Articles producció científica> Química Analítica i Química Orgànica

Yoghurt standardization using real-time NIR prediction of milk fat and protein content

  • Identification data

    Identifier: imarina:9364441
    Authors:
    Castro-Reigía, D.Ezenarro, J.Azkune, M.Ayesta, I.Ostra, M.Amigo, J.M.García, I.Ortiz, M.C.
    Abstract:
    A system based on near-infrared (NIR) spectroscopy has been developed for the in-line control of the composition of the milk used as raw material for yoghurt production to control the content of protein and fat in the final product, and, therefore, to reduce variability in the production process. Firstly, after selecting the appropriate method for preprocessing NIR data, Partial Least Squares Regression models were built to predict fat and protein content in milk, obtaining good performances. The variance explained of y-block in prediction (R2P) was 0.99 and 0.80, while the Root Mean Square Error of Prediction (RMSEP), was 0.26 and 0.16 for fat and protein, respectively. With those models, it was possible to determine the fat and protein contents in milk in real time, and therefore, the quantity of milk powder and cream added in the manufacturing process of yoghurt could be readjusted. The presented strategy allows the improvement of the homogeneity of the final product, reducing the variability of the nutritional values in more than 70% with respect to the traditional recipe, and also meet the target values according to yoghurt producers for fat and protein content, that is, 10% of fat and 5% of protein.
  • Others:

    Project code: Grant agreement No. 824769
    Keywords: Yoghurt Protein Proof of concept Partial least squares regression (plsr) Near-infrared (nir) In-line Fat
    Record's date: 2024-11-16
    Papper version: info:eu-repo/semantics/publishedVersion
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Food Composition And Analysis. 128 106015-
    APA: Castro-Reigía, D.; Ezenarro, J.; Azkune, M.; Ayesta, I.; Ostra, M.; Amigo, J.M.; García, I.; Ortiz, M.C. (2024). Yoghurt standardization using real-time NIR prediction of milk fat and protein content. Journal Of Food Composition And Analysis, 128(), 106015-. DOI: 10.1016/j.jfca.2024.106015
    Publication Type: Journal Publications
    Project code 3: 2021PMF-BS-12
    Author, as appears in the article.: Castro-Reigía, D.; Ezenarro, J.; Azkune, M.; Ayesta, I.; Ostra, M.; Amigo, J.M.; García, I.; Ortiz, M.C.
    Department: Química Analítica i Química Orgànica
    URV's Author/s: EZENARRO GARATE, JOKIN
    Abstract: A system based on near-infrared (NIR) spectroscopy has been developed for the in-line control of the composition of the milk used as raw material for yoghurt production to control the content of protein and fat in the final product, and, therefore, to reduce variability in the production process. Firstly, after selecting the appropriate method for preprocessing NIR data, Partial Least Squares Regression models were built to predict fat and protein content in milk, obtaining good performances. The variance explained of y-block in prediction (R2P) was 0.99 and 0.80, while the Root Mean Square Error of Prediction (RMSEP), was 0.26 and 0.16 for fat and protein, respectively. With those models, it was possible to determine the fat and protein contents in milk in real time, and therefore, the quantity of milk powder and cream added in the manufacturing process of yoghurt could be readjusted. The presented strategy allows the improvement of the homogeneity of the final product, reducing the variability of the nutritional values in more than 70% with respect to the traditional recipe, and also meet the target values according to yoghurt producers for fat and protein content, that is, 10% of fat and 5% of protein.
    Program founding action 2: Action of the European Union-NextGenerationEU
    Thematic Areas: Zootecnia / recursos pesqueiros Saúde coletiva Química Nutrição Medicina veterinaria Medicina ii Medicina i Materiais Interdisciplinar Geociências Food science & technology Food science Farmacia Engenharias iii Engenharias ii Engenharias i Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, applied Biotecnología Biodiversidade Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: jokin.ezenarro@urv.cat jokin.ezenarro@urv.cat
    Author identifier: 0000-0001-9234-7877 0000-0001-9234-7877
    Founding program action 3: Universitat Rovira i Virgili - Banco Santander
    Founding program 2: INVESTIGO programe
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S0889157524000498
    Founding program 3: Contratos de personal investigador predoctoral en formación
    Article's DOI: 10.1016/j.jfca.2024.106015
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2024
    Funding program action: Action of the European Union’s Horizon 2020 research and innovation programme
  • Keywords:

    Chemistry, Applied,Food Science,Food Science & Technology
    Yoghurt
    Protein
    Proof of concept
    Partial least squares regression (plsr)
    Near-infrared (nir)
    In-line
    Fat
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Química
    Nutrição
    Medicina veterinaria
    Medicina ii
    Medicina i
    Materiais
    Interdisciplinar
    Geociências
    Food science & technology
    Food science
    Farmacia
    Engenharias iii
    Engenharias ii
    Engenharias i
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Chemistry, applied
    Biotecnología
    Biodiversidade
    Astronomia / física
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