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Proceedings Paper

Peach maturity/quality assessment using hyperspectral imaging-based spatially resolved technique
Author(s): Haiyan Cen; Renfu Lu; Fernando A. Mendoza; Diwan P. Ariana
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Paper Abstract

The objective of this research was to measure the absorption (μa) and reduced scattering coefficients (μs') of peaches, using a hyperspectral imaging-based spatially-resolved method, for their maturity/quality assessment. A newly developed optical property measuring instrument was used for acquiring hyperspectral reflectance images of 500 'Redstar' peaches. μa and μs' spectra for 515-1,000 nm were extracted from the spatially-resolved reflectance profiles using a diffusion model coupled with an inverse algorithm. The absorption spectra of peach fruit presented several absorption peaks around 525 nm for anthocyanin, 620 nm for chlorophyll-b, 675 nm for chlorophyll-a, and 970 nm for water, while μs' decreased consistently with the increase of wavelength for most of the tested samples. Both μa and μs' were correlated with peach firmness, soluble solids content (SSC), and skin and flesh color parameters. Better prediction results for partial least squares models were obtained using the combined values of μa and μs' (i.e., μa × μs' and μeff) than using μa or μs', where μeff = [3 μaa + μs')]1/2 is the effective attenuation coefficient. The results were further improved using least squares support vector machine models with values of the best correlation coefficient for firmness, SSC, skin lightness and flesh lightness being 0.749 (standard error of prediction or SEP = 17.39 N), 0.504 (SEP = 0.92 °Brix), 0.898 (SEP = 3.45), and 0.741 (SEP = 3.27), respectively. These results compared favorably to acoustic and impact firmness measurements with the correlation coefficient of 0.639 and 0.631, respectively. Hyperspectral imaging-based spatially-resolved technique is useful for measuring the optical properties of peach fruit, and it also has good potential for assessing fruit maturity/quality attributes.

Paper Details

Date Published: 2 June 2011
PDF: 15 pages
Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270L (2 June 2011); doi: 10.1117/12.883573
Show Author Affiliations
Haiyan Cen, Michigan State Univ. (United States)
Renfu Lu, Michigan State Univ. (United States)
Fernando A. Mendoza, Michigan State Univ. (United States)
Diwan P. Ariana, Michigan State Univ. (United States)

Published in SPIE Proceedings Vol. 8027:
Sensing for Agriculture and Food Quality and Safety III
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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