Green and rapid quantitative detection of selenium in selenium-enriched kefir grain based on Fourier transform infrared spectroscopy. Food chemistry Rapid monitoring of total and organic selenium content in kefir grain was essential for microbial screening and selenium-enriched food development. Firstly, spectral information of selenium-enriched kefir grain was obtained using an attenuated total reflection Fourier transform infrared spectrometer. Secondly, the performance of the quantitative prediction models established by the four-variable screening method with three machine learning algorithms, respectively, was compared. For the prediction of total selenium, the competitive adaptive reweighted sampling - least squares support vector machine model performed the best, with prediction set relative coefficient (R) and relative prediction deviation (RPD) values of 0.97 and 4.36, respectively. For the prediction of organic selenium, the IRF-LSSVM model had a R and RPD value of 0.95 and 6.44, respectively. The proposed method achieves scientific, rapid (within 1 min) and green detection of total selenium (237.72-2330.82 μg/g) and organic selenium (102.20-1483.59 μg/g) content in selenium-enriched Kefir grain. 10.1016/j.foodchem.2024.142056