[en] Description of the subject. Given the current low price of milk, a lot of producers have decided to process their milk into
products with a higher added-value, including butter. However, all milks are not suitable to be transformed into butter. It would
thus be useful to be able to predict milk processing properties.
Objectives. The aim of this paper was to study the ability of milk to be processed into butter using infrared spectrophotometry.
Method. A normalized protocol for the production of butter was developed. Milk samples (n = 110) collected between 2013
and 2016 were analyzed by near and medium infrared spectrometry (315 spectra). Butter samples were also analyzed by
visible-near infrared spectrometry (220 spectra). Composition of the products was subsequently assessed using validated
prediction equations. Principal components analyses were performed to discriminate samples.
Results. Butter properties seemed to be influenced by seasons and feedings. Water content and color parameters could be
predicted on the basis of butter infrared spectra.
Conclusions. It was possible to correlate butter characteristics with milk properties. However, it was not possible to predict
butter characteristics on the basis of milk near infrared spectra. It could be interesting to try predictions from milk medium
infrared spectra.
Disciplines :
Food science
Author, co-author :
Lefebure, Emilie ; Université de Liège - ULiège > Département GxABT > Chimie des agro-biosystèmes
Troch, Thibault ; Université de Liège - ULiège > Département GxABT > Chimie des agro-biosystèmes
Noutfia, Younès; Université de Liège – Gembloux Agro-Bio Tech > Laboratoire Qualité et Sécurité des Produits Agro-alimentaires
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Abbas O., Dardenne P. & Baeten V., 2012. Near-infrared, mid-infrared, and raman spectroscopy. In: Picó Y., ed. Chemical analysis of food: techniques and applications. Amsterdam: Elsevier, 59-90.
AFNOR, 1995. NF EN ISO 3727 Décembre 1995. Beurre – Détermination des teneurs en eau, matière sèche non grasse et en matière grasse sur la même prise d’essai (méthode de référence). La Plaine Saint-Denis, France: AFNOR.
Agence wallonne pour la Promotion d’une Agriculture de Qualité, 2021. Informations générales sur les produits laitiers dérivés wallons, https://www.apaqw.be/fr/informations-generales-sur-les-produits-laitiers-derives-wallons, (11/02/2021).
Amiot J. et al., 2002. Composition, propriétés physicochimiques, valeur nutritive, qualité technologique et techniques d’analyse du lait. In: Vignola C.L., ed. Science et technologie du lait. Transformation du lait. Québec, Canada: Presses Internationales Polytechniques, 1-73.
Baeten V. et al., 2014. Vibrational spectroscopy methods for the rapid control of agro-food products. In: Nollet L.M.L., ed. Handbook of food analyses. 3rd ed. Boca Raton, FL, USA: CRC Press, 591-622.
Bobe G. et al., 2003. Texture of butter from cows with different milk fatty acid compositions. J. Dairy Sci., 86, 3122-3127, doi.org/10.3168/jds.S0022-0302(03)73913-7
Bobe G. et al., 2007. Butter composition and texture from cows with different milk fatty acid compositions fed fish oil or roasted soybeans. J. Dairy Sci., 90, 2596-2603, doi.org/10.3168/jds.2006-875.
Boutonnier J.L., 2007. Matière grasse laitière – Crème et beurre standard. Techn. Ing., F 6321, 1-16.
Brochu E. et al., 1984. Science et technologie du lait: principes et applications. Québec, Canada: La Fondation de Technologie laitière du Québec.
Carroll S.M. et al., 2006. Milk composition of Holstein, Jersey, and Brown Swiss cows in response to increasing levels of dietary fat. Anim. Feed Sci. Technol., 90, 451-473, doi.org/10.1016/j.anifeedsci.2006.06.019
Chilliard Y., Martin C., Rouel J. & Doreau M., 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci., 92, 5199-5211, doi. org/10.3168/jds.2009-2375
Codex Alimentarius, 2018. CXS 279-1971 Norme pour le beurre, http://www.fao.org/fao-who-codexalimentarius/sh-proxy/fr/?lnk=1&url=https%253A%252F%252Fw orkspace.fao.org%252Fsites%252Fcodex%252FStand ards%252FCXS%2B279-1971%252FCXS_279f.pdf, (8/2/2021).
Colinet F. et al., 2013. Potentiel d’utilisation de la spectrométrie moyen infrarouge pour prédire le rendement fromager du lait et étudier sa variabilité génétique. In: 20è Rencontres Recherches Ruminants, 4-5 décembre 2013, Institut de l’Élevage, Paris. Paris: INRA, 153-156.
