Abstract

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 3502200nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding– doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses.

© 2008 Optical Society of America

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References

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]

2007

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review,” Postharvest Biol. Technol. 46, 99-118 (2007).
[CrossRef]

J. Xing, W. Saeys, and J. De Baerdemaeker, “Combination of chemometric tools and image processing for bruise detection on apples,” Comput. Electron. Agric. 56, 1-13 (2007).
[CrossRef]

M. L. Oey, E. Vanstreels, J. De Baerdemaeker, E. Tijskens, H. Ramon, M. Hertog, and B. M. Nicolaï, “Effect of turgor on micromechanical and structural properties of apple tissue: a quantitative analysis,” Postharvest Biol. Technol. 44, 240-247 (2007).
[CrossRef]

B. M. Nicolaï, B. E. Verlinden, M. Desmet, S. Saevels, W. Saeys, K. Theron, R. Cubeddu, A. Pifferi, and A. Torricelli, “Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear,” Postharvest Biol. Technol. 47, 68-74 (2007).
[CrossRef]

M. A. Velazco-Roa and S. N. Thennadil, “Estimation of complex refractive index of polydisperse particulate systems from multiple-scattered ultraviolet-visible-near-infrared measurements,” Appl. Opt. 46, 3730-3735 (2007).
[CrossRef] [PubMed]

2006

Y. Liu, Y. Ying, H. Yu, and X. Fu, “Comparison of the HPLC method and FT-NIR analysis for quantification of glucose, fructose and sucrose in intact apple fruits,” J. Agric. Food Chem. 54, 2810-2815 (2006).
[CrossRef] [PubMed]

2005

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of the subcutaneous adipose tissue in the spectral range 400-2500 nm,” Opt. Spectrosc. 99, 836-842 (2005).
[CrossRef]

A. Peirs, A. Schenk, and B. M. Nicolaï, “Effect of natural variability among apples on the accuracy of VIS-NIR calibration models for optimal harvest date predictions,” Postharvest Biol. Technol. 35, 1-13 (2005).
[CrossRef]

V. A. McGlone, P. J. Martinsen, C. J. Clark, and R. B. Jordan, “On-line detection of Brownheart in Braeburn apples using near infrared transmission measurements,” Postharvest Biol. Technol. 37, 142-151 (2005).
[CrossRef]

2004

E. Mehinagic, G. Royer, R. Symoneaux, D. Bertrand, and F. Jourjon, “Prediction of the sensory quality of apples by physical measurements,” Postharvest Biol. Technol. 34, 257-269(2004).
[CrossRef]

2003

B. Park, J. A. Abbott, K. J. Lee, C. H. Choi, and K. H. Choi, “Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of Delicious and Gala apples,” Trans. ASAE 46, 1721-1731 (2003).

D. G. Frazer, R. B. Jordan, R. Künnemeyer, and V. A. McGlone, “Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy,” Postharvest Biol. Technol. 27, 185-196 (2003).
[CrossRef]

2001

1999

G. de Vries, J. F. Beek, G. W. Lucassen, and M. J. C. van Gemert, “The effect of light losses in double integrating spheres on optical properties estimation,” IEEE J. Quantum Electron. 5, 944-947 (1999).
[CrossRef]

1998

J. Lammertyn, B. Nicolaï, K. Ooms, V. De-Smedt, and J. De Baerdemaeker, “Nondestructive measurement of acidity, soluble solid, and firmness of Jonagold apples using NIR-spectroscopy,” Trans. ASAE 41, 1089-1094 (1998).

1993

1973

Appl. Opt.

Comput. Electron. Agric.

J. Xing, W. Saeys, and J. De Baerdemaeker, “Combination of chemometric tools and image processing for bruise detection on apples,” Comput. Electron. Agric. 56, 1-13 (2007).
[CrossRef]

IEEE J. Quantum Electron.

G. de Vries, J. F. Beek, G. W. Lucassen, and M. J. C. van Gemert, “The effect of light losses in double integrating spheres on optical properties estimation,” IEEE J. Quantum Electron. 5, 944-947 (1999).
[CrossRef]

J. Agric. Food Chem.

Y. Liu, Y. Ying, H. Yu, and X. Fu, “Comparison of the HPLC method and FT-NIR analysis for quantification of glucose, fructose and sucrose in intact apple fruits,” J. Agric. Food Chem. 54, 2810-2815 (2006).
[CrossRef] [PubMed]

J. Biomed. Opt.

T. L. Troy and S. N. Thennadil, “Optical properties of human skin in the near infrared wavelength range of 1000 to 2200 nm,” J. Biomed. Opt. 6, 167-176 (2001).
[CrossRef] [PubMed]

Opt. Spectrosc.

A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of the subcutaneous adipose tissue in the spectral range 400-2500 nm,” Opt. Spectrosc. 99, 836-842 (2005).
[CrossRef]

Postharvest Biol. Technol.

