Albert Robbat

New England India Pale Ales (NEIPAs) are characterized by their hazy appearance and intense hop-derived aroma. These characteristics are central to their consumer appeal and market identity, yet the chemical drivers of these qualities remain poorly defined. This study aimed to investigate how hop profile influences NEIPA chemistry, emphasizing the role of Citra in defining volatile composition. Four profiles were evaluated: Single Hop, Citra; Multiple Hops, with Citra; Single Hop, Other; and Multiple Hops, without Citra. Volatile compounds were analyzed using a combination of sequential two-dimensional gas chromatography-mass spectrometry (GC–GC/MS) and one-dimensional gas chromatography-mass spectrometry (GC-MS). Multivariate statistical analysis was used to identify which compounds differentiated the four hop profiles. Esters, monoterpenes, and sesquiterpenes all contributed to differences across hop profiles. Inclusion of Citra hops yielded distinct volatile chemistry marked by isoamyl acetate, methyl geranate, ethyl cinnamate, and (E,E)-farnesol. By contrast, blended hop profiles showed greater chemical diversity. These findings demonstrate that hop blending alters beer chemistry beyond the sum of individual hops and identify key compounds that may serve as markers of blended versus single-hop NEIPAs. This work provides new insights into the chemical drivers of hop aroma complexity and establishes a framework for connecting hop usage, beer chemistry, and sensory outcomes.

Gas chromatography/mass spectrometry (GC/MS) is a long-standing technique for the analysis of volatile organic compounds (VOCs). When coupled with the Ion Analytics software, GC/MS provides unmatched selectivity in the analysis of complex mixtures and it reduces the reliance on high-resolution chromatography to obtain clean mass spectra. Here, we present an application of spectral deconvolution, with mass spectral subtraction, to identify a wide array of VOCs in green and roasted coffees. Automated sequential, two-dimensional GC-GC/MS of a roasted coffee sample produced the retention index and spectrum of 750 compounds. These initial analytes served as targets for subsequent coffee analysis by GC/MS. The workflow resulted in the quantitation of 511 compounds detected in two different green and roasted coffees. Of these, over 100 compounds serve as candidate differentiators of coffee quality, AAA vs. AA, as designated by the Coopedota cooperative in Costa Rica. Of these, 72 compounds survive the roasting process and can be used to discriminate green coffee quality after roasting.

This study applied an untargeted–targeted (UT) fingerprinting approach, based on comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF MS), to assess the effects of rainfall and temperature (both seasonal and elevational) on the tea metabolome. By this strategy, the same compound found in multiple samples need only to be identified once, since chromatograms and mass spectral features are aligned in the data analysis process. Primary and specialized metabolites of leaves from two Chinese provinces, Yunnan (pu′erh) and Fujian (oolong), and a farm in South Carolina (USA, black tea) were studied. UT fingerprinting provided insight into plant metabolism activation/inhibition, taste and trigeminal sensations, and antioxidant properties, not easily attained by other analytical approaches. For example, pu′erh and oolong contained higher relative amounts of amino acids, organic acids, and sugars. Conversely, black tea contained less of all targeted compounds except fructose and glucose, which were more similar to oolong tea. Findings revealed compounds statistically different between spring (pre-monsoon) and summer (monsoon) in pu′erh and oolong teas as well as compounds that exhibited the greatest variability due to seasonal and elevational differences. The UT fingerprinting approach offered unique insights into how differences in growing conditions and commercial processing affect the nutritional benefits and sensory characteristics of tea beverages.

Climatic conditions affect the chemical composition of edible crops, which can impact flavor, nutrition and overall consumer preferences. To understand these effects, we sampled tea (Camellia sinensis (L.) Kuntze) grown in different environmental conditions. Using a target/nontarget data analysis approach, we detected 564 metabolites from tea grown at two elevations in spring and summer over 3 years in two major tea-producing areas of China. Principal component analysis and partial least squares-discriminant analysis show seasonal, elevational, and yearly differences in tea from Yunnan and Fujian provinces. Independent of location, higher concentrations of compounds with aromas characteristic of farmers’ perceptions of high-quality tea were found in spring and high elevation teas. Yunnan teas were distinct from Fujian teas, but the effects of elevation and season were different for the two locations. Elevation was the largest source of metabolite variation in Yunnan yet had no effect in Fujian. In contrast seasonal differences were strong in both locations. Importantly, the year-to-year variation in chemistry at both locations emphasizes the importance of doing multi-year studies, and further highlights the challenge farmers face when trying to produce teas with specific flavor/health (metabolite) profiles.

Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena.

Consumers are increasingly concerned about the environmental and social impacts of their purchases. Prior research has assessed willingness to pay (WTP) for environmental and ethical attributes on foods and beverages such as locally grown, fairly traded, and organically produced. However, few studies have examined WTP for agricultural greenhouse gas (GHG) mitigation, especially in the U.S. and to date, no prior study has examined how knowledge or concerns about climate change motivate WTP for climate-friendly products. The objective of this study was to estimate WTP for agricultural GHG mitigation and examine variability in WTP across consumer characteristics, climate change knowledge and risk perception. A sensory-grounded choice experiment and survey assessing climate change knowledge and risk perception was administrated to specialty food and beverage shoppers in the Midwest and Northeastern U.S. Male and lower-income participants, as well as those at the Midwestern study site were willing to pay a higher premium for agricultural GHG mitigation, relative to females, higher income participants, and those in the Northeastern U.S. Knowledge of climate change and level of concerns for the risks it poses were not significantly associated with increased WTP for agricultural GHG mitigation. This suggests that if consumer demand is going to play a role in driving agricultural GHG mitigation, motivations for such purchasing behavior must be more fully understood.