal drying conditions and established that temperature is the single most significant factor in determining the drying time of sturgeon bone marrow. It is expected that this system will have broader application in other food drying requirements.

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“…Selected salts used for the preparation of saturated salt solutions and their corresponding equilibrium relative humidity at the experimental temperatures were presented in Table 1. To prevent mold or bacterial growth, capillary tube containing toluene (3ml) was also placed in each container with high equilibrium relative humidity (ERH60%) (Sahin and Sumnu 2006;Yogendrarajah et al, 2015;Tatar et al, 2014). The sample weight was recorded at regular intervals until the equilibrium was achieved.…”

Section: Determination Of Adsorption Isothermsmentioning

confidence: 99%

“…The standard gravimetric method recommended by the COST 90 project (Wolf et al, 1985) was used to determine the moisture sorption isotherms at 25°C and 45°C. The whole dried sample was weighted in perforated aluminum cups and placed on a platform inside sealed containers with different levels of relative humidity maintained by eight different saturated salt solutions (Tatar et al, 2014;Yogendrarajah et al, 2015). Selected salts used for the preparation of saturated salt solutions and their corresponding equilibrium relative humidity at the experimental temperatures were presented in Table 1.…”

Section: Determination Of Adsorption Isothermsmentioning

confidence: 99%

Adsorption Isotherms of Kiwifruit Dried by Different Drying Methods

Kizmaz

Altıok

Mazı

2019

Mühendislik Bilimleri Ve Tasarım Dergisi

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The aim of this study was to determine the moisture adsorption isotherms of kiwifruit dried by hot air, vacuum and freeze drying methods. Equilibrium moisture contents (EMC) of dried kiwifruits were obtained by using the standard gravimetric method at two different temperatures (25 and 45°C) within a range of water activities from 0.112 to 0.936. The adsorption isotherms of dried kiwifruits decreased with increasing temperature and exhibited type III behavior. Six different isotherm equations (GAB (Guggenheim-Anderson-de Boer), BET (Brunauer-Emmett-Teller), Oswin, Henderson, Halsey and Peleg) were employed to describe the experimental adsorption isotherm data. Adsorption data obtained at both 25 and 45°C for vacuum dried samples and data obtained at 45°C for hot air and freeze dried samples were best represented by Peleg equation. For the samples dried by hot air and freeze drying, GAB equation gave the best description of the experimental data obtained at 25°C.

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Effect of Hemicellulose as a Coating Material on Water Sorption Thermodynamics of the Microencapsulated Fish Oil and Artificial Neural Network (ANN) Modeling of Isotherms

Cited by 10 publications

References 36 publications

Formation and prediction of heterocyclic amines and N‐nitrosamines in smoked sausages using back propagation artificial neural network

Formation and prediction of heterocyclic amines and N‐nitrosamines in smoked sausages using back propagation artificial neural network

Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow

Adsorption Isotherms of Kiwifruit Dried by Different Drying Methods

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