To study the dynamic changes in volatile flavor compounds during the steaming process of giant salamander tail, sensory evaluation, gas chromatography-ion mobility spectrometry (GC-IMS) combined with chemometrics was utilized to analyze the variation of volatile organic compounds after steaming for 0, 2, 5 and 8 min. The results indicated that the sensory score of giant salamander tails was highest after 5 min of steaming. A total of 29 volatile organic compounds were identified, including two alcohols, four aldehydes, three ketones, nine esters, eight ethers, two acids, and one phenol. Compared to those at 0 min, after 2 min of steaming, the contents of esters and ethers decreased, while the relative contents of alcohols and aldehydes increased. As the steaming time increased from 2 to 8 min, the content of esters and aldehydes gradually decreased, while the proportions of alcohols and ethers increased. A stable predictive model was established using partial least squares discriminant analysis (PLS-DA). Based on variable importance in projection (VIP), 10 characteristic volatile flavor compounds were selected from 29 volatile organic compounds (VIP values > 1). These included one alcohol (3-methylbutan-1-ol), one phenol (4-methylguaiacol), three ethers (allyl methyl disulfide, dipropyl disulfide, and isobutyl propyl sulfide), one acid (3-methylpentanoic acid), two aldehydes ((E)2-hexenal, and (E)-2-methyl-2-butenal), and two esters (ethyl acetate and methyl salicylate). Principal component analysis (PCA) showed that the cumulative contribution rate of the first two principal components was 97.6%, allowing for good discrimination of the steaming stages of giant salamander tails based on these differential volatile compounds.

In this study, non-targeted metabolomics based on ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to explore the change in the metabolite profile in giant salamander meat during cold storage at 4 ℃ (0, 2, 4 and 8 days). The differences within each group and between the 0- and 2-day storage groups were small, while the intra- and inter-group differences between days 4 and 8 of storage were large. As the storage time increased, the number of differential metabolites between adjacent groups increased gradually. Using the variable importance in the projection (VIP) value of the first principal component in the partial least squares discriminant analysis (PLS-DA) model greater than or equal to two, and the P-value of t-test less than or equal to 0.001 as criteria, a total of 125 differential metabolites were selected, including organic acids and their derivatives (17), esters and their derivatives (53), amino acids and their derivatives (25), nucleotides and their derivatives (13), alcohols (3) and other compounds (14). The abundance of most of the metabolites decreased significantly on 8 day of storage (P < 0.05). The cumulative change in the abundance of the organic acids and their derivatives (A1) had a similar trend to that of the amino acids and their derivatives (A3), that is, there was a small increase from days 0 to 2, a small decrease from days 2 to 4, and a rapid decrease from days 4 to 8. The cumulative changes in the abundance of the esters and their derivatives (A2) as well as the nucleotides and their derivatives (A4) showed a downward trend, but the abundance of the esters and their derivatives (A2) decreased slowly from days 0 to 4 and rapidly from days 4 to 8, while the abundance of the nucleotides and their derivatives (A4) showed a linear downward trend. The results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Pearson correlation analysis showed that histidine metabolism, arginine and proline metabolism, arginine biosynthesis, lysine degradation, taurine and sub taurine metabolism, valine, leucine and isoleucine biosynthesis, and aminoacyl-tRNA biosynthesis and other metabolic pathways had a good correlation with the changes of giant salamander meat quality; at the same time, creatine, L-histidinol, L-glutamate, histidine, ornithine, L-arginine and phytosphingosine could be used as potential markers for evaluating the quality change of giant salamander meat during cold storage. The results of this study provide a theoretical basis for understanding postmortem metabolism in giant salamander muscle and for quality control of giant salamander meat during cold storage.

