Discriminant analysis and quantitative study of antibiotics in infant milk powder based on hyperspectral detection
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 (Rp) 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.
Section snippets
Experimental materials and devices
The infant milk powder used in this experiment was under the brand of FIRMUS; the antibiotic samples were purchased from the Aladdin website (www.aladdin-e.com). Among them, the purity of aureomycin hydrochloride was ≥80.0 % (USP grade), the purity of doxycycline ≥98.0 %, and the purity of oxytetracycline hydrochloride ≥95.0 %. According to the designed doping ratio (quality ratio), the samples were prepared according to the designed concentration gradient (concentration 0.07 %, 0.008 %, 0.01
Hyperspectral contrast analysis of different antibiotic samples
Hyperspectrum has abundant data and message. In this experiment, the frequency range of hyperspectral is 957.49∼2567.67 nm, with a total of 288 band points. Fig. 3 shows the spectral comparison of the three pure antibiotics, it can be seen that the reflectance of the three antibiotics as a whole had a similar trend. As the wavelength increased, the reflectance gradually decreased, but there were certain differences in different wavelengths. Oxytetracycline has the highest reflectivity among the
Conclusion
This paper verified the feasibility of qualitative and quantitative determination of antibiotics in infant milk powder by using hyperspectral detection technology. PLS-DA and RF qualitative discrimination models were established respectively, and the accuracy of the RF qualitative discrimination model was up to 100 %. In the establishment of the quantitative model, wavelength selection was conducted on the original spectrum using the UVE, CARS and SPA algorithm, then the LS-SVM model was
Author statement
Jun Hu: Conceptualization, Methodology, Software, Writing- Reviewing and Editing. Zhen Xu : Data curation, Software, Writing-Original draft preparation. Maopeng Li: Visualization, Investigation. Yong He: Supervision. Yande Liu: Validation, Funding acquisition, Project administration.
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgement
The authors gratefully acknowledged the financial support of National 863 Program (SS2012AA101306), “the 12th Five-Year Plan”, Jiangxi Advantageous Science and Technology Innovation Team Construction Plan (20153BCB24002), Collaborative Innovation Center Project of Intelligent Management Technology and Equipment for Southern Mountain Orchards (G.J.G.Z. [2014] No.60),National Natural Science Foundation of China (2002017018).Science and Technology Research Youth Project of Jiangxi Education
Jun Hu, male, born in 1992 in Xiaogan, Hubei Province,doctor; research direction: THz spectroscopy applications in agricultural product quality and safety testing.
References (29)
- et al.
Multi-color quantum dot-based fluorescence immunoassay array for simultaneous visual detection of multiple antibiotic residues in milk
Biosens. Bioelectron.
(2015) - et al.
Simultaneous determination of 38 veterinary antibiotic residues in raw milk by UPLC-MS/MS
Food Chem.
(2015) - et al.
Latex bead and colloidal gold applied in a multiplex immunochromatographic assay for high-throughput detection of three classes of antibiotic residues in milk
Food Control
(2017) - et al.
Terahertz biosensing with a graphene-metamaterial heterostructure platform
Carbon
(2019) - et al.
Determination of rice root density from Vis-NIR spectroscopy by support vector machine regression and spectral variable selection techniques
Exp. Cell Res.
(2017) - et al.
Quantitative analysis of melamine in milk powders using near-infrared hyperspectral imaging and band ratio
J. Food Eng.
(2016) - et al.
VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits
Chemom. Intell. Lab. Syst.
(2016) - et al.
Using terahertz spectroscopy to identify transgenic cottonseed oil according to physicochemical quality parameters
Optik: Zeitschrift fur Licht- und Elektronenoptik: Journal for Light-and Electronoptic
(2017) - et al.
Detection of adulterants in dietary supplements with Ginkgo biloba extract by attenuated total reflectance Fourier transform infrared spectroscopy and multivariate methods PLS-DA and PCA
Spectrochim. Acta A. Mol. Biomol. Spectrosc.
(2019) National Health and Family Planning Commission of the People’s Republic of China
(2014)
Characterization of the fruits and seeds of Alpinia oxyphylla miq by high-performance liquid chromatography (HPLC) and near-infrared spectroscopy (NIRS) with partial least-squares (PLS) regression
Anal. Lett.
A comprehensive review on food applications of terahertz spectroscopy and imaging
Compr. Rev. Food Sci. Food Saf.
Terahertz Spectroscopy Determination of Benzoic Acid Additive in Wheat Flour by Machine Learning
J. Infrared Millim. Terahertz Waves
Terahertz spectroscopy and chemometric tools for rapid identification of adulterated dairy product
Opt. Quantum Electron.
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Jun Hu, male, born in 1992 in Xiaogan, Hubei Province,doctor; research direction: THz spectroscopy applications in agricultural product quality and safety testing.
Yande Liu, female, born in 1967 in Ji’an, Jiangxi Province, doctor, professor; research direction: spectroscopic diagnostic technology in agricultural product quality and safety.
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Received: Oct 5, 2020.