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Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging

by 1,* and 2
1
Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
2
United States Department of Agriculture Agricultural Research Service, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Alejandro Castillo
Foods 2021, 10(5), 1094; https://doi.org/10.3390/foods10051094
Received: 20 March 2021 / Revised: 5 May 2021 / Accepted: 13 May 2021 / Published: 14 May 2021
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers. View Full-Text
Keywords: pickling cucumber; chilling injury; chlorophyll fluorescence imaging; machine learning pickling cucumber; chilling injury; chlorophyll fluorescence imaging; machine learning
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MDPI and ACS Style

Lu, Y.; Lu, R. Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging. Foods 2021, 10, 1094. https://doi.org/10.3390/foods10051094

AMA Style

Lu Y, Lu R. Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging. Foods. 2021; 10(5):1094. https://doi.org/10.3390/foods10051094

Chicago/Turabian Style

Lu, Yuzhen, and Renfu Lu. 2021. "Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging" Foods 10, no. 5: 1094. https://doi.org/10.3390/foods10051094

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