Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis.

The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.

Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: a review.

This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) and coupled to mass spectrometry (GC×GC-MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.