A Molecular-Imprinted Bipolymer Infused Capacitive Sensor for Inositol Detection in Fruits

This study presents a cost-effective, reusable, and stable molecular-imprinted polymer (MIP) technology-based capacitive sensor for the detection of inositol (IS) in fruits. As a sensitive composite, molecular-imprinted polydopamine-polyethylene glycol (MI-2P-IS@C) was introduced on copper (Cu)-cladded FR-4 plates of the capacitive sensor. For the sensing material characterization, a couple of techniques: 1) scanning electron microscope (SEM) and 2) X-ray diffraction (XRD), were employed. The studies of parallel capacitance (
$C_{\mathrm {pis}}$
), impedance (
$Z_{\mathrm {pis}}$
), and phase angle (
$\theta _{\mathrm {pis}}$
) variation as a function of frequency were performed for the MI-2P-IS@C sensor. The parallel capacitance
$C_{\mathrm {pis}}$
was considered to be the key parameter for exploring the detection capability of the sensor. The linearity range of the sensor was found to be from 0.125 to 1 ppm, and the lower limit of detection (LOD) was estimated to be as low as 1.8 ppb. A couple of linear regression models: 1) principal component regression (PCR) method and 2) partial least square regression (PLSR) was exercised for the prediction of IS in the fruits, and the prediction accuracies were found to be as high as 99.91% and 99.90% for the two methods, respectively. The root-mean-square error of calibration (RMSEC) was estimated to be 0.0008 for both PLSR and PCR using three latent variables. Moreover, the sensor offers high selectivity in detecting IS and exhibits high stability as the maximum parallel capacitance (
$C_{\mathrm {pism}}$
) increases only by 0.5% even after three months of usage.