E. Mehinagic, G. Royer, R. Symoneaux, D. Bertrand, and F. Jourjon, “Prediction of the sensory quality of apples by physical measurements,” Postharvest Biol. Technol. 34, 257-269(2004).
[CrossRef]

M. L. Oey, E. Vanstreels, J. De Baerdemaeker, E. Tijskens, H. Ramon, M. Hertog, and B. M. Nicolaï, “Effect of turgor on micromechanical and structural properties of apple tissue: a quantitative analysis,” Postharvest Biol. Technol. 44, 240-247 (2007).
[CrossRef]

B. M. Nicolaï, B. E. Verlinden, M. Desmet, S. Saevels, W. Saeys, K. Theron, R. Cubeddu, A. Pifferi, and A. Torricelli, “Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear,” Postharvest Biol. Technol. 47, 68-74 (2007).
[CrossRef]

D. G. Frazer, R. B. Jordan, R. Künnemeyer, and V. A. McGlone, “Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy,” Postharvest Biol. Technol. 27, 185-196 (2003).
[CrossRef]

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review,” Postharvest Biol. Technol. 46, 99-118 (2007).
[CrossRef]

A. Peirs, A. Schenk, and B. M. Nicolaï, “Effect of natural variability among apples on the accuracy of VIS-NIR calibration models for optimal harvest date predictions,” Postharvest Biol. Technol. 35, 1-13 (2005).
[CrossRef]

V. A. McGlone, P. J. Martinsen, C. J. Clark, and R. B. Jordan, “On-line detection of Brownheart in Braeburn apples using near infrared transmission measurements,” Postharvest Biol. Technol. 37, 142-151 (2005).
[CrossRef]

Trans. ASAE

B. Park, J. A. Abbott, K. J. Lee, C. H. Choi, and K. H. Choi, “Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of Delicious and Gala apples,” Trans. ASAE 46, 1721-1731 (2003).

J. Lammertyn, B. Nicolaï, K. Ooms, V. De-Smedt, and J. De Baerdemaeker, “Nondestructive measurement of acidity, soluble solid, and firmness of Jonagold apples using NIR-spectroscopy,” Trans. ASAE 41, 1089-1094 (1998).

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Figures (12)

Fig. 1
Fig. 1

Schematic of the different measurement configurations: (a) Collimated transmittance measurement (b) Total diffuse transmittance measurement (c) Total diffuse reflectance measurement.

Fig. 2
Fig. 2

Bulk optical properties μ a (top) and μ s (bottom) in the 450 1800 nm range for a monodisperse polystyrene suspension (diameter of 0.45 μm and concentration of 0.15% b y weight of solids) in water estimated by means of Mie theory (circles) and by means of the IAD program with fixed g (stars).

Fig. 3
Fig. 3

Bulk optical properties μ a (top), μ s (middle), and g (bottom) in the 450 1800 nm range for a monodisperse polystyrene suspension (diameter of 0.45 μm and concentration of 0.15% by weight of solids) in water estimated by means of Mie theory (circles) and by means of the extended IAD program (stars).

Fig. 4
Fig. 4

Average bulk optical properties μ a (top), μ s (middle), and g (bottom) of apple skin in the 350 2200 nm range for the cultivars Royal Gala (solid curve), Granny Smith (dashed curve), and Braeburn (dotted curve) estimated by means of the extended IAD program. The presented curves are the average of 28 estimated curves per cultivar (14 apples, 2 spots each).

Fig. 5
Fig. 5

Average bulk optical properties μ a (top), μ s (middle), and g (bottom) of apple flesh in the 350 1900 nm range for the cultivars Royal Gala (solid curve), Granny Smith (dashed curve), and Braeburn (dotted curve) estimated by means of the extended IAD program. The presented curves are the average of 28 estimated curves per cultivar (14 apples, 2 spots each).

Fig. 6
Fig. 6

Average bulk optical properties μ a (top), μ s (bottom) of apple skin in the 350 2200 nm range for the cultivars Royal Gala (solid curve), Granny Smith (dashed curve), and Braeburn (dotted curve) estimated by means of the IAD program with fixed anisotropy factors g of 0.70, 0.63, and 0.67, respectively; The presented curves are the average of 28 estimated curves per cultivar (14 apples, 2 spots each).

Fig. 7
Fig. 7

Average bulk optical properties μ a (top), μ s (bottom) of apple flesh in the 350 1900 nm range for the cultivars Royal Gala (solid line), Granny Smith (dashed line), and Braeburn (dotted line) estimated by means of the IAD program with fixed anisotropy factors g of 0.66, 0.63, and 0.64, respectively. The presented curves are the average of 28 estimated curves per cultivar (14 apples, 2 spots each).

Fig. 8
Fig. 8

Average bulk optical properties μ a (top), μ s (bottom) of apple skin in the 350 2200 nm range for the Royal Gala apples estimated by means of the extended IAD program (solid curves) and the IAD program with fixed anisotropy factor (dashed curves) g of 0.70. The presented curves are the average of 28 estimated curves (14 apples, 2 spots each).

Fig. 9
Fig. 9

Average bulk optical properties μ a (top), μ s (bottom) of apple flesh in the 350 1900 nm range for the Royal Gala apples estimated by means of the extended IAD program (solid curves) and the IAD program with fixed anisotropy factor (dashed curves) g of 0.66. The presented curves are the average of 28 estimated curves (14 apples, 2 spots each).

Fig. 10
Fig. 10

Relative variation of the estimated bulk optical properties μ a (top), μ s (middle), and μ s (bottom) of apple skin in the 350 2200 nm range for the Royal Gala apples estimated by means of the extended IAD program (solid curves) and the IAD program with fixed anisotropy factor (dashed curves) g of 0.70. The presented curves are calculated from 28 estimated curves (14 apples, 2 spots each).

Fig. 11
Fig. 11

Relative variation of the estimated bulk optical properties μ a (top), μ s (middle), and μ s (bottom) of apple flesh in the 350 1900 nm range for the Royal Gala apples estimated by means of the extended IAD program (solid curves) and the IAD program with fixed anisotropy factor (dashed curves) g of 0.66. The presented curves are calculated from 28 estimated curves (14 apples, 2 spots each).

Fig. 12
Fig. 12

Comparison of the average bulk optical properties μ a (top) and μ s (bottom) in the 650 1000 nm range for skin (dashed curve) and flesh (solid curve) tissue of Granny Smith apples obtained by the IAD approach to the values presented in [9] (stars) obtained by TRS on intact apples.

Equations (1)

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μ s = μ s ( 1 g ) .

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