Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging
Fecha
2016Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Impacto
|
10.1016/j.foodcont.2016.06.001
Resumen
Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising
represents substantial economic losses every year. As the tubers do not show external symptoms, bruise
detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of
identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to ...
[++]
Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising
represents substantial economic losses every year. As the tubers do not show external symptoms, bruise
detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of
identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect
other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully
automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been
investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named VisibleNear Infrared (Vis-NIR) and another covering the 1000e2500 nm range, called Short Wave Infrared
(SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were
manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA
were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall
correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results
were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of
early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results
in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is
possible to detect blackspot areas in each potato tuber with high accuracy. [--]
Materias
Vis-NIR,
SWIR,
Hyperspectral imaging,
Solanum tuberosum L.,
Potato,
Blackspot
Editor
Elsevier
Publicado en
Food Control, 70 (2016) 229-241
Departamento
Universidad Pública de Navarra. Departamento de Proyectos e Ingeniería Rural /
Nafarroako Unibertsitate Publikoa. Landa Ingeniaritza eta Proiektuak Saila
Versión del editor
Entidades Financiadoras
The funding of this work has been covered by the Universidad
Pública de Navarra through the concession of both a predoctoral
research grant (Res. 1753/2012) and a mobility grant (Res. 1506/
2013), by the National Institute for Agricultural and Food Research
and Technology (INIA) project: “Mejora genetica de la patata:
caracterizacion reol ogica y por tecnología NIRS del material” RTA2013-00006-C03-03, and by the Agency for Innovation by Science and Technology in Flanders (IWT) through the Chameleon
(SB-100021) project.