Brian C. Smith

Here, we discuss how to perform a validation, how to calculate the Standard Error of Prediction, and the best metric of calibration quality.

Here, we discuss how to spot outliers and potential ways to correct them in data sets.

Here, we discuss the importance of calibration metrics and how to measure calibration quality.

A practical discussion of how to plot calibration lines and how to calculate calibration line equations.

Here, we will introduce systematic error, discuss how to measure signal-to-noise ratios, and more.

Here, we begin a new column series on calibration science. The goal of these columns will be to teach you the foundational theory behind how to calibrate analytical instruments that are used for quantitative analysis in the cannabis analysis industry, such as potency and pesticide analyzers.

It is impossible to obtain potency accuracy in cannabis analysis. Here, we suggest some solutions to the problem.

Here, we explore how the different parameters in the Master Resolution Equation relate to Fs, and what that means for chromatographic experiments.

We finally present the Master Resolution Equation for chromatography, which combines all three measures of separation quality, and an understanding of these parameters will go a long way towards allowing you to rationally develop chromatographic methods.

Here we discuss diffusion and see how a quantity called plate number is used to measure the amount of band spreading.

A review of the concept of separation factors and a discussion on developing a method for measuring peak width including triangulation followed by the introduction of the concept of chromatographic resolution for a pair of peaks.

Part IV of this series discusses the capacity factor of chromatographic peaks to calculate separation factors, whose value tells us how well two peaks are or aren’t separated. We then introduce the reality of peak widths and how they impact separation quality.

Part III of this series will delve into understanding how to optimize methods for chromatographic separations, Smith will also introduce the concepts of capacity factor and selectivity.

Part II of this series looks at the mechanism via which molecules move through HPLC columns and leads us to a discussion on the equations used to characterize separations.

In part I in a new series of columns, we begin a discussion of the theory of chromatographic separations. Our goal is to derive equations that will help you better perform experiments to optimize your separations.

The final part in this series of pitfalls to avoid and practicalities covers the importance of cleanliness, how many standards to run, how to spot outliers, and important experimental variables to control.

The third part in this series of pitfalls to avoid and practicalities to know about discusses the importance of validating and checking your calibration to ensure quality.

Part II of this series discusses how to measure absorbances properly, and compares the use of peak heights and peak areas in creating calibrations.

Here we discuss the importance of using standards that bracket the expected concentration range of standards in the unknowns, using the same chemical components in standards and unknowns, and making sure to minimize the error in concentration measurements of standards.

Part III discusses more on Beer’s Law and why absorptivity is important along with some of its advantages and limitations.

In part II of this series, we establish the physical basis of Beer’s Law and derive it from first principles then wrap up with a discussion of how the assumptions made in the derivation affect Beer’s Law experiments.

In this introduction to Beer’s Law, we review its use, history, and begin to cover the theory behind the equation.

A discussion on the selection of a “golden” potency method including what this method should look like, how we should go about choosing it, and more.

How does the pharmaceutical industry test better to avoid mislabeling problems?

In part I of this series on statistical concepts related to cannabis analysis, we introduce the normal distribution, standard deviation, and how to calculate precision and accuracy.

The inter-laboratory variation problem still exists, which means laboratory certification is necessary, but not sufficient to guarantee your samples are analyzed properly. Here, Smith offers suggestions to help improve the situation.

How can cannabis businesses start formulating like the pharmaceutical industry and why it’s important to do so.

A number of misconceptions people have about how to test, what to test, and when to test in the cannabis industry are dispelled.

Why the USDA’s requirements on what part of the plant to sample and how to obtain samples from the field are rife with problems and how to solve them.

Mislabeled cannabis medicines are an on-going problem; here we discuss appropriate technologies for cannabis dispensaries to do their own testing.