Confédération belge de l’Industrie laitière, 2019. Rapport annuel 2019, https://bcz-cbl.be/media/382772/2019_ jaarverslag_bcz_fr.pdf, (11/02/2021)
Couvreur S. et al., 2006. The linear relationship between the proportion of fresh grass in the cow diet, milk fatty acid composition, and butter properties. J. Dairy Sci., 89, 1956-1969, doi.org/10.3168/jds.S0022-0302(06)72263-9
Dal Zotto R. et al., 2008. Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy. J. Dairy Sci., 91, 4103-4112, doi.org/10.3168/jds.2007-0772
Dvorak L., Luzova T. & Sustova K., 2016. Comparison of butter quality parameters available on the Czech market with the use of FT NIR technology. Mljekarstvo, 66, 73-80, doi.org/10.15567/mljekarstvo.2016.0108
El-Hajjaji S. et al., 2019. Overview of the local production process of raw milk butter in Wallonia (Belgium). Int. J. Dairy Technol., 72, 466-471, doi.org/10.1111/1471-0307.12608
El-Hajjaji S. et al., 2020. Assessment of growth and survival of Listeria monocytogenes in raw milk butter by durability tests. Int. J. Food Microbiol., 321, 108541, doi.org/10.1016/j.ijfoodmicro.2020.108541
Fagan C.C., O’Donnell C.P., Rudzik L. & Wüst E., 2009. Milk and dairy products. In: Sun D.W., ed. Infrared spectroscopy for food quality analysis and control. Amsterdam: Elsevier.
Funahashi H. & Horiuchi J., 2008. Characteristics of the churning process in continuous butter manufacture and modelling using an artificial neural network. Int. Dairy J., 18, 323-328.
Gori A. et al., 2012. A rapid method to discriminate season of production and feeding regimen of butters based on infrared spectroscopy and artificial neural networks. J. Food Eng., 109, 525-530, doi.org/10.1016/j. jfoodeng.2011.10.029
Hermida M., Gonzalez J.M., Sanchez M. & Rodriguez-Otero J.L., 2001. Moisture, solids-non-fat and fat analysis in butter by near infrared spectroscopy. Int. Dairy J., 11, 93-98, doi.org/10.1016/S0958-6946(01)00039-5.
Heussen P.C.M., Janssen H.G., Samwel I.B.M. & van Duynhoven J.P.M., 2007. The use of multivariate modelling of near infra-red spectra to predict the butter fat content of spreads. Anal. Chim. Acta, 595, 176-181, doi.org/10.1016/j.aca.2007.01.048
Holroyd S.E., 2013. Review: the use of near infrared spectroscopy on milk and milk products. J. Near Infrared Spectrosc., 21, 311-322, doi.org/10.1255%2Fjnirs.1055
Hurtaud C. & Peyraud J.L., 2007. Effects of feeding Camelina (seeds or meal) on milk fatty acid composition and butter spreadability. J. Dairy Sci., 90, 5134-5145, doi.org/10.3168/jds.2007-0031
Jeantet R. et al., 2008. Les produits laitiers. Paris: Lavoisier, Éditions Tec & Doc.
Jensen R.G., 2002. The composition of bovine milk lipids: January 1995 to December 2000. J. Dairy Sci., 85, 295-350, doi.org/10.3168/jds.S0022-0302(02)74079-4
Karoui R. & De Baerdemaeker J., 2007. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chem., 102, 621-640, doi.org/10.1016/j. foodchem.2006.05.042
Keogh M.K., 2006. Chemistry and technology of butter and milk fat spreads. In: Fox P.F. & McSweeney P.L.H., eds. Advanced dairy chemistry. Vol. 2. Lipids. Berlin, Germany: Springer, 333-363.
La Spina S., 2016. Pistes d’avenir pour le secteur laitier wallon. Jambes, Belgique: Nature & Progrès.
MacGibbon A.K.H. & Taylor M.W., 2006. Composition and structure of bovine milk lipids. In: Fox P.F. & McSweeney P.L.H., eds. Advanced dairy chemistry. Vol. 2. Lipids. Berlin, Germany: Springer, 1-42.
Mahaut M., Jeantet R. & Brulé G., 2003. Initiation à la technologie fromagère. Paris: Lavoisier, Éditions Tec & Doc.
Massart D.L. et al., 1997. Data handling in science and technology. In: Vandeginste B.G.M. & Rutan S.C., eds. Handbook of chemometrics and qualimetrics. Part A. Amsterdam: Elsevier, 867.
Soyeurt H. et al., 2009. Potential estimation of major mineral contents in cow milk using mid-infrared spectrometry. J. Dairy Sci., 92, 2444-2454, doi.org/10.3168/jds.2008-1734
Soyeurt H. et al., 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. J. Dairy Sci., 94, 1657-1667, doi. org/10.3168/jds.2010-3408
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.