The roasting process is one of the critical points to obtain a product of the highest quality with certain sensorial properties including the aroma of coffee. Samples of coffee beans were roasted at different thermal treatment intensities with the aim of obtaining aromatic compounds detected with an electronic device. Sensory analysis, volatile compound profiling, and electronic nose analysis were carried out. Through principal component analysis (95.8% of the total variance of the data was explained by PC1 and PC2) and partial least squares discriminant analysis (the sum of the diagonal elements gave a hit rate of 94%), it could be demonstrated that the E-nose is able to discriminate roasted coffee beans subjected to different thermal treatments. Aromatic profiling was carried out by a testing panel and volatile compounds (VOCs) for the discrimination of roasted coffee samples. Alcohols, aromatics, esters, ketones and furanone were found in higher proportions in samples at the lowest thermal treatment. The VOCs with positive attributes were 1-(4-nitrophenyl)-3-phenylamino-propenone, carboxylic acids, 2-methoxy-4-vinylphenol, and 2-phenylethyl alcohol, while the compounds with negative ones were 2-methyl-furan, 2,5-dimethyl-pyridine, 2-methyl-butanal, and 2-furfurylthiol. The PLS model allows for the quantification of the positive and negative aromas ($R_{CV}^2$ = 0.92) of roasted coffee by using the E-nose. Therefore, the E-nose, that is, an inexpensive and nondestructive instrument, could be a chemometric tool able to discriminate between different qualities of coffee during processing.

The degradation process of virgin olive oil (VOO) is related to storage time and the type of storage container used. The aim of this work is to explore the evolution of the VOO quality stored in different container types over a defined storage period in order to predict the organoleptic characteristics using a non-destructive technique such as the electronic-nose (E-nose). The “Picual” variety VOO was stored in different containers over a period of 21 months and monitored using sensory analysis, volatile compounds, and an E-nose. The panelists showed that oil stored in dark glass bottles and in green polyethylene bottles began to show defects after 12 and 15 weeks, respectively. However, oil stored in tin containers retained its quality throughout the 21 months studied. A total of 31 volatile compounds were identified, and the evolution of the volatile profile in the different containers during the storage period was studied. The E-nose data were able to classify oil quality by container using principal component analysis (PCA). Furthermore, the E-nose data combined with partial least squares (PLS) regression enabled the building of a predictive model to quantify sensory defect values ($R_{CV}^2$ = 0.92; $R_{CV}^2$ = 0.86), evidencing that this technique would be an appropriate screening tool to support a sensory panel.

The purpose of this study was to investigate the use of an experimental electronic nose (E-nose) as a predictive tool for detecting the formation of chemical process contaminants in roasted almonds. Whole and ground almonds were subjected to different thermal treatments, and the levels of acrylamide, hydroxymethylfurfural (HMF) and furfural were analysed. Subsequently, the aromas were detected by using the electronic device. Roasted almonds were classified as positive or negative sensory attributes by a tasting panel. Positive aromas were related to the intensity of the almond odour and the roasted aroma, whereas negative ones were linked to a burnt smell resulting from high-intensity thermal treatments. The electronic signals obtained by the E-nose were correlated with the content of acrylamide, HMF, and furfural ($R_{CV}^2$ > 0.83; $R_P^2$ > 0.76 in whole roasted almonds; $R_{CV}^2$ > 0.88; $R_{P }^2$ > 0.95 in ground roasted almonds). This suggest that the E-nose can predict the presence of these contaminants in roasted almonds. In conclusion, the E-nose may be a useful device to evaluate the quality of roasted foods based on their sensory characteristics but also their safety in terms of the content of harmful compounds, making it a useful predictive chemometric tool for assessing the formation of contaminants during almond processing.

Californian-style black olives can undergo different chemical changes during the sterilization process that can affect their sensory and phenol characteristics. Thus, these olives were stuffed with flavoured hydrocolloids and submitted to different thermal sterilization treatments to assess sensory categories. The triangular test indicated that the panellists were able to discriminate between samples from different categories according to their aromas with more than 85% success. The results indicated that the negative aroma detected by tasters was related to burn defects. The highest level of defects was found in standard olives, while the lowest was identified in the extra category. Furthermore, olives submitted to the lowest thermal sterilization treatment (extra) presented significantly higher phenol profile content, such as for hydroxytyrosol, tyrosol, oleuropein and procyanidin B1. The electronic nose (E-nose) discriminated between samples from different categories according to the specific aroma (PC1 = 82.1% and PC2 = 15.1%). The PLS-DA classified the samples with 90.9% accuracy. Furthermore, the volatile organic compounds responsible for this discrimination were creosol, copaene, benzaldehyde and diallyl disulphide. Finally, the models established by the PLS analysis indicated that the E-nose could predict olives according to their aroma and total phenol profile $(R_{CV}^2$ values were 0.89 and 0.92, respectively). Thus, this device could be used at the industrial level to discriminate between olives with different sensory aromas to determine those with the highest quality.

Valencian paella is a world-famous dish that is originally from the Valencia Spanish region, in which rice is the basic ingredient along with others such as extra virgin olive oil, vegetables, seafood and/or meat. During the cooking process, the paella rice suffers a loss of moisture and the socarrat is formed, being crunchy and brown in color. The objective of this work was to evaluate the aromas generated during the formation of socarrat in paella rice (P) by an electronic nose (E-nose), discriminating against the aromatic profile of white rice (WR), and validate it with sensory analysis and gas chromatography. The results of the sensory analysis showed a decrease in positive fruity and sweet aromas of some volatile compounds such as hexanal and nonanal, among others, and an increase in roasted aromas due to the appearance of furans and furanones compounds, which is probably associated with socarrat formation. The acrylamide content increased by 33.8–48.3% as the intensity of the thermal treatment rose. The higher value of acrylamide (179.5 ng g⁻¹) was achieved in P. The E-nose was sensitive to changes in the aromatic profile, and the PCA analysis explained 85.7% and 91.6% of the variance for WR and P, respectively. Furthermore, a strong clustering in the thermal treatments was observed, which is related to the composition of volatile compounds.