Trace Determination of Hormones in Peptide Powders Based on Multi-Molecular Infrared Spectroscopic and Multivariate Data Fusion

Abstract

A direct, high-throughput and accurate method, multi-molecular infrared spectroscopic (MMIR) data fusion, was established for the detection of trace of estrone, diethylstilbestrol, betamethasone and prednisone acetate in peptide powders based on near-infrared and mid-infrared fusion datasets. Tri step MMIR with progressively improved resolution captured key fingerprint features of hormones in the regions of 3430-3250, 2950-2850, 1750-1550 and 1335-1050 cm^-1 which were exclusively selected as the datasets. The back propagation neural network (BPNN) model with the best performance used a mid-level fusion strategy and achieved excellent root mean square error of prediction (RMSEP, 1.101 mg kg^-1) and residual prediction deviation (RPD, 9.890). Compared to those of single dataset models, the RMSEP was reduced by more than 82.03%. This method had remarkable practical significance for the direct and accurate quality evaluation of peptide powder products with favorable limit of detection (LOD) but without pretreatment and chemical agents, verifying the feasibility of multiple analytical instruments for rapid and comprehensive detection of powdered foods.

Keywords:
multi-molecular infrared spectroscopy; data fusion; peptide powder; hormones