Development and Validation of an HPLC Method for the Analysis of Flowers of Wild-Growing Primula veris from Epirus, Greece

An HPLC-PDA method was developed for the determination of the flavonoids in the flowers of Primula veris from Epirus, Greece. The aim was to investigate the chemical content of the over-harvested P. veris populations of Epirus and to develop and optimize an extraction protocol to allow fast, exhaustive, and repeatable extraction. Qualitative analysis revealed that the P. veris flowers from Epirus were particularly rich in flavonoids, especially flavonol triglycosides including derivatives of quercetin, isorhamnetin, and kaempferol. A phytochemical investigation of a 70% hydromethanolic extract from the flowers afforded a new flavonoid, namely, isorhamnetin-3-Ο-β-glucopyranosyl-(1 → 2)-β-glucopyranosyl-(1 → 6)-β-glucopyranoside, which is also the main constituent of the flower extracts. Its structure elucidation was carried out by means of 1D and 2D NMR and mass spectrometry analyses. The HPLC-PDA method was developed and validated according to the International Council for Harmonisation guidelines. Since the main flavonol glycoside of the plant is not commercially available, rutin was used as a secondary standard and the response correction factor was determined. Finally, the overall method was validated for precision (% relative standard deviation ranging between 1.58 and 4.85) and accuracy at three concentration levels. The recovery ranged between 93.5 and 102.1% with relative standard deviation values < 5%, within the acceptable limits. The developed assay is fast and simple and will allow for the quality control of the herbal drug.

The high-frequency response correction of eddy covariance fluxes – Part 2: An experimental approach for analysing noisy measurements of small fluxes

Fluxes measured with the eddy covariance (EC) technique are subject to flux losses at high frequencies (low-pass filtering). If not properly corrected for, these result in systematically biased ecosystem–atmosphere gas exchange estimates. This loss is corrected using the system's transfer function which can be estimated with either theoretical or experimental approaches. In the experimental approach, commonly used for closed-path EC systems, the low-pass filter transfer function (H) can be derived from the comparison of either (i) the measured power spectra of sonic temperature and the target gas mixing ratio or (ii) the cospectra of both entities with vertical wind speed. In this study, we compare the power spectral approach (PSA) and cospectral approach (CSA) in the calculation of H for a range of attenuation levels and signal-to-noise ratios (SNRs). For a systematic analysis, we artificially generate a representative dataset from sonic temperature (T) by attenuating it with a first order filter and contaminating it with white noise, resulting in various combinations of time constants and SNRs. For PSA, we use two methods to account for the noise in the spectra: the first is the one introduced by Ibrom et al. (2007a) (PSAI07), in which the noise and H are fitted in different frequency ranges, and the noise is removed before estimating H. The second is a novel approach that uses the full power spectrum to fit both H and noise simultaneously (PSAA21). For CSA, we use a method utilizing the square root of the H with shifted vertical wind velocity time series via cross-covariance maximization (CSAH,sync). PSAI07 tends to overestimate the time constant when low-pass filtering is low, whilst the new PSAA21 and CSAH,sync successfully estimate the expected time constant regardless of the degree of attenuation and SNR. We further examine the effect of the time constant obtained with the different implementations of PSA and CSA on cumulative fluxes using estimated time constants in frequency response correction. For our example time series, the fluxes corrected using time constants derived by PSAI07 show a bias between 0.1 % and 1.4 %. PSAA21 showed almost no bias, while CSAH,sync showed bias of ±0.4 %. The accuracies of both PSA and CSA methods were not significantly affected by SNR level, instilling confidence in EC flux measurements and data processing in set-ups with low SNR. Overall we show that, when using power spectra for the empirical estimation of parameters of H for closed-path EC systems the new PSAA21 outperforms PSAI07, while when using cospectra the CSAH,sync approach provides accurate results. These findings are independent of the SNR value and attenuation level.

