Discriminant analysis and quantitative study of antibiotics in infant milk powder based on hyperspectral detection

Abstract

ObjectiveFood quality and safety has become the focus of attention for people from all walks of life. As antibiotic residues in food will cause serious harm to human health, it is necessary to realize the rapid and non-destructive detection of antibiotic residues in food. The problem of antibiotic residues is among the most urgent problems to be tackled in the quality problems of milk powder, so it is very important to conduct accurate qualitative identification and quantitative detection of antibiotics in milk powder. MethodBased on hyperspectral technology and combined with chemometrics, this research took the three common residual antibiotics (doxycycline, chlortetracycline and oxytetracycline) in milk powder as the research objects to monitor the quality of milk powder. Firstly, Samples were prepared by grinding, drying, weighing, mixing and performing successively according to the designed concentration gradient. Then, the spectral of pure sample (infant milk powder and pure antibiotic) and samples containing three types of antibiotic residues were acquired characteristics and compared. Thirdly, to establish a qualitative discriminant model for different antibiotic residues in infant milk powder, the Partial Least Squares Discriminant Analysis (PLS-DA) and Random Forest (RF) models were established to identify antibiotic residues in milk powder. Fourthly, to establish a quantitative discriminant model for antibiotic residues in infant milk powder, to simplify the models and reduce the computational complexity, three methods, namely, Successive projection Algorithm (SPA), Uninformative Variable Elimination (UVE), and Competitive Adaptive Reweighted Sampling (CARS) were used to select the wavelengths for the optimal method. Then the Least Squares Support Vector Machine (LS-SVM) model was established to conduct quantitative detection of residual antibiotics. ResultIn the qualitative analysis, PLS-DA model can roughly identify three antibiotics, with an accuracy rate of 96.2 %. RF model has better effect, with an identification accuracy reaching 100 %. In the establishment of quantitative detection model, after the spectrum wavelengths of three types of milk powder samples was selected by CARS algorithm, the CARS-LS-SVM model which was established by using only 7% of the data showed good effect. Among them, the prediction set correlation coefficient Rp and Root Mean Square Error of Prediction Set (RMSEP) of milk powder samples containing aureomycin, doxycycline and oxytetracycin residues were 0.9990 and 0.08 %, 0.9996 and 0.05 %, 0.9997 and 0.04 %, respectively. The LOD(Limit of Detection) of aureomycin, doxycycline, and oxytetracycline were 2.44 × 10−3, 1.51 × 10−3, 1.2 × 10-3, respectively. ConclusionThe identification of infant milk powder can be well realized by using hyperspectral technology combined with RF algorithm. The LS-SVM models were established by hyperspectral technology combined with CARS algorithm can then be used to set up better quantitative determination models of antibiotic residues in infant milk powder. This research can provide a theoretical basis for the detection of antibiotics in other types of food and can guarantee food safety to a certain extent.