Non-destructive evaluation of soluble solids content of apples using a developed portable Vis/NIR device. (May 2020)

• Record Type: Journal Article
• Authors: Fan, Shuxiang; Wang, Qingyan; Tian, Xi; Yang, Guiyan; Xia, Yu; Li, Jiangbo; Huang, Wenqian
• Is Part Of: Biosystems engineering. Volume 193(2020)
• Page Start: 138
• Page End: 148
• Publication Date: 2020-05
• Subjects: Soluble solids content -- NIR -- apple -- Portable device -- On-tree; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2020.02.017
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : A portable visible and near-infrared (Vis/NIR) device could evaluate and monitor internal qualities of fruit on-tree, as well as during storage conditions after harvest. A portable Vis/NIR device which consisted of a commercial spectrometer in the spectral range of 400–1000 nm, an interactance fibre optic probe, a novel switch system, and a microcontroller, was developed and its ability for apple soluble solids content (SSC) prediction was evaluated. A switch system was designed for spectra collection, resulting in the acquisition of three spectra for each measurement of apple fruit, namely the white reference, dark reference, and sample spectrum, which can be used to correct the spectrum of apple fruit dynamically. The results showed that the dynamic correction was more promising than the static correction in which the reference spectra were obtained only once. A model for SSC was built using partial least square (PLS), with the coefficient of determination of prediction ( R p 2 ), the root mean square error of prediction (RMSEP), and the ratio of the standard deviation of the reference destructive SSC to the RMSEP (RPD) of 0.777, 0.561%, and 2.114, respectively. The model was then embedded in the custom software to make it possible for the portable device to predict SSC of apple directly, followed by validation using independent sets. The validation results gave R p 2, RMSEP, and RPD of 0.764, 0.672%, 2.029, respectively for SSC prediction under laboratory conditions, and 0.684, 2.777%, and 0.381, respectively for apples on-tree. The prediction results in the field were improved dramatically using the model built by the field data, with R p 2, RMSEP and RPD of 0.690, 0.604%, and 1.794, respectively. The overall results showed that the developed device had considerable potential to detect the SSC of apple in practical situations. Highlights: A prototype of portable Vis/NIR device for apple SSC detection was developed. The spectrum of apple fruit was corrected dynamically for each measurement. The device showed potential to predict SSC of apples in the lab and in the field.