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Several analytical methods are available to evaluate virgin olive oil (VOO) minor compounds; however, multi-class methodologies are yet rarely studied. Herewith, LC-MS and GC–MS platforms were used to develop two methods capable of simultaneously determine more than 40 compounds belonging to different VOO minor chemical classes within a single run. A non-selective and highly efficient liquid-liquid extraction protocol was optimized for VOO minor components isolation. The separation and detection conditions were adjusted for determining phenolic and triterpenic compounds, free fatty acids and tocopherols by LC-MS, plus sterols and hydrocarbons by GC–MS. Chromatographic analysis times were 31 and 50 min, respectively. A comparative assessment of both methods in terms of analytical performance, easiness, cost and adequacy to the analysis of each class was carried out. The emergence of this kind of multi-class analytical methodologies greatly increases throughput and reduces cost, while avoiding the complexity and redundancy of single-chemical class determinations.

This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.

Crushing is a key step during olive oil extraction. Among commercial crushers, the hammer mill is the most widely used due to its robustness and high throughput. In the present work, the impact of hammer mill rotor speed on extraction yield and overall quality of super-high-density Arbosana olive oils were assessed in an industrial facility. Our results show that increasing the rotor speed from 2400rpm to 3600rpm led to a rise in oil yield of 1.2%, while conserving quality parameters. Sensory analysis showed more pungency with increased rotation speed, while others attributes were unaffected. Volatile compounds showed little variation with the differences in crusher speed; however, total phenols content, two relevant secoiridoids, and triterpenoids levels increased with rotor speed. Hammer mill rotor speed is a processing variable that can be tuned to increase the extraction efficiency and modulate the chemical composition of extra virgin olive oil.

UV–Vis spectrometry and chemometric techniques were used to classify aqueous extracts of Brazilian ground roast coffee with respect to type (caffeinated/decaffeinated) and conservation state (expired and non-expired shelf-life). Two classification methods were compared: soft independent modelling of class analogy (SIMCA) and linear discriminant analysis (LDA) with wavelength selection by the successive projections algorithm (SPA). The best results were obtained by SPA–LDA, which correctly classified all test samples. The classification accuracy of this model remained high (96%) even after the introduction of artificial spectral noise. These results suggest that UV–Vis spectrometry and SPA–LDA modelling provide a promising alternative for assessment of conservation state and decaffeination condition of coffee samples.