A Study On The Liquid Identification Method Based On The Drop Analysis Technology And The Liquid Drop Fingerprint

The liquid drop fingerprint (LDF) can be obtained through drop analysis technology (DAT) and related instruments, which reveals the variation laws of the coupled light signal passing through the liquid drop along with the drop growth. LDF externalizes the overall properties of tested liquids and it is unique and definite for a certain liquid under certain testing conditions. This thesis aims to make a research into the method of liquid fine discrimination and liquid identification based on LDF.The working principle and structural design of the drop analyzer is introduced in detail, including the liquid drop sensor, signal processing circuits, program-controlled microflow-feeding pump and computer interface. The drop sensor and the pump are improved essentially to solve some problems found in experiments, so that the signal-to-noise ratio (SNR) is highly increased. An idea of normalization is put forward to make LDFs of different liquids comparable, and the specific processing algorithm is described. Normalization ensures the reproducibility of LDFs against the variation the feeding speed of the pump from the aspect of data format.A conception for feature extraction of LDF is presented, which converts the LDF into a point in the multidimensional space through constructing an eigen vector composed of multiple eigenvalues for LDF. Waveform analysis method, correlation comparison method, data compression method and polynomial regression method are developed to realize feature extraction. Resolving sensitivity (RS) is defined to describe the response of eigenvalues to the variation of LDF quantitatively.A general idea for liquid identification is presented, which compares LDF of the test liquid and that of the reference liquid. The identification result is true if the difference between two liquids falls within the threshold range, and is false if it falls outside. According to various descriptions of differences, three specific identification methods are developed, which are based on the graph study of LDF, based on the eigenvector of LDF and based on the eigenvalue of LDF. The threshold is determined according to the uncertainty of measurement and the dispersibility of reference liquid. Sampling experiments are carried out, and the effectiveness and applicability of feature extraction methods and liquid identification methods are studied. Some recommendations for common liquids are given.Analysis given in this thesis proves that liquid identification based on the liquid drop fingerprint is a simple novel effective method with a wide applicability. It can be used for attacking the counterfeits, liquid process control and environment monitoring and other fields where fine discrimination of liquid is needed.