An electronic nose based on ultra-fast gas chromatography (UFGC E-nose) was used to analyze the volatile flavor components of 24 samples of whole milk powder for infants, students, adults and middle-aged and elderly people. The aroma characteristics of milk powders for different age groups were classified and predicted by linear discriminant analysis (LDA) and linear support vector machine (LSVM). The results showed that a total of 54 volatile flavor components were detected in all samples, among which 37 components were common to these samples including propionaldehyde, acetone, hexanal, and caproic acid. Methyl propionate and 1-octen-3-one were exclusively found in infant milk powder. (E,E)-2,4-Decadienal and γ-unsecalactone were unique to students’ milk powder. Heptanal was detected only in adult milk powder. 2-Butanone and benzaldehyde were only found in middle-aged and elderly milk powder. In total, 23 volatile components contributing to the aroma were identified according to their relative odor activity values and aroma radar map, and 10 of them were the key aroma compounds in milk powders for the four age groups, all of which had milky, vegetable-like, fruity, flowery, grassy, caramel-like and tar-like aromas. In addition, infant milk powder had a characteristic apricot kernel-like aroma. The sensory evaluation results showed that the flavor of students’ milk powder and adult milk powder was better than that of infant milk powder and middle-aged and elderly milk powder, which was significantly different from the aroma radar map results. The classification and prediction accuracy of LDA and LSVM for milk powder for different age groups were 93.3% and 94.2%, respectively.

The volatile compounds of 25 brands of fragrant vinegar, rice vinegar and aged vinegar were detected by ultrafast gas chromatography-electronic nose and were analyzed qualitatively and quantitatively by retention index and external standard method. A flavor wheel for vinegar was developed based on the odor activity values (OAV) of volatile compounds, and the volatile aroma profiles of the different kinds of vinegar were compared and their relationship with the content of volatile compounds was evaluated. Linear discriminant analysis (LDA) and random forest (RF) were used to investigate the difference in the composition of volatile compounds of different kinds of vinegar in order to distinguish them. The results showed that in total 44 volatile compounds were detected in the three kinds of vinegar, mainly including alcohols, esters, aldehydes, ketones, acids, alkanes and heterocycles, the most abundant ones being acids, followed in decreasing order by alcohols, aldehydes and alkanes. The OAVs and odor radar chart of vinegar samples showed that acetic acid, 3-methylbutyraldehyde, phenylacetaldehyde, 5-methylfurfural and guaiacol contributed a lot to the aroma. The sourness, and fruity and cocoa-like aroma of fragrant vinegar were higher than those of rice vinegar and aged vinegar, but the flowery aroma of aged vinegar was higher than that of the other two types. The results of LDA showed that there were significant differences among different brands and types of vinegar. Using random forest machine learning, the three types of vinegar were discriminated with an accuracy of 100%, and 3-methylbutanol, guaiacol, benzaldehyde and acetone were selected as important compounds to distinguish different kinds of vinegar.

An ultra-fast gas chromatography-based electronic nose was used to qualitatively and quantitatively analyze the volatile flavor components in 36 samples of whole milk, low-fat milk, and skimmed milk subjected to ultra-high temperature (UHT) treatment. Using a combination of principal component analysis (PCA) and multi-layer perceptron neural network (MLP-NN), UHT-treated milk samples were classified and predicted according to their aroma characteristics. The results showed that 29 volatile flavor components were detected in all milk samples, mainly including acetone, n-butanol, and δ-decalactone, acetone and decanal being the most and least abundant, respectively. Isobutanol, acetoin, 1-pentanol, E-3-hexenal and decanal only existed in UHT-treated whole milk. 3-Methylheptane, 2,6-dimethylpyrazine, and E-2-nonen-1-ol were exclusively detected in UHT-treated low-fat milk. α-Pinene, 5-methylfurfural and decanoic acid were only detected in UHT-treated skimmed milk. The major aroma characteristics of UHT-treated milk were vegetable-like, creamy, grassy, fruity, and malty aroma notes. Among them, vegetable-like, creamy and malty aromas were the most widely distributed in UHT-treated whole fat milk. Fruity aroma was the most widely distributed in UHT-treated low-fat milk. Grassy aroma was the most widely distributed in UHT-treated skimmed milk. PCA showed that there was a significant difference between different brands of UHT-treated milk. The prediction accuracy of MLP-NN for UHT-treated milk was as high as 98.6%.