Abstract The contamination of foodborne Escherichia coli in fresh-cut products has become a major problem of public health around the world, so that early and rapid detection of contamination is crucial. This study explored the potential of hyperspectral imaging (HSI) measurement of contamination on the surface of fresh-cut potato slices in visible-near infrared (Vis-NIR, 400-1000 nm) region. Four preprocessing methods and the genetic algorithm (GA) were explored to handle spectral data and select characteristic wavelengths so as to establish linear and non-linear regression models. The performance of the back-propagation neural network (BP-NN) model based on full-spectrum was satisfactory, with an overall accuracy of 97.6 % and residual predictive deviation (RPD) of 6.7. Based on the BP-NN model, the research successfully explored the optimum treatment time (20 min) of a non-thermal and environmental-friendly method to inactivate the E. coli on the surface of fresh-cut potato slices, thus confirming the potential application of HSI for the first time. The overall results showed that HSI could provide a rapid and non-destructive approach for the detection of foodborne pathogens on the surface of fresh-cut products.

中文翻译：

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高光谱成像法快速无损检测鲜切马铃薯片表面大肠杆菌及应用

摘要 鲜切产品中食源性大肠杆菌的污染已成为全球公共卫生的重大问题，因此及早快速检测污染至关重要。本研究探讨了高光谱成像 (HSI) 在可见-近红外 (Vis-NIR, 400-1000 nm) 区域对鲜切马铃薯切片表面污染进行测量的潜力。探索了四种预处理方法和遗传算法（GA）来处理光谱数据和选择特征波长，从而建立线性和非线性回归模型。基于全谱的反向传播神经网络（BP-NN）模型的性能令人满意，总体准确率为97.6%，残差预测偏差（RPD）为6.7。基于BP-NN模型，该研究成功探索了一种非热环境友好方法灭活鲜切马铃薯切片表面大肠杆菌的最佳处理时间（20分钟），从而首次证实了HSI的潜在应用。总体结果表明，HSI 可为鲜切产品表面的食源性病原体检测提供一种快速、无损的方法。