|Authors: ||I.R. Donis-González, D.E. Guyer, R. Lu|
|Keywords: ||food safety, quality, classification, inline, non-destructive, postharvest|
This research was designed to develop and test an automatic image analysis algorithm to detect the presence (pre-dicing) of undesirable fibrous carrot (Daucus carota L.) tissue.
Fibrous carrot dices are difficult to detect, and are highly problematic when found in ready-to-eat infant food, where they might represent a choking hazard (safety concern). A Visible/Near-InfraRed (Vis/NIR) hyperspectral imaging (321-1088 nm) system was used to obtain a set of 520-images per sample, from 1233 sections (samples). Carrots were collected during the 2013 and 2014 harvesting seasons.
Classification accuracy per sample was evaluated by comparing the classes obtained using Vis/NIR hyperspectral images against their undesirable fibrous tissue class, based on the industry-simulated invasive quality assessment (% of fiber). Class-0 represents fibrous-free samples, and class-1 denotes samples containing fibrous tissue.
After Vis/NIR image preprocessing, cropping, selection, and segmentation, 3135 grayscale intensity and textural features were extracted per sample from 15 selected Vis/NIR images.
A 4-fold cross-validation Neural-Network-Classifier with a performance accuracy of 86.4±2.1% was developed using 140 relevant features, which were selected using a sequential forward selection algorithm with the Fisher discriminant objective function.
Findings showed that this methodology is an objective, accurate, and reliable tool to determine the presence of undesirable fibrous tissue in processing carrots, and would be applicable to an automated noninvasive inline sorting system.
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