Revising the high frequency response correction of scalar fluxes measured by closed-path eddy covariance systems

&lt;p&gt;Eddy covariance (EC) scalar flux loss at high frequency is due to the incapability of the measurement system to detect small-scale variation of atmospheric turbulent signals. This systematic bias is particularly important for closed-path systems, and it is mainly related to inadequate sensor frequency response, sensor separation and the air sampling trough tubes and filters. Here, we investigate the limitations of current approaches, based on measured power spectra (PSA) or cospectra (CSA), to empirically estimated the spectral transfer function of the EC system needed for the frequency response correction of measured fluxes. We performed a systematic analysis by using EC data from a wetland and forest site for a wide range of attenuation levels and signal-to-noise ratio. We proposed a novel approach for PSA that uses simultaneously the noise and the turbulent signals present in the power spectrum, providing robust estimates of spectral transfer function for all conditions. We further theoretically derived a new transfer function to be used in the CSA approach which specifically accounts for the interaction between the low-pass filtering induced phase shift and the high frequency attenuation. We show that current CSA approaches neglect such effect, giving a non-negligible systematic bias to the estimated scalar fluxes from the studied sites. Based on these findings, we recommend that spectral correction methods, implemented in EC data processing algorithms, are revised accordingly.&lt;/p&gt;

The high frequency response correction of eddy covariance fluxes. Part 1: an experimental approach for analysing noisy measurements of small fluxes

Fluxes measured with the eddy covariance (EC) technique are subject to flux losses at high frequencies (low-pass filtering). If not properly corrected for, these result in systematically biased ecosystem-atmosphere gas exchange estimates. This loss is corrected using the system's transfer function which can be estimated with either theoretical and experimental approaches. In the experimental approach, commonly used for closed-path EC systems, the low-pass filter transfer function (H) can be derived from the comparison of either (i) the measured power spectra of sonic temperature and the target gas mixing ratio or (ii) the cospectra of both entities with vertical wind speed. In this study, we compare the power spectral approach (PSA) and cospectral approach (CSA) in the calculation of H for a range of attenuation levels and signal-to-noise ratios (SNRs). For a systematic analysis, we artificially generate a representative dataset from sonic temperature (T) by attenuating it with a first order filter and contaminating it with white noise, resulting in various combinations of time constants and SNRs. For PSA, we use two methods to account for the noise in the spectra: the first is the one introduced by Ibrom et al. (2007a) (PSAI07), where the noise and H are fitted in different frequency ranges and the noise is removed before estimating H. The second is a novel approach that uses the full power spectrum to fit both H and noise simultaneously (PSAA20). For CSA, we use three different methods: (1) a plain version of Lorentzian equation describing the H (CSAH), (2) a square-root of the H (CSA√H), and (3) a square-root of the H with shifted vertical wind velocity time series via cross-covariance maximisation (CSA√H,sync). PSAI07 tends to overestimate the time constant when low-pass filtering is low, whilst the new PSAA20 successfully estimates the expected time constant regardless of the degree of attenuation and SNR. CSAH underestimates the time constant with decreasing accuracy as attenuation increases due to the omission of the quadrature spectrum. CSA√H overestimates, but its accuracy increases with time-lag correction in the CSA√H,sync. We further examine the effect of the time constant obtained with the different implementations of PSA and CSA on cumulative fluxes using estimated time constants in frequency response correction. For our example time series, the fluxes corrected using time constants derived by PSAI07 show a bias of &amp;pm;2 %. PSAA20 showed a similar variation, yet slightly better accuracy. CSAH underestimated fluxes by up to 4 %, while CSA√H overestimated them by up to 3 %, a bias which was mostly eliminated with time-lag correction in the CSA√H,sync (−2 % to 1 % ). The accuracies of both PSA and CSA methods were not affected by SNR level, instilling confidence in EC flux measurements and data processing in setups with low SNR. Overall we show that, when using power spectra for the empirical estimation of parameters of H for closed-path EC systems the new PSAA20 outperforms PSAI07, while when using cospectra the CSA√H,sync approach provides the most accurate results. These findings are independent of the SNR value and attenuation level.