Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion

For efficient and comprehensive detection of the staling degree of Chinese steamed bread (CSB), staled CSB samples stored for 0-16 days were prepared and analyzed using near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy combined with data fusion. Among three data fusion schemes, decision-level fusion ach-ieved the best performance when quantifying the CSB staling degree according to the soluble starch amylose fraction, relative crystallinity, and hardness, with determination coefficients and root mean square errors for the validation set in the range of 0.928-0.986 and 0.015-1.290, respectively. The relative percent deviation values of the three indicators increased to 8.362, 4.735, and 3.617, respectively. These results indicate that the combi-nation of NIR, MIR, and Raman spectroscopy as a decision-level fusion scheme can achieve efficient, compre-hensive, and accurate quantification of the staling degree of CSB. This research has important applications for food quality, safety, and shelf-life evaluations.