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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: Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Authors
item Leiva-Valenzuela, Gabriel -
item LU, RENFU
item Aguilera, Jose Miguel -

Submitted to: Journal of Food Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 2, 2012
Publication Date: January 15, 2013
Citation: Leiva-Valenzuela, G., Lu, R., Aguilera, J. 2013. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering. 115(1):91-98.

Interpretive Summary: Blueberry consumption worldwide has increased significantly over the past decade, because consumers like its flavor and become more aware of its antioxidant benefits. To ensure quality and marketability, blueberries are currently sorted and inspected for color, size and/or defect using optically-based computer vision technique, and for firmness (or softness) using mechanical vibration or impact technique, in many modern packinghouses. A new generation inspection technique is needed for effectively inspecting and grading blueberries for both external and internal quality attributes (i.e., firmness and soluble solids content). Research was carried out to predict the firmness and soluble solids content of blueberries, using a hyperspectral reflectance imaging system for the spectral range of 500-1000 nm. The inhouse developed hyperspectral imaging system acquired both spectral (wavelength) and spatial (image) information simultaneously from 302 blueberries moving on a conveying unit in each of two fruit orientations (i.e., stem and calyx ends facing upward). Image processing and analysis algorithms were developed to extract important spectral information from each fruit, which was then used to estimate the firmness and soluble solids content of the fruit measured using destructive compression and refractometric methods. The hyperspectral imaging system was able to predict the firmness of blueberries with the correlation of 0.87, while it had a lower correlation of 0.79 for soluble solids content prediction. Fruit orientation had an insignificant effect on the prediction of fruit firmness and soluble solids content. Further analysis showed that the technique could sort blueberries into two classes of firmness (i.e., soft and firm). This research has demonstrated the feasibility of hyperspectral imaging technique for online sorting and grading of blueberries for internal quality.

Technical Abstract: Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packinghouses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and internal quality attributes of individual blueberries. This paper reports on the use of hyperspectral imaging technique for predicting the firmness and soluble solids content (SSC) of blueberries. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 302 blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 500-1,000 nm. Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R = 0.87) were obtained, compared to SSC predictions (R = 0.79). Fruit orientation had no or insignificant effect on the firmness and SSC predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting blueberries for firmness and possibly SSC to enhance product quality and marketability.

Last Modified: 9/29/2014