This chapter shows the usefulness of n-way techniques when there are legal requirements to meet the performance characteristics of analytical methods for the determination of residues of pharmacologically active substances, pesticides, migrants, or additives in food. Specifically, parallel factor analysis (PARAFAC) and PARAFAC2 n-way techniques are used, both can handle data cubes to extract the information underlying the chemical signals. This methodology allows solving different analytical problems, such as the coelution of interferents that have an absorbance spectrum similar to the target analyte (HPLC-DAD) or interferents that share m/z ions (GC–MS, LC–MS–MS) or solving overlapping spectra in the case of fluorescence spectroscopy. The most important novelty is the use of the “second-order advantage” to unequivocally identify and quantify analytes. In addition, the PARAFAC decomposition of trilinear arrays enables the optimization of several analytical procedures (derivatization reaction, microextraction optimization, etc.) that are previous (and necessary) to the chromatographic determination of complex samples.
Multiway data analysis holds high potential for the evaluation of spectroscopic data, in particular for information-rich mid-infrared spectra. This chapter provides an overview of recent applications of multiway data analysis in (laser-based) infrared spectroscopy of proteins. Correlation-optimized warping and wavelet transform are presented as useful techniques to alleviate instrument imperfections and improve noise levels of experimentally recorded data with optical setups. Multivariate curve resolution-alternating least squares was found to be an excellent tool to predict percentages of individual secondary structures in static protein spectra and to reveal profiles of spectral components and display their progression when monitoring dynamic changes in protein secondary structure. Finally, the outlined examples demonstrate that (laser-based) IR spectroscopy paired with multiway analysis is a powerful combination for monitoring and in-depth interpreting of protein folding.
Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC–MS) is now an integral analytical technique for the characterization of volatile and semivolatile compounds. With the rise of this technique in various application fields, the size and complexity of data sets have also grown, making manual interpretation of GC × GC–MS data sets cumbersome and tedious. However, for these comparative analysis studies, chemometric methods can efficiently discover chemical differences between samples. This chapter discusses the recent developments in chemometric analysis of GC × GC–MS data. As the application of these computational approaches is dependent on the quality of the data, instrumentation and data preprocessing methods are outlined. Key principles of both unsupervised and supervised nontargeted methods and their application in a pixel-based, peak table-based, and tile-based fashion are also reviewed. Finally, this chapter explores the application of targeted methods during a comparative analysis workflow to improve analyte identification and quantification.
This chapter focuses on the recent advances in theoretical and experimental procedures on third-order/four-way and fourth-order/five-way analytical methods. For higher-order data generation, most of the published works report the use of excitation–emission luminescence matrices technique coupled to some experimental variable as the third mode, e.g., kinetics, chromatographic resolution, pH gradient, luminescence lifetime, or other physical–chemical treatment. The success of these protocols has been demonstrated for a wide range of applications in the environmental, bioanalytical, and food fields, and is described here. In addition, the analytical benefits achieved by performing third- and fourth-order data modeling for the quantitative/qualitative/descriptive analysis of complex samples are detailed throughout this chapter.
Process analysis applies analytical science to the monitoring and control of industrial process; in this line, process analytical chemistry (PAC) has been largely used especially for quality assurance in analytical field. Most of the application relies on the use of first-order spectroscopic data, once the instrumentation is simpler to be implemented, on in-line or on-line process, which is interesting due to the possibility of an automatic feedback. However, multiway data analysis evolves in such a way that makes it possible to reduce the time dedicated to sample preparation and instrumental analysis, which enable the use of techniques, generally used with off-line approach, as at-line or even in an on-line process. In addition, it reduces the requirement of a high number of samples to be analyzed due to the amount of information present in the multiway data, which significantly impact even on sampling step or on the model update. This chapter presents the history of PAC and how this approach evolves together with the development of the analytical instrumentation and chemometrics.
