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.

中文翻译：

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基于高光谱检测的婴儿奶粉中的抗生素判别分析和定量研究

客观的

食品质量和安全已成为各界人士关注的焦点。由于食品中的抗生素残留会对人体健康造成严重危害，因此有必要实现食品中抗生素残留的快速无损检测。奶粉质量问题中最迫切需要解决的是抗生素残留问题，因此对奶粉中的抗生素进行准确的定性鉴定和定量检测非常重要。

方法

本研究以高光谱技术为基础，结合化学计量学，以奶粉中三种常见的残留抗生素（强力霉素，金霉素和土霉素）为研究对象，以监测奶粉的质量。首先，根据设计的浓度梯度，通过研磨，干燥，称重，混合并依次进行制备样品。然后，获得了纯样品（婴儿奶粉和纯抗生素）和含有三种类型抗生素残留的样品的光谱特征并进行了比较。第三，为了建立婴幼儿奶粉中不同抗生素残留的定性判别模型，建立了偏最小二乘判别分析（PLS-DA）和随机森林（RF）模型以鉴定奶粉中的抗生素残留。第四，为了建立婴幼儿奶粉中抗生素残留的定量判别模型，简化模型并降低计算复杂度，这三种方法分别是连续投影算法（SPA），无信息变量消除（UVE）和竞争性自适应加权抽样（CARS） ）被用来选择最佳方法的波长。然后建立最小二乘支持向量机（LS-SVM）模型，对残留抗生素进行定量检测。使用竞争自适应加权采样（CARS）来选择最佳方法的波长。然后建立最小二乘支持向量机（LS-SVM）模型，对残留抗生素进行定量检测。使用竞争自适应加权采样（CARS）来选择最佳方法的波长。然后建立最小二乘支持向量机（LS-SVM）模型，对残留抗生素进行定量检测。

结果

在定性分析中，PLS-DA模型可以粗略地鉴定出三种抗生素，准确率为96.2％。RF模型效果更好，识别精度达到100％。在建立定量检测模型时，用CARS算法选择了三种奶粉样品的光谱波长，仅用7％的数据建立的CARS-LS-SVM模型显示出良好的效果。其中，含有金霉素，强力霉素和土霉素的奶粉样品的预测集相关系数R _p和预测均方根误差（RMSEP）分别为0.9990和0.08％，0.9996和0.05％，0.9997和0.04％。金霉素，强力霉素和土霉素的检出限为2.44×10 ^-3，分别为1.51×10 ^-3，1.2 ×10 ^-3。

结论

高光谱技术结合射频算法可以很好地实现婴幼儿奶粉的识别。通过高光谱技术结合CARS算法建立了LS-SVM模型，可用于建立更好的婴儿奶粉中抗生素残留定量测定模型。该研究可为其他类型食品中抗生素的检测提供理论依据，并在一定程度上保证食品安全。