{'fraction_non_alphanumeric': 0.016727272727272726, 'fraction_numerical': 0.0, 'mean_word_length': 5.521327014218009, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Data analysis and calibration for a bulk-refractive-index-compensated surface plasmon resonance affinity sensor

Surface plasmon resonance (SPR) affinity sensing, the problem of bulk refractive index (RI) interference in SPR sensing, and a sensor developed to overcome this problem are briefly reviewed. The sensor uses a design based on Texas Instruments' Spreeta SPR sensor to simultaneously measure both bulk and surface RI. The bulk RI measurement is then used to compensate the surface measurement and remove the effects of bulk RI interference. To achieve accurate compensation, robust data analysis and calibration techniques are necessary. Simple linear data analysis techniques derived from measurements of the sensor response were found to provide a versatile, low noise method for extracting measurements of bulk and surface refractive index from the raw sensor data. Automatic calibration using RI gradients was used to correct the linear estimates, enabling the sensor to produce accurate data even when the sensor has a complicated nonlinear response which varies with time. The calibration procedure is described, and the factors influencing calibration accuracy are discussed. Data analysis and calibration principles are illustrated with an experiment in which sucrose and detergent solutions are used to produce changes in bulk and surface RI, respectively.