聚乙烯醇比色纤维膜结合神经网络学习技术用于细菌污染检测

Polyvinyl Alcohol Colorimetric Fiber Membrane Combined with Neural Network Learning Technique for Bacterial Contamination Level Detection

中文关键词:  比色指示  神经网络学习技术  细菌污染  静电纺丝

英文关键词:colorimetric indicator  artificial neural networks learning technique  bacterial contamination  electrostatic spinning

中文摘要:

      目的 细菌污染是影响食品安全的主要因素，开发更准确、快速、无创的检测技术对保障饮食安全有重要意义。方法 本实验使用静电纺丝技术制备了聚乙烯醇(PVA)/花青素(cy)纳米纤维膜(C-PVA cy)，结合人工神经网络学习技术(Artificial Neural Networks，ANN)建立了对细菌污染程度的预测模型，实现了通过颜色变化精确预测细菌浓度。使用扫描电子显微镜和傅里叶红外光谱仪测定C-PVA cy结构及成分;再测定其对pH和大肠杆菌的颜色响应性，研究其检测性能;再使用ANN技术对膜的颜色变化进行学习并建立预测模型。结果 C-PVA cy具有粗细均匀的纳米纤维丝(直径为747 nm)，cy被成功引入其中。纤维膜在不同pH下有明显的颜色差异，其对不同浓度的大肠杆菌呈现了从深红色到棕红色的颜色变化，检测限为9.8×10¹ cfu/mL。使用ANN成功建立了C-PVA cy颜色值(L*，a*，b*值)与细菌浓度的预测模型，验证准确率可达96%。结论 C-PVA cy纳米纤维颜色指示膜结合ANN实现了细菌污染程度的精确预测，操作便捷，准确度较高，为食品安全性快速检测提供了新思路。

英文摘要:

      Bacterial contamination is a major factor affecting food safety, and the development of more accurate, rapid, and noninvasive detection techniques is important for ensuring dietary safety. In this experiment, polyvinyl alcohol (PVA)/anthocyanin (cy) nanofibrous membranes (C-PVA cy) were prepared by electrostatic spinning technology, and a prediction model for the degree of bacterial contamination was established by combining with Artificial Neural Networks (ANN) learning technique to realize the accurate prediction of bacterial concentration by color change. Scanning electron microscopy and Fourier infrared spectroscopy were used to determine the structure and composition of C-PVA cy. Then, its color responsiveness to pH and E. coli was determined to study its detection performance. Next, the ANN technique was used to learn the color change of the membrane and establish a prediction model. The C-PVA cy had uniformly thick and thin nanofiber filaments (747 nm in diameter) into which cy was successfully introduced. The fiber membrane had obvious color differences at different pH values, showing a color change from dark red to brownish red for different concentrations of E. coli, with a detection limit of 9.8 × 101 cfu/mL. A prediction model for the color values (L, a, b values) of C-PVA cy versus bacterial concentration was successfully established by ANN, with a validation accuracy of up to 96%. The C-PVA cy nanofiber color indicator film combined with ANN achieves the precise prediction of bacterial contamination level with convenient operation and high accuracy, which provides a new idea for the rapid detection of food safety.