Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

This column is the continuation of a series (1-5) dealing with the rigorous derivation of the expressions relating the effect of instrument (and other) noise to its effects on the spectra we observe. Our first column in this series was an overview. While subsequent columns dealt with other types of noise sources, the ones listed analyzed the effect of noise on spectra when the noise is constant detector noise (that is, noise that is independent of the strength of the optical signal). Inasmuch as we are dealing with a continuous series of columns, on this branch in the thread of the discussion, we again continue the equation numbering and use of symbols as though there were no break. The immediately previous column (5) was the first part of this set of updates of the original columns.

In the second part of this series, columnists Jerome Workman, Jr. and Howard Mark continue their discussion of the limitations of analytical accuracy and uncertainty.

This tutorial reviews the mathematical models for dealing with interelement effects in optical emission and X-ray fluorescence spectrochemical analysis. Line overlaps and matrix effect corrections are examined.

In this month's installment of "Chemometrics in Spectroscopy," the authors again explore that vital link between statistics and chemometrics, this time with an emphasis on the statistics side.

Combining the three techniques of LC, MS, and NMR into one integrated system provides optimal use of NMR intrument time by using information-rich MS data to automatically guide the NMR operation. Here, the authors explore just this type of system.

In this month's installment of "Chemometrics in Spectroscopy," the authors explore that vital link between statistics and chemometrics, with an emphasis on the chemometrics side.

At this point in our series dealing with linearity, we have determined that the data under investigation do indeed show a statistically significant amount of nonlinearity, and we have developed a way of characterizing that nonlinearity. Our task now is to come up with a way to quantify the amount of nonlinearity, independent of the scale of either variable, and even independent of the data itself.

At this point in our series dealing with linearity, we have determined that the data under investigation do indeed show a statistically significant amount of nonlinearity, and we have developed a way of characterizing that nonlinearity. Our task now is to come up with a way to quantify the amount of nonlinearity, independent of the scale of either variable, and even independent of the data itself.

This column presents results from some computer experiments designed to assess a method of quantifying the amount of non-linearity present in a dataset, assuming that the test for the presence of non-linearity already has been applied and found that a measurable, statistically significant degree of non-linearity exists.

This third part in a series on non-linearity looks at other tests and how they can be applied in laboratories that must meet FDA regulations.

The authors begin a discussion of the statistical tools available to compare and correlate two or more data sets.

This book makes serious efforts to include examples of the various ways in which information about the underlying behavior of a system can be extracted from data obtained from the system.