Abstract The potential application of laser biospeckle imaging technique and its methods of image processing to detect chilling and freezing disorders in orange was assessed. Four temperature treatments were applied to fruits for 16 h including no chilling and freezing, chilled at 1 °C, placed in freezer at −7 °C, and placed in freezer at −20 °C, respectively to simulate sound, chilled, moderate freezing, and extreme freezing conditions. Two coherent laser lights were then used to illuminate the samples in both back-scattering and forward-scattering arrangements. Biospeckle images were processed by using graphical and numerical procedures. Twelve features were extracted from the biospeckle images and they were then used to classify the oranges. The classifiers were included soft independent modeling of class analogy, linear discriminant analysis, quadratic discriminant analysis, artificial neural networks (ANN), and support vector machines. The results showed that both graphical analysis and biospeckle laser parameter could monitor biological activity resulting from the action of temperature changes responsible for chilling and freezing damages. Moreover, the forward-scattering arrangement resulted in the best prediction power with equal classification accuracies of 100 % for sound, chilled, moderate freezing, and extreme freezing classes by the ANN classifier. We conclude that the biospeckle imaging technique could be considered an alternative and useful tool for chilling and freezing assessments of oranges.

中文翻译：

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生物散斑激光成像在橙子寒冻病早期检测中的应用

摘要 评估了激光生物散斑成像技术及其图像处理方法在检测橙子冷冻病中的潜在应用。对水果应用四种温度处理 16 h，包括不冷藏和冷冻、1 °C 冷藏、-7 °C 冷冻和 -20 °C 冷冻，分别模拟声音、冷冻、适度冷冻，以及极端冰冻条件。然后使用两束相干激光以后向散射和前向散射方式照射样品。通过使用图形和数字程序处理生物斑点图像。从生物斑点图像中提取了 12 个特征，然后将它们用于对橙子进行分类。分类器包括类类比的软独立建模、线性判别分析、二次判别分析、人工神经网络 (ANN) 和支持向量机。结果表明，图形分析和生物散斑激光参数都可以监测由于温度变化引起的冷和冻害作用而引起的生物活动。此外，前向散射排列产生了最佳预测能力，ANN 分类器对声音、冷冻、中等冷冻和极端冷冻类的相同分类精度为 100%。我们得出的结论是，生物斑点成像技术可以被视为一种替代和有用的工具，用于对橙子进行冷藏和冷冻评估。结果表明，图形分析和生物散斑激光参数都可以监测由于温度变化引起的冷和冻害作用而引起的生物活动。此外，前向散射排列产生了最佳预测能力，ANN 分类器对声音、冷冻、中等冷冻和极端冷冻类的相同分类精度为 100%。我们得出的结论是，生物斑点成像技术可以被视为一种替代和有用的工具，用于对橙子进行冷藏和冷冻评估。结果表明，图形分析和生物散斑激光参数都可以监测由于温度变化引起的冷和冻害作用而引起的生物活动。此外，前向散射排列产生了最佳预测能力，ANN 分类器对声音、冷冻、中等冷冻和极端冷冻类的相同分类精度为 100%。我们得出的结论是，生物斑点成像技术可以被视为一种替代和有用的工具，用于对橙子进行冷藏和冷冻评估。ANN 分类器的中等冻结和极端冻结类别。我们得出的结论是，生物斑点成像技术可以被视为一种替代和有用的工具，用于对橙子进行冷藏和冷冻评估。ANN 分类器的中等冻结和极端冻结类别。我们得出的结论是，生物斑点成像技术可以被视为一种替代和有用的工具，用于对橙子进行冷藏和冷冻评估。