Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN

Abstract

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 µM to 3.9 µM based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk.

Keywords: amoxicillin; back propagation-artificial neural network; breast milk; colorimetric methods; image preprocessing.

Scheme 1

Schematic illustration of the detection of amoxicillin in breast milk. When different concentrations of amoxicillin were bound to AuNPs-ssDNA, different color reactions were produced. The results were captured by portable image acquisition equipment and processed by artificial intelligence algorithms.

Figure 1

(A) Transmission electron microscopy results of AuNPs. (B) Spectral scan results when different components were present in the solution (AuNPs; AuNPs+NaCl; AuNPs+ssDNA; AuNPs+ssDNA+NaCl; AuNPs+ssDNA+AMO+NaCl). When AuNPs were dispersed, the absorption peak was around 520 nm, and the absorption peak was around 620 nm when aggregated. (C) Color changes when different components were present in the solution (AuNPs; AuNPs+NaCl; AuNPs+ssDNA; AuNPs+ssDNA+NaCl; AuNPs+ssDNA+AMO+NaCl).

Figure 2

(A) Aggregation of different concentrations of ssDNA coupled to AuNPs in the presence of different concentrations of NaCl. (B) Effect of the incubation time of ssDNA with AuNPs on the protection of AuNPs by ssDNA. (C) Effect of reaction time between AMO and AuNPs-ssDNA. (D) Selectivity of the reaction system in aqueous solution. The concentrations of AMP, CFM, CEF, TET, and CHL were 6 µM, the mixture solutions were mixed at the same concentration and the concentration of AMO was 3 µM. (E) Interference resistance of the reaction system. Interference including amino acids and cations, the concentrations of interference used were all 50 µM and AMO was 3 µM. (F) Spectral scan result of the decided AMO concentrations. (G) Color change of the decided AMO concentrations.

Figure 3

The effect of image preprocessing for the colorimetric image. Three steps were included: segmentation, Gaussian filtering, and cropping.

Figure 4

(A) The training curves. The maximum number of training events was 1000 epochs, and the training mean square error was 0.07. (B) The specific training set fitting situation. (C) The specific test set fitting situation.