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A recent FAN blog described non-destructive impedance sensors as a tool to classify meat freshness.

In this paper (open access) the authors have used the same principle and developed a classification model for potato varieties based on the effect of their dry matter content on an electrical impedance sensor.  The test is destructive as the potato must be sliced.  The authors built a reference database from data from 9 cultivars (Actrice, Ambra, Constance, El Mundo, Fontane, Gaudi, Jelly, Monalisa and Universa) sourced directly from the grower.  These cultivars were chosen because they cover a wide range of dry matter content.  The authors collected multivariate analytical data from the impedance sensor; impedance magnitude and phase data along with derived parameters such as the minimum phase point of each spectrum, the ratio between the low- and high-frequency values of the impedance magnitude,  the dissipation factor, the distance between the zero and the maximum value of the Nyquist plot, and  the Cole model equivalent circuit parameters.

They conclude that machine learning methods for predicting potato dry matter and varieties, based on impedance data, can achieve an equivalent (sub-optimal) performance to conventional methods and that they hold promise for future improvement to surpass conventional methods. An improved deeper analysis could aim to reduce the root-mean-squared error and increase the coefficient of determination value, thereby enhancing the accuracy of dry matter data predictions. To achieve this, various techniques such as feature engineering, hyperparameter tuning, and advanced modelling approaches (e.g. convolutional neural networks) could be explored. The authors consider that alternate chemometric methods like the Kennard-Stone algorithm, which selects representative samples based on distance criteria, could lead to more robust dataset partitioning. Additionally, incorporating data fusion with results obtained through infrared spectroscopy could further improve the model’s performance.

Photo by Rodrigo dos Reis on Unsplash

Impedance is a complex Cartesian function describing the difference between an inputting and exiting sinusoidal electrical signal.  It can be depicted graphically as a plot (vector) of resistance vs reactance.  The linearity of this plot, and the angle of the vector, are distinctive.  In a sample of meat or fish, impedance is affected by the cell structure and the water content.  Both of which are an indicator of freshness.  An impedance sensor, comparing the result with a “normal” database, can therefore be used to detect unfresh meat or meat that has been prior frozen and defrosted without declaration.

This review (open access) describes published applications, comparing the technique with other approaches such as HADH Enzyme measurement (see FAN method explainers).  It concludes that the development of Impedance Sensor methods is now at a stage where the technique is ideal as a cheap, non-destructive inline check in the food industry, particularly if coupled with machine learning to spot unusual or anomalous samples.

Photo by Victoria Shes on Unsplash