Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry as a Tool for the Untargeted Study of Hop and Their Metabolites.

Since hop secondary metabolites have a direct correlation with the quality of beer and other hop-based beverages, and the volatile fraction of hop has a complex composition, requiring effective separation, here we explore the application of headspace solid-phase microextraction as a sample preparation method, coupled with comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) analysis. The methodology involved the use of a DVB/PDMS fibre with 500 mg of hop cone powder, extracted for 40 min at 50 °C, for both GC-MS and GC×GC-MS. The varieties Azacca, Cascade, Enigma, Loral, and Zappa were studied comprehensively. The results demonstrate that GC×GC-MS increases the number of peaks by over 300% compared to classical GC-MS. Overall, 137 compounds were identified or tentatively identified and categorised into 10 classes, representing between 87.6% and 96.9% of the total peak area. The composition revealed the highest concentration of sesquiterpene hydrocarbons for Enigma, whilst Zappa showed a relatively significant concentration of monoterpene hydrocarbons. Principal component analysis for all compounds and classes, along with hierarchical cluster analysis, indicated similarities between Zappa and Cascade, and Azacca and Loral. In conclusion, this method presents an optimistic advancement in hop metabolite studies with a simple and established sample preparation procedure in combination with an effective separation technique.

Multivariate Analysis of the Influence of Microfiltration and Pasteurisation on the Quality of Beer during Its Shelf Life.

Gas chromatography-mass spectrometry (GC-MS), physicochemical and microbiological analyses, sensory descriptive evaluation, and multivariate analyses were applied to evaluate the efficiencies of microfiltration and pasteurization processes during the shelf life of beer. Samples of microfiltered and pasteurised beer were divided into fresh and aged groups. A forced ageing process, which consisted of storing fresh samples at 55° C for 6 days in an incubator and then keeping them under ambient conditions prior to analysis, was applied. Physicochemical analysis showed that both microfiltered and pasteurised samples had a slight variation in apparent extract, pH, and bitterness. The samples that underwent heat treatment had lower colour values compared with those that were microfiltered. Chromatographic peak areas of vicinal diketones increased in both fresh and aged samples. The results of the microbiological analysis revealed spoilage lactic acid bacteria (Lactobacillus) and yeasts (Saccharomyces and non-Saccharomyces) in fresh microfiltered samples. In the GC-MS analysis, furfural, considered by many authors as a heat indicator, was detected only in samples that underwent forced ageing and not in samples that were subjected to thermal pasteurisation. Finally, sensory analysis found differences in the organoleptic properties of fresh microfiltered samples compared with the rest of the samples.

Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees.

The Specialty Coffee Association (SCA) sensory analysis protocol is the methodology that is used to classify specialty coffees. However, because the sensory analysis is sensitive to the taster's training, cognitive psychology, and physiology, among other parameters, the feasibility of instrumental approaches has been recently studied for complementing such analyses. Spectroscopic methods, mainly near infrared (NIR) and mid infrared (FTIR-Fourier Transform Infrared), have been extensively employed for food quality authentication. In view of the aforementioned, we compared NIR and FTIR to distinguish different qualities and sensory characteristics of specialty coffee samples in the present study. Twenty-eight green coffee beans samples were roasted (in duplicate), with roasting conditions following the SCA protocol for sensory analysis. FTIR and NIR were used to analyze the ground and roasted coffee samples, and the data then submitted to statistical analysis to build up PLS models in order to confirm the quality classifications. The PLS models provided good predictability and classification of the samples. The models were able to accurately predict the scores of specialty coffees. In addition, the NIR spectra provided relevant information on chemical bonds that define specialty coffee in association with sensory aspects, such as the cleanliness of the beverage.

Mid infrared spectroscopy and chemometrics as tools for the classification of roasted coffees by cup quality.

