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- W3177688007 abstract "Thermal treatments induce variable physical and chemical modifications in milk molecules, which determine the quality of dairy products. Discrimination of the samples involved in different degrees of heat treatment can be used to direct further ingredient formulations and target products. A new approach was developed to discriminate the degree of heat treatment applied to milk using infrared absorption profile coupled with chemometric measurements. Samples were submitted to different heating procedures that combined various times and temperatures. Infrared absorption spectra of the samples were obtained without any preparation. Principal component analysis was employed to extract characteristic absorption wavelengths of heat-treated milk by modeling the raw data of the absorption profiles after the samples underwent various heat treatments. The accuracy of the random forest model was as high as 0.97, which confirmed as the best performance classifier compared with support vector machine, k-nearest neighbor and linear discriminant analysis. Thus, the present method, which globally exhibits the structural or chemical changes in heat-treated milk by means of IR measurements, could be a comprehensive and accurate approach for heat treatment indicators." @default.
- W3177688007 created "2021-07-19" @default.
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- W3177688007 date "2021-12-01" @default.
- W3177688007 modified "2023-09-27" @default.
- W3177688007 title "A novel approach to temperature-dependent thermal processing authentication for milk by infrared spectroscopy coupled with machine learning" @default.
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- W3177688007 doi "https://doi.org/10.1016/j.jfoodeng.2021.110740" @default.
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