Adulteration of extra virgin olive oil (EVOO) with cheaper edible oils is of considerable concern in the olive oil industry. The potential of Raman spectroscopy combined with multivariate statistics has been investigated for evaluating the authenticity (or purity) and concentration of EVOO irrespective of it being adulterated with one or more adulterants. The adulterated oil samples were prepared by blending different concentrations of EVOO (10–100% v/v) randomly with cheaper edible oils such as corn, soybean and rapeseed oil. As a result, a Raman spectral database of oil samples (n = 214 spectra) was obtained from 11 binary mixtures (EVOO and rapeseed oil), 16 ternary mixtures (EVOO, rapeseed and corn oil) and 44 quaternary mixtures (EVOO, rapeseed, corn and soybean oil). Partial least squares (PLS) calibration models with 10-fold cross validation were constructed for binary, ternary and quaternary oil mixtures to determine the purity of spiked EVOO. The PLS model on the complex dataset (binary + ternary + quaternary) where the spectra obtained with different measurement parameters and sample conditions can able to determine the purity of spiked EVOO inspite of being blended with one or more cheaper oils. As a proof of concept, in this study, we used single batch of commercial oil bottles for estimating the purity of EVOO. The developed method is not only limited to EVOO, but can be applied to clean EVOO obtained from the production site and other types of food.

A trace-gas sensor, based on quartz-enhanced photoacoustic spectroscopy (QEPAS), consisting of two acoustically coupled micro-resonators (mR) with an off-axis 20 kHz quartz tuning fork (QTF) is demonstrated. The complete acoustically coupled mR system is optimized based on finite-element simulations and is experimentally verified. The QEPAS sensor is pumped resonantly by a nanosecond pulsed single-mode mid-infrared optical parametric oscillator. The sensor is used for spectroscopic measurements on methane in the 3.1–3.5 μm wavelength region with a resolution bandwidth of $1{\mathrm{cm}}^{-1}$ and a detection limit of 0.8 ppm. An Allan deviation analysis shows that the detection limit at the optimum integration time for the QEPAS sensor is 32 ppbv at 190 s, and that the background noise is due solely to the thermal noise of the QTF.

The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria. For this we have implemented a spectral transformer model for hyper-spectral Raman images of bacteria. We show that our model outperforms the standard convolutional neural network models on a multitude of classification problems, both in terms of accuracy and in terms of training time. We attain more than 96% classification accuracy on a dataset consisting of 15 different classes and 95.6% classification accuracy for six MR–MS bacteria species. More importantly, our results are obtained using only fast and easy-to-produce training and test data.

A compact versatile photoacoustic (PA) sensor for trace gas detection is reported. The sensor is based on an integrating sphere as the PA absorption cell with an organ pipe tube attached to increase the sensitivity of the PA sensor. The versatility and enhancement of the sensitivity of the PA signal is investigated by monitoring specific ro-vibrational lines of CO₂ in the 2 μm wavelength region and of NO₂ in the 405 nm region. The measured enhancement factor of the PA signal exceeds 1200, which is due to the acoustic resonance of the tube and the absorption enhancement of the integrating sphere relatively to a non-resonant single pass cell. It is observed that the background absorption signals are highly attenuated due to the thermal conduction and diffusion effects in the polytetrafluoroethylene cell walls. This demonstrates that careful choice of cell wall materials can be highly beneficial to the sensitivity of the PA sensor. These properties makes the sensor suitable for various practical sensor applications in the ultraviolet (UV) to the near infrared (NIR) wavelength region, including climate, environmental and industrial monitoring.

We demonstrate an online (in-situ) sensor for continuous detection of oil contamination in compressed air systems complying with the ISO-8573 standard. The sensor is based on the photo-acoustic (PA) effect. The online and real-time PA sensor system has the potential to benefit a wide range of users that require high purity compressed air. Among these are hospitals, pharmaceutical industries, electronics manufacturers, and clean room facilities. The sensor was tested for sensitivity, repeatability, robustness to molecular cross-interference, and stability of calibration. Explicit measurements of hexane (C₆H₁₄) and decane (C₁₀H₂₂) vapors via excitation of molecular C-H vibrations at approx. 2950 cm⁻¹ (3.38 μm) were conducted with a custom made interband cascade laser (ICL). For the decane measurements a (1 σ) standard deviation (STD) of 0.3 ppb was demonstrated, which corresponds to a normalized noise equivalent absorption (NNEA) coefficient for the prototype PA sensor of 2.8×10⁻⁹ W cm⁻¹ Hz^1/2.

We demonstrate the usefulness of a nanosecond-pulsed single-mode mid-infrared (MIR) optical parametric oscillator (OPO) for photoacoustic (PA) spectroscopic measurements. The maximum wavelength ranges for the signal and idler are 1.4 μm to 1.7 μm and 2.8 μm to 4.6 μm, respectively, with a MIR output power of up to 500 mW, making the OPO useful for different spectroscopic PA trace-gas measurements targeting the major market opportunity of environmental monitoring and breath gas analysis. We perform spectroscopic measurements of methane (${\mathrm{CH}}_4$), nitrogen dioxide (${\mathrm{NO}}_2$), and ammonia (${\mathrm{NH}}_3$) in the 2.8 μm to 3.7 μm wavelength region. The measurements were conducted with a constant flow rate of 300 mL/min, thus demonstrating the suitability of the gas sensor for real-time trace-gas measurements. The acquired spectra are compared with data from the HITRAN database, and good agreement is found, demonstrating a resolution bandwidth of $1.5{\mathrm{cm}}^1$. An Allan deviation analysis shows that the detection limit for methane at optimum integration time for the PA sensor is 8 ppbV (nmol/mol) at 105 s of integration time, corresponding to a normalized noise equivalent absorption coefficient of $2.9\times {10}^{-7}\mathrm{W}{\mathrm{cm}}^{-1}{\mathrm{Hz}}^{-1/2}$.