NDLI: Nondestructive Determination of Soluble Solids Content of ‘Fuji’ Apples Produced in Different Areas and Bagged with Different Materials During Ripening

Content Provider	Springer Nature Link
Author	Dong, Jinlei Guo, Wenchuan Wang, Zhuanwei Liu, Dayang Zhao, Fan
Copyright Year	2015
Abstract	To investigate the feasibility of hyperspectral imaging technique in nondestructive determination of soluble solids content (SSC) of fruits produced in different places and bagged with different materials during ripening, the near infrared hyperspectral reflectance images were acquired on 196 ‘Fuji’ apples picked from four orchards in different areas and bagged with polyethylene film or light-impermeable paper. Mean reflectance spectrum from the regions of interest in the hyperspectral image of each apple was extracted. Standard normal variate (SNV) was used to eliminate the effect of instrument and environment on spectra. The sample set partitioning based on joint x–y distances method was applied to divide the samples into calibration set and prediction set as the ratio of 3:1. Successive projection algorithm (SPA) and uninformative variable elimination (UVE) method were used to select effective wavelengths (EWs) from the full spectra. Partial least squares (PLS), least squares support vector machine (LSSVM), and extreme learning machine (ELM) were used to develop SSC determination models. The results showed that 24 and 122 EWs were selected by SPA and UVE, respectively. The selection of EWs was helpful to SSC determination performance improvement. The optimal SSC prediction model was LSSVM based on selected EWs by SPA, with the correlation coefficient and root-mean-square error of prediction set of 0.878 and 0.908 °Brix, respectively. This study indicates that hyperspectral imaging technique could be used to determine SSC of intact apples produced in different places and bagged with different materials during ripening.
Starting Page	1087
Ending Page	1095
Page Count	9
File Format	PDF
ISSN	19369751
Journal	Food Analytical Methods
Volume Number	9
Issue Number	5
e-ISSN	1936976X
Language	English
Publisher	Springer US
Publisher Date	2015-08-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Apples Soluble solids content Hyperspectral Nondestructive Modeling Food Science Chemistry/Food Science Microbiology Analytical Chemistry
Content Type	Text
Resource Type	Article
Subject	Analytical Chemistry Safety Research Safety, Risk, Reliability and Quality Applied Microbiology and Biotechnology Food Science

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