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

Highlights

• To simplify the calculation and improve the model accuracy, the SPA, UVE, and CARS are used to select the effective spectroscopy bands.

• In the qualitative analysis, 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, rp and 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.

Abstract

Objective

Food 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.

Method

Based 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.

Result

In 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 R_p 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.

Conclusion

The 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.

Introduction

Infant milk powder, as a necessity in the growing period of infants and young children, is the main nutrition source for them. Therefore, it is particularly important to ensure the safety of infant milk powder, which is related to the future of the country [1]. Antibiotics are not only used as veterinary drugs to treat animal diseases, but also as feed additives to prevent animal diseases and promote animal growth. However, excessive use of antibiotics could lead to excessive antibiotic residues in milk source, seriously threatening the health of people [2].In order to seek economic benefits, some milk powder manufacturers ignore the quality supervision in the process of milk powder production, resulting in excessive antibiotic content in infant milk powder that pose serious threats to the safety of infants and young children. Therefore, it is necessary to strengthen the detection of antibiotic residues in milk powder to ensure the quality and safety of milk powder.

The research of antibiotics in dairy products has always been the focus of academic attention. Currently, most of the analytical methods for antibiotics are based on the physical and chemical analysis, and the biological determination method. Traditional physical and chemical analysis methods mainly include High Performance Liquid Chromatography (HPLC), Thin Layer Chromatography (TLC), Gas Chromatography (GC), Mass Spectrometry (MS), and the combination of these techniques [3]. The mentioned above methods require complex pretreatment, and the detection process is tedious, time-consuming and expensive [4].Traditional biological determination methods include immune analysis method and biosensors method [5]. The biological determination methods are all destructive detection methods with high cost and complicated operation, which cannot meet the requirements of speed and cost for quality detection of infant milk powder [6]. Although the pure antibiotics have obvious characteristic absorption peaks in the terahertz spectrum, the terahertz technology still has the disadvantages of low laser source power and low detection sensitivity at this stage, and it is currently difficult to use in actual milk powder safety testing [7]. Although some scholars have studied the use of metamaterials to enhance detection sensitivity, the structure design of metamaterials is relatively complex, the processing is difficult, and the metamaterials are not universal [8,9]. Therefore, it is urgent to explore a rapid and non-destructive method for detecting antibiotic residues in milk powder.

As a kind of non-destructive testing technology, hyperspectral detection technology has been widely used in various fields in recent years. Many researchers have applied it to the detection of food and chemical substances. Jie Dengfei et al. [10] used hyperspectral technology to study the prediction models of soluble solid content in different parts of citrus, with the correlation coefficient of the prediction (R_p) of 0.950 and Root Mean Square Error of Prediction (RMSEP) of 0.636 %. Wang Guanghui et al. [11] used hyperspectral technology combined with chemometrics to model and analyze the aflatoxin B1 and gibberellin in moldy corn, with the prediction accuracy of aflatoxin B1 content was 98.74 %, and the correct rate of gibberellin content prediction of 100 %.Yin Yong et al. [12] used hyperspectral technology to classify the mildew degree of corn, and the identification accuracy of 6 grades of moldy corn was 98.6 %. Liu Zheng et al. [13] used hyperspectral technology to detect the nitrite content of sausages in different storage periods, and the coefficient of determination and the root mean square error of the accuracy of the established partial least square regression model were 0.9829 and 0.0592, respectively. The results showed that the spectral information at full wavelength was more suitable for establishing hyperspectral detection model of nitrite content in sausage storage. Hyperspectral technology is particularly suitable for quality and safety research in food and agricultural products, but hyperspectral imaging technology has rarely been reported in antibiotics detection.

In this paper, the infant milk powder and the widely used aureomycin, doxycycline and oxytetracycline were used as the research objects to detect the antibiotic residues in milk powder. The PLS-DA and RF algorithm were established to find superior discrimination model of antibiotic residues type. To optimize the model, SPA, UVE and CARS algorithm were used to select the spectral wavelengths of the samples [[14], [15], [16]]. Finally, different quantitative models were establish to detect the antibiotic residues in milk powder, and to explore the optimal model. This study can provide relevant basis and theoretical reference for the subsequent research on antibiotics in food.