An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Abstract

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.

Keywords: flavor metabolites; fruit flavor; machine learning; pepino.

Figure 1

Stats-violin plot of testers’ sensory evaluation of pepino in three regions.

Figure 2

Each metabolite node is sized based on its betweenness centrality. The thickness of the line connecting the metabolites is indicated by the weighted value (Tables S5 and S6). Identification of each metabolite is indicated by a number in the legend.

Figure 3

Contribution of metabolite groups to sensory characteristics.

Figure 4

Model prediction of sensory characteristics.

Figure 5

Role of metabolites on sensory panel. β coefficient from the Bayes A (X-axis), variable importance from the Neural Net (Y-axis).

Figure 6

PCA plots of the scores (a) and loadings (b) of the three regional pepino.