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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #188260
Title: IMPROVING APPLE FRUIT FIRMNESS PREDICTIONS BY EFFECTIVE CORRECTION OF MULTISPECTRAL SCATTERING IMAGES

Author
item PENG, YANKUN - MICHIGAN ST UNIVERSITY
item Lu, Renfu

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/18/2006
Publication Date: 8/1/2006
Citation: Peng, Y., Lu, R. 2006. Improving apple fruit firmness predictions by effective correction of multispectral scattering images. Postharvest Biology and Technology. 41(3):266-274.

Interpretive Summary: Fruit firmness is an important parameter in the quality grading of apples, and it directly influences consumer acceptability and satisfaction. Nondestructive measurement of fruit firmness would enable the fruit industry to provide consistent, high quality fruit for the market. Considerable research has been reported on nondestructive measurement of fruit firmness by using different mechanical methods including sonic vibration and impact. However, these methods generally do not correlate well or consistently with the standard destructive firmness measurement method. We recently developed a new technique of using spectral scattering at selected wavelengths to detect apple fruit firmness. The technique shows great potential for grading and sorting apple fruit for firmness. This paper reports on improved methods for acquiring and analyzing spectral scattering information from apple fruit. The methods took into account image noise, fruit size/shape and light source effects for more accurate quantification of light scattering from apples. These methods showed a marked impact on improving firmness predictions for "Golden Delicious" and "Red Delicious" apples, with the correlation and prediction error being improved by 10% and 25%, respectively, for both varieties. The improved methods will be incorporated into a prototype multispectral scattering system so that it can meet the firmness sorting and grading requirements. The US apple industry generated more than 1.7 billion dollar revenue at the farm gate in 2004. Nondestructive firmness sorting/grading will allow the apple industry to better manage fruit after harvest and produce more premium grade fresh fruit, thus improving its profitability.

Technical Abstract: Firmness is an important parameter in determining the maturity and quality grade of apple fruit. Nondestructive measurement of fruit firmness will help the industry provide better fruit for the consumer. The objective of this research was to improve the multispectral imaging system used in our previous studies and refine scattering analysis methods for more effectively measuring apple fruit firmness. An improved multispectral imaging system equipped with a light intensity controller was used to measure light scattering from "Red Delicious" apples at seven wavelengths and "Golden Delicious" apples at eight wavelengths. A correction method was proposed to reduce noise signals in the scattering images during radial averaging of image pixels. Apple shape/size affected scattering intensity and distance, and two methods for correcting their effects were proposed. The corrected scattering images were reduced to spatial symmetrical profiles by radial averaging. A modified Lorentzian distribution (MLD) function with four parameters was used to fit the scattering profiles. Firmness prediction models were developed by multi-linear regression against MLD parameters for two apple cultivars. The improved system yielded better firmness predictions with the correlation (r) of 0.898 and the standard error of validation (SEV) of 6.41 N for "Red Delicious" apples and r=0.897 and SEV=6.14 N for "Golden Delicious" apples.