{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:01:14Z","timestamp":1790676074130,"version":"4.1.0"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Lipolysis is a longstanding and appealing topic within the dairy industry. Having been extensively researched throughout the twentieth century, lipolysis is often prematurely dismissed as a settled or outdated field of study. In recent years, the advent of robotic milking systems and increased milking frequencies have led to a rise in lipolysis levels, sparking renewed interest in the monitoring of milk quality. Predicting lipolysis more effectively relies on the analysis of free fatty acid (FFA) concentrations. Recent research has demonstrated the feasibility of quantifying individual FFA in raw milk. In particular, Fourier-transform mid-infrared (FT-MIR) spectroscopy has shown considerable potential for predicting these compounds. However, that study presented several limitations, including the use of non-standardized reference methods and the reliance on artificially lipolyzed samples to enhance variability within the calibration dataset. To overcome these limitations, FT-MIR FFA models have been used as a preliminary screening tool in order to select samples (n = 248) with high lipolysis, and the selected samples were analyzed using the ISO reference method. A total of 248 samples were selected, of which 208 were obtained through the Walloon DHI in Belgium and 40 were sampled at the CRA-W experimental farm. A comparison between the two reference methods was performed and highlighted the need to harmonize reference analysis as the two reference methods are only comparable for short-chain free fatty acids (SCFFA). A mixed model for SCFFA combining both references was built using different algorithms such as Partial Least Squares (PLS), Kernel Ridge Regression (KRR), Support Vector Machine Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The model developed for SCFFA showed predictive performances comparable to those reported in the literature with an R2val of 0.80 and an RMSEval of 32.08 mg\/L. Qualitative models for SCFFA were created with a threshold highlighted in the literature using the same algorithms as the quantitative model and gave an accuracy of correct classification of 85% applied on the test dataset. This work has highlighted the necessity to correctly select the reference method in order to build the calibration dataset. Non-linear algorithms provided analyte-dependent improvements over PLS, particularly for C6, C8 and SCFFA prediction.<\/jats:p>","DOI":"10.3390\/foods15193423","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:29:53Z","timestamp":1790267393000},"page":"3423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing FT-MIR Prediction of Short-Chain Free Fatty Acids in Milk Through High-Lipolysis Field Samples, Reference Method Evaluation and Machine Learning Approaches"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9037-8079","authenticated-orcid":false,"given":"Octave S.","family":"Christophe","sequence":"first","affiliation":[{"name":"Walloon Agricultural Research Center (CRA-W), 24 Chauss\u00e9e de Namur, 5030 Gembloux, Belgium"},{"name":"Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, University of Li\u00e8ge, 5030 Gembloux, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Didier","family":"Veselko","sequence":"additional","affiliation":[{"name":"Comit\u00e9 du Lait de Battice, Route de Herve 104, 4651 Battice, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Leblois","sequence":"additional","affiliation":[{"name":"Elev\u00e9o Asbl, AWE Group, 4, Rue des Champs Elys\u00e9es, 5590 Ciney, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6733-4334","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Dehareng","sequence":"additional","affiliation":[{"name":"Walloon Agricultural Research Center (CRA-W), 24 Chauss\u00e9e de Namur, 5030 Gembloux, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9883-9047","authenticated-orcid":false,"given":"H\u00e9l\u00e8ne","family":"Soyeurt","sequence":"additional","affiliation":[{"name":"Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, University of Li\u00e8ge, 5030 Gembloux, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3945\/ajcn.115.114769","article-title":"A Randomized Trial to Assess the Potential of Different Beverages to Affect Hydration Status: Development of a Beverage Hydration Index1","volume":"103","author":"Maughan","year":"2016","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32527","DOI":"10.3402\/fnr.v60.32527","article-title":"Milk and Dairy Products: Good or Bad for Human Health? An Assessment of the Totality of Scientific Evidence","volume":"60","author":"Thorning","year":"2016","journal-title":"Food Nutr. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1046\/j.1471-0307.2001.00015.x","article-title":"Milk Fats as Ingredients","volume":"54","author":"Burgess","year":"2001","journal-title":"Int. J. Dairy Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.3168\/jds.S0022-0302(05)73037-X","article-title":"Milk Quality and Automatic Milking: Fat Globule Size, Natural Creaming, and Lipolysis","volume":"88","author":"Abeni","year":"2005","journal-title":"J. Dairy Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.idairyj.2005.08.011","article-title":"Lipoprotein Lipase and Lipolysis in Milk","volume":"16","author":"Deeth","year":"2006","journal-title":"Int. Dairy J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"620","DOI":"10.3168\/jds.S0022-0302(89)79152-9","article-title":"Influence of Bovine Mastitis on Lipolysis and Proteolysis in Milk","volume":"72","author":"Murphy","year":"1989","journal-title":"J. Dairy Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1017\/S0022029900033057","article-title":"Effect