Multi-wavelength deep-ultraviolet absorbance detector based upon program-controlled pulsing light-emitting diodes.

A novel approach for multi-wavelength ultraviolet (UV) absorbance detection has been introduced employing a single board computer (SBC) with a field programmable gate array (FPGA), Red Pitaya SBC, to generate separated micro pulses for three deep-ultraviolet light-emitting diodes (DUV-LEDs), λmax = 235, 250, and 280 nm, along with data acquisition and processing via a custom-made program. The pulse set generation and data acquisition were synchronized using the SBC. The outputs of the three pulsing DUV-LEDs were combined and transmitted to the flow cell via a solarisation resistant trifurcated optical fiber (OF). An ultra-fast responding photodiode was connected to the optical-fiber-compatible flow cell to record the intensity of the DUV pulses. Upper limit of detector linearity (A95 %) was found to be 1917 mAU, 2189 mAU, and 1768 mAU at 235 nm, 250 nm, and 280 nm, respectively, with stray light ≤0.9 %. In addition, the effective path length (Leff) was estimated to be ≥98.0 % of the length of the used flow cell (50 mm). The new pulsed multi-LEDs absorbance detector (PMLAD) has been successfully coupled with a standard liquid chromatograph and utilized for the analysis of pharmaceuticals. Paracetamol, caffeine, and aspirin were simultaneously determined at 250, 280, and 235 nm, respectively, using the PMLAD. The absorbance ratios between the different wavelengths were applied to further confirm the identity of the studied compounds. Excellent linearity was achieved over a range of 0.1-3.2 µg/mL for paracetamol, 0.4-6.4 µg/mL for caffeine, and 0.8-12.8 µg/mL for aspirin with a regression correlation coefficient (r2) ≥ 0.99996. The quantitation limits (LOQs) were 0.10 µg/mL, 0.38 µg/mL, and 0.66 µg/mL for paracetamol, caffeine, and aspirin, respectively.

Chemometric optimisation of enzymatic hydrolysis of beechwood xylan to target desired xylooligosaccharides.

Generation of specific xylooligosaccharides (XOS) is attractive to the pharmaceutical and food industries due to the importance of their structure upon their application. This study used chemometrics to develop a comprehensive computational modelling set to predict the parameters maximising the generation of the desired XOS during enzymatic hydrolysis. The evaluated parameters included pH, temperature, substrate concentration, enzyme dosage and reaction time. A Box-Behnken design was combined with response surface methodology to develop the models. High-performance anion-exchange chromatography coupled with triple-quadrupole mass spectrometry (HPAEC-QqQ-MS) allowed the identification of 22 XOS within beechwood xylan hydrolysates. These data were used to validate the developed models and demonstrated their accuracy in predicting the parameters maximising the generation of the desired XOS. The maximum yields for X2-X6 were 314.2 ± 1.2, 76.6 ± 4.5, 38.4 ± 0.4, 17.8 ± 0.7, and 5.3 ± 0.2 mg/g xylan, respectively. These values map closely to the model predicted values 311.7, 92.6, 43.0, 16.3, and 4.9 mg/g xylan, respectively.

Comparison of chemometric assisted targeted and untargeted approaches for the prediction of radical scavenging activity of ylang-ylang essential oils.

Essential oils have been used for centuries for their preservative properties. An example is ylang-ylang Cananga odorata [Lam.] Hook. f. & Thomson essential oil, which exists in four different distillation grades, where the fraction with the longest distillation time has the highest radical scavenging activity (RSA). Gas chromatography mass spectrometry (GC-MS) followed by multivariate statistical analysis is a powerful approach for determination of RSA. Herein the performance of such multivariate statistical analysis using three data sets derived from gas chromatography mass spectrometry (GC-MS) analysis, is compared to that achieved using two direct and fast spectroscopic techniques, for the prediction of RSA using partial least squares (PLS) regression analysis. The three GC-MS data sets were, 'full chemical composition', 'total chromatogram average mass spectra (TCAMS)' and 'segment average mass spectra (SAMS)', whilst two spectroscopic techniques, namely attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Raman spectroscopy, provided the spectroscopic data sets for comparison. PLS models created using ATR-FTIR and 'full chemical composition' data sets provided the lowest relative error of prediction (REP) and mean error of prediction (MEP) in validation, whilst in independent test sets, the PLS models created using ATR-FTIR and SAMS data sets delivered the lowest REP and MEP. The three GC-MS derived data sets were further compared for value in determination of compounds contributing to the RSA. PLS regression analysis of the full chemical composition data set revealed that germacrene D and (E,E)-α-farnesene were the major contributors to the RSA, whilst average mass spectrum based data sets, TCAMS and SAMS, also highlighted eugenol as another contributor to the RSA.

Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation.

We present a method, utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated essential oils (EOs). Firstly, the approach was evaluated for discrimination of pure EOs from those adulterated with solvent, namely benzyl alcohol. In the case of ylang-ylang EO, three different types of adulteration were examined, adulteration with solvent, cheaper vegetable oil and a lower price EO. Random Forest and partial least square discrimination analysis (PLS-DA) showed excellent performance in discriminating pure from adulterated EOs, whilst the same time identifying the type of adulteration. Also, utilising partial least squares regression analysis (PLS) all adulterants, namely benzyl alcohol, vegetable oil and lower price EO, were quantified based on spectra recorded using the smartphone Raman spectrometer, with relative error of prediction (REP) being between 2.41-7.59%.

Radical scavenging activity and metabolomic profiling study of ylang-ylang essential oils based on high-performance thin-layer chromatography and multivariate statistical analysis.

Ylang-ylang (YY) essential oil (EO) is distilled from the fresh-mature flowers of the Annonaceae family tropical tree Cananga odorata [Lam.] Hook. f. & Thomson, and is widely used in perfume and cosmetic industries for its fragrant character. Herein, two different metabolomic profiles obtained using high-performance thin-layer chromatography (HPTLC), applying different stains, namely 2,2-diphenyl-1-picrylhydrazyl (DPPH·) and p-anisaldehyde, were used for discrimination of 52 YY samples across geographical origins and distillation grades. The first profile is developed using the DPPH· stain based on the radical scavenging activity (RSA) of YY EOs. Results of the HPTLC-DPPH· assay confirmed that RSA of YY EOs is in proportion to the length of distillation times. Major components contributing to the RSA of YY EOs were tentatively identified as germacrene D and α-farnesene, eugenol and linalool, by gas chromatography-mass spectrometry (GC-MS) and GC-flame ionisation detector (GC-FID). The second profile was developed using the general-purpose p-anisaldehyde stain based on the general chemical composition of YY EOs. Untargeted metabolomic discrimination of YY EOs from different geographical origins was performed based on the HPTLC-p-anisaldehyde profiles, followed by principal component analysis (PCA). A discrimination and prediction model for identification of YY distillation grade was developed using PCA and partial least squares regression (PLS) based on binned HPTLC-ultraviolet (254 nm) profiles, which was successfully applied to distillation grade determination of blended YY Complete EOs.

Data handling and data analysis in metabolomic studies of essential oils using GC-MS.

Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.

Characterisation of complex perfume and essential oil blends using multivariate curve resolution-alternating least squares algorithms on average mass spectrum from GC-MS.

Quality control of essential oil blends and the discovery of potential adulterations and product fraud is a significant challenge within the natural oil and perfume industry. In this research, total chromatogram average mass spectra (TCAMS), created from the GC-MS three-way raw data, were employed for the characterisation of complex samples of perfumes and essential oil blends. A multivariate approach for curve resolution was used to resolve the TCAMS of pure essential oils within such perfume and essential oil blends. Resolved TCAMS, in combination with unsupervised pattern recognition approaches revealed the distillation grade and origin of used ylang-ylang oils in perfume mixtures. TCAMS resolved from the essential oil blends were used with a supervised machine learning classification model to identify oils, used in creating the blends. Quantification was performed using a multivariate curve resolution approach, resulting in relative errors of prediction lower than 17.84% with root mean square errors of prediction smaller than 3.43.

Determination of acidity constants at 37 °C through the internal standard capillary electrophoresis (IS-CE) method: internal standards and application to polyprotic drugs.

