Non-destructive prediction of soluble solids and dry matter contents in eight apple cultivars using near-infrared spectroscopy

Soluble solids content (SSC) is an important factor for assessing quality of apples as it is linked to consumer taste preferences. Fruit dry matter content (DMC) is dominated by soluble sugar and starch concentrations at harvest, and therefore the DMC at the time of harvest can be strongly correlated with the post-storage SSC. The objective of this study was to develop models based on near-infrared (NIR) spectroscopy using a commercially available handheld instrument to predict SSC and DMC of fruit at harvest and after storage. ‘Gala’, ‘Honeycrisp’, ‘McIntosh’, ‘Jonagold’, ‘NY1?, ‘NY2?, ‘Red Delicious’ and ‘Fuji’ apples were tested. Partial least square regression was used to build calibration models for prediction of SSC and DMC. Models were also built for individual and multiple cultivars. Internal and external validations were applied to test the accuracy and precision of both models. In general, the individual- and multi-cultivar models have similar calibration performance. In internal validations, R2 and RMSE from multi-cultivar and individual-cultivar models were similar, but the slope values were higher in individual-cultivar than multi-cultivar models, indicating that the prediction using individual-cultivar model was more accurate. However, for individual-cultivar models, data-overfitting and the reference values distribution may lead to poor prediction in external validation. Overall the results support use of a portable NIR-based instrument to predict SSC and DMC, but to obtain precision and accurate predictions, calibration models should be built based on individual cultivars and the variability from seasonal and regional effects have to be taken into consideration. SourceNon-destructive prediction of soluble solids and dry matter contents in eight apple cultivars using near-infrared spectroscopyYiyi Zhang, Jacqueline F. Nock, Yosef Al Shoffe, Christopher B. WatkinsPostharvest Biology and Technology, Volume 151, May 2019, Pages 111-118https://doi.org/10.1016/j.postharvbio.2019.01.009 PictureF-750 Produce Quality Meter