Abstract
This study collected the near infrared (NIR) hyperspectral images of 150 astringent persimmons, with the spectra are in 900~1700 nm. Monte Carlo-uninformative variable elimination (MC-UVE) algorithm and successive projections algorithm (SPA) were adopted to the optimization of wavelengths obtained from the region of interest (ROI). Eight wavelengths were selected by MC-UCE-SPA. These feature wavelengths were 924.69, 928.05, 1 112.72, 1 270.91, 1 365.3, 1 402.42, 1 453.06 and 1 547.69 nm, respectively. The spectral reflectance of the 8 feature wavelengths were applied to establish the detective model for the soluble solid content (SSC) of persimmon by partial least squares regression (PLSR) method. The correlation coefficient and root mean square error of prediction set are rpre=0.942, RMSEP=1.009 °Brix. The results indicated that MC-UVE-SPA could effectively extract the characteristic information related to the SSC and develop a better predictive model with fewer wavelengths. This work can provide technical support and research basis for the nondestructive detection, grading and processing equipment for persimmon quality.
Publication Date
10-28-2017
First Page
52
Last Page
55
DOI
10.13652/j.issn.1003-5788.2017.10.011
Recommended Citation
Xuan, WEI; Jincheng, HE; Dapeng, YE; and Dengfei, JIE
(2017)
"Research on Non-destructive methods for soluble solid content detection of astringent persimmon based on near-infrared hyperspectral technology,"
Food and Machinery: Vol. 33:
Iss.
10, Article 11.
DOI: 10.13652/j.issn.1003-5788.2017.10.011
Available at:
https://www.ifoodmm.cn/journal/vol33/iss10/11
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