Label-free methods neither cause cell damage nor contribute to any change in cell composition and intrinsic characteristics. Indeed, there is much interest in the scientific community to learn more from existing methods and to develop new label-free based methods for detection and classification of cells. Cell classification using optical measurements has been frequently utilized. When cells interact with light, due to differences in the composition of different types of cells, changes in the optical absorption and transmission response result. This work combined the advancement in optical measurements and Prony techniques to enhance the classification of cells based on their measured optical profiles. In this work, six types of cells, HeLa, 293T, lung-cancer and normal, and liver-cancer and normal, were suspended in their corresponding medium and their transmission characteristics were assessed. After media de-embedding, the transmission profiles were fitted with a sum of exponentially decaying signals using the Prony algorithm. After that, the optical response of each cell was modeled with a set of extracted parameters: amplitude, frequency, phase, and damping factor. The four parameters extracted via the Prony method are related to the coefficients and locations of the poles for each fitted model. A figure of merit (FOM) has been introduced, whose distribution in the complex z-plane plays a major role in the classification of cell type. The changes in the values of FOM are due to the changes in cell composition and intrinsic characteristics of different cells.

Label free based methods received huge interest in the field of bio cell characterizations because they do not cause any cell damage nor contribute any change in its compositions. This work takes a close outlook of cancerous cells discrimination from normal cells utilizing parametric modeling approach. Autoregressive (AR) modeling technique is used to fit the measured optical transmittance profiles of both cancer and normal cells. The transmitted light intensity, when passes through the cells, gets affected by their intercellular compositions and membrane properties. In this study, four types of cells: lung-cancerous and normal, liver-cancerous and normal, were suspended in their corresponding medium and their transmission characteristics were collected and processed. The AR coefficients of each type of the cell were analyzed with the statistical technique called Analysis of variance (ANOVA), which provided the significant coefficients. The poles extracted from the significant coefficients resulted in an improved demarcation for normal and cancer cells. These outcomes can be further utilized for cell classification using statistical tools.