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Quality Control in the Cannabis Industry: A Depressing Update, by Brian C. Smith, PhD
Calibration Science: The Six Part Series
Check out the complete series from our Cannabis Analysis column.
Calibration Science: The Six Part Series from our Cannabis Analysis column
Started over a year ago and now compiled into one article for easy access, this series from our Cannabis Analysis column covers a wide range of instructional information by Brian C. Smith, PhD, Editorial Board Member of Cannabis Science and Technology, and President of Spectros Associates. As explained by Dr. Smith: “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.”
1. Calibration Science, Part I: Precision, Accuracy, and Random Error
In the first part of this six-part series, Dr. Smith begins with an in-depth discussion of accuracy, precision, random error sources, and how to reduce them. Precision is the spread of values of a measurement made multiple times. Accuracy is how far away we are from the true value, he explains; measured values vary because of random error. He then discusses the many experimental sources of random error and how to correct these problems.
Next, Dr. Smith introduces a second type of error besides random error, called systematic error. He also discusses how accuracy measures both types of error, how to quantitate error by measuring signal-to-noise ratios, and how to improve data quality despite the presence of error by averaging observations together.
3. Calibration Science, Part III: Calibration Lines and Correlation Coefficients
In the third installment of the calibration science series, Dr. Smith discusses how to plot calibration lines and how to calculate calibration line equations. Next, he introduces a measure of calibration line quality, the correlation coefficient. Lastly, he emphasizes the importance of only using a calibration line in the range where calibration data exists.
4. Calibration Science, Part IV: Calibration Metrics
Dr. Smith next discusses how to measure the quality of calibration lines and how variance is measured. Then, using illustrations and equations, he explains how to calculate the standard deviation of a data set, which will give a measure of accuracy; how to calculate the correlation coefficient, which was introduced in the third installment; and the F for Regression, a measure of a thing called the robustness of a calibration.
5. Calibration Science, Part V: Spotting Outliers
Dr. Smith continues the calibration quality control theme in this installment. Sometimes in a data set used to generate a calibration, he explains, one or more data points will stand out as being not like the others and these data points are called outliers. He explains how to spot outliers and potential ways to correct them.
6. Calibration Science, Part VI: Validation
In the final installment in this series, Dr. Smith explains how to validate a calibration. A validation is an independent test of the quality and robustness of a calibration and is the best measure we know of to determine calibration quality, he explains. Additionally, he shows how to perform a validation and how to calculate the Standard Error of Prediction, the best metric of calibration quality.
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