of Stage of Lactation, Stage of Pregnancy, Milk Yield and Herd Management on Seasonal Variation in Spontaneous Lipolysis in Bovine Milk","volume":"53","author":"Chazal","year":"1986","journal-title":"J. Dairy Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8959","DOI":"10.3168\/jds.2025-26537","article-title":"Milking Frequency and Dairy Cow Susceptibility to Lipolysis Interact to Alter Milk Lipolysis and Composition","volume":"108","author":"Hurtaud","year":"2025","journal-title":"J. Dairy Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11697","DOI":"10.3168\/jds.2024-24958","article-title":"Impact of Milking Interval and Time on Milk Spontaneous Lipolysis and Composition in Dairy Cows","volume":"107","author":"Hurtaud","year":"2024","journal-title":"J. Dairy Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.3168\/jds.2016-12094","article-title":"Effects of Breed, Feeding System, and Lactation Stage on Milk Fat Characteristics and Spontaneous Lipolysis in Dairy Cows","volume":"100","author":"Vanbergue","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13085","DOI":"10.3168\/jds.2025-26829","article-title":"Going Deeper in Lipolysis: Prediction of Individual Free Fatty Acid Contents in Milk by Mid-Infrared Spectroscopy","volume":"108","author":"Christophe","year":"2025","journal-title":"J. Dairy Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fox, P.F., and McSweeney, P.L.H. (2006). Lipolytic Enzymes and Hydrolytic Rancidity. Advanced Dairy Chemistry Volume 2 Lipids, Springer.","DOI":"10.1007\/0-387-28813-9"},{"key":"ref_13","first-page":"50","article-title":"Hydrolytic Rancidity in Milk. A Simplified Method for Estimating the Extent of Its Development","volume":"17","author":"Thomas","year":"1955","journal-title":"Am. Milk Rev."},{"key":"ref_14","unstructured":"IDF (1991). Determination of Free Fatty Acids in Milk and Milk Products. IDF Bulletin No.265.Br., International Dairy Federation."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3168\/jds.S0022-0302(80)82913-4","article-title":"Modified Copper Soap Solvent Extraction Method for Measuring Free Fatty Acids in Milk","volume":"63","author":"Shipe","year":"1980","journal-title":"J. Dairy Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1093\/clinchem\/17.6.512","article-title":"Rapid, Reliable Method for Measuring Serum Lipase Activity","volume":"17","author":"Yang","year":"1971","journal-title":"Clin. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.foodchem.2015.02.145","article-title":"A Reliable and Reproducible Method for the Lipase Assay in an AOT\/Isooctane Reversed Micellar System: Modification of the Copper-Soap Colorimetric Method","volume":"182","author":"Kwon","year":"2015","journal-title":"Food Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.3168\/jds.S0022-0302(65)88530-7","article-title":"Contribution of Free Fatty Acids to the Flavor of Rancid Milk1, 2","volume":"48","author":"Scanlan","year":"1965","journal-title":"J. Dairy Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, X., Wang, F., Chen, M., Wang, J., and Zhang, Y. (2023). Quantification of Free Short-Chain Fatty Acids in Raw Cow Milk by Gas Chromatography-Mass Spectrometry. Foods, 12.","DOI":"10.3390\/foods12071367"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1021\/acs.jafc.8b05462","article-title":"Development and Validation of a Novel Free Fatty Acid Butyl Ester Gas Chromatography Method for the Determination of Free Fatty Acids in Dairy Products","volume":"67","author":"Mannion","year":"2019","journal-title":"J. Agric. Food Chem."},{"key":"ref_21","unstructured":"Milk and Milk Products\u2014Quantification of Free Fatty Acids\u2014Capillary Gas Chromatographic Method (Standard No. ISO 24191|IDF 263)."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1017\/S0022029900023153","article-title":"A Method for the Quantitative Determination of Individual Free Fatty Acids in Milk by Ion Exchange Resin Adsorption and Gas-Liquid Chromatography","volume":"50","author":"Needs","year":"1983","journal-title":"J. Dairy Res."},{"key":"ref_23","first-page":"144","article-title":"A Study of Ester Preparation for the Gas Chromatographic Analysis of the Fatty Acids Composition of Butterfat","volume":"22","author":"Parodi","year":"1967","journal-title":"Aust. J. Dairy Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.3168\/jds.S0022-0302(06)72409-2","article-title":"Estimating Fatty Acid Content in Cow Milk Using Mid-Infrared Spectrometry","volume":"89","author":"Soyeurt","year":"2006","journal-title":"J. Dairy Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Christophe, O.S., Grelet, C., Bertozzi, C., Veselko, D., Lecomte, C., H\u00f6eckels, P., Werner, A., Auer, F.-J., Gengler, N., and Dehareng, F. (2021). Multiple Breeds and Countries\u2019 Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry. Foods, 10.","DOI":"10.3390\/foods10092235"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1017\/S1751731112000456","article-title":"Potential Use of Milk Mid-Infrared Spectra to Predict Individual Methane Emission of Dairy Cows","volume":"6","author":"Dehareng","year":"2012","journal-title":"Animal"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.3168\/jds.2019-17910","article-title":"Potential of Milk Mid-Infrared Spectra to Predict Nitrogen Use Efficiency of Individual Dairy Cows in Early Lactation","volume":"103","author":"Grelet","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"27083","DOI":"10.1038\/s41598-025-12332-9","article-title":"Stress Detection\/Assement in Dairy Cows by the Determination of Cortisol in Hair by Near-Infrared (NIR), Mid-Infrared (MIR) and Raman