Evaluation of the Olfactory Quality of Roasted Coffee Beans Using a Digital Nose

Comparison of Patient Characteristics and Olfactory Sensitivity for Trigger Odorants in Parosmia and Phantosmia

OBJECTIVES

This study aimed to determine the characteristics of patients with qualitative olfactory dysfunction (qualOD) and whether individuals with parosmia exhibit increased olfactory sensitivity to previously reported odorous triggers of parosmia.

METHODS

This study included individuals aged ≥18 years, divided into quantitative OD only, parosmia, and phantosmia groups. Data collected included: clinical-demographic data, "Sniffin' Sticks" scores, questionnaires (depression scale, importance of olfaction), and information about parosmia and phantosmia. A proportion of patients underwent trigger odor threshold testing for 2-Furfurylthiol [FFT] found in coffee and 2,6-nonadienal [Nonadienal] found in cucumber.

RESULTS

Those with parosmia were typically younger women, with shorter OD duration due to post-viral OD (PVOD), hyposmic/normosmic, and experienced parosmia more severely. Parosmia was 3.5 times more likely in PVOD. Those with phantosmia were older, with longer OD duration due to idiopathic OD, hyposmic/anosmic, and experienced phantosmia less severely. There were no significant differences between FFT and Nonadienal threshold scores in patients with parosmia, phantosmia, or only quantitative OD, but all groups had significantly increased olfactory sensitivity for trigger odors compared to phenyl ethyl alcohol (PEA).

CONCLUSION

Parosmia and phantosmia patients have distinct characteristics. This may provide clinicians with a better understanding of possible olfactory outcomes in these patients. The higher olfactory sensitivity of all groups to trigger odors compared to PEA raises interesting points about parosmia triggers and odors in the context of warning for danger, in relation to the pathophysiology of parosmia that may be worth exploring in future studies.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3277-3285, 2024.

Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization

This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin-Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation.

Electronic Prediction of Chemical Contaminants in Aroma of Brewed Roasted Coffee and Quantification of Acrylamide Levels

The aim of this research was to apply an electronic device as indirect predictive technology to evaluate toxic chemical compounds in roasted espresso coffee. Fresh coffee beans were subjected to different thermal treatments and analyzed to determine volatile organic compounds, content of acrylamide and 5-hydroxymethylfurfural, sensory characteristics and electronic nose data. In total, 70 different volatile compounds were detected and grouped into 15 chemical families. The greatest percentage of these compounds were furans, pyrazines, pyridines and aldehydes. The positive aroma detected had the intensity of coffee odor and a roasted aroma, whereas the negative aroma was related to a burnt smell. A linear relationship between the toxic substances and the sensory defect was established. A high sensory defect implied a lower content of acrylamide and a higher content of 5-hydroxymethylfurfural. Finally, electronic signals were also correlated with the sensory defect. This relationship allowed us to predict the presence of these contaminants in the roasted coffee beverage with an indirect method by using this electronic device. Thus, this device may be useful to indirectly evaluate the chemical contaminants in coffee beverages according to their sensory characteristics.

Inhibition of Botrytis cinerea in tomatoes by allyl-isothiocyanate release from black mustard (Brassica nigra) seeds and detection by E-nose

The aim of this work is to delayed the postharvest development of Botrytis cinerea in tomatoes by releasing allyl-isothiocyanate (AITC) from mustard seeds at room temperature, and to discriminate the aromatic profile by using an electronic device (E-nose). Olfactory sensory analysis showed that tomatoes inoculated in the presence of AITC did not develop the microorganisms until the eighth day of storage. The highest inhibitory concentration of AITC was found in Day 3 (175.18 ppb). However, tomatoes inoculated without the presence of AITC developed a moldy aroma on the third day of storage. The most prominent chemical groups were esters, ketones and alcohols. The compounds associated with a positive aroma were trimethyl orthoacetate, styrene, tridecan-7-amine and acetaldehyde, while compounds related to B. cinerea were hepten-2-one and butanoic acid. The E-nose was able to discriminate tomatoes based on their aromatic characteristics during the storage period. Moreover, it successfully quantified the mold defect aroma with the established Partial Least Squares (PLS) model.

Electronic Nose and Gas Chromatograph Devices for the Evaluation of the Sensory Quality of Green Coffee Beans

The aim of this work is to discriminate between the volatile org9anic compound (VOC) characteristics of different qualities of green coffee beans (Coffea arabica) using two analysis approaches to classify the fresh product. High-quality coffee presented the highest values for positive attributes, the highest of which being fruity, herbal, and sweet. Low-quality samples showed negative attributes related to roasted, smoky, and abnormal fermentation. Alcohols and aromatic compounds were most abundant in the high-quality samples, while carboxylic acids, pyrazines, and pyridines were most abundant in the samples of low quality. The VOCs with positive attributes were phenylethyl alcohol, nonanal and 2-methyl-propanoic acid, and octyl ester, while those with negative attributes were pyridine, octanoic acid, and dimethyl sulfide. The aroma quality of fresh coffee beans was also discriminated using E-nose instruments. The PLS-DA model obtained from the E-nose data was able to classify the different qualities of green coffee beans and explained 96.9% of the total variance. A PLS chemometric approach was evaluated for quantifying the fruity aroma of the green coffee beans, obtaining an RP2 of 0.88. Thus, it can be concluded that the E-nose represents an accurate, inexpensive, and non-destructive device for discriminating between different coffee qualities during processing.