Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.

Plant-specific transcription factors such as the TCP family play crucial roles in light responses and lateral branching. The commercial development of S. muricatum has been influenced by the ease with which its lateral branches can be germinated, especially under greenhouse cultivation during the winter with supplemented LED light. The present study examined the TCP family genes in S. muricatum using bioinformatics analysis (whole-genome sequencing and RNA-seq) to explore the response of this family to different light treatments. Forty-one TCP genes were identified through a genome-wide search; phylogenetic analysis revealed that the CYC/TB1, CIN and Class I subclusters contained 16 SmTCP, 11 SmTCP and 14 SmTCP proteins, respectively. Structural and conserved sequence analysis of SmTCPs indicated that the motifs in the same subcluster were highly similar in structure and the gene structure of SmTCPs was simpler than that in Arabidopsis thaliana; 40 of the 41 SmTCPs were localized to 12 chromosomes. In S. muricatum, 17 tandem repeat sequences and 17 pairs of SmTCP genes were found. We identified eight TCPs that were significantly differentially expressed (DETCPs) under blue light (B) and red light (R), using RNA-seq. The regulatory network of eight DETCPs was preliminarily constructed. All three subclusters responded to red and blue light treatment. To explore the implications of regulatory TCPs in different light treatments for each species, the TCP regulatory gene networks and GO annotations for A. thaliana and S. muricatum were compared. The regulatory mechanisms suggest that the signaling pathways downstream of the TCPs may be partially conserved between the two species. In addition to the response to light, functional regulation was mostly enriched with auxin response, hypocotyl elongation, and lateral branch genesis. In summary, our findings provide a basis for further analysis of the TCP gene family in other crops and broaden the functional insights into TCP genes regarding light responses.