Proceedings Article | 3 June 2011
KEYWORDS: Scattering, Near infrared, Sensors, Atrial fibrillation, Acoustics, Calibration, Solids, Data fusion, Data modeling, Near infrared spectroscopy
Several nondestructive technologies have been developed for assessing the firmness and soluble solids content (SSC) of
apples. Each of these technologies has its merits and limitations in predicting the two quality parameters. With the
concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the
quality prediction of apples. In this research, four sensing systems (i.e., an acoustic sensor, a bioyield firmness tester, a
miniature near-infrared (NIR) spectrometer, and an online hyperspectral scattering system) were evaluated and
combined for nondestructive prediction of firmness and SSC of 'Jonagold' (JG), 'Golden Delicious' (GD), and
'Delicious' (RD) apples. A total of 6,535 apples harvested in 2009 and 2010 were used for analysis. Each of the four
sensors showed various degrees of ability to predict apple quality. Better predictions of the firmness and, in most cases,
of the SSC were obtained using sensors fusion than using individual sensors, as measured by number of latent variables,
correlation coefficient, and standard error of prediction (SEP). Results obtained from the two harvest seasons with the
multi-sensor fusion approach were quite consistent, confirming the validity and robustness of the proposed approach.
The SEPs for firmness measurement of JG, GD and RD using the best combination of two-sensor data were reduced by
13.3, 19.7 and 7.9% for the 2009 data and 16.0, 12.6 and 4.7% for the 2010 data; and using all four-sensor data by 21.8,
25.6 and 13.6% in 2009, and 14.9, 21.9, and 7.9% in 2010, respectively. For SSC prediction, using the two-sensor data
(i.e., NIR and scattering) improved predictions for JG, GD and RD apples harvested in 2009, with their SEP values
being reduced by 10.4, 6.6 and 6.8%, respectively. This research demonstrated that the fused systems provided more
complete complementary information and, thus, were more powerful than individual sensors in prediction of apple
quality.
