Examination of the quality of spinach leaves using hyperspectral imaging
By Belén Diezma, L. Lleó, Jean-Michel Roger, A. Herrero‐Langreo, Loredana Lunadei, M. Ruiz-Altisent. 2013
69 citations recorded by the source corpus.
The present research is focused on the application of hyperspectral images for the supervision of quality deterioration in ready to use leafy spinach during storage (Spinacia oleracea). Two sets of samples of packed leafy spinach were considered: (a) a first set of samples was stored at 20 °C (E-20) in order to accelerate the degradation process, and these samples were measured the day of reception in the laboratory and after 2 days of storage; (b) a second set of samples was kept at 10 °C (E-10), and the measurements were taken throughout storage, beginning the day of reception and repeating the acquisition of Images 3, 6 and 9 days later. Twenty leaves per test were analyzed. Hyperspectral images were acquired with a push-broom CCD camera equipped with a spectrograph VNIR (400–1000 nm). Calibration set of spectra was extracted from E-20 samples, containing three classes of degradation: class A (optimal quality), class B and class C (maximum deterioration). Reference average spectra were defined for each class. Three models, computed on the calibration set, with a decreasing degree of complexity were compared, according to their ability for segregating leaves at different quality stages (fresh, with incipient and non-visible symptoms of degradation, and degraded): spectral angle mapper distance (SAM), partial least squares discriminant analysis models (PLS-DA), and a non linear index (Leafy Vegetable Evolution, LEVE) combining five wavelengths were included among the previously selected by CovSel procedure. In sets E-10 and E-20, artificial images of the membership degree according to the distance of each pixel to the reference classes, were computed assigning each pixel to the closest reference class. The three methods were able to show the degradation of the leaves with storage time.
Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Plant Science
View Paper: https://www.semanticscholar.org/paper/e3862c602b0be0a454149543a646be502cfc0896
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In Analytical Chemistry
In Industrial and Manufacturing Engineering
In Plant Science
Citations in the corpus
- Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, background): “Evolution [60]”