Sensory (cup) analysis is a reliable methodology for green coffee quality evaluation, but faces barriers when applied to commercial roasted coffees due to lack of information on roasting conditions. The aim of this study was to examine the potential of mid-infrared spectroscopy for predicting cup quality of arabica coffees of different roasting degrees. PCA analysis showed separation of arabica and robusta. A two-level PLS-DA Hierarchical strategy was employed, with coffee being classified as high or low quality in the first level and then separated according to cup quality in the second level. Validation results showed that the second level models exhibited 100% sensitivity and specificity in the training sets. For the test set, sensitivity ranged from 67% (rio zona) to 100% (soft) while specificity ranged from 71% (rio) to 100% (rioysh, hard). Thus, the proposed method can be used for the quality evaluation of arabica coffees regardless of roasting conditions.

Calibration transfer from powder mixtures to intact tablets: A new use in pharmaceutical analysis for a known tool.

Calibration transfer is commonly used for spectra obtained in different spectrometers or other conditions. This paper proposed the use of calibration transfer between spectra recorded for the same samples in different physical forms. A new method was developed for the direct determination of nevirapine in solid pharmaceutical formulations based on diffuse reflectance near infrared spectroscopy (NIRS) and partial least squares (PLS). This method was developed with 50 powder mixtures and then, successfully extended to the quantification in intact tablets by using calibration transfer with double window piecewise direct standardization (DWPDS). This chemometric strategy provided good results with a small number of tablet transfer samples, only seven, prepared out of the narrow range of active principle ingredients (API) content around the nominal value of the formulation (100%). The method was fully validated in the working range of 83.0-113.9% of nevirapine and the use of DWPDS allowed to significantly decreasing the root mean square error of prediction (RMSEP) from 4.8% (tablets predicted by a model built with only powder samples) to 2.6%. The range of relative errors decreased from -5.1/8.7% to -4.6/3.3%. Considering that the amount of raw materials demanded for preparing tablets is up to ten times higher than for powder mixtures, this type of application is of particular interest in pharmaceutical analysis. In the context of process analytical technology (PAT), the use of the same multivariate model in different steps of the production is very advantageous, saving time and labor.

Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA.

This paper proposed a new screening method for the simultaneous detection of five common adulterants in raw cow milk by using attenuated total reflectance (ATR) mid infrared spectroscopy and multivariate supervised classification (partial least squares discrimination analysis - PLSDA). The method was able to detect the presence of the adulterants water, starch, sodium citrate, formaldehyde and sucrose in milk samples containing from one up to five of these analytes, in the range of 0.5-10% w/v. A multivariate qualitative validation was performed, estimating specific figures of merit, such as false positive and false negative rates, selectivity, specificity and efficiency rates, accordance and concordance. The proposed method does not need any sample pretreatment, requires a small amount of sample (30 µL), is fast and simple, being suitable for the control of raw milk in a dairy industry or for the quality inspection of commercialized milk.

Development and analytical validation of a simple multivariate calibration method using digital scanner images for sunset yellow determination in soft beverages.

This paper proposed a novel methodology for the quantification of an artificial dye, sunset yellow (SY), in soft beverages, using image analysis (RGB histograms) and partial least squares regression. The developed method presented many advantages if compared with alternative methodologies, such as HPLC and UV/VIS spectrophotometry. It was faster, did not require sample pretreatment steps or any kind of solvents and reagents, and used a low cost equipment, a commercial flatbed scanner. This method was able to quantify SY in isotonic drinks and orange sodas, in the range of 7.8-39.7 mg L(-1), with relative prediction errors lower than 10%. A multivariate validation was also performed according to the Brazilian and international guidelines. Linearity, accuracy, sensitivity, bias, prediction uncertainty and a recently proposed tool, the ß-expectation tolerance intervals, were estimated. The application of digital images in food analysis is very promising, opening the possibility for automation.

Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil.

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)-MS] is used to obtain fingerprints of aqueous-methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS-DA) protocol aiming at discriminating the above-mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS-DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1-7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils.