This study focuses on the analysis and early detection of freeze-damage in tangerines using a specific double-needle sensor and Electrochemical Impedance Spectroscopy (EIS). Freeze damage may appear in citrus fruits both in the field and in postharvest processes resulting in quality loss and a difficult commercialization of the fruit. EIS has been used to test a set of homogeneous tangerine samples both fresh and later frozen to analyze electrochemical and biological differences. A double-needle electrode associated to a specifically designed electronic device and software has been designed and used to send an AC electric sinusoidal signal 1 V in amplitude and frequency range [100Hz to 1MHz] to the analyzed samples and then receive the electrochemical impedance response. EIS measurements lead to distinct values of both impedance module and phase of fresh and frozen samples over a wide frequency range. Statistical treatment of the received data set by Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) shows a clear classification of the samples depending on the experienced freeze phenomenon, with high sensitivity (1.00), specificity (≥ 0.95) and confidence level (95%). Later Artificial Neural Networks (ANN) analysis based on 20-3-1 architecture has allowed to create a mathematical prediction model able to correctly classify 100% of the analyzed samples (CCR =100% for training, validation and test phases, and overall classification), being fast, easy, robust and reliable, and an interesting alternative method to the traditional laboratory analyses.

中文翻译：

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快速电化学阻抗谱和基于传感器的橘子冷冻损伤监测方法

这项研究的重点是使用特定的双针传感器和电化学阻抗谱（EIS）对橘子中的冷冻损伤进行分析和早期检测。在田间和收获后的过程中，冷冻损害都可能出现在柑橘类水果中，从而导致品质下降和水果的商业化困难。EIS已用于测试一组均质的橘子样品，包括新鲜的和随后冷冻的均质橘子样品，以分析电化学和生物学差异。已经设计了与专门设计的电子设备和软件相关联的双针电极，该双针电极用于将振幅和频率范围为[100Hz至1MHz]的1V交流电正弦信号发送到被分析的样品，然后接收电化学阻抗响应。EIS测量可在很宽的频率范围内得出不同的阻抗模块值，以及新鲜和冷冻样品的相位。通过主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）对接收到的数据集进行统计处理，可以根据经历的冻结现象对样品进行清晰分类，具有高灵敏度（1.00），特异性（≥0.95） ）和置信度（95％）。后来基于20-3-1架构的人工神经网络（ANN）分析允许创建数学预测模型，该模型能够正确分类100％的分析样本（CCR = 100％用于训练，验证和测试阶段以及总体分类） ），快速，简便，强大且可靠，是传统实验室分析的一种有趣的替代方法。