Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy

Abstract

The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges.

Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

“…In the specific case of assays with frozen lemons, fruits were first exposed to a laboratory freezing night simulation by introducing them in a freezer (LIEBHERR Model GGU 1500 Premium, Liebherr-International Deutschland GmbH, Biberach an der Riß, Germany) long enough to reach and slightly exceed the freezing temperature of the fruit. Then, after the temperature stabilization of the product for at least 12 hours, they were tested again following the same protocol [25].…”

“…As electrical impedance is an intrinsic property correlated with the internal structure of the samples, EIS measurements [24] determine or infer information about them as long as the event in analysis presents a change in their electrical behavior. In fact, EIS has already been successfully used in the field of food technology, particularly in the quality control of several parameters of fruits and vegetables [25,26] such as bananas [27], kiwis [28], mangoes [29], eggplants [24], tomatoes [30], carrots [31,32] potatoes [33], manufactured products [34] and their waste valorization [35,36].…”

“…Consequently, principal components analysis (PCA) and partial least squares (PLS) have been used in a first phase, as they have thus far given very satisfactory results when working with this type of data [37,38,39]. Here, a discriminant analysis by partial least squares was selected, as there was a large amount of data per sample, and it was clearly organized in groups with different electrochemical responses [25,40]. Next, artificial neural networks (ANNs) were used to improve the aforementioned methods since they are very flexible and adaptable, easily fitting to non-linear systems and able to learn from their own mistakes [41,42].…”

“…Accordingly, and attending to the preliminary experience in this field [25], the goal of this study was to determine the ability of a system combining EIS analysis by using a specific sensor and an adequate data treatment tool (ANN) to detect freeze-damage in lemons in a rapid, easy, economic and reliable way.…”

Monitoring Freeze-Damage in Grapefruit by Electric Bioimpedance Spectroscopy and Electric Equivalent Models

“…Standardization and mean centering by autoscale were used for preprocessing, and "Venetian blinds" were used for cross-validation. Statistical validity of the models was studied by the coefficient of determination (R 2 ) and the corresponding root mean square errors of cross-validation (RMSCV) and prediction (RMSEP) [67]. Additionally, an ANN-based data treatment was conducted in order to better understand the multivariable dependence of the EIS samples responses and, thus, create simpler and adaptive models suitable for implementation in a microprocessor [48,49].…”