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United States Department of Agriculture

Agricultural Research Service

Research Project: TECHNOLOGIES FOR QUALITY MEASUREMENT AND GRADING OF FRUITS AND VEGETABLES

Location: Sugarbeet and Bean Research

Title: Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content

Authors
item Mendoza, Fernando
item Lu, Renfu
item Haiyan, Cen -

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 25, 2012
Publication Date: November 1, 2012
Citation: Mendoza, F., Lu, R., Haiyan, C. 2012. Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biology and Technology. 73:89-98.

Interpretive Summary: Nondestructive, accurate assessment of firmness and soluble solids content (SSC), two primary quality parameters for apples, is challenging because they are influenced by many physiological and environmental factors. Four nondestructive sensors (i.e., sonic firmness, bioyield firmness, visible/near-infrared spectroscopy, and spectral scattering) have been developed recently for assessing the firmness and/or SSC of apples. This research compared the four sensors and evaluated different sensor combinations for improving apple firmness and SSC prediction. A total of 6,535 'Jonagold', 'Golden Delicious', and 'Delicious' apples harvested in 2009 and 2010 were tested using each sensing technique. Features were extracted from the data of each sensor and were then used individually and collectively for developing apple firmness and SSC prediction models. Visible/near-infrared and spectral scattering techniques had better correlation (R=0.897-0.961) with the fruit firmness of the three apple varieties, compared with the sonic and bioyield firmness sensors (R=0.589-0.871). Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by correlation coefficient and standard error for the independent sets of apple samples. The standard errors for the firmness using the best combinations of two-sensor data were reduced by 7.3-20.0% for 2009 and 6.2-14.6% for 2010; and using the best three or four fused sensor data by 13.9-24.9% in 2009 and 8.9-23.6% in 2010, respectively. The combination of visible/near infrared spectroscopy and scattering data improved SSC predictions for ‘Delicious’ apples, with the standard errors being reduced by 5.8% and 6.0% for 2009 and 2010, respectively. This research demonstrated that the fused systems provided more complete and complementary information and, thus, were more effective than individual sensors in prediction of apple quality. The sensor fusion approach is useful for the development of a more accurate and robust system for online sorting and grading of apples.

Technical Abstract: Four nondestructive technologies (i.e., acoustic firmness, bioyield firmness, visible/near-infrared (NIR) spectroscopy, and spectral scattering) have been developed in recent years for assessing the firmness and/or 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, the four sensing systems were evaluated and combined for nondestructive prediction of the 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. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by correlation coefficient and standard error of prediction (SEP). The SEPs for the firmness of JG, GD and RD using the best combination of two-sensor data were reduced by 13.5%, 20.0% and 7.3% for the 2009 data and 14.6%, 14.2% and 6.2% for the 2010 data; and using the best three or four fused sensor data by 19.1%, 24.9% and 13.9% in 2009, and 15.7%, 23.6%, and 8.9% in 2010, respectively. The combination of NIR and scattering data improved SSC predictions for RD apples, with the SEP values being reduced by 5.8% and 6.0% for 2009 and 2010, respectively. This research demonstrated that the fused systems provided more complete and complementary information and, thus, were more effective than individual sensors in prediction of apple quality.

Last Modified: 7/24/2014
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