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Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks
- Source :
- Foods, Vol 9, Iss 558, p 558 (2020), Foods, Volume 9, Issue 5
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 &times<br />10&minus<br />5 mg&middot<br />g&minus<br />1&middot<br />d&minus<br />1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.
- Subjects :
- spectroscopy
Health (social science)
Brassica oleracea var. italica
Plant Science
lcsh:Chemical technology
01 natural sciences
Health Professions (miscellaneous)
Microbiology
Article
chemistry.chemical_compound
0404 agricultural biotechnology
statistical analysis
vegetable
chlorophyll
lcsh:TP1-1185
Mathematics
Second derivative
Artificial neural network
nondestructive analysis
010401 analytical chemistry
Nondestructive analysis
Hyperspectral imaging
04 agricultural and veterinary sciences
040401 food science
Reflectivity
0104 chemical sciences
Chlorophyll concentration
chemistry
Green color
Chlorophyll
shelf life
Biological system
mathematical model
Food Science
Subjects
Details
- Language :
- English
- ISSN :
- 23048158
- Volume :
- 9
- Issue :
- 558
- Database :
- OpenAIRE
- Journal :
- Foods
- Accession number :
- edsair.doi.dedup.....1d8782637c194ecc55f034b4601f88ec