A significant amount of noteworthy articles reviewing different label-free biosensors are being published in the last years. Most of the times, the comparison among the different biosensors is limited by the procedure used of calculating the limit of detection and the measurement uncertainty. This article clarifies and establishes a simple procedure to determine the calibration function and the uncertainty of the concentration measured at any point of the measuring interval of a generic label-free biosensor. The value of the limit of detection arises naturally from this model as the limit at which uncertainty tends when the concentration tends to zero. The need to provide additional information, such as the measurement interval and its linearity, among others, on the analytical systems and biosensor in addition to the detection limit is pointed out. Finally, the model is applied to curves that are typically obtained in immunoassays and a discussion is made on the application validity of the model and its limitations.

This article presents the results of an LMM-R-2019 interlaboratory comparison. Such comparisons of different families of measuring instruments are one of the activities conducted among the calibration laboratories to maintain their ISO 17025 accreditation. Given that the study of surface roughness is becoming increasingly important in the field of dimensional metrology, the comparison focused on determining the Ra parameter on a pseudorandom metallic roughness standard using two types of measuring instruments: physical contact (stylus instruments) and optical (confocal microscopes). Among the aspects studied was whether the roughness measurements obtained using calibrated confocal microscopes could be compared with those using traditional methods since optical instruments obtain measurements more quickly and responsively than do stylus instruments. The results showed that roughness measurements using confocal microscopes are comparable with those from a traditional stylus instrument.

This work describes a procedure to obtain the limit of detection (LoD) of a biosensor device from a set of measurements that we call immunoassays, which consists of testing the device’s response to different concentrations of the molecule, which has to be detected throughout its working range. The procedure, based on the recommendations of international organizations, provides general expressions to estimate the uncertainty throughout its working range and the LoD. In the metrological model, the contributions to the uncertainty, due to the determination of the parameters of the calibration curve, the resolution, the lack of repeatability and others are taken into account. The model is applied to experimental data from the calibration of a biosensor. Using iterative weighted least squares techniques, a general logistic function (6-parameters) has been fitted and used as a calibration curve. The example studied shows that the lack of repeatability is not always the most important contribution to the LoD. The final expression of the LoD is equivalent to that of the expanded uncertainty (for a coverage probability of 99.9%) assigned to the concentration when . This result permits seeing the LoD as the smallest concentration measured with the device, whereby uncertainty interval does not include negative values.

In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s²) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.