Column: Chemometrics in Spectroscopy

Howard Mark serves on the Editorial Advisory Board of 

 and runs a consulting service, Mark Electronics that provides assistance, training, and consultation in near-IR spectroscopy as well as custom hardware and software design and development.

Articles

A previous analysis of data is compared to the results achieved using classical least squares and principal component analysis. What did we learn?

More About CLS, Part 2: Spectral Results (Not Needing Constituent Values) and CLS

Alignment of the instrument y-axis is a critical step for quantitative and qualitative measurements using spectroscopy. Here, we explain in detail how to use photometric standards for ultraviolet, visible, near infrared, infrared, and Raman spectroscopy.

Using Reference Materials, Part II: Photometric Standards

A newly discovered effect can introduce large errors in many multivariate
spectroscopic calibration results. The CLS algorithm can be used to explain this effect. Having found this new effect that can introduce large errors in calibration results, an investigation of the effects of this phenomenon to calibrations using principal component regression (PCR) and partial least squares (PLS) is examined.

More About CLS, Part 1: Expanding the Concept

The use of reference materials to align or test the wavelength–wavenumber axis for optical spectroscopy is essential for quantitative and qualitative methods. This article provides details for using reference materials with ultraviolet, visible, near-infrared, infrared, and Raman spectroscopy methods.

Using Reference Materials, Part I: Standards for Aligning the X-Axis

What are the steps to take once an outlier is discovered? There are several options.

Outliers, Part III: Dealing with Outliers

Calibration transfer involves several strategies and mathematical techniques for applying a single calibration database consisting of samples, reference data, and calibration equations to two or more instruments. In this installment, we review the chemometric and tactical strategies used for the calibration transfer process.

Calibration Transfer Chemometrics, Part II: A Review of the Subject

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

How can you detect the presence of an outlier when it is mixed with multiple other, similar, samples?

Outliers, Part II: Pitfalls in Detecting Outliers

Calibration transfer involves multiple strategies and mathematical techniques for applying a single calibration database to two or more instruments. Here, we explain the methods to modify the spectra or regression vectors to correct differences between instruments.

Calibration Transfer Chemometrics, Part I: Review of the Subject

Outliers are fundamentally a very fuzzy notion. Here, we try to clear up what outliers are and how they affect your data.

Outliers, Part I: What Are Outliers?

As we have previously discussed, the most time consuming and bothersome issue associated with calibration modeling and the routine use of multivariate models for quantitative analysis in spectroscopy are the constant intercept (bias) or slope adjustments. These adjustments must be routinely performed for every product and each constituent model. For transfer and maintenance of multivariate calibrations this procedure must be continuously implemented to maintain calibration prediction accuracy over time. Sample composition, reference values, within and between instrument drift, and operator differences may be the cause of variation over time. When calibration transfer is attempted using instruments of somewhat different vintage or design type the problem is amplified. In this discussion of the problem we continue to delve into the issues causing prediction error, bias and slope changes for quantitative calibrations using spectroscopy.