Artificial neural network model of pork meat cubes osmotic dehydratation

Pezo, Lato; Ćurčić, Biljana Lj.; Filipović, Vladimir; Nićetin, Milica; Koprivica, Gordana; Mišljenović, Nevena; Lević, Ljubinko

doi:10.2298/hemind120529082p

Cited by 69 publications

(94 citation statements)

References 49 publications

(60 reference statements)

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“…The optimization procedures to minimize the error function between network and experimental outputs was used during ANN training cycle (23,24), and the sum of squares (SOS) was evaluated according to the BFGS algorithm, to speed up and stabilize convergence of the results (25). ANN models were used to predict experimental variables, reasonably well, for a broad range of the process variables.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Serum Concentration of Hepcidin as an Indicator of Iron Reserves in Children

Ćulafić¹,

Kolarovic²,

Pezo³

et al. 2018

Journal of Medical Biochemistry

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Section: Artificial Neural Network Modelmentioning

confidence: 99%

Serum Concentration of Hepcidin as an Indicator of Iron Reserves in Children

Ćulafić¹,

Kolarovic²,

Pezo³

et al. 2018

Journal of Medical Biochemistry

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“…• C [11,14,24] for 12 hours in order to determine the amount of time needed to achieve a moisture content of 55g/100g of product established by Pezo et al [20]. Moisture content was determined by duplicate every 2 hours according to AOAC method 934.01 [25].…”

Section: Previous Testsmentioning

confidence: 99%

“…It is done by immersion of the product in a hypertonic solution, forming an osmotic pressure gradient because of the difference of concentrations between them. As a result, three main mass transfers occur in this process: (1) water flow from the product to the solution, (2) solutes are transferred from the solution to the product and (3) a loss of the product's own solutes into de hypertonic solution (Lück, 1995;Pezo et al, 2013). Energy savings and waste reduction are the main advantages of OD compared to other drying techniques.…”

Section: Introductionmentioning

confidence: 99%

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Efecto del tiempo de secado y de distintos cortes de músculos de res en las características fisicoquímicas y sensoriales de carne seca (cecina)

Ramírez-Cárdenas¹

2015

Av. Cienc. Ing. (Quito)

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Meat products have a broad market in Ecuador; however, jerky (dry meat) is rarely known and there are no products preserved using osmotic dehydration (OD). OD improves organoleptic characteristics of meat, thus it allows to use less economically valued meat cuts. The objective of this work was to evaluate the effect of drying time on three beef cuts, regarding the physicochemical characteristics (Moisture Content, Moisture:Protein Relation, MPR, and Water Activity Aa) and sensory characteristics in the elaboration of jerky that was previously dehydrated by OD, employing a 60• Brix hypertonic solution. OD procedure was considered to be finished when a moisture content of 55g/100g of product was achieved for all treatments. During the air drying stage, a Completely Randomized Design (CRD) with factorial arrangement 3 2 was used (Time: 4h, 5h, and 6h and muscle cut: Infraspinatus, Rectus femoris, Pectoralis profundus) at constant temperature (68.5• C). Five hours drying time treatments (T2, T5 and T8) presented better results for all three muscles types on physicochemical characterization and were evaluated by 65 consumer panelists through a preference test, in which no significant difference among treatments was observed. Pectoralis profundus is considered to have lesser market value compared to the other two muscles, hence its use for this type of products is highly recommended.Keywords. Jerky, Infraspinatus, Rectus femoris, Pectoralis profundus, Moisture Protein Ratio, Osmotic Dehydration. ResumenPese al gran posicionamiento que tienen los productos cárnicos en el Ecuador, el Charqui (carne seca) es poco conocido y no existen productos conservados usando deshidratación osmótica (DO). La DO mejora las características organolépticas de la carne por lo que permite utilizar cortes de menor costo económico. El objetivo de este trabajo fue evaluar el efecto de diferentes tiempos de secado en tres tipos de músculo de res sobre las características fisicoquímicas (contenido de Humedad, Relación Humedad: Proteína MPR, y Actividad de Agua Aa) y organolépticas en la preparación de Charqui, previamente deshidratado mediante DO utilizando una solución hipertónica 60• Brix. La DO se consideró terminada para los tratamientos al alcanzar un contenido de Humedad de 55g/100g de producto. Para la etapa de secado se utilizó un diseño completamente al azar (DCA) con arreglo factorial 3 2 , correspondiente a 2 factores (Tiempo: 4h, 5h, y 6h y tipo de músculo: Infraspinatus, Rectus femoris, Pectoralis profundus) a temperatura constante (68.5• C). Los tratamientos de 5 horas de secado para todos los músculos (T2, T5 y T8) presentaron los mejores resultados en la caracterización físico-química y al ser evaluados sensorialmente por 65 consumidores a través de una prueba de preferencia no presentaron diferencia significativa. Pectoralis profundus es un músculo con un valor en el mercado considerablemente menor a los dos anteriores, por lo que su utilización puede ser altamente recomendada en este tipo de productos.Palabras Clave.

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“…Th e fi rst estimation of the number of neurons can be obtained from the following equation [15,16]:…”

Section: Artifi Cial Neural Network Modelingmentioning

confidence: 99%

Prediction and Optimization of Heavy Clay Products Quality

Arsenović

Pezo

Mančić

et al. 2014

Advanced Materials for Agriculture, Food, and Environmental Safety

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Th e eff ects of chemical composition, fi ring temperature (800-1100 °C), and several shape formats of laboratory brick samples on the fi nal product quality were investigated. Prediction of the fi nal laboratory products parameters was evaluated by second order polynomial regression models (SOPs) and artifi cial neural networks (ANNs), and aft erwards compared to experimental results. SOPs showed high r 2 values (0.897-0.913 for compressive strength models, 0.942-0.962 for water absorption, 0.928 for fi ring shrinkage, 0.988-0.991 for water loss during fi ring, and 0.941 for volume mass of cubes models). An ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy: 0.866-0.939 for compressive strength models, 0.954-0.974 for water absorption, 0.882 for fi ring shrinkage, 0.982-0.988 for water loss during fi ring, and 0.920 for volume mass of cubes models. Th e optimal samples' chemical composition and fi ring temperature were chosen depending on a fi nal usage of the raw material in heavy clay brick industry.

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Artificial neural network model of pork meat cubes osmotic dehydratation

Cited by 69 publications

References 49 publications

Serum Concentration of Hepcidin as an Indicator of Iron Reserves in Children

Serum Concentration of Hepcidin as an Indicator of Iron Reserves in Children

Efecto del tiempo de secado y de distintos cortes de músculos de res en las características fisicoquímicas y sensoriales de carne seca (cecina)

Prediction and Optimization of Heavy Clay Products Quality

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