Advanced and sensitive method by discrete geometrical invariants for detection of differences between complex fluids

A new method based on the so-called discrete geometrical invariants (DGIs) is proposed to highlight the statistical differences between random sequences that can be presented in the form of two dimensions curves. The effectiveness of the proposed method is validated experimentally. Different complex fluids (i.e. sunflower and olive oils from different producers, isopropanol (IPA), sodium chloride, glucose, and distilled water) are considered to carry out a set of measurements for identifying the differences between fluids. The experimental results are obtained by two different approaches that are based on microwave and optical characterization. Moreover, an algorithm is provided to apply the DGIs method to both types of results and, in general, to implement the identification method in an automatic way also to other fluids. The new methodology is specified by the combination of the peculiar statistical analysis with the employed experimental technique. It opens a wide scenario of practical applications in detection and identification of hidden differences between complex fluids like vegetable and industrial oils, wines, milk, etc., without having a detailed knowledge of the complicated chemical content. It is expected that the proposed methodology can be applied to different technologies and systems, such as in production of different beverages, chemical fluids and solutions.