Articles
Detection of russet on pome fruit using a spectral index (SRI)
Article number
1346_100
Pages
783 – 790
Language
English
Abstract
Russet is a brown necrotic appearance or physiological peel disorder of pome fruit.
The objective of the present work was to identify russet in-situ on the fruit peel using new non-invasive technology.
Colorimetry, spectrometry, 3D profilometry, and a luster sensor were employed on green Conference and red Williams Christ pears.
Non-invasive true and false color images at 40x magnification with a 3D-profilometer type VR-3000 (Keyence) allowed identification of russet on Conference pear with a larger roughness Ra 3.3 µm (SE 0.4) compared with Ra 2.5 µm (SE 0.3) on russet-devoid fruit peel.
The contactless russet detection using the luster sensor CZ-H72 failed to detect a significant difference (8 a.u.) in glossiness between russet (54.5 a.u.) and non-russet (62.5 a.u.) on Conference (green) in contrast to red Williams Christ with a ca.2-fold statistically significant difference between russet (38.4 a.u) and non-russet (67.5. a.u.) surface.
The situation reversed with colorimetry: For the red pears of Williams Christ, differentiation between russet (66.8 °hue) and russet-devoid peel (62.4 °hue) was impossible when using contactless colorimetry.
Russet could be successfully detected for all cultivars examined using portable contactless spectrometry (190-1100 nm). A new spectral russet index (SRI) was proposed relating the two peaks of light reflection (550-600 nm and 775-785 nm) to the trough (667-685 nm). Russeted peel (e.g., 3.6) exhibited ca. 25% smaller SRI values than russet-devoid peel (4.8) of both cultivars Conference (green) and Sweet Sensation(red). Overall, both portable non-invasive techniques, 3D-profilometer and spectral light reflection with the novel russet index, proved suitable for real-time, in-situ russet detection in pears.
In particular, the study has identified colorimetry as a suitable measure for russet detection for green and luster sensors for red pear cultivars, which offers new possibilities for contactless russet detection in the field or on a grading line.
The objective of the present work was to identify russet in-situ on the fruit peel using new non-invasive technology.
Colorimetry, spectrometry, 3D profilometry, and a luster sensor were employed on green Conference and red Williams Christ pears.
Non-invasive true and false color images at 40x magnification with a 3D-profilometer type VR-3000 (Keyence) allowed identification of russet on Conference pear with a larger roughness Ra 3.3 µm (SE 0.4) compared with Ra 2.5 µm (SE 0.3) on russet-devoid fruit peel.
The contactless russet detection using the luster sensor CZ-H72 failed to detect a significant difference (8 a.u.) in glossiness between russet (54.5 a.u.) and non-russet (62.5 a.u.) on Conference (green) in contrast to red Williams Christ with a ca.2-fold statistically significant difference between russet (38.4 a.u) and non-russet (67.5. a.u.) surface.
The situation reversed with colorimetry: For the red pears of Williams Christ, differentiation between russet (66.8 °hue) and russet-devoid peel (62.4 °hue) was impossible when using contactless colorimetry.
Russet could be successfully detected for all cultivars examined using portable contactless spectrometry (190-1100 nm). A new spectral russet index (SRI) was proposed relating the two peaks of light reflection (550-600 nm and 775-785 nm) to the trough (667-685 nm). Russeted peel (e.g., 3.6) exhibited ca. 25% smaller SRI values than russet-devoid peel (4.8) of both cultivars Conference (green) and Sweet Sensation(red). Overall, both portable non-invasive techniques, 3D-profilometer and spectral light reflection with the novel russet index, proved suitable for real-time, in-situ russet detection in pears.
In particular, the study has identified colorimetry as a suitable measure for russet detection for green and luster sensors for red pear cultivars, which offers new possibilities for contactless russet detection in the field or on a grading line.
Publication
Authors
M. Schuesseler, M. Blanke
Keywords
pear (Pyrus communis L.), consumer appeal, fruit quality, glossiness, non-invasive detection, surface roughness
Online Articles (107)