Non-destructive prediction of soluble solids and dry matter concentrations in apples using near-infrared spectroscopy

Soluble solids content (SSC) is a major quality attribute for apple fruit that influences consumer purchases. Prediction of fruit quality at harvest and after storage has become a significant focus to the postharvest value chain. The objectives of this study were to evaluate the relationship between fruit dry matter content (DMC) with SSC at harvest and after storage for 'McIntosh', 'Red Delicious' and 'Fuji' apples and to develop models based on near-infrared (NIR) spectroscopy (729-975 nm) to predict SSC and DMC. Fruit were harvested multiple times at one week intervals. Results showed that fruit DMC and SSC at harvest were closely related and the relationship was improved during maturation and storage. Partial least square regression (PLS) was used to build calibration models for prediction of SSC and DMC. Coefficient of determination (R²) values for calibration models ranged from 0.77 to 0.85 for SSC, and from 0.75 to 0.85 for DMC. Root mean square error (RMSE) of calibration models ranged from 0.44 to 0.62% for SSC, and from 4.25 to 4.92 g kg^‑1 for DMC. A strong linear relationship was found between DMC and SSC and NIR spectroscopy shows great potential for use as a non-destructive method for predicting SSC and DMC.