“The application of direct mass spectrometry in the form of SIFT-MS has greatly simplified the identification of odor sources”

William Kerr Application Scientist

Dr. Will Kerr

BSc(Hons), PhD

Environmental Technical Specialist

Public emissions of unpleasant odors are as embarrassing for industrial facilities as they are for individual people. Analyzing industrial odor presents a challenge to some laboratory techniques due to the chemical diversity of odor compounds, low human odor thresholds and the dynamic nature of the odors themselves. Fortunately for the operators at Gelita, a gelatin manufacturing plant in Christchurch New Zealand, selected ion flow tube mass spectrometry (SIFT-MS) has been successfully deployed to meet these challenges.

Gelatin production uses various chemical processes to extract collagen from animal hides, producing pungent odors that can be released into the surrounding property, prompting complaints from neighbors. To mitigate this, Gelita evaluated the odor-eliminating photoionization technology Neutralox®. By using SIFT-MS, key malodor VOCs (ammonia, trimethylamine, methyl mercaptan, ethyl mercaptan, carbon disulfide, ethanol, formaldehyde and acetic acid) could be quantified at the source (inlet air), and after passing through the Neutralox® air deodorizer (outlet air). The figure below shows the reported concentrations recorded for trimethylamine, methyl mercaptan and formaldehyde in the hide shed of the Gelita factory during different processes (salting the hide, moving the salted hide and cutting the hide). The photoionization device was shown to reduce the concentrations of odorants to below the odor threshold – reducing the risk unfavorable emissions in future.



The ability of SIFT-MS to directly quantify these chemically diverse odorants within a single scan, and at a sensitivity below the human odor threshold, made this evaluation possible. Not only that, the Voice200ultra SIFT-MS unit did not object to sampling these objectionable odors.

Reference: “Comprehensive Instrumental Odor Analysis Using SIFT-MS: A Case Study”

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