Journal
POSTHARVEST BIOLOGY AND TECHNOLOGY
Volume 143, Issue -, Pages 112-118Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.postharvbio.2018.05.003
Keywords
Cherry fruit; SSC; pH; Visualization; Near infrared; Hyperspectral imaging
Categories
Funding
- National Natural Science Foundation of China [31771676]
- Technological Research Program of the Chongqing Municipal Education Commission [KJ1501128]
- Zhejiang Province Public Technology Research Program [2015C02008, 2017C02027]
- Zhejiang Province Public Welfare Technology Application Research Project [2014C32091]
- Special Funding Projects for Basic Scientific Research Projects in Universities [2015QNA6005]
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The relationships between soluble solids content (SSC) and pH cherry fruit of different maturity stages has been investigated using near-infrared (NIR) hyperspectral imaging technology. Using 550 fruit, 11 hyperspectral images in the 874-1734 nm region were captured and compared with SSC and pH measured by standard methods. Two types of models based on full bands, namely principal components regression model and partial least squares regression model, showed similar predictive ability. To reduce the modeling complexity based on full bands, a genetic algorithm (GA) and a successive projections algorithm were employed to select feature bands; both algorithms were tested by multiple linear regression (MLR). By comparing the results of different modeling methods, GA-MLR was selected as the final modeling method with a ratio of standard deviation of prediction set to standard deviation of prediction error of 2.7 for SSC and 2.4 for pH. SSC and pH distribution maps were generated by inputting the feature bands of each pixel into GA-MLR models. Classification of fruit maturity stages was studied, and a linear discrimination analysis method produced a correct classification ratio of 96.4%. We conclude that it is feasible to detect the quality of cherry fruit by NIR hyperspectral imaging technology.
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