Detection of Brownheart in 'Braeburn' apple by transmission NIR spectroscopy

'Braeburn' apples containing the internal browning disorder, Brownheart, were examined by transmission NIR spectroscopy to determine whether sample orientation and degree of browning were significant factors requiring consideration in the design of online detection systems. Two hundred and forty fruit were preselected using a clinical MRI system to have a uniform range of the disorder from 0 to 100%. This was later checked by dissecting fruit and calculating the degree of browning in transverse equatorial slices. Four arrangements of light source, fruit-orientation and detector were investigated. Pronounced spectral changes were observed in the range 700-900 nm as the percentage of browned tissue increased. Regression-based Ratio, MLR and PLS calibration models applied to independent prediction sets gave correlations (R-p(2)) of between 0.69 and 0.91 for the different geometries across the full range of affected fruit. Corresponding root mean square error of prediction (RMSEP) statistics of area affected were between 7.9 and 15.4%, and were elevated, in part, because of the asymmetric distribution of browning in individual fruits. PLS models tended to produce lower error and better goodness-of-fit statistics than the simpler two- or three-wavelength Ratio or MLR models. The best individual model was obtained by averaging spectra from opposite sides of the fruit where the stem-calyx axis was horizontal and the light source and detector were located at right-angles to one another at the equator. Our results suggest that single NIR transmission measurements using the models above could lead to a worthwhile reduction in the incidence of Brownheart in commercial lines containing affected fruit. However, to produce an optimal grading system, techniques to reduce the prediction error that overcome the asymmetric distribution of affected flesh, such as averaging multiple views, are still required. (C) 2002 Elsevier Science B.V. All rights reserved.