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.

Quartz-enhanced photoacoustic sensing is a promising method for low-concentration trace-gas monitoring due to the resonant signal enhancement provided by a high-Q quartz tuning fork. However, quartz-enhanced photoacoustic spectroscopy (QEPAS) is associated with a relatively slow acoustic decay, which results in a reduced spectral resolution and signal-to-noise ratio as the wavelength tuning rate is increased. In this work, we investigate the influence of wavelength scan rate on the spectral resolution and signal-to-noise ratio of QEPAS sensors. We demonstrate the acquisition of photoacoustic spectra from 3.1 $\mathsf{\mu }$m to 3.6 $\mathsf{\mu }$m using a tunable mid-infrared optical parametric oscillator. The spectra are attained using wavelength scan rates differing by more than two orders of magnitude (from 0.3 nm s${}^{-1}$ to 96 nm s${}^{-1}$). With this variation in scan rate, the spectral resolution is found to change from 2.5 cm${}^{-1}$ to 9 cm${}^{-1}$. The investigated gas samples are methane (in nitrogen) and a gas mixture consisting of methane, water, and ethanol. For the gas mixture, the reduced spectral resolution at fast scan rates significantly complicates the quantification of constituent gas concentrations.

We report on the use of quartz-enhanced photoacoustic spectroscopy for continuous carbon-dioxide measurements in humid air over a period of six days. The presence of water molecules alters the relaxation rate of the target molecules and thus the amplitude of the photoacoustic signal. Prior to the measurements, the photoacoustic sensor system was pre-calibrated using CO₂ mole fractions in the range of 0–10⁻³ (0–1000 ppm) and at different relative humidities between 0% and 45%, while assuming a model hypothesis that allowed the photoacoustic signal to be perturbed linearly by H₂O content. This calibration technique was compared against an alternative learning-based method, where sensor data from the first two days of the six-day period were used for self-calibration. A commercial non-dispersive infrared sensor was used as a CO₂ reference sensor and provided the benchmark for the two calibration procedures. In our case, the self-calibrated method proved to be both more accurate and precise.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H${}_2$O), ammonia (NH${}_3$), and methane (CH${}_4$) were measured in the mid-infrared (MIR) wavelength range using a mid-infrared (MIR) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra are a common challenge for gas spectroscopy. To mitigate this, we used a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts per million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here, in this proof-of-concept experiment, we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H${}_2$O, NH${}_3$, and CH${}_4$ and with an absolute sensitivity of approximately 300 (±50) ppm/V, 50 (±5) ppm/V, and 5 (±2) ppm/V for water, ammonia, and methane, respectively. This demonstrates that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photo-acoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3) and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a MIR (mid-infrared) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra is a common challenge for gas spectroscopy. To mitigate this, we use a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts-per-million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here in this proof-of-concept experiment we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3 and CH4 and with an absolute sensitivity of approximately 300(±50) ppm/V, 50(±5) ppm/V and 5(±2) ppm/V for water, ammonia and methane, respectively. Thus, demonstrating that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.