Real-time label-free surface plasmon resonance biosensing with gold nanohole arrays fabricated by nanoimprint lithography.

In this work we present a surface plasmon resonance sensor based on enhanced optical transmission through sub-wavelength nanohole arrays. This technique is extremely sensitive to changes in the refractive index of the surrounding medium which result in a modulation of the transmitted light. The periodic gold nanohole array sensors were fabricated by high-throughput thermal nanoimprint lithography. Square periodic arrays with sub-wavelength hole diameters were obtained and characterized. Using solutions with known refractive index, the array sensitivities were obtained. Finally, protein absorption was monitored in real-time demonstrating the label-free biosensing capabilities of the fabricated devices.

Low-cost visible-near infrared sensor for on-line monitoring of fat and fatty acids content during the manufacturing process of the milk.

This paper describes the calibration, validation and testing process of a low-cost on-line visible-near infrared (400-1100 nm) sensor for the monitoring of fat and fatty acids content in milk during the manufacturing process of milk. The optical, mechanical and electronic designs of the sensor have been developed in Tekniker IK4 research centre just as its manufacturing process. The measurement range of the sensor is 400-1100 nm thus it covers the visible range (400-780 nm) and the short-wave near infrared (780-1100 nm). Chemometric techniques were applied with the purpose of obtaining a predictive model for each parameter correlating the spectra obtained by the sensor with the parameters analysed in the laboratory. The models were developed by Partial Least Squares Regression (PLS) obtaining one model for each parameter. The raw milk samples used in this work were provided by CAPSA (Asturias, Spain).

Chemometric methods applied to the calibration of a Vis-NIR sensor for gas engine's condition monitoring.

This paper describes the calibration process of a Visible-Near Infrared sensor for the condition monitoring of a gas engine's lubricating oil correlating transmittance oil spectra with the degradation of a gas engine's oil via a regression model. Chemometric techniques were applied to determine different parameters: Base Number (BN), Acid Number (AN), insolubles in pentane and viscosity at 40 °C. A Visible-Near Infrared (400-1100 nm) sensor developed in Tekniker research center was used to obtain the spectra of artificial and real gas engine oils. In order to improve sensor's data, different preprocessing methods such as smoothing by Saviztky-Golay, moving average with Multivariate Scatter Correction or Standard Normal Variate to eliminate the scatter effect were applied. A combination of these preprocessing methods was applied to each parameter. The regression models were developed by Partial Least Squares Regression (PLSR). In the end, it was shown that only some models were valid, fulfilling a set of quality requirements. The paper shows which models achieved the established validation requirements and which preprocessing methods perform better. A discussion follows regarding the potential improvement in the robustness of the models.