NIR sensor for aqueous urea solution film thickness and concentration measurement using a broadband light source

Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Film Thickness and Glycerol Concentration Mapping of Falling Films Based on Fluorescence and Near-Infrared Technique

Falling film evaporation processes involve high fluid velocities with continuous variations in local film thickness, fluid composition, and viscosity. This contribution presents a parallel and complementary film thickness and concentration mapping distribution in falling films using a non-invasive fluorescence and near-infrared imaging technique. The experiments were performed with a mixture of glycerol/water with a mass fraction from 0 to 0.65 gglycgtotal-1 and operating ranges similar to evaporation processes. The measurement system was designed by integrating two optical measurement methods for experimental image analysis. The film thickness was evaluated using a VIS camera and high-power LEDs at 470 nm. The local glycerol concentration gglycgtotal-1 was determined using a NIR camera and high-power LEDs at 1050, 1300, 1450 and 1550 nm. A multiwavelength analysis with all NIR wavelengths was implemented with a better correlation for falling films at low flow velocity. The results show an improvement in the analysis of falling films with high flow velocities up to almost 500 mm/s by using only the 1450 nm wavelength and the fluorescence measurement. Simultaneous imaging analysis of film thickness and concentration in falling films provides further insight into understanding mass and heat transport and thus supports the optimization of falling film evaporators.

Simultaneous measurement of liquid-film thickness and solute concentration of aqueous solutions of two urea derivatives using NIR absorption

We present a method to simultaneously measure the film thickness and individual concentrations of two urea derivates (urea CH₄N₂O and dimethylurea C₃H₈N₂O) mixed in an aqueous solution at constant temperature using near-infrared (NIR) absorption at multiple specific wavelengths. Fourier transform infrared (FTIR) spectra of aqueous mixtures of urea and dimethylurea solutions were recorded in the 1250-2500 nm wavelength range in thin-layer quartz cuvettes at room temperature. The spectra reveal suitable detection wavelengths, i.e., 1450, 1933, 2200, and 2270 nm, for which both the absorption coefficient and its variation with the species concentration are large enough to achieve satisfactory detection sensitivity and selectivity. For validation measurements, samples were prepared in thin-layer quartz transmission cells with known path lengths and mixture compositions in the range 100-1000 µm and 0-40 wt.%, respectively. Film thickness and mass fractions of both species were determined from measured absorbance ratios in the determined characteristic wavelength bands.