Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions

To use salmon protein hydrolysates as food ingredients and to mask the fish odor, Maillard reactions were associated with enzymatic production of hydrolysates. The study explored an original approach based on regression trees (RT) and random forest (RF) methodologies to predict hydrolysate odor prof...

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Published in:Food Research International
Main Authors: Cardinal, Mireille, Chaussy, Marianne, Donnay-moreno, Claire, Cornet, Josiane, Rannou, Cecile, Fillonneau, Catherine, Prost, Carole, Baron, Regis, Courcoux, Philippe
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier BV 2020
Subjects:
Online Access:https://archimer.ifremer.fr/doc/00624/73590/73024.pdf
https://doi.org/10.1016/j.foodres.2020.109254
https://archimer.ifremer.fr/doc/00624/73590/
id ftarchimer:oai:archimer.ifremer.fr:73590
record_format openpolar
spelling ftarchimer:oai:archimer.ifremer.fr:73590 2023-05-15T15:32:34+02:00 Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions Cardinal, Mireille Chaussy, Marianne Donnay-moreno, Claire Cornet, Josiane Rannou, Cecile Fillonneau, Catherine Prost, Carole Baron, Regis Courcoux, Philippe 2020-08 application/pdf https://archimer.ifremer.fr/doc/00624/73590/73024.pdf https://doi.org/10.1016/j.foodres.2020.109254 https://archimer.ifremer.fr/doc/00624/73590/ eng eng Elsevier BV https://archimer.ifremer.fr/doc/00624/73590/73024.pdf doi:10.1016/j.foodres.2020.109254 https://archimer.ifremer.fr/doc/00624/73590/ info:eu-repo/semantics/openAccess restricted use Food Research International (0963-9969) (Elsevier BV), 2020-08 , Vol. 134 , P. 109254 (11p.) Sensory characteristics Volatile compounds HS-SPME/GC-MS Regression tree Random forest Hydrolysate Maillard reactions text Publication info:eu-repo/semantics/article 2020 ftarchimer https://doi.org/10.1016/j.foodres.2020.109254 2021-09-23T20:35:00Z To use salmon protein hydrolysates as food ingredients and to mask the fish odor, Maillard reactions were associated with enzymatic production of hydrolysates. The study explored an original approach based on regression trees (RT) and random forest (RF) methodologies to predict hydrolysate odor profiles from volatile compounds. An experimental design with four factors: enzyme/substrate ratio, quantity of xylose, hydrolysis and cooking times was used to create a range of enzymatic hydrolysates. Twenty samples were submitted to a trained panel for sensory descriptions of odor. Hydrolysate volatile compounds were extracted by means of Headspace Solid Phase MicroExtraction (HS-SPME) and analyzed using gas chromatography/mass spectrometry (GC-MS). The results showed that RT and RF methodologies can be useful tools for predicting an entire sensory profile from volatile compounds. Four main volatile compounds made it possible to separate hydrolysates into five groups according to their specific sensory profile. 2,5-dimethylpyrazine, 1-hydroxy-2-propanone and 3-hydroxy-2-pentanone were identified as the main predictors of the roasted odor, whereas methanethiol was associated with a mud odor. These results also suggest the appropriate process conditions for obtaining a typical roasted odor. Article in Journal/Newspaper Atlantic salmon Salmo salar Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) Food Research International 134 109254
institution Open Polar
collection Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer)
op_collection_id ftarchimer
language English
topic Sensory characteristics
Volatile compounds
HS-SPME/GC-MS
Regression tree
Random forest
Hydrolysate
Maillard reactions
spellingShingle Sensory characteristics
Volatile compounds
HS-SPME/GC-MS
Regression tree
Random forest
Hydrolysate
Maillard reactions
Cardinal, Mireille
Chaussy, Marianne
Donnay-moreno, Claire
Cornet, Josiane
Rannou, Cecile
Fillonneau, Catherine
Prost, Carole
Baron, Regis
Courcoux, Philippe
Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions
topic_facet Sensory characteristics
Volatile compounds
HS-SPME/GC-MS
Regression tree
Random forest
Hydrolysate
Maillard reactions
description To use salmon protein hydrolysates as food ingredients and to mask the fish odor, Maillard reactions were associated with enzymatic production of hydrolysates. The study explored an original approach based on regression trees (RT) and random forest (RF) methodologies to predict hydrolysate odor profiles from volatile compounds. An experimental design with four factors: enzyme/substrate ratio, quantity of xylose, hydrolysis and cooking times was used to create a range of enzymatic hydrolysates. Twenty samples were submitted to a trained panel for sensory descriptions of odor. Hydrolysate volatile compounds were extracted by means of Headspace Solid Phase MicroExtraction (HS-SPME) and analyzed using gas chromatography/mass spectrometry (GC-MS). The results showed that RT and RF methodologies can be useful tools for predicting an entire sensory profile from volatile compounds. Four main volatile compounds made it possible to separate hydrolysates into five groups according to their specific sensory profile. 2,5-dimethylpyrazine, 1-hydroxy-2-propanone and 3-hydroxy-2-pentanone were identified as the main predictors of the roasted odor, whereas methanethiol was associated with a mud odor. These results also suggest the appropriate process conditions for obtaining a typical roasted odor.
format Article in Journal/Newspaper
author Cardinal, Mireille
Chaussy, Marianne
Donnay-moreno, Claire
Cornet, Josiane
Rannou, Cecile
Fillonneau, Catherine
Prost, Carole
Baron, Regis
Courcoux, Philippe
author_facet Cardinal, Mireille
Chaussy, Marianne
Donnay-moreno, Claire
Cornet, Josiane
Rannou, Cecile
Fillonneau, Catherine
Prost, Carole
Baron, Regis
Courcoux, Philippe
author_sort Cardinal, Mireille
title Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions
title_short Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions
title_full Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions
title_fullStr Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions
title_full_unstemmed Use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of Atlantic salmon (Salmo salar) by-products combined with Maillard reactions
title_sort use of random forest methodology to link aroma profiles to volatile compounds: application to enzymatic hydrolysis of atlantic salmon (salmo salar) by-products combined with maillard reactions
publisher Elsevier BV
publishDate 2020
url https://archimer.ifremer.fr/doc/00624/73590/73024.pdf
https://doi.org/10.1016/j.foodres.2020.109254
https://archimer.ifremer.fr/doc/00624/73590/
genre Atlantic salmon
Salmo salar
genre_facet Atlantic salmon
Salmo salar
op_source Food Research International (0963-9969) (Elsevier BV), 2020-08 , Vol. 134 , P. 109254 (11p.)
op_relation https://archimer.ifremer.fr/doc/00624/73590/73024.pdf
doi:10.1016/j.foodres.2020.109254
https://archimer.ifremer.fr/doc/00624/73590/
op_rights info:eu-repo/semantics/openAccess
restricted use
op_doi https://doi.org/10.1016/j.foodres.2020.109254
container_title Food Research International
container_volume 134
container_start_page 109254
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