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.