Escuela Técnica Superior de Ingeniería del Diseño, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Unidad Mixta Universitat Politècnica de València - Universitat de València, Camí de Vera s/n, 46022, Valencia, Spain.
Sensors (Basel). 2019 Sep 19;19(18):4051. doi: 10.3390/s19184051.
Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).
柠檬是最敏感的柑橘类水果，对寒冷非常敏感。因此，在果实仍在树上以及随后的商业化过程中，检测和避免可能损害果实的温度至关重要。为了快速识别这种果实的霜害，我们使用了一种基于电化学阻抗谱技术（EIS）的系统。该系统由一个信号发生器装置与个人计算机（PC）相连，用于控制该系统，以及一个双针不锈钢电极。通过对一组自然和随后冷冻解冻的果实进行测试，我们能够通过单一主成分分析（PCA）和偏最小二乘判别分析（PLS-DA），根据样品是否事先冷冻来区分阻抗值的行为。人工神经网络（ANNs）被用来生成一个预测模型，能够在冷害发生后 24 小时内识别出受损的果实，具有足够的稳健性和可靠性（CCR = 100%）。
