Impedance Spectroscopy: A New Tool for Dairy Analysis
Authors: Pérez González Clara , Salvo Comino Coral , García Cabezón Cristina , Garcia Hernandez Celia , Rodriguez-Mendez Maria Luz , Martin-Pedrosa Fernando 
Published: September 2023

In this work, an impedimetric bioelectronic tongue (bioET) formed by an array of interdigitated microelectrodes sensors was developed and applied in the discrimination of chemical components usually found in milk samples. Few studies of impedimetric bioETs applied in the dairy industry can be found in the bibliography due to the complexity of the characterization of milk matrixes. Therefore, the development and study of the capabilities of multisensory systems containing different advanced materials such as nanomaterials combined with biological molecules is a new field of research.
In order to develop an array of sensors with enhanced sensitivity and selectivity, sensors were modified with silver nanoparticles (AgNPs) and enzymes including galactose oxidase (GaOx), glucose oxidase (GOx), beta-galactosidase (β-gal), urease (Ure) and lipase (Lip) were immobilized on the sensors surface. Silver nanoparticles have proved to be an excellent option to enhance the performance of biosensors due to their electrochemical properties, their high electrical conductivity, and their ability to amplify bio electrochemical signals.
Atomic force microscopy (AFM) was used to study the distribution of AgNPs and enzymes in the sensor surface. Silver nanoparticles showed a homogeneous arrangement along the entire surface, moreover after the enzyme immobilisation a pronounced increase in the sensors surface roughness was observed ensuring the process of the biological molecule (Figure 1.A and Figure 1.B).
The impedance analysis of standard solutions of interest in milk (glucose, galactose, lactose, urea and triglycerides) demonstrated that sensors with a higher concentration of silver nanoparticles (AgNPs) in their structure reported higher sensitivities towards compounds found in milk with a reduction in the impedance module, indicating that there has been an improvement in electronic transfer due to the interaction of the nanomaterial and the electrode surface an example of the sensor behaviour is shown in Figure 1.C. Moreover, sensors that combined the nanomaterial with an enzyme showed a greater ability to differentiate between increasing concentrations of the enzymes target molecules (Figure 1.D). Taking as an example the case of glucose the sensor modified with AgNPs and glucose oxidase achieved a lower impedance module compared with the other sensors thanks to its lower resistance to electronic transfer in the sensor due to the enzymatic activity enhanced by the silver nanoparticles.
Once all the standard solutions had been analysed, the chemometric analysis of their response was carried out applying a principal components analysis (PCA) approach which showed that the bioET developed was able to discriminate between the standard solutions (glucose, galactose, lactose, urea and triglycerides), resulting in five clusters according to their nature with a 79% of the variance of the original data obtained by the first two components of the system. Sugars appear in the positive region of the first component divided into three groups (lactose, galactose and glucose). In the negative region of the first component appears urea and the suspension of triglycerides that can be differentiated over the second component.
Lastly, a first approach was made to the analysis of milk samples with different nutritional characteristics such as their fat, lactose and urea content. The utilization of the bioelectronic tongue showed how the different contents of molecules of interest was directly related with the response of the impedimetric sensors due to changes in the interaction of the sensors surface and the milk matrix. As it was expected, higher concentrations of fat caused an increase in the impedance module due to their interference in the electronic transfer process. Furthermore, it was confirmed that the biosensors developed showed specific responses towards milk samples with higher values of sugar and proteins contents ensuring a high degree of cross-selectivity in the system.
Figure 1: A) Characterization by AFM of the sensor modified with AgNPs. B) Characterization by AFM of the biosensor with AgNPs and GOx. C) Nyquist diagram of sensors with 20 (orange), 30 (black) and 40 μl (blue) of AgNPs in 0.1M glucose. D) Nyquist diagram of the response of a sensor with AgNPs and GOx in glucose 0.1M (red), 0.01M (green) and 0.001M (grey).

Figure 1