The Need for Chemometrics

Until fairly recently, IR spectroscopy was scarcely used in quantitative analysis owing to its many inherent shortcomings (e.g. extensive band overlap, failure to fulfil Beer s law over wide enough concentration ranges, irreproducible baselines, elevated instrumental noise, low sensitivity). The advent of FTIR spectroscopy, which overcomes some of these drawbacks, in addition to the development of powerful chemometric techniques for data processing, provides an effective means for tackling the analysis of complex mixtures without the need for any prior separation of their components. 

Thus, my interest in 2 or more factor chemometric models of your simulation is in line with this view of chemometrics. I agree with the need for better physical understanding of instrument responses as well as of the spectra themselves. I would not choose PCR/PLS or MLR to construct such physical models, however. (Fred Cahn)... 

A third and often neglected reason for the need for care fill application of chemometric methods is the problem of the type of distribution of environmental data. Most basic and advanced statistical methods are based on the assumption of normally distributed data. But in the case of environmental data, this assumption is often not valid. Figs. 1-7 and 1-8 demonstrate two different types of experimentally found empirical data distribution. Particularly for trace amounts in the environment, a log-normal distribution, as demonstrated for the frequency distribution of N02 in ambient air (Fig. 1-7), is typical. 

Advances in herbal medicines have hastened the need for high-throughput CE methods that can effectively screen and resolve numerous compounds in a short period of time. Chemometric experimental design and optimization techniques will continue to increase as new developments in sample preparation, method optimization, and data processing in (3E analysis of herbal medicines occur. 

Other examples presented in the literature are those based on the use of copolymeric disk-based sorbent materials for the isolation and preconcentration of nitro-substituted phenol isomers and the concurrent removal of potentially interfering matrix components followed by onhne simultaneous determination of individual species by diode array spectrophotometry, via chemometric deconvolution of the overlapped spectra, without the need for chromatographic separation. In contrast to bead-extraction, no flow impedance is observed when using extraction disks while better enrichment factors are obtained because of the improved specific surface area. Compared with earlier methods for isolation/preconcentration of nitrosubstituted phenols based on liquid-liquid extraction, these systems should be regarded as environmentally friendly approaches because the use of harmful organic solvents is circumvented. 

Despite the fact that direct analysis methods exclude a cost-intensive separation step overall analysis cost may still be high, namely by the need for more sophisticated instrumentation (allowing for a physical rather than chemical separation of components) or extensive application of chemometric techniques. The wide variety of additives that are commercially available and employed complicate spectroscopic data analysis. For multicomponent analysis some kind of physical separation of additive signals is often quite helpful, e.g. based on mobility (as in LR-NMR or NMRI), diffusion coefficient (as in DOSY NMR), thermal behaviour (as in a thermal analysis and pyrolysis techniques) or mass (as in tandem mass spectrometry). The power of signal processing techniques (such as multi-wavelength techniques, derivative spectrophotometry) is also used to the fullest extent. 

The above questions are difficult to answer without comprehensive and relevant information. Such information will almost invariably be multivariate in nature in order to comprehensively describe the complex underlying problems. Therefore, the need for advanced experimental planning and subsequent advanced data analysis is obvious. Chemometrics provides the necessary tools for digging into food-related problems. This book is a highly needed and relevant contribution to the food research area in this respect. The book provides an impressive, very detailed and illustrative tour deforce through the chemometric landscape. 

A final means to circumvent the need for perfect chemical selectivity is to utilize the response from several sensors, applying the techniques of chemometrics or pattern recognition to identify and quantify the analyte(s). In this Chapter and within the context of SAW device-based sensor systems, we examine two variations on this theme. The first may be considered a form of spectroscopy the utilization of a multifrequency SAW device to probe the interactions between various analytes and... 

Procedures used vary from trial-and-error methods to more sophisticated approaches including the window diagram, the simplex method, the PRISMA method, chemometric method, or computer-assisted methods. Many of these procedures were originally developed for HPLC and were apphed to TLC with appropriate changes in methodology. In the majority of the procedures, a set of solvents is selected as components of the mobile phase and one of the mentioned procedures is then used to optimize their relative proportions. Chemometric methods make possible to choose the minimum number of chromatographic systems needed to perform the best separation. 

It is therefore not surprising that the interest in PyMS as a typing tool diminished at the turn of the twenty-first century and hence why taxonomists have turned to MS-based methods that use soft ionization methods such as electrospray ionization (ESI-MS) and matrix-assisted laser desorption ionization (MALDI MS). These methods generate information-rich spectra of metabolites and proteins, and because the molecular ion is seen, the potential for biomarker discovery is being realized. The analyses of ESI-MS and MALDI-MS data will still need chemometric methods, and it is hoped that researchers in these areas can look back and learn from the many PyMS studies where machine learning was absolutely necessary to turn the complex pyrolysis MS data into knowledge of bacterial identities. 

However, society likes to have decisions made in a black and white manner and to know whether something is there or not. This situation suggests that the analytical error should drop to zero. While this result is the goal of all analytical work, it is simply not realistic. Our basic need, then, is to simplify error determinations and explanations and to educate the public both for the reasons and for the interpretations of error. The goal of this volume is to further the use of mathematical and statistical tools—the field of chemometrics—for chemical and, specifically, trace chemical analyses of pesticides and environmental contaminants. 

From the outset acoustic chemometrics is fully dependent upon the powerful ability of chemometric full spectrum data analysis to elucidate exactly where in the spectral range (which frequencies) the most influential information is found. The complete suite of chemometric approaches, for example PCA, PLS regression, SIMCA (classification/discrimination) are at the disposition of the acoustic spectral data analyst there is no need here to delve further into this extremely well documented field. (See Chapter 12 for more detail.)... 

The equipment needed for acoustic chemometric monitoring can be divided into three categories ... 

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