In order to establish the correlation between physicochemical indexes and muscle metabolites in giant salamander meat during cold storage, differential metabolites between giant salamander muscle stored for different periods (0, 2, 4 and 8 days) at 4 ℃ were analyzed by gas chromatography-mass spectrometry (GC-MS) non-targeted metabolomics combined with multivariate statistical model. The results showed that the composition of metabolites in giant salamander muscle refrigerated for 8 days was significantly different from those stored for 0, 2 and 4 days. According to partial least squares-discriminant analysis (PLS-DA) and variable importance in projection (VIP) (VIP ≥ 1, P < 0.05 in t-test), 69 differential metabolites were identified including organic acids and their derivatives (21), amino acids and their derivatives (14), sugars and their derivatives (7), nucleotides and their derivatives (10), amines and their derivatives (6), and other compounds (11). According to the hierarchical cluster heatmap, the giant salamander meat samples could be divided into three groups: early (days 0–2), middle (day 4) and late stages (day 8). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the important metabolic pathways during the cold storage of giant salamander meat were purine metabolism, aminoacyl-tRNA biosynthesis, glyoxylic acid and dicarboxylic acid metabolism, pyruvate metabolism and the tricarboxylic acid cycle. Metabolic pathway mapping and Pearson’s correlation analysis showed that L-lysine, L-serine, L-isoleucine, L-methionine, pyruvic acid, succinic acid, glycine could be used as potential markers for the change in meat quality of giant salamander.

Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and similarity analysis were used to evaluate the differences in the volatile flavor compounds of five different colored unpolished rices (red, yellow, green, purple and black) from Yangxian county, Hanzhong city after cooking. The results showed that a total of 61 volatile flavor compounds were identified, including 35 aldehydes (49.83%–57.06%), 13 ketones (accounting for 34.40%–41.45%), 5 alcohols (accounting for 1.42%–1.96%), 2 pyrazines (accounting for 0.02%–0.07%), 2 acids (accounting for 0.19%–0.49%), 1 ester (accounting for 0.08%–0.67%), 1 furan (accounting for 5.61%–8.23%), 1 ether (accounting for 0.02%–0.10%), and 1 phenol (accounting for 0.04%–0.22%). The content of aldehydes in cooked unpolished red rice was relatively higher, the content of acids in cooked unpolished yellow rice was relatively higher; the contents of alcohols and ethers in cooked unpolished green rice were relatively higher; the content of furans in cooked unpolished purple rice was relatively higher; and the contents of ketones, esters, pyrazines and phenols in cooked unpolished black rice were relatively higher. Principal component analysis (PCA) showed that the cumulative contribution rate of the first two principal components was 74.1%, which could better explain the characteristics of the original sample data. HS-GC-IMS spectral data could be used to distinguish the volatile flavor components of cooked unpolished rices of different colors. A volatile component fingerprint of cooked unpolished rice of different colors from Yangxian county was established in this study, which can visually presents the contour information of volatile flavor components and enrich the information about the eating quality of the five colored rices from Yangxian.

