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United States Department of Agriculture

Agricultural Research Service

Title: Predicting Apple Fruit Firmness and Sugar Content Using Near-Infrared Scattering Properties

Author
item Lu, Renfu

Submitted to: Agricultural Engineering International Conference
Publication Type: Proceedings
Publication Acceptance Date: May 30, 2003
Publication Date: July 27, 2003
Citation: Lu, R. 2003. Predicting apple fruit firmness and sugar content using near-infrared scattering properties. Agricultural Engineering International Conference Proceedings.

Interpretive Summary: Today, consumers in the U.S. and other industrialized nations are demanding more consistent quality fruit. Because of the array of choices consumers have at the grocery store, poor quality fruit will not be considered for purchase. Technologies that can sense quality and allow grading and sorting fruit for texture and flavor are key methodologies for assuring the quality and consistency of fruit for purchase by the consumer. Firmness and sweetness are two important quality attributes that directly influences consumer purchase of apples. By grading and sorting apples into different firmness and sweetness grades, the apple industry can better meet the expectations of consumers; thus, increasing consumer satisfaction and improving the apple industry's profitability. Research was conducted to see if a new sensing technique using multispectral imaging,an optical imaging technology that acquires images at selected wavelengths of light, can predict the firmness and sweetness of apple fruit. This novel multispectral imaging system allowed us to quantify light scattering and absorption in apple fruit at selected wavelengths and relate them to fruit firmness and sweetness. The system was able to predict the firmness of apple fruit with the correlation coefficient of 0.87 and the prediction error less than 6.0 N, the minimum resolving firmness value a trained panelist can ascertain. The system also gave good predictions of apple sweetness with the correlation coefficient of 0.77 and the prediction error of 0.8%. With further research, the multispectral imaging system can be used to grade and sort apple fruit for firmness and sweetness. The technology will add value to the fruit industry by delivering superior quality and more consistent fruit to the consumer.

Technical Abstract: Firmness and soluble solids content (SSC or total sugar content) are important attributes for apples and many other fresh fruits. This research investigated the feasibility of using multispectral imaging to quantify light backscattering profiles from apple fruit for predicting firmness and SSC. Backscattering images, generated from a focused broadband beam (0.8 mm diameter), were obtained from Red Delicious apples for five selected spectral bands (10 nm bandpass) between 670 nm and 1060 nm. Ratios of scattering profiles for different spectral bands were used as inputs to a neural network to predict fruit firmness and SSC. Three ratio combinations with four spectral bands (680 nm, 880 nm, 905 nm, and 940 nm) gave the best predictions of fruit firmness, with r = 0.87 and the standard error of prediction (SEP) = 5.8 N. Two ratio combinations with three spectral bands (880 nm, 905 nm and 940 nm) were needed to predict the SSC with r =0.77 and SEP = 0.78 deg Brix. The multispectral imaging technique is promising for grading and sorting of fruit for firmness and sweetness.

Last Modified: 9/23/2014
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