Prediction of loquat soluble solids and titratable acid content using fruit mineral elements by artificial neural network and multiple linear regression

•Artificial neural network (ANN) architecture of 9:12:1 can accurately predict soluble solids content (SSC) (R2 = 0.9597).•ANN architecture of 9:11:1 can accurately predict titratable acids content (TAC) (R2 = 0.9580).•ANN architecture of 9:11:1 can accurately predict the ratio of soluble solids to titratable acid content (R2 = 0.9658).•The N, P, K, Mg content in fruits contribute relatively largely to the SSC, TAC and SSC/TAC of loquat. Mineral nutrient elements have an important impact on fruit quality, especially on soluble solids (SSC), titratable acid content (TAC) and the ratio of soluble solids to titratable acid (SSC/TAC) in fruits, which are the most important factors determining the taste and flavor of fruits. In this study, multiple linear regression (MLR) and artificial neural networks (ANN) were used to assess the predictive ability of models to predict SSC, TAC, and SSC/TAC in fruits based on mineral elements in fruits. The results showed that compared with the MLR model (R2 = 0.6772, R2 = 0.5520 and R2 = 0.6025, respectively), the ANNs predicted SSC, TAC, SSC/TAC with higher accuracy and effectiveness (R2 = 0.9597, R2 = 0.9580 and R2 = 0.9658, respectively). These results indicated the ANN is an effective tool with good performance in predicting SSC, TAC, and SSC/TAC of loquat. Meanwhile, we also conducted sensitivity analysis to analyze the relative contribution of mineral nutrients in the fruit to SSC, TAC and SSC/TAC. In terms of relative contribution, the N, P, K, Mg contents in fruits contributed relatively largely to SSC, TAC, and SSC/TAC of loquat.