Calibration modelling for non-destructive estimation of external and internal quality parameters of ‘Marsh’ grapefruit using Vis/NIR spectroscopy

Consumer preference for fruit without disorder influences purchase of fruit at both local and international markets. Recent trends in horticulture show that consumer preference is influenced by assurance that external appearance is linked with rewarding internal sensory quality. Therefore, the need for non-destructive evaluation of external and internal quality parameters is important. This study was conducted to develop and test calibration models for integrated prediction of external and internal quality of ‘Marsh’ grapefruit. Visible to near infrared (Vis/NIR) spectroscopy (Vis/NIRS) was used to acquire spectral information from 522 intact fruit. Reference quality parameters such as colour indices (luminosity (L*), greenness (a*) and yellowness (b*)), rind dry matter (DM), rind total phenolics concentration, BrimA, carbohydrates, sweetness index (SI) and total sweetness index (TSI) were obtained using conventional methods. Principal component analysis was applied to analyse spectral data to identify outliers. Savitzky-Golay second derivative with second order polynomial was employed as pre-processing method to correct light scattering properties of the spectra. The spectra were subjected to a test set validation by categorising the spectra into calibration (60%) and validation (40%) sets. Partial least square regression was used as chemometric tool to develop models for predicting each parameter. The model validation results showed that external and internal quality parameters of grapefruit could be predicted with satisfactory accuracy with R2 value of 0.99 for rind quality parameters (L*, a*, b*, DM) and 0.77, 0.99, 0.99 for BrimA, SI and TSI, respectively. The residual predictive deviation (RPD) results for L*, a*, b*, DM, BrimA, SI and TSI were 64.1, 61.4, 123.4, 12.9, 1.4, 9.0 and 13.9, respectively. Vis/NIR calibration and validation results demonstrated that quality parameters of ‘Marsh’ grapefruit could be predicted using Vis/NIRS.