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Challenges and Trends in GC Headspace Analysis

Gary A. Reineccius, PhD, and Vadoud Niri, PhD, discuss current trends and challenges in GC headspace analysis

Rachel Muenz

Gary A. Reineccius, PhD, is a professor in the Department of Food Science and Nutrition at the University of Minnesota. His research has tended to follow the needs of the industry, which have evolved over time. Dr. Reineccius’s greatest interest currently lies in the encapsulation of food ingredients, emulsions, and flavoromics.

Q: What does your lab do?

A: We look into how volatile compounds contribute to the aroma of food in one way or another. They can be what make a food taste good, or what creates an objectionable flavor in the food. We need to look at both.

Q: What are some of the main changes or trends you’ve seen in GC headspace analysis?

A: Certainly we have seen automation, and that’s been helpful. It really expands your capabilities to collect data. Also, over time, we have seen the ability to concentrate the headspace so we can actually see more compounds. [Still], we don’t see much by headspace; for example, looking at a freshly brewed cup of coffee, if we look at the coffee itself and the aroma compounds from the coffee, there have been over 900 volatile compounds found in coffee. If we take the headspace and simply check it on a gas chromatograph, we might see 30 of 900 compounds, and that’s why we need to do some kind of concentration so we can see more compounds and get better representation. As I see it, one of the major advances has been in how we concentrate headspace to make it more broadly applicable to trace components.

Q: What are some of the major techniques used to concentrate the headspace?

A: The first one was what we call a purge and trap method, [where] we put the sample into a container of some kind and then we purge the sample with an inert gas. Then that purged gas picks up volatile compounds and we run that gas through a trap, [which is a] porous polymer; Tenax is the trapping material you’ll commonly find sold. It’s a small particulate material, and you can run aroma compounds and purged gas through it. They get absorbed onto the particles. You take that trapping material and heat it to about 250 degrees Centigrade, and the trapped volatiles elute into your gas chromatograph. That’s been our first method, and it’s interesting to me that it’s still one of our best methods.

There is a method that sometimes replaces that Tenax trap called SPME—solid-phase microextraction. [SPME] is the most commonly used research method for aroma isolation from foods and, unfortunately, it’s one of our worst methods in terms of sensitivity, reproducibility, and so on. But it is so convenient that people use it and they do not think of the drawbacks. It concentrates some things, but you still miss more than you can imagine. I would guess probably 80 percent of the work that is published today on flavors uses that method. That is one of my biggest frustrations—that people really do not think about the advantages and disadvantages of the method they are choosing. [People assume] that because so many researchers publish with it, it must be great. It does have its place, but maybe not quite so many places. There’s also a stir-bar [concentration method], which involves a glass magnetic stir bar that has an absorbent on it. That is a much better methodology than SPME for most applications. We have seen some other innovations in the concentration of volatiles, but they have not been profound. There is still a need for improvement in this area of analysis.

Q: What are some of the major challenges you run into with GC headspace analysis?

A: Sensitivity is our biggest issue. We also have a problem with how we get quantitative data. We measure flavor compounds in a food, but it’s not in the food, it’s above the food in the headspace, and how you relate something in the headspace to the food is extremely complicated. Whenever we ask “We measured this, but how much is really there?”, it’s hard to answer that question. The best way is to do stable isotopes. If you want to monitor cherry flavor (measure benzaldehyde, for example), ideally, you would want to have a carbon 13 labeled benzaldehyde standard, but these standards may cost $10,000 or more, so quantification is extremely complicated as well.

Q: What advice do you have for researchers who are new to the technique of GC headspace analysis?

A: They need to know the limitations of their methodology, and that means they have got to read both the literature and critical reviews of that literature. You do not look at what somebody else has done, because that could be wrong. You need to read good critical reviews before you start; otherwise, you waste your time and you absolutely waste your money. So go to the library, go online, find the critical reviews of the techniques, and do not read just one. Pick up three or four, and then you’ll get a much better view of “Is this good for me? Does this work for me? What are the challenges?”

Q: When it comes to GC headspace analysis, what plans do you have for the future?

A: Right now, we are interested primarily in proteins and flavor reactions with proteins. [Proteins] are problematic to flavor. You may be able to flavor them initially, but then the flavors chemically react with the protein and they start tasting not so good after a while. If you look at high-protein bars, they can be really good when they are made and really bad by the time they get to market. So we want to determine the source of the problem and how it happens. We want to know [whether] one protein is more likely to react with flavors. Or does it react less, and why? We also do a little bit of encapsulation work. For that, we end up using flavor analysis to determine “Did we encapsulate? Was it a good encapsulation?” So [we’re basically looking at the] performance of an encapsulated material.

Vadoud Niri, assistant professor and director of the chemistry graduate program at State University of New York–Oswego, holds a PhD in analytical chemistry. Besides teaching analytical chemistry courses at both undergraduate and graduate levels, his research group in the Analytical Chemistry Research Lab (ACRL) focuses on developing new analytical methods for analyzing different types of compounds in a variety of environmental, biological, and food samples.

Q: What does your lab do?

A: The focus of our research group in the ACRL is to develop analytical methods for monitoring chemical pollutants that negatively affect public health and the environment (air, water, soil, and sediment media) and investigating the efficiency of possible removal/remediation techniques for these compounds. We also focus on analyzing flavors/off-flavors and toxic compounds (e.g., pesticides and preservatives) in food samples, measuring/monitoring drugs in pharmaceutical products and biological media, analyzing organic compounds such as fragrances emitted from living flowers and plants, and chemical analysis of electronic cigarettes.

Q: What are some of the key applications you’re using GC headspace analysis for at the moment?

A: The current projects where we use headspace analysis are as follows: Analyzing high molecular weight alcohols and flavors from Scotch samples from different regions of Scotland, analyzing microbial volatile organic compounds (MVOCs) from mold samples to develop a method for early detection of mold growth, and analyzing fragrances from plants used for extracting essential oils.

Q: What are some of the main changes or trends you’ve seen in GC headspace analysis since you first started using this technique?

A: One of the important aspects of headspace analysis is reducing sample preparation steps, especially for samples with complicated matrices. Since volatile analytes are collected from the gaseous space above the sample, the matrix won’t affect the analysis directly. So using the headspace method, in most cases, saves us sample preparation time and provides cleaner results.

Q: What are some of the major challenges you run into with GC headspace analysis?

A: Headspace analysis works effectively for analyzing volatile compounds; however, it can be challenging for semi-volatile or non-volatile compounds.

Q: How do you handle those challenges?

A: In the case of semi-volatile compounds, increasing the temperature is the easiest way to solve this problem. This approach has its own limitations based on the sampling method used. For example, if you are using solid-phase microextraction for extraction from a headspace, increasing the temperature will be useful until some point; after that, the extraction efficiency will go down because of increasing desorption from the fiber at the higher temperature. In this case, we usually find an optimal temperature by plotting the temperature profile. For non-volatile compounds, we usually don’t use headspace methods or even GC and prefer using HPLC instead.

Q: What resources have you found to be most useful when it comes to GC headspace analysis?

A: Papers by other researchers in this area, both published as articles or presented at a conference, are great sources to find new improvements in this area. Also, you won’t learn or improve a technique unless you do it yourself. The experience that we get in the lab over time teaches us a great deal.

Q: When it comes to GC headspace analysis, what plans do you have for the future?

A: Time is always money! Shortening analytical methods can save huge [amounts of] time and expenses, especially when we are dealing with a large number of samples. We are also looking to lower the detection limits of analytical methods [in order] to be able to analyze lower concentrations.