Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins
Protein hydrolysates are, in general, mixtures of amino acids and small peptides able to supply the body with the constituent elements of proteins in a directly assimilable form. They are therefore characterised as products with high nutritional value. However, hydrolysed proteins display an unpleas...
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/10/6/1312 |
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| author | Dahlia Daher Barbara Deracinois Philippe Courcoux Alain Baniel Sylvie Chollet Rénato Froidevaux Christophe Flahaut |
| author_facet | Dahlia Daher Barbara Deracinois Philippe Courcoux Alain Baniel Sylvie Chollet Rénato Froidevaux Christophe Flahaut |
| author_sort | Dahlia Daher |
| collection | DOAJ |
| description | Protein hydrolysates are, in general, mixtures of amino acids and small peptides able to supply the body with the constituent elements of proteins in a directly assimilable form. They are therefore characterised as products with high nutritional value. However, hydrolysed proteins display an unpleasant bitter taste and possible off-flavours which limit the field of their nutrition applications. The successful identification and characterisation of bitter protein hydrolysates and, more precisely, the peptides responsible for this unpleasant taste are essential for nutritional research. Due to the large number of peptides generated during hydrolysis, there is an urgent need to develop methods in order to rapidly characterise the bitterness of protein hydrolysates. In this article, two enzymatic hydrolysis kinetics of micellar milk caseins were performed for 9 h. For both kinetics, the optimal time to obtain a hydrolysate with appreciable organoleptic qualities is 5 h. Then, the influence of the presence or absence of peptides and their intensity over time compared to the different sensory characteristics of hydrolysates was studied using heat maps, random forests and regression trees. A total of 22 peptides formed during the enzymatic proteolysis of micellar caseins and influencing the bitterness the most were identified. These methods represent simple and efficient tools to identify the peptides susceptibly responsible for bitterness intensity and predict the main sensory feature of micellar casein enzymatic hydrolysates. |
| first_indexed | 2024-03-10T10:38:11Z |
| format | Article |
| id | doaj.art-889c603f5c5443c49674c78a1e7c2dfd |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T10:38:11Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-889c603f5c5443c49674c78a1e7c2dfd2023-11-21T23:08:20ZengMDPI AGFoods2304-81582021-06-01106131210.3390/foods10061312Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar CaseinsDahlia Daher0Barbara Deracinois1Philippe Courcoux2Alain Baniel3Sylvie Chollet4Rénato Froidevaux5Christophe Flahaut6UMR Transfrontalière 1158 BioEcoAgro, Univ. Lille, INRAe, Univ. Liège, UPJV, JUNIA, Univ. Artois, Univ. Littoral Côte d’Opale, ICV—Institut Charles Viollette, 59000 Lille, FranceUMR Transfrontalière 1158 BioEcoAgro, Univ. Lille, INRAe, Univ. Liège, UPJV, JUNIA, Univ. Artois, Univ. Littoral Côte d’Opale, ICV—Institut Charles Viollette, 59000 Lille, FranceOniris, StatSC, rue de la Géraudière, 44322 Nantes, FranceIngredia S.A. 51 Av. Lobbedez-CS 60946, CEDEX, 62033 Arras, FranceUMR Transfrontalière 1158 BioEcoAgro, Univ. Lille, INRAe, Univ. Liège, UPJV, JUNIA, Univ. Artois, Univ. Littoral Côte d’Opale, ICV—Institut Charles Viollette, 59000 Lille, FranceUMR Transfrontalière 1158 BioEcoAgro, Univ. Lille, INRAe, Univ. Liège, UPJV, JUNIA, Univ. Artois, Univ. Littoral Côte d’Opale, ICV—Institut Charles Viollette, 59000 Lille, FranceUMR Transfrontalière 1158 BioEcoAgro, Univ. Lille, INRAe, Univ. Liège, UPJV, JUNIA, Univ. Artois, Univ. Littoral Côte d’Opale, ICV—Institut Charles Viollette, 59000 Lille, FranceProtein hydrolysates are, in general, mixtures of amino acids and small peptides able to supply the body with the constituent elements of proteins in a directly assimilable form. They are therefore characterised as products with high nutritional value. However, hydrolysed proteins display an unpleasant bitter taste and possible off-flavours which limit the field of their nutrition applications. The successful identification and characterisation of bitter protein hydrolysates and, more precisely, the peptides responsible for this unpleasant taste are essential for nutritional research. Due to the large number of peptides generated during hydrolysis, there is an urgent need to develop methods in order to rapidly characterise the bitterness of protein hydrolysates. In this article, two enzymatic hydrolysis kinetics of micellar milk caseins were performed for 9 h. For both kinetics, the optimal time to obtain a hydrolysate with appreciable organoleptic qualities is 5 h. Then, the influence of the presence or absence of peptides and their intensity over time compared to the different sensory characteristics of hydrolysates was studied using heat maps, random forests and regression trees. A total of 22 peptides formed during the enzymatic proteolysis of micellar caseins and influencing the bitterness the most were identified. These methods represent simple and efficient tools to identify the peptides susceptibly responsible for bitterness intensity and predict the main sensory feature of micellar casein enzymatic hydrolysates.https://www.mdpi.com/2304-8158/10/6/1312bitternessenzymatic hydrolysismicellar caseinsoff-flavourspeptidomicsrandom forests |
| spellingShingle | Dahlia Daher Barbara Deracinois Philippe Courcoux Alain Baniel Sylvie Chollet Rénato Froidevaux Christophe Flahaut Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins Foods bitterness enzymatic hydrolysis micellar caseins off-flavours peptidomics random forests |
| title | Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins |
| title_full | Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins |
| title_fullStr | Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins |
| title_full_unstemmed | Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins |
| title_short | Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins |
| title_sort | sensopeptidomic kinetic approach combined with decision trees and random forests to study the bitterness during enzymatic hydrolysis kinetics of micellar caseins |
| topic | bitterness enzymatic hydrolysis micellar caseins off-flavours peptidomics random forests |
| url | https://www.mdpi.com/2304-8158/10/6/1312 |
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