Abstract:To accurately identify the three different types of high-temperature sauce-flavor Daqu, 30 samples were collected from storage and subjected to mid-infrared spectra. The three Daqu categories (black, yellow, and white) clustered separately in the principal component analysis. Furthermore, a pattern recognition model was established based on midinfrared spectroscopy combed with partial least squares discriminant analysis (PLS-DA). With an R2Y of 0.956 and a Q2 of 0.906, the model effectively distinguished the different qualities and types of Daqu, offering a data-driven basis for feeding the materials during production. A near-infrared spectroscopy based quantitative model involving 60 samples during different fermentation processes was established to rapidly quantitate melanoidins in Daqu. The obtained spectrum was processed by multiplicative scatter correction (MSC) and first-order derivatives. The PLS model achieved optimal results in the range of 10 000~4 000 cm-1 and when the principal component was 8. The coefficient of determination for the calibration set (R2Cal) was 0.987 7, root mean square error of calibration (RMSEC) was 0.169 6, coefficient of determination for the validation set (R2Val) was 0.900 7, and cross-validation root mean square error (RMSECV) was 0.491 1. An external prediction with 15 samples was conducted to validate the reliability of the model, yeilding a root mean square error of prediction (RMSEP) of 0.460 6. The ratio of the standard deviation to the prediction standard deviation (RPD) was 2.63. Furthermore, there is no significant differences between the near-infrared method and the reference method (P=0.772). Therefore, this model can effectively predict melanoidin content in unknown Daqu samples. This method could be applied to the rapid quality evaluation of Daqu due to its convenience, with a detection time of only 10~15 min and an efficiency that is at least eight times higher than the traditional method.
