2020
DOI: 10.1016/j.infrared.2020.103529
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Determination of bagged ‘Fuji’ apple maturity by visible and near-infrared spectroscopy combined with a machine learning algorithm

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Cited by 40 publications
(25 citation statements)
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“…To improve the data stability, we calibrated the light source intensity using a standard whiteboard (WR-D97, material PTFE) so that the light source intensity was adjusted to the standard spectrum every time the device was turned on. The dark spectrum was obtained by turning off the light source, and the original spectrum was corrected according to Equation (1) [10]:…”
Section: Spectral Data Acquisitionmentioning
confidence: 99%
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“…To improve the data stability, we calibrated the light source intensity using a standard whiteboard (WR-D97, material PTFE) so that the light source intensity was adjusted to the standard spectrum every time the device was turned on. The dark spectrum was obtained by turning off the light source, and the original spectrum was corrected according to Equation (1) [10]:…”
Section: Spectral Data Acquisitionmentioning
confidence: 99%
“…According to the SPI, the maturity of apples was characterized by three levels: immature, harvest mature, and eatable mature [10]. The number of apples obtained in 2019 was 270, 320, and 246 for each maturity level.…”
Section: Data Measurementmentioning
confidence: 99%
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“…In the available literature, there are reports on the use of machine learning and image analysis for objective evaluation of food products, including the results on cultivar discrimination (e.g., Alipasandi, Ghaffari, & Alibeyglu, 2013; Ronald & Evans, 2016; Ropelewska, 2020; Ropelewska, 2021a; Ropelewska, 2021b; Ropelewska, 2021c; Sofu, Erb, Kayacan, & Cetişli, 2016). In addition to cultivar discrimination, machine learning was successfully applied, among others, for the maturity determination of apples with the combination with visible and near‐infrared spectroscopy (Zhang et al, 2020). The evaluation of apple maturation was also performed using color and spectral data by Pourdarbani et al (2020).…”
Section: Introductionmentioning
confidence: 99%
“…A PLS regression model was built for joint analysis of fruit in four locations and three growing seasons in a crop field. Zhang et al. (2020) determined the maturity of Fuji apples.…”
Section: Introductionmentioning
confidence: 99%