Revolutionizing Tissue Analysis: Femtosecond Double-Pulse LIBS with Machine Learning Breaks New Ground

By Will Wetzel

November 12th 2024

A recent study presents a new technique that combines femtosecond double-pulse laser-induced breakdown spectroscopy (fs-DP-LIBS) with machine learning (ML) algorithms to significantly enhance tissue discrimination and signal quality, paving the way for more precise biomedical diagnostics.

Spreadsheets: A Sound Foundation for a Lack of Data Integrity?

September 1st 2020Spreadsheets are often used to perform GMP-related calculations, but can lead to serious problems and unnecessary risk. We explain why the use of spreadsheets is heavily discouraged in a regulated laboratory environment.

A Survey of Chemometric Methods Used in Spectroscopy

August 1st 2020We provide a scorecard of chemometric techniques used in spectroscopy. The tables and lists of reference sources given here provide an indispensable resource for anyone seeking guidance on understanding chemometric methods or choosing the most suitable approach for a given analysis problem.

Book Review: Chemometrics in Spectroscopy, 2nd Edition, by Howard Mark and Jerry Workman, Jr.

November 1st 2019Chemometrics in Spectroscopy is a collection of column articles that the authors published in Spectroscopy over a period spanning more than two decades. Each article is generally arranged as a chapter in the book, and chapters dealing with the same or similar topics are arranged closely as a section block rather than following the original sequence in the magazine. Although each article or series of articles only discusses one specific topic, collectively, the articles form a comprehensive reference that is a valuable source for readers wanting to learn chemometrics, especially with its applications in spectroscopy.

More About CLS, Part 1: Expanding the Concept

June 1st 2019A 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.

Calibration Transfer Chemometrics, Part I: Review of the Subject

October 1st 2017Calibration 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.

Optimizing the Regression Model: The Challenge of Intercept–Bias and Slope “Correction”

July 1st 2015The archnemesis of calibration modeling and the routine use of multivariate models for quantitative analysis in spectroscopy is the confounded bias or slope adjustments that must be continually implemented to maintain calibration prediction accuracy over time. A perfectly developed calibration model that predicted well on day one suddenly has to be bias adjusted on a regular basis to pass a simple bias test when predicted values are compared to reference values at a later date. Why does this problem continue to plague researchers and users of chemometrics and spectroscopy?

Statistics and Chemometrics for Clinical Data Reporting, Part I

June 1st 2009This article describes the application of chemometric methods and statistics for reporting clinical quantitative measurement methods. The equations and terminology are consistent with the Clinical and Laboratory Standards Institute (CLSI) guidelines. These chemometric and statistical methods describe the accuracy and precision of a test method compared to a reference method for a single analyte determination. Part I will introduce these concepts and Part II will discuss the statistical underpinnings in greater detail.

The Long, Complicated, Tedious, and Difficult Route to Principal Components: Part VI

February 1st 2009This column is a continuation of the set we have been working on to explain and derive the equations behind principal components (1–5). As we usually do, when we continue the discussion of a topic through more than one column, we continue the numbering of equations from where we left off.

Addendum to Chemometrics in Spectroscopy

June 1st 2007This 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.

Fully Integrated Analysis of Metabolites, Impurities, and Degradants Using LC–NMR–MS

May 1st 2006Combining 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.

Linearity in Calibration: Quantifying Nonlinearity, Part II

January 1st 2006At 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.

Chemometrics in Spectroscopy ? Linearity in Calibration: Quantifying Nonlinearity, Part II (PDF)

January 1st 2006At 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.

Chemometrics in Spectroscopy Linearity in Calibration: Quantifying Non-linearity

December 1st 2005This 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.