To explore the microbial community and special spoilage bacteria in pallet-packaged giant salamander (Andrias davidianus) meat during cold storage, the total volatile basic nitrogen (TVB-N) content and total bacterial count of giant salamander meat were evaluated after different refrigeration durations (0, 2, 4, 6 and 8 d) at 4 ℃ , and the changes and diversity of the microbial flora were explored by Illumina MiSeq sequencing. The results showed that the total count of bacteria and TVB-N levels in salamander meat showed an upward trend during refrigeration, and exceeded the safety limit on the 6th and 8th days, respectively. The results of high-throughput sequencing showed that the microbial abundance in giant salamander meat decreased with storage time. The dominant phyla of bacteria were Bacteroidetes and Firmicutes during the early storage period (0 and 2 d), and Proteobacteria during the middle (4 d) and late (6 and 8 d) storage periods. The dominant genera were Bacteroidetes and Faecalibacterium during the early storage period, and Pseudomonas, Aeromonas, Hafnia-Obesumbacterium and Serrella during the middle and late storage periods. Principal coordinate analysis showed that there were great differences in microbial community structure between the three storage periods, and the degree of superposed interpretation of the two principal coordinates was 80.92%. Linear discriminant analysis effect size (LEfSe) analysis showed that Proteobacteria, Actinobacteria, Bacteroides, Fibrinobacteria and Firmicutes were the major bacterial phyla that significantly changed during cold storage. The main bacterial genera that significantly changed during storage were Pseudoxanthomonas, Bauldia, Serrella, Acinetobacter, Aeromonas, Pseudomonas, ambiguous_taxa, Hafnia-Obesumbacterium, Rikenellaceae_RC9_gut_group, Prevotella_9, Bacteroides, Fibrobacter, Lachnospira, Faecalibacterium, Clostridium_sensu_stricto_1. Evolutionary analysis showed that there was a strong correlation between the succession of microflora and storage time. Overall, the dominant spoilage microorganisms in giant salamander meat are Pseudomonas, Aeromonas, Hafnia-Obesumbacterium and Serrella. This study provides a reference for the targeted bacteriostasis of spoilage bacteria in giant salamander meat during cold storage to extend its shelf life in the future.

In order to explore the flavor characteristics of different colored millet porridges, the differences in volatile organic compounds in black, green, white and yellow millet porridges were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS). The results showed that a total of 48 volatile organic compounds were identified from all samples, including 22 aldehydes, 11 ketones, 9 alcohols, 2 furans, 2 esters and 2 ethers, accounting for 68.58%-80.66%, 6.52%-9.89%, 5.99%-14.91%, 2.09%-5.66%, 1.13%-1.72% and 0.39%-0.69% of the total amount of volatile organic compounds, respectively. The major volatile organic compounds identified were aldehydes, alcohols and ketones, followed by furans, esters and ethers. The relative contents of aldehydes, ketones and esters in black millet porridge were higher, and the relative content of ketones in green millet porridge was highest. The relative contents of alcohols and furans in white millet porridge were highest. The relative contents of aldehydes and esters in yellow millet porridge were highest. Principal component analysis (PCA) and Euclidean distance analysis showed that the GC-IMS data of volatile organic compounds could be used to distinguish different colored millet porridge samples. A prediction model with good stability was established using orthogonal partial least squares-discriminant analysis (OPLS-DA), and 13 differential volatile organic compounds (variable importance in the projection (VIP) > 1) were identified, which could be used for classification of different colored millet porridges. This study provides useful information for enriching the eating quality characteristics of different colored millet.

The volatile components in giant salamander oil adulterated with different amounts of peanut oil (0%, 5%, 10%, 20%, 30%, and 100%) were studied by gas chromatography-ion mobility spectroscopy (GC-IMS) combined with chemometrics. The results showed that a total of 41 volatile compounds were identified in all samples, including 21 aldehydes, 6 ketones, 4 alcohols, 4 heterocyclic compounds, 3 esters, 2 sulfur-containing compounds and 1 acid. With increasing adulteration level, the contents of aldehydes, heterocycles, acids and esters increased, while the contents of ketones, alcohols and sulfur compounds decreased. A partial least squares regression (PLSR) model between volatile components and adulteration level was established. The correlation coefficient (R²) values for the calibration and verification sets were 0.9924 and 0.9882, respectively, indicating that the reliability of the model. Principal component analysis (PCA) showed that the cumulative contribution rate of the first two principal components (PC) was 94.3%, indicating that the different adulteration levels could be well distinguished by volatile components. Thirteen differential volatile compounds with variable importance for the projection (VIP) scores greater than one, including seven aldehydes, three ketones, one alcohol, one sulfur compound and one ester, were selected by partial least squares-discriminant analysis (PLS-DA). PCA and cluster analysis showed that these differential volatile components could also be used to distinguish the different adulterated salamander oil samples. This study can provide technical support for the nondestructive rapid identification of adulterated giant salamander oil.