2005 Fiscal Year Final Research Report Summary
Study on Inspection System of Quality for Fruit and Vegetables using Spectral Imaging(UV-NIR).
| Project/Area Number |
15380175
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Agricultural environmental engineering
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| Research Institution | University of Miyazaki |
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Principal Investigator |
NAGATA Masateru Univ.of Miyazaki, Dept.of Agriculture, Professor, 農学部, 教授 (80041002)
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| Co-Investigator(Kenkyū-buntansha) |
GEJIMA Yoshinori Univ.of Miyazaki, Dept.of Agriculture, Associate Professor, 農学部, 助教授 (10253808)
KAWASUE Kikuhito Univ.of Miyazaki, Dept.of Engineering, Associate Professor, 工学部, 助教授 (20214645)
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| Project Period (FY) |
2003 – 2005
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| Keywords | Hyperspectral Imaging / Spectral Image / Strawberry / Quality Estimation / Soluble Solids Content / Firmness / Near Infrared spectral method / Non-destroying measurement |
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| Research Abstract |
Consumers are demanding for higher quality and better safety of agricultural produce. While external food quality inspection is now more commonly done during post-harvest processing, the non-destructive measurement of internal quality such as sugar content and firmness is becoming more important. Thus, more accurate quality measurement techniques are needed. The main goal of this research is to develop prediction models that can estimate soluble solids content (SSC) and firmness in strawberries and SSC in tomatoes using hyperspectral imaging in visible (VIS) and near-infrared (NIR).
1.Strawberries
1)A VIS liquid crystal tunable filter-based hyperspectral imaging that took images from 450 nm to 650 nm at 2-nm intervals was used to relate spectral reflectance data with firmness. In the technically ripe sample sets, the five-predictor firmness model (510, 650, 644, 628, and 598 nm) had an SEP of 0.364 and a correlation coefficient r of 0.784.
2)Similarly, using NIR hyperspectral images (650-1000nm at 5-nm intervals) were taken to develop calibration models for firmness and SSC using stepwise multiple linear regression. The three-wavelength prediction model for firmness had a correlation of 0.786 and SEP of 0.350 (50% to Full-ripe group). While for SSC, the five-wavelength prediction model yielded a correlation of 0.870 and SEP of 0.530 (70% to Full-ripe group).
2.Tomatoes
Using NIR hyperspectral images from 650 nm to 1100 nm at 10-nm intervals calibration models were developed to relate the second derivative of the absorbance spectral data to the fruit's SSC. The five-wavelength model had a correlation coefficient r of 0.939 and SEC of 0.094 for the Full-ripe samples.
Hyperspectral imaging was found very useful for the non-destructive estimation of internal quality of fruits and fruit-vegetables.
Papers based on this research were chosen to receive the IET Select Paper Award during the international meetings of ASAE in 2004 and 2005.
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