Detecting pits in tart cherries by hyperspectral transmission imaging

Jianwei Qin; Renfu Lu

doi:10.1117/12.571359

19 November 2004 Detecting pits in tart cherries by hyperspectral transmission imaging

Jianwei Qin, Renfu Lu

Proceedings Volume 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources; (2004) https://doi.org/10.1117/12.571359
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.

Citation Download Citation

Jianwei Qin and Renfu Lu "Detecting pits in tart cherries by hyperspectral transmission imaging", Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); https://doi.org/10.1117/12.571359

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