Multispectral images of peach related to firmness and maturity at harvest

By L. Lleó, Pilar Barreiro, M. Ruiz-Altisent, A. Herrero. 2009

90 citations recorded by the source corpus.

Abstract Two multispectral maturity classifications for red soft-flesh peaches (‘Kingcrest’, ‘Rubyrich’ and ‘Richlady’ n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak).

Subfields: Analytical Chemistry, Food Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/dff0ff020b4d4c66ed276bc13a011e81cf1ba1e3

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In Analytical Chemistry

In Food Science

L. Lleó

In Plant Science

Citations in the corpus

Monitoring of fresh-cut spinach leaves through a multispectral vision system (2012, methodology): “Additionally, this method gave rise to successful results in previous work investigating fruit ripeness and browning (Lleó et al., 2009; Herrero et al, 2011; Lunadei et al, 2011).”
Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening (2011, methodology): “The present research proposes two new indexes, Indi and Ind2, to be compared with two previously used indexes, Ind3 (Lleó et al., 2009; Sims and Gamón, 2002) and IAD (Ziosi et al.”
Fruit Quality Evaluation Using Spectroscopy Technology: A Review (2015, background): “[103] compared two multispectral classification methods based on red images (R) and a combination of R and infrared images (R/IR) respectively.”
Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment (2012, methodology): “In a different study aimed at estimating maturity, Lleó et al. (2009) used a multispectral imaging system to classify peaches into different levels of maturity at harvest and to compare this classification with reference measurements such as firmness or reflectance at 680 nm achieved with a visible ”
Hyperspectral and multispectral imaging for evaluating food safety and quality (2013, background): “Peach Maturity evaluation Area-scan reflectance 450, 675, 800 Lleo et al. (2009)”
A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices (2011, result): “Besides, this method gave rise o successful results in previous works investigating fruit ripeness Lleó et al., 2009; Herrero et al., 2011).”