Determination of Monascus Pigments in Red Yeast Rice Using Near Infrared Spectroscopy
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
NIR combined with chemometrics was proposed to predict orange, red and yellow pigments content in red yeast rice. Stepwise multiple linear regression (SMLR), partial least squares (PLS) and principal component regression (PCR) were used to built prediction models. Correlation coefficient of calibration (R), root mean square error of calibration (RMSEC), root mean square error of predication (RMSEP) and ratio of prediction to deviation (RPD) were suggested to evaluate the performance of models. The results showed that MSC and SNV could eliminate spectral scattering causing by uneven red yeast rice particles. Derivative treatment could eliminate the baseline drift. Three models for orange, red and yellow pigments all had good robustness. The three models were used to predict unknown monascus pigments, which all had better performance of prediction (RPD, 2.86~5.39). Therefore, the models could be used to accurately predict monascus pigments. The study shows that near-infrared spectroscopy technology has the potential beneficial for measuring the pigments content in red yeast rice online and conducive to intelligent quality control.
