Man vs. Machine: A Junior-Level Laboratory Exercise Comparing Human and Instrumental Detection Limits

Ryan J. Elias, Helene Hopfer, Amanda N. Hofstaedter, John E. Hayes

Research output: Contribution to journalArticlepeer-review

Abstract

The human nose is a very sensitive detector and is able to detect potent aroma compounds down to low ng/L levels. These levels are often below detection limits of analytical instrumentation. The following laboratory exercise is designed to compare instrumental and human methods for the detection of volatile odor active compounds. Reference standards of 3-mercapto-1-hexanol (3MH), a secondary thiol that is important to food quality, are analyzed by gas chromatography with flame ionization detection (GC-FID), and these raw data are provided to students. Students also perform a series of 3-alternative forced choice (3-AFC) sensory tests to determine the human detection limits in a series of samples. For both data sets, 2 methods of data analysis (standard deviation of the response and the slope and signal-to-noise ratio for GC-FID data; forced-choice ascending concentration series method of limits and linear regression for 3-AFC data) will be used to estimate instrumental detection limits and human thresholds. GC-FID and 3-AFC results are then compared by the students to demonstrate the importance of instrumental and human methods for food analysis, and to provide an experiential learning opportunity to critically think through multiple methods of analysis and compare the outcomes of those methods. In completing the laboratory exercise and discussion questions, students will gain an understanding of the advantages and disadvantages of human and instrumental measurements in food analysis, and compare the outcome of common data analysis methods for instrumental and sensory data.

Original languageEnglish (US)
Pages (from-to)72-76
Number of pages5
JournalJournal of Food Science Education
Volume16
Issue number3
DOIs
StatePublished - Jul 2017

All Science Journal Classification (ASJC) codes

  • Food Science
  • Education

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