Simplifying chromatographic analysis of the volatile fraction of foods

The Effect of Co-Culture with Different Pichia kluyveri and Saccharomyces cerevisiae on Volatile Compound and Characteristic Fingerprints of Mulberry Wine

In this study, changes in volatile compounds co-fermented by different Pichia kluyveri with Saccharomyces cerevisiae were analyzed using GC-IMS and compared with S. cerevisiae fermentation, to investigate the production of aroma in mulberry wine during the fermentation process. A total of 61 compounds were accurately identified, including 21 esters, 10 alcohols, 8 aldehydes, 6 ketones, and 19 other volatiles. Compared with the single strain fermentation (S. cerevisiae), the content of 2-methylpropyl acetate, allyl Isothiocyanate, ethyl crotonate, isobutyl propanoate, and butyl 2-methylbutanoate, co-fermentation groups (S. cerevisiae with different P. kluyveri) showed a significant decrease. Alcohols, aldehydes, ketones, and organic acid were lower in both the F(S-P1) and F(S-P2) groups than in the F(S) group throughout fermentation. The 2-methylpentanoic acid only was contained in the F(S) group. The co-fermentation with different P. kluyveri could also be well distinguished. The content of Benzaldehyde and 4-methylphenol in the F(S-P1) group was significantly lower than that in the F(S-P2) group. The PCA results revealed effective differentiation of mulberry wine fermented by different fermentation strains from GC-IMS. The result showed that P. kluyveri could establish a new flavor system for mulberry wine, which plays a crucial role in enhancing the flavor of fruit wine.

Diversity of Volatile Compounds in Raw Milk with Different n-6 to n-3 Fatty Acid Ratio

Simple Summary

In production, milk that is more beneficial to human health is obtained by adjusting the ratio of n-6 and n-3 fatty acids; however, the effect the regulation will have on the volatile substances in milk is unknown. In this study, gas chromatography–ion mobility spectrometry combined with principal component analysis was used to establish the fingerprint of volatile substances in raw milk to identify the types of volatile substances. The results show that a total of 34 target compounds were identified, and there were differences in the types and contents of volatile compounds among different treatment groups. The main reason for these differences is that lipid is degraded and aldehydes and ketones are produced in the adjusted-proportion group.

Abstract

Fatty acid profiles may affect the flavor of milk. The diversity of volatile compounds in raw milk with different ratios of n-6 to n-3 fatty acids (8:1, 4:1, and 3:1) was studied. Gas chromatography–ion mobility spectroscopy (GC–IMS) is a promising technology for the accurate characterization and detection of volatile organic compounds in agricultural products, but its application in milk is rare or even unavailable. In this experiment, GC–IMS fingerprints along with principal component analysis (PCA) were used to study the flavor fingerprints of fresh milk samples with different percentages. Thirty-four typical target compounds were identified in total. A diversity of flavor compounds in raw milk with different n-6/n-3 was observed. After reduction of the proportion, the concentrations of volatile compounds, such as hexanoic acid (dimer and monomer), ethyl acetate, and 2-methylpropanoic acid (dimer and monomer) decreased, while those of 4-methyl-2-pentanone, pentanal, and acetone increased. We carried out PCA according to the signal strength of the identified volatile compounds, and the examination showed that it could precisely make a distinction among the samples in a comparative space. In conclusion, the results show that the volatile compounds are different as the proportion is different. The volatile compounds in raw milk are mainly hexanoic acid, ethyl acetate, and 2-methylpropanoic acid. After adjustment of the ratio, the flavor substances of the medium-ratio (MR) group were mainly ketones, while those of the low-ratio (LR) group were aldehydes. Therefore, in production, reducing the impact on volatile substances while adjusting the proportion of n-6 and n-3 fatty acids to obtain functional dairy products should be taken into consideration.

HS-GC-IMS with PCA to analyze volatile flavor compounds across different production stages of fermented soybean whey tofu

The variations in flavor substances across the different stages of fermented soybean whey tofu (FSWT) production were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with principal component analysis (PCA). The results revealed 24 representative flavor compounds in the samples across all production stages. After heating, the signal intensity of hexanal, 1-octen-3-ol, heptanal, and (E)-2-hexenol, which are unpleasant flavor substances found in raw soymilk, weakened, whereas those of some aroma substances increased. Furthermore, fermented flavor compounds, namely, 2-heptanone, 2-pentylfuran, pentanal, and 2,3-butanedione, were produced after the addition of fermented soybean whey as a coagulant. A PCA based on the signal intensity of the detected volatile compounds revealed effective differentiation of samples from different stages into comparatively independent spaces. These results showed that the flavor fingerprints of the samples from different stages of FSWT production can be successfully built using HS-GC-IMS and PCA based on the detected volatile compounds.

Determination of volatile compounds by GC-IMS to assign the quality of virgin olive oil

The characterisation of different olive oil categories (extra virgin, virgin and lampante) using Ion Mobility Spectrometry (IMS) was improved by replacing the multicapillary column (MCC) with a capillary column (CC). The data obtained with MCC-IMS and CC-IMS were evaluated, studying both the global and the specific information obtained after the analysis of the volatile fraction of olive oils. A better differentiation of the oil categories was obtained employing CC vs MCC, since the classification percentage obtained with the CC-IMS was 92% as opposed to 87% obtained with MCC-IMS; although in productivity analytical terms, MCC offer a faster analysis than GC. The specific information obtained was also used to build a database, with a view to facilitating the characterization of specific attributes of olive oils. A total of 26 volatile metabolites (aldehydes, ketones, alcohols and esters) were identified. Finally, as revealed by an ANOVA test, some volatiles differed markedly in content among the different categories of oil. The data obtained confirms the potential of IMS as a reliable analytical screening technique, which can be used to assign the correct category to an olive oil sample.

Review: highlights in recent applications of electronic tongues in food analysis

This paper examines the main features of modern electronic tongues (e-tongues) and their most important applications in food analysis in this new century. The components of an e-tongue (automatic sampler, array of chemical sensors, and data processing system) are described. Applications commented include process monitoring, freshness evaluation and shelf-life investigation, authenticity assessment, foodstuff recognition, quantitative analysis, and other quality control studies. Finally, some interesting remarks concerning the strengths and weaknesses of e-tongues in food analysis are also mentioned.