Spectroscopy","volume":"15","author":"Christophe","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10186","DOI":"10.3168\/jds.2025-26502","article-title":"Can We Get Information on Dairy Cow Chronic Stress Biomarkers from Milk Mid-Infrared Spectra?","volume":"108","author":"Grelet","year":"2025","journal-title":"J. Dairy Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.3168\/jds.2014-8764","article-title":"Standardization of Milk Mid-Infrared Spectra from a European Dairy Network","volume":"98","author":"Grelet","year":"2015","journal-title":"J. Dairy Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.ymeth.2020.07.012","article-title":"Large-Scale Phenotyping in Dairy Sector Using Milk MIR Spectra: Key Factors Affecting the Quality of Predictions","volume":"186","author":"Grelet","year":"2021","journal-title":"Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1366\/0003702884428266","article-title":"Partial Least-Squares Quantitative Analysis of Infrared Spectroscopic Data. Part I: Algorithm Implementation","volume":"42","author":"Fuller","year":"1988","journal-title":"Appl. Spectrosc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Luo, Z., and Vovk, V. (2013). Kernel Ridge Regression. Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, Springer.","DOI":"10.1007\/978-3-642-41136-6"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"25139","DOI":"10.1038\/s41598-025-10417-z","article-title":"Combination of Machine Learning and Raman Spectroscopy for Prediction of Drug Release in Targeted Drug Delivery Formulations","volume":"15","author":"Mahdi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support Vector Regression. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Apress.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11585","DOI":"10.3168\/jds.2020-18870","article-title":"A Comparison of 4 Different Machine Learning Algorithms to Predict Lactoferrin Content in Bovine Milk from Mid-Infrared Spectra","volume":"103","author":"Soyeurt","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, B., Ma, M., and Chang, J. (2012). New Machine Learning Algorithm: Random Forest. Information Computing and Applications, Springer.","DOI":"10.1007\/978-3-642-34062-8_32"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Franceschini, S., Fastr\u00e9, C., Nickmilder, C., Santschi, D.E., Warner, D., Bahadi, M., Bertozzi, C., Veselko, D., Dehareng, F., and Gengler, N. (2025). Detection of Dairy Herd Management Issues Using Fatty Acid Profiles Predicted by Mid-Infrared Spectrometry. Animals, 15.","DOI":"10.3390\/ani15111575"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Contla Hern\u00e1ndez, B., Lopez-Villalobos, N., and Vignes, M. (2021). Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods. Animals, 11.","DOI":"10.3390\/ani11082154"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1039\/D0JA00524J","article-title":"The Spectral Fusion of Laser-Induced Breakdown Spectroscopy (LIBS) and Mid-Infrared Spectroscopy (MIR) Coupled with Random Forest (RF) for the Quantitative Analysis of Soil pH","volume":"36","author":"Chen","year":"2021","journal-title":"J. Anal. At. Spectrom."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","article-title":"A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions","volume":"85","author":"Schulz","year":"2018","journal-title":"J. Math. Psychol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.biosystemseng.2018.09.013","article-title":"Developing an Intelligent System for the Prediction of Soil Properties with a Portable Mid-Infrared Instrument","volume":"177","author":"Soto","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.3168\/jds.S0022-0302(65)88529-0","article-title":"Major Free Fatty Acids in Milk","volume":"48","author":"Kintner","year":"1965","journal-title":"J. Dairy Sci."},{"key":"ref_44","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cheng, Y.Y., and Yu, J.Z. (2020). Minimizing Contamination from Plastic Labware in the Quantification of C16 and C18 Fatty Acids in Filter Samples of Atmospheric Particulate Matter and Their Utility in Apportioning Cooking Source Contribution to Urban PM2.5. Atmosphere, 11.","DOI":"10.3390\/atmos11101120"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3544","DOI":"10.1021\/acs.analchem.3c05431","article-title":"Studies of Labware Contamination During Lipid Extraction in Mass Spectrometry-Based Lipidome Analysis","volume":"96","author":"Canez","year":"2024","journal-title":"Anal. Chem."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhu, H., and Xu, J.-L. (2020). Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms. Molecules, 25.","DOI":"10.3390\/molecules25214987"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"105006","DOI":"10.1016\/j.prevetmed.2020.105006","article-title":"Predicting Physiological Imbalance in Holstein Dairy Cows by Three Different Sets of Milk Biomarkers","volume":"179","author":"Foldager","year":"2020","journal-title":"Prev. Vet. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4596","DOI":"10.3168\/jds.S0022-0302(06)72509-7","article-title":"Influence of Air Intake on the Concentration of Free Fatty Acids and Vacuum Fluctuations During Automatic Milking","volume":"89","author":"Rasmussen","year":"2006","journal-title":"J. Dairy Sci."}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/19\/3423\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:35:06Z","timestamp":1790674506000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/19\/3423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":49,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["foods15193423"],"URL":"https:\/\/doi.org\/10.3390\/foods15193423","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}