Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors.

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 (RCV2 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.

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

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 (RCV2 = 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.