This work provides the pKa at the biorelevant temperature of 37 °C for a set of compounds proposed as internal standards for the internal standard capillary electrophoresis (IS-CE) method. This is a high throughput method that allows the determination of the acidity constants of compounds in a short time, avoiding the exact measurement of the pH of the buffers used. pH electrode calibration at 37 °C can be avoided too. In order to anchor the pKa values obtained through the IS-CE method in the pH scale, the acidity constant at 37 °C of some of the standards has been determined also by the reference potentiometric method. In general, a decrease in the pKa value is observed when changing the temperature from 25 to 37 °C, and the magnitude of the change depends on the nature of the compounds. Once the pKa values at 37 °C of the internal standards have been established, the method is applied to the determination of the acidity constants of seven polyprotic (5 diprotic and 2 triprotic) drugs. The obtained mobility-pH profiles show well-defined curves, and the fits provide precise pKa values. Due to the lack of reference data at 37 °C only the pKa values of labetalol can be compared to values from the literature, and a very good agreement is observed.

Comprehensive characterisation of ylang-ylang essential oils according to distillation time, origin, and chemical composition using a multivariate approach applied to average mass spectra and segmented average mass spectral data.

Analyses of the complex essential oil samples using gas chromatography hyphenated with mass spectrometry (GC-MS) generate large three-way data arrays. Processing such large data sets and extracting meaningful information in the metabolic studies of natural products requires application of multivariate statistical techniques (MSTs). From the GC-MS raw data several different input data sets for the MSTs can be created, including total chromatogram average mass spectra (TCAMS), segmented average mass spectra (SAMS) and chemical composition. Herein, we compared the performance of MSTs on average mass spectrum based data sets, TCAMS and SAMS, against chemical composition and attenuated total reflectance - Fourier transformation infrared (ATR-FTIR) spectroscopy in the evaluation of quality of ylang-ylang essential oils, based on their grade, geographical origin and chemical composition, using principal component analysis (PCA), partial least squares regression (PLS) and discriminatory analysis (PLS-DA). PCA based on TCAMS, SAMS and chemical composition showed clear trends amongst the samples based on increase in grade (distillation time). PLS-DA applied to TCAMS, SAMS and ATR-FTIR discriminated between all geographical origins. Predicted relative abundances of the 18 most important compounds, using PLS regression models on TCAMS, SAMS and ATR-FTIR, were successfully applied to ylang-ylang essential oil quality assessment based on comparison with the ISO 3063:2004 standard, where the SAMS data set showed superior performance, compared to other data sets.

Random Forests machine learning applied to gas chromatography - Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils.

Differences in chemical profiles of various essential oils (EOs) come from the fact that each plant species and chemotype has a distinctive secondary metabolism. Therefore, these differences can be used as the chemical markers for EO classification and determination of their quality. Herein, the Random Forests (RF) machine learning algorithm was applied to the classification of 20 different EOs. From three-way raw gas chromatography - mass spectra data, total chromatogram average mass spectra (TCAMS) and segment average mass spectra (SAMS) were created. TCAMS was generated by averaging response of each m/z over the whole chromatogram and SAMS by averaging the response of each fragment across a certain time segment within the chromatogram. The RF model was applied to the two data sets and optimised through the evaluation of pre-processed data, number of trees, and number of variables used in each node split. The performance of the model was evaluated through a cross-validation process, repeated 50 times by dividing the whole sample set into training and validation subsets. The calculated average out-of-bag error (OOBE), over 50 different training TCAMS data sets was 3.22 ±â€¯1.29%, while for SAMS it was found to be 2.28 ±â€¯1.33%. The minimal number of variables necessary for EO classification was determined by a nested cross-validation process. The amount of reduced variables in each step was 10%. It was shown that the TCAMS data set with 6 variables had similar prediction power as the SAMS with 30 variables. OOBE for classification of 20 EOs was 2.89 ±â€¯1.44% and 3.70 ±â€¯1.73%, for TCAMS and SAMS, respectively. Proximity between samples was used to evaluate their qualities. Samples with greater intra-class proximity had good similarity, while the lower ones indicated greater variations in the chemical profiles. The SAMS data set showed superior potential for quality assurance, compared with TCAMS.