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Journal of Food Measurement and Characterization

, Volume 11, Issue 4, pp 1676–1680 | Cite as

Parameter optimization in soluble solid content prediction of entire bunches of grape based on near infrared spectroscopic technique

  • Jing Yu
  • Hui Wang
  • Xiangyu Sun
  • Weidong HuangEmail author
Original Paper
  • 171 Downloads

Abstract

The object of this research was to evaluate grape’s soluble solid content (SSC) nondestructively based on near infrared spectroscopic technique using a detection probe designed in house and focus on optimization of three detection parameters that were light power (P), outer diameter of the probe (D), distance between the light source to the probe (L). Statistical models between diffuse transmittance spectra with grape’s SSC were developed using partial least square (PLS) regression. Orthogonal experiment was applied to choose optimal parameters. Comprehensive performance of different models under each combination of factor levels was assessed in terms of correlation coefficient of calibration (r c ), root mean square error of prediction (RMSEP) and differential values of RMSEP and root mean square error of calibration (Δ). The PLS model obtained the best results with the r c of 0.83, the RMSEP of 0.76 °Brix and the Δ of 0.84 °Brix on condition that P, D and L were 70 W, 70 and 85 mm, respectively. The results in the study show that SSC prediction of grape using the testing probe is feasible and parameter L has more impact on the performance of models than others. Better models can be obtained through reasonable parameters combination. This research can provide reference for the nondestructive detection of the entire bunches of grapes.

Keywords

Near infrared spectroscopy Soluble solid content Grape Nondestructive detection Orthogonal experiment 

Notes

Funding

This study was funded by the National Key Research and Development Plan Support (2016YFD0400504 and 2016YFD0400501).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Food Science and Nutritional Engineering, Beijing Key Laboratory of Viticulture and EnologyChina Agricultural UniversityBeijingPeople’s Republic of China
  2. 2.Chinese Academy of Agricultural Mechanization SciencesBeijingPeople’s Republic of China

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