Determination of 10-HDA in royal jelly by ATR-FTMIR and NIR spectral combining with data fusion strategy

The attenuated total reflectance-Fourier transform mid-infrared (ATR-FTMIR) and near-infrared (NIR) spectra of royal jelly samples were collected. Low- and mid-level data fusion strategies in combination with the partial least squares (PLS) regression algorithm were used for quantitative modeling analysis of 10-hydroxy-2-decenoic acid (10-HDA) content in royal jelly samples. In lowlevel data fusion, each raw spectrum was pre-processed before splicing into a new data matrix for PLS model construction and analysis. In the mid-level data fusion, synergy interval (SI)-PLS was used for variable selection and principal component analysis/independent component analysis was used for feature extraction to obtain variables. The extracted variables were spliced before inputting into the PLS model for modeling and analysis. The results showed that the PLS analysis model constructed by mid-level data fusion is better than the PLS models constructed by independent data and low-level data fusion. Among these models, the PLS model that was constructed by the mid-level data fusion after SI-PLS variable selection had the best 10-HDA content prediction accuracy, with RMSEP = 0.1118(%) and R-P = 0.9585. Therefore, a mid-level data fusion strategy based on the ATR-FTMIR and NIR spectra can be used as a reliable tool for 10-HDA quantitation.