Application of dielectric power spectroscopy with a parallel plate sensor for freshness detection of milk

Document Type : Research Article


1 M.Sc. Student, Dept. of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University

2 Assistant Prof., Dept. of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University

3 Assistant Prof., Dept. of Animal Science, Faculty of Agriculture, Shahrekord University

4 Associate Prof., Dept. of Health and Food quality control, Faculty of Veterinary Medicine, Shahrekord University


Milk and dairy products as an animal protein source with a high nutritional value has a particular importance in human diet. Development of non-destructive and quick methods for assessing the quality and freshness of milk is of particular importance in ensuring product safety before consumption. The aim of this study was to measure the dielectric power with the use of a parallel-plate sensor to develop a new method for rapid assessment of milk freshness. Three milks with commercial names of Pak, Kaleh and Damdaran and a milk from dairy farm were dielectrically measured during seven storage days. Sinusoidal voltage wad applied to the milk samples and capacitor power within the frequency range of 0-150 MHz was measured using a spectrum analyzer. Results proved that the frequency range of 75-100 MHz varied considerably with storage time so that the frequency of the valley within this frequency range increased with storage time. The frequency of the valley as a spectrum characteristic predicted the storage day by a linear regression model with R2 and RMSE of 0.84 and 0.8, respectively. The correlation of milk pH and the frequency of the valley showed that during the first four days of storage with a minor change in pH, the frequency of valley varied significantly indicating further physico-chemical changes in milk with storage that affects the dielectric properties of milk. The dielectric power at the frequencies of 2.7 and 89.4 as the most effective frequencies, predicted the storage day and pH, respectively with RMSEs of 0.4 and 0.22. Multiple linear regression models decreased the errors of prediction to 0.26 and 0.11 for storage day and pH, respectively. Based on the findings of this study, dielectric power spectroscopy can be implemented as a non-destructive, simple and accurate method to milk pH and freshness determination.


Main Subjects

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