Comparative genomics and fermentation flavor characterization of five selected lactic acid bacteria provide predictions for flavor biosynthesis metabolic pathways in fermented muskmelon puree

Abstract Five species of plant‐derived lactic acid bacteria (LAB), including Pediococcus pentosaceus, Lactococcus garvieae, Lactiplantibacillus paraplantarum, Weissella paramesenteroides, and Lactococcus lactis, were used for muskmelon fermentation to improve flavor characteristics. Comparative genomic analysis showed that a high proportion of annotated genes were related to carbohydrate metabolism and amino acid metabolism in all five strains. L. lactis P‐1 (Ll1) contained the highest proportion (2.02%) of genes from the glycoside hydrolase family associated with carbohydrate metabolism. Gas chromatography–mass spectrometry results showed that 89 volatile compounds in muskmelon purees were identified; the largest number of volatile compounds was detected in fermented muskmelon puree (FMP) inoculated with W. paramesenteroides FL3, followed by L. paraplantarum FL‐8. A total of 25 key volatile compounds were screened in muskmelon puree, and some unique key volatile compounds (e.g., β‐damascenone, phenylacetaldehyde, and 3‐penten‐2‐one) and taste compounds (e.g., mannitol) were produced by different LAB strains. L. garvieae Pa‐2 and Ll1 were considered to have greater potential due to their ability to produce a greater category of pleasant key aromas. Based on the correlations of potential pathways analyzed using comparative genomics and detected flavor profiles, flavor synthesis pathways in FMP were predicted. Some carbohydrate, amino acid, and fatty acid metabolism pathways, including tricarboxylic acid cycle, glycolysis, and cysteine and methionine metabolism pathways, are common to all five LAB strains. Furthermore, differences in the predicted flavor biosynthesis metabolic pathways in monoterpenoid biosynthesis, phenylalanine metabolism, fatty acid biosynthesis, and fructose and mannose metabolism pathways explained the differences in volatile profiles produced by different strains

A Framework to Represent and Mine Knowledge Evolution from Wikipedia Revisions

State-of-the-art knowledge representation in semantic web employs a triple format (subject-relation-object). The limitation is that it can only represent static information, but cannot easily encode revisions of semantic web and knowledge evolution. In reality, knowledge does not stay still but evolves over time. In this paper, we first introduce the concept of “quintuple representation ” by adding two new fields, state and time, wherestate has two values, either in or out, to denote that the referred knowledge takes effective or becomes expired at the given time. We then discuss a twostep statistical framework to mine knowledge evolution into the proposed quintuple representation. Utilizing extracted quintuple properly, it not only can reveal knowledge changing history but also detect expired information. We evaluate the proposed framework on Wikipedia revisions, as well as, common web pages currently not in semantic web format