Interlaboratory study of a supercritical fluid chromatography method for the determination of pharmaceutical impurities: Evaluation of multi-systems reproducibility.

Modern supercritical fluid chromatography (SFC) is now a well-established technique, especially in the field of pharmaceutical analysis. We recently demonstrated the transferability and the reproducibility of a SFC-UV method for pharmaceutical impurities by means of an inter-laboratory study. However, as this study involved only one brand of SFC instrumentation (Waters®), the present study extends the purpose to multi-instrumentation evaluation. Specifically, three instrument types, namely Agilent®, Shimadzu®, and Waters®, were included through 21 laboratories (n = 7 for each instrument). First, method transfer was performed to assess the separation quality and to set up the specific instrument parameters of Agilent® and Shimadzu® instruments. Second, the inter-laboratory study was performed following a protocol defined by the sending lab. Analytical results were examined regarding consistencies within- and between-laboratories criteria. Afterwards, the method reproducibility was estimated taking into account variances in replicates, between-days and between-laboratories. Reproducibility variance was larger than that observed during the first study involving only one single type of instrumentation. Indeed, we clearly observed an 'instrument type' effect. Moreover, the reproducibility variance was larger when considering all instruments than each type separately which can be attributed to the variability induced by the instrument configuration. Nevertheless, repeatability and reproducibility variances were found to be similar than those described for LC methods; i.e. reproducibility as %RSD was around 15 %. These results highlighted the robustness and the power of modern analytical SFC technologies to deliver accurate results for pharmaceutical quality control analysis.

Profiling of contemporary beer styles using liquid chromatography quadrupole time-of-flight mass spectrometry, multivariate analysis, and machine learning techniques.

Although all beer is brewed using the same four classes of ingredients, contemporary beer styles show wide variation in flavor and color, suggesting differences in their chemical profiles. A selection of 32 beers covering five styles (India pale ale, blonde, stout, wheat, and sour) were investigated to determine chemical features, which discriminate between popular beer styles. The beers were analyzed in an untargeted fashion using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). The separation and detection method were tuned to include compounds from important beer components, namely iso-α-acids and phenolic compounds. Due to the sheer number of unknown compounds in beer, multivariate analysis and machine learning techniques were used to pinpoint some of the compounds most influential in distinguishing beer styles. It was determined that while many phenols and iso-α-acids were present in the beers, they were not the compounds most responsible for the variations between styles. However, it was possible to discriminate each beer style using multivariate analysis. Principal component analysis (PCA) was able to separate and cluster the individual beer samples by style. A combination of statistical tools were used to predict formulas for some of the most influential metabolites from each style. Machine learning models accurately classified patterns in the five beer styles, indicating that they can be precisely distinguished by their nonvolatile chemical profile.

Target profiling of beer styles by their iso-α-acid and phenolic content using liquid chromatography-quadrupole time-of-flight-mass spectrometry.

Beer styles show wide variation in color, flavor, and clarity, due to differences in their chemical content. Some of the major flavor compounds in beer are isomerized alpha acids and phenolic compounds. These were investigated as potentially discerning features between beer styles. A selection of 32 American beers covering five styles was analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry, which resulted in high mass accuracy chromatograms of the studied analytes. Distinctions between the presence or relative concentrations of certain compounds were observed and related back to brewing ingredients and procedures. For example, vanillin was only observed in stout beers due to the use of roasted barley malts for brewing, while chlorogenic acid isomers were found in two sours at relatively high concentrations (189 and 34 mg/L) because of the fruits used to flavor the beers. Distinctions were further confirmed using multivariate analysis techniques, which separated three of the five beer styles (India pale ales, stouts, and sours).

Minimizing UV noise in supercritical fluid chromatography. I. Improving back pressure regulator pressure noise.

Pressure fluctuations and resulting refractive index changes, induced by the back pressure regulator (BPR) can be a significant source of UV detector noise in supercritical fluid chromatography (SFC). The refractive index (RI) of pure carbon dioxide (CO(2)) changes ≈0.2%/bar at the most commonly used conditions in supercritical fluid chromatography (SFC) (40 °C and 100 bar), compared to 0.0045%/bar for water (CO(2) IS 44× worse). Changes in RI cause changes in the focal length of the detector cell which results in changes in UV intensity entering the detector. The change in RI (ΔRI/bar) of CO(2) decreases 8-fold at 200 bar, compared to 100 bar. A new back pressure regulator (BPR) design representing an order of magnitude improvement in the state of the art is shown to produce peak to peak pressure noise (PN(p-p)) as low as 0.1 bar, at 200 bar, and 20Hz, compared to older equipment that attempted to maintain PN(p-p)<1bar, at <5Hz. With this lower PN(p-p), changes in baseline UV offsets could be measured as a function of very small changes in pressure. A pressure change of ±1 bar at 100 bar, common with some older BPR's, produced a UV baseline offset >0.5 mAU. A pressure change of ±0.5 bar representing the previous state-of-the-art, resulted in a UV offset of 0.3m AU. Baseline noise <0.05 is required to validate methods for trace analysis. The new BPR, with a PN(p-p) of 0.1 bar, demonstrated UV peak to peak noise (N(p-p))<0.02 mAU with a >0.03 min (10Hz) electronic filter under some conditions. This new low noise level makes it possible to validate SFC methods for the first time.