Enzymatic hydrolysis kinetics and nitrogen recovery in the protein hydrolysate production from pig bones

Pagán, Jordi; Ibarz, Albert; Falguera, Víctor; Benítez, Ricardo

doi:10.1016/j.jfoodeng.2013.06.040

Cited by 44 publications

(24 citation statements)

References 33 publications

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“…Effects of the initial substrate and the initial enzyme concentrations on DH A c c e p t e d M a n u s c r i p t 10 sources, including soy protein (Govindaraju and Srinivas, 2007), camel and bovine milk proteins (Salami et al, 2008), Atlantic salmon (Salmo salar) muscle proteins (Chabanon et al, 2007), and pig bone proteins (Pagán et al, 2013).…”

Section: Resultsmentioning

confidence: 99%

“…Several studies on proteolysis kinetics have been conducted Rasco, 2000a, 2000b;Pagán et al, 2013), and their task was to predict the optimal proteolysis conditions, which limited the hydrolysis of the undesired peptide fragments (bitter peptides), or to provide accumulation of the desired peptides (bioactive peptides) Ahn et al, 2012;Ktari et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Enzymatic Hydrolysis of Alaska Pollock Proteins Based on Kinetics Model and Lysine Biosensor–Neural Network Model

Hou

Fan

et al. 2016

Journal of Aquatic Food Product Technology

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2016): Enzymatic hydrolysis of Alaska Pollock proteins based on kinetics model and lysine biosensor -neural network model, Journal of Aquatic Food Product Technology, Abstract Controlled proteolysis is important for target hydrolysates. The hydrolysis kinetics of pollock bone protein (PBP) were obtained by mathematic deduction and experimental analysis. The equation of degree of hydrolysis was as follows: DH = 2.801 ln│1 + (0.06044 E 0 /S 0 -0.1005)t│, which could predict the hydrolysis process of PBP when hydrolysis condition was strictly controlled. However, the hydrolysis reaction was affected by several factors, and the predicted value may be against the experimental result. Therefore, on-line monitoring was necessary for obtaining the target peptides. Biosensor and artificial neural network (ANN) were employed for monitoring hydrolysis process on-line. Free lysine level was chosen as the monitoring factor determined by immobilized lysine oxidase electrodes. Based on the lysine biosensor and ANN, the hydrolysis monitoring model LYS-ANN was built, including 3 input layer nodes (E 0 , S 0 , and Lys), 1 output layer node (DH), and 11 hidden layer nodes. R 2 value between sample value and simulation value was 0.9964. Simulation error was in the range of 0-4.56%, and the average relative error was 0.94%. The verification tests of PBP hydrolysis showed that the LYS-ANN model could forecast hydrolysis process successfully with high efficiency and accuracy even if the hydrolysis conditions varied.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enzymatic Hydrolysis of Alaska Pollock Proteins Based on Kinetics Model and Lysine Biosensor–Neural Network Model

Hou

Fan

et al. 2016

Journal of Aquatic Food Product Technology

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“…Small peptide content, DH and free amino nitrogen content reflect the hydrolytic capacity of the proteases for soybean protein. DH is the percentage of free amino acid groups cleaved from proteins; this index can be calculated from the ratio of α-amino nitrogen to total nitrogen (Pagán et al 2013). α-Amino nitrogen was used for formaldehyde titration, and total nitrogen content was measured with a Kaytype nitrogen determination apparatus from Hanon company in China (version number K9860) (Dajanta et al 2011).…”

Section: Assay Of Hydrolysatesmentioning

confidence: 99%

Analysis of the Hydrolytic Capacities of Aspergillus oryzae Proteases on Soybean Protein Using Artificial Neural Networks

Hong

et al. 2015

Journal of Food Processing and Preservation

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An artificial neural network (ANN) model was established to predict the hydrolytic capacities of Aspergillus oryzae proteases on soybean protein. The available training data were split in two subsets: training and testing data, which comprised 25 and six groups of proteases, respectively. These data served as the inputs of ANN to predict small peptide content, degree of hydrolysis and free amino nitrogen content. This network included three neurons in the single hidden layer with a low mean squared error. The predicted results were similar to the actual values (R 2 > 0.92) and were superior to those of multiple linear regression. Sensitivity analysis revealed that there is a correlation between protease and soy protein hydrolysates. It was also verified that protease and soy protein hydrolysates could serve as inputs and outputs in the ANN. Among the tested proteases, aminopeptidase showed the highest hydrolytic capacity for soybean protein with sensitivity analysis. PRACTICAL APPLICATIONSThe artificial neural network model is a powerful technique to predict the hydrolytic capacities of Aspergillus oryzae proteases for soybean protein. The results of this study could be used to test the amount of yielded hydrolysates of soybean protein under one combination protease, and also explained the mechanism underlying the protease-catalyzed hydrolysis of soybean.

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“…The types of protease and protein substrate have an effect on the functional properties of the protein hydrolysate. So it is essential to control the process parameters to make it is possible to produce hydrolysate with the desired composition and properties (Pagán, Ibarz, Falguera, & Benítez, 2013).…”

Section: Introductionmentioning

confidence: 99%

Preparation of the rainbow trout bone peptides directed by nutritional properties and flavor analyses

Fan

Tan

et al. 2018

Food Science & Nutrition

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Rainbow trout bone proteins were prepared by heating at 121°C for 30 min, followed by filtration, concentration, and lyophilization. Nutritional properties and flavor analyses of hydrolysates digested by five different enzymes were investigated, respectively. Results showed that the crude protein content of rainbow trout bone was 15.90% and had a well‐balanced nutritional value. The content of total amino acids was 983.64 mg/g. The amount of free amino acids of hydrolysates digested by alkaline protease, neutral protease, flavourzyme, papain, and trypsin for 3 hr was 207.83, 224.13, 1,001.59, 283.26, and 303.64 mg/g, respectively. During the hydrolysis, the main flavor compounds were identified by GC‐MS to be alcohols, aldehydes, ketones, acids, and alkanes. After hydrolysis, the main molecular weight of peptides was focused on the range of 1,000–3,000 Da in all enzymatic hydrolysates. This study provided a theoretical basis to comprehensive utilization of rainbow trout bone in food industry.

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Enzymatic hydrolysis kinetics and nitrogen recovery in the protein hydrolysate production from pig bones

Cited by 44 publications

References 33 publications

Enzymatic Hydrolysis of Alaska Pollock Proteins Based on Kinetics Model and Lysine Biosensor–Neural Network Model

Enzymatic Hydrolysis of Alaska Pollock Proteins Based on Kinetics Model and Lysine Biosensor–Neural Network Model

Analysis of the Hydrolytic Capacities of Aspergillus oryzae Proteases on Soybean Protein Using Artificial Neural Networks

Preparation of the rainbow trout bone peptides directed by nutritional properties and flavor analyses

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