Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers

Y. Liu; Yud-Ren Chen; C. Y. Wang; D. E. Chan; Moon S. Kim

doi:10.1117/12.597550

19 November 2004 Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers

Y. Liu, Yud-Ren Chen, C. Y. Wang, D. E. Chan, Moon S. Kim

Proceedings Volume 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources; (2004) https://doi.org/10.1117/12.597550
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

Hyperspectral images of cucumbers were acquired before and during cold storage treatments as well as during subsequent room temperature (RT) storage to explore the potential for the detection of chilling induced damage in whole cucumbers. Region of interest (ROI) spectral features of chilling injured areas, resulting from cold storage treatments at 0°C or 5°C, showed a reduction in reflectance intensity during multi-day post chilling periods of RT storage. Large spectral differences between good-smooth skins and chilling injured skins occurred in the 700-850 nm visible/NIR region. A number of data processing methods, including simple spectral band algorithms, second difference, and principal component analysis (PCA), were attempted to discriminate the ROI spectra of good cucumber skins from those of chilling injured skins. Results revealed that using either a dual-band ratio algorithm (Q_811/756) or a PCA model from a narrow spectral region of 733-848 nm could detect chilling injured skins with a success rate of over 90%. Furthermore, the dual-band algorithm was applied to the analysis of images of cucumbers at different conditions, and the resultant images showed more correct identification of chilling injured spots than other processing methods.

Citation Download Citation

Y. Liu, Yud-Ren Chen, C. Y. Wang, D. E. Chan, and Moon S. Kim "Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers", Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); https://doi.org/10.1117/12.597550

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