Near-infrared reflection spectroscopy and partial least squares regression to predict α-farnesene and conjugated trienol content in apples during storage

•The study evaluates the potential of NIRS for predicting α-farnesene and CTols content in apples.•Highly efficient prediction models using PLS regression were obtained by inclusion of two harvest seasons, two varieties, different growing sites and various storage conditions.•Principal component analysis was applied on NIR spectral data to discriminate storage conditions.

The synthesis of α-farnesene and its degradation to the corresponding oxidation products such as conjugated trienols (CTols) in apple skins is strongly correlated with the incidence of superficial scald, a physiological disorder occurring in apple during and after storage. This study aimed to evaluate the potential of near-infrared spectroscopy (NIRS) for the prediction of α-farnesene and CTols (CT258 and CT281) content, developing a rapid and non-invasive method to support the postharvest decision systems. Applying partial least squares (PLS) regression, positive correlations were found for α-farnesene and CTols obtaining correlation coefficients of calibration (rcal) above 0.90. The calibration model for one apple variety was validated with apples from a second season resulting in correlation coefficients of validation (rval) of 0.51, 0.71 and 0.76 for α-farnesene, CT258 and CT281, respectively. A global calibration model including two cultivars and two growing seasons led to standard errors of prediction (SEP) of 139, 60 and 59 μmol m−2 for α-farnesene, CT258 and CT281, respectively. These results demonstrate the potential of NIRS for rapid and non-destructive prediction of the scald-disorder-related compounds α-farnesene and CTols in apple fruit.