Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum
Abstract
:1. Introduction
2. Materials and Methods
2.1. Coffee Samples
2.2. Pre-Concentration Methods
2.2.1. Activated Charcoal Strip Analysis + Headspace (ACS + HS)
2.2.2. Direct Headspace (HS)
2.3. GC-IMS Analysis
2.4. Data Analysis
- Area. The total area calculated as the sum of the VOCs areas obtained by the two pre-concentration methods—ACS and non-preconcentrated samples. For that purpose, the area of each compound was selected and calculated using LAV software (Figure 2A). Then, the sum of all the areas was used to determine the optimal conditions to obtain the maximum signal (area) corresponding to the total VOC content and to evaluate the headspace differences between the pre-concentrated samples and non-pretreated samples.
- Ion mobility sum spectrum (IMSS). Once the optimal sample preparation method had been established, a total of 30 samples were analyzed under those conditions. IMSS was used to evaluate the capacity of the HS-GC-IMS to discriminate between the Arabica, Robusta, and mixture samples. IMSS is defined as the sum of intensities across the chromatographic profile, this results in a spectrum in which each drift time acts as a “sensor,” and the total volatile compounds intensity collected at each drift time is equivalent to a multiple sensor signal. It supposes that no chromatographic information has been used (Figure 2B). i.e., data on the total intensities at 4500 drift times, from 0.000 ms to 4.500 ms (relatives to RIP). The reaction ion peak (RIP) represents the total available ions generated by the ion source and this signal is used as the reference to determine each compound drift time. A specific zone was selected, as it was the range where volatiles compounds were detected, resulting in a spectrum with a total of 599 drift times from 1.187 ms to 1.786 ms. Each spectrum was normalized by assigning one unit value to its maximum intensity. The data obtained from the IMSS was arranged in matrixes named Dm×n where m is the number of samples and n is the number of drift times.
2.5. Standardization Procedure
3. Results
3.1. Comparison between the Two Pre-Concentration Methods
3.1.1. Activated Charcoal Strip Analysis + Headspace (ACS + HS)
3.1.2. Headspace (HS)
3.2. Discrimination of Arabica, Robusta, and Mixed Coffee Samples by HS-GCIMS
3.3. Greenness Assessment of the Developed Analytical Procedures
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Time (min) | Average Sum Area ± Standard Deviation |
---|---|
5 a | 1839 ± 24 |
15 b | 2309 ± 42 |
30 b | 2431 ± 12 |
Sample Weight (g) | Average Sum Area ± Standard Deviation |
---|---|
0.5 a | 9209 ± 2 |
0.4 b | 9141 ± 23 |
0.3 c | 9062 ± 78 |
0.2 d | 8920 ± 62 |
0.1 d | 8412 ± 11 |
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Piotr Konieczka, P.; Aliaño-González, M.J.; Ferreiro-González, M.; Barbero, G.F.; Palma, M. Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum. Sensors 2020, 20, 3123. https://doi.org/10.3390/s20113123
Piotr Konieczka P, Aliaño-González MJ, Ferreiro-González M, Barbero GF, Palma M. Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum. Sensors. 2020; 20(11):3123. https://doi.org/10.3390/s20113123
Chicago/Turabian StylePiotr Konieczka, Paweł, María José Aliaño-González, Marta Ferreiro-González, Gerardo F. Barbero, and Miguel Palma. 2020. "Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum" Sensors 20, no. 11: 3123. https://doi.org/10.3390/s20113123
APA StylePiotr Konieczka, P., Aliaño-González, M. J., Ferreiro-González, M., Barbero, G. F., & Palma, M. (2020). Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum. Sensors, 20(11), 3123. https://doi.org/10.3390/s20113123