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Grapefruit is a cold-sensitive citrus fruit, and freezing can spoil the harvest when the fruit is still on the tree and even later during manufacturing and transport due to inappropriate postharvest management. This study performed a specific Electric Impedance Spectroscopy (EIS) analysis and statistical data treatment to obtain an EIS and Artificial Neural Networks (ANN)-based model for early freeze-damage detection in grapefruit showing a Correct Correlation Rate of 100%. Additionally, Cryo-Field Emission Scanning Electron Microscopy observations were conducted on both fresh and frozen/thawed samples, analyzing the different impedance responses in order to understand the biological changes in the tissue. Finally, a modified Hayden electric equivalent model was parameterized to simulate the impedance response electrically and link the electric behavior of biological tissue to the change in its properties due to freezing. The developed technique is introduced as an alternative to the traditional ones, as it is fast, economic, and easy to carry out.

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.

A new electronic tongue to monitor the presence of glyphosate (a non-selective systemic herbicide) has been developed. It is based on pulse voltammetry and consists in an array of three working electrodes (Pt, Co and Cu) encapsulated on a methacrylate cylinder. The electrochemical response of the sensing array was characteristic of the presence of glyphosate in buffered water (phosphate buffer 0.1 mol·dm−3, pH 6.7). Rotating disc electrode (RDE) studies were carried out with Pt, Co and Cu electrodes in water at room temperature and at pH 6.7 using 0.1 mol·dm−3 of phosphate as a buffer. In the presence of glyphosate, the corrosion current of the Cu and Co electrodes increased significantly, probably due to the formation of Cu2+ or Co2+ complexes. The pulse array waveform for the voltammetric tongue was designed by taking into account some of the redox processes observed in the electrochemical studies. The PCA statistical analysis required four dimensions to explain 95% of variance. Moreover, a two-dimensional representation of the two principal components differentiated the water mixtures containing glyphosate. Furthermore, the PLS statistical analyses allowed the creation of a model to correlate the electrochemical response of the electrodes with glyphosate concentrations, even in the presence of potential interferents such as humic acids and Ca2+. The system offers a PLS prediction model for glyphosate detection with values of 098, −2.3 × 10−5 and 0.94 for the slope, the intercept and the regression coefficient, respectively, which is in agreement with the good fit between the predicted and measured concentrations. The results suggest the feasibility of this system to help develop electronic tongues for glyphosate detection.

The effectiveness of microwave-assisted sodium hydroxide pretreatments to enhance the saccharification performance of pineapple waste was evaluated. Microwave alkali pretreatments for short exposure times (up to 60 s) significantly improved the yield of the enzymatic hydrolysis compared with non-pretreated waste. The greatest increase of fermentable (35.7%) and total sugars (33.5%) was obtained at 6.375 W/g for 5 s. However, longer exposure times resulted in sugar degradation and released fermentation inhibitors, such as phenols or hydroxymethylfurfural (HMF), as a consequence of thermal degradation. Nevertheless, the obtained phenols values were not sufficient to inhibit subsequent fermentation. Scanning electron microscope (SEM) images confirmed that applying microwaves for short exposure times promoted structural changes that improved enzymatic hydrolysis. By contrast, an increase in the severity of the treatment led to a compacted structure, which hindered access to enzymes and consequently reduced the release of sugars into the medium.

Electrochemical Impedance Spectroscopy (EIS) has been used to develop a methodology able to identify and quantify fermentable sugars present in the enzymatic hydrolysis phase of second-generation bioethanol production from pineapple waste. Thus, a low-cost non-destructive system consisting of a stainless double needle electrode associated to an electronic equipment that allows the implementation of EIS was developed. In order to validate the system, different concentrations of glucose, fructose and sucrose were added to the pineapple waste and analyzed both individually and in combination. Next, statistical data treatment enabled the design of specific Artificial Neural Networks-based mathematical models for each one of the studied sugars and their respective combinations. The obtained prediction models are robust and reliable and they are considered statistically valid (CCR% > 93.443%). These results allow us to introduce this EIS-based technique as an easy, fast, non-destructive, and in-situ alternative to the traditional laboratory methods for enzymatic hydrolysis monitoring.

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%).

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.

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R2) and root mean square errors of prediction (RMSEP) were determined as R2 > 0.944 and RMSEP < 1.782 for PLS and R2 > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step.