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Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network

The potential of a combination of a low cost visible and shortwave near infrared (VIS-SWNIR) spectrometer and an artificial neural network in the non-invasive soluble solids content assessment of pineapple was evaluated. Four data sets (i.e. VIS-SWNIR spectra and soluble solids content reference) of...

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Main Authors: Kim, Seng Chia, Abdul Rahim, Herlina, Abdul Rahim, Ruzairi
Format: Article
Published: Elsevier 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/33470/
http://dx.doi.org/10.1016/j.biosystemseng.2012.07.003
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spelling my.utm.334702018-11-30T06:35:36Z http://eprints.utm.my/id/eprint/33470/ Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network Kim, Seng Chia Abdul Rahim, Herlina Abdul Rahim, Ruzairi TK Electrical engineering. Electronics Nuclear engineering The potential of a combination of a low cost visible and shortwave near infrared (VIS-SWNIR) spectrometer and an artificial neural network in the non-invasive soluble solids content assessment of pineapple was evaluated. Four data sets (i.e. VIS-SWNIR spectra and soluble solids content reference) of pineapple samples from different days were acquired and independently processed. Baseline shift effect in the reflectance spectra was removed using a first order derivative coupled with a first order Savitzky-Golay smoothing filter. The dispersion of the spectral data was reduced by applying robust principal component analysis. Potential outliers were identified via an externally studentised residual approach. An artificial neural network was trained using one of the four data sets and validated using the other three data sets. From interpolation analysis, the root mean square error of calibration (RMSEC), correlation coefficient of calibration (r c), root mean square error of prediction (RMSEP) and correlation coefficient of prediction (r p) of the artificial neural network with the first two robust principal components were 0.84 °Brix, 0.85, 0.87 °Brix and 0.68, respectively. The predicted results by using three data sets from different days suggest that the use of a low cost VIS-SWNIR spectrometer is promising for the non-invasive soluble solids content assessment of pineapple. Elsevier 2012-10 Article PeerReviewed Kim, Seng Chia and Abdul Rahim, Herlina and Abdul Rahim, Ruzairi (2012) Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network. Biosystems Engineering, 113 (2). pp. 158-165. ISSN 1537-5110 http://dx.doi.org/10.1016/j.biosystemseng.2012.07.003 DOI:10.1016/j.biosystemseng.2012.07.003
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Kim, Seng Chia
Abdul Rahim, Herlina
Abdul Rahim, Ruzairi
Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
description The potential of a combination of a low cost visible and shortwave near infrared (VIS-SWNIR) spectrometer and an artificial neural network in the non-invasive soluble solids content assessment of pineapple was evaluated. Four data sets (i.e. VIS-SWNIR spectra and soluble solids content reference) of pineapple samples from different days were acquired and independently processed. Baseline shift effect in the reflectance spectra was removed using a first order derivative coupled with a first order Savitzky-Golay smoothing filter. The dispersion of the spectral data was reduced by applying robust principal component analysis. Potential outliers were identified via an externally studentised residual approach. An artificial neural network was trained using one of the four data sets and validated using the other three data sets. From interpolation analysis, the root mean square error of calibration (RMSEC), correlation coefficient of calibration (r c), root mean square error of prediction (RMSEP) and correlation coefficient of prediction (r p) of the artificial neural network with the first two robust principal components were 0.84 °Brix, 0.85, 0.87 °Brix and 0.68, respectively. The predicted results by using three data sets from different days suggest that the use of a low cost VIS-SWNIR spectrometer is promising for the non-invasive soluble solids content assessment of pineapple.
format Article
author Kim, Seng Chia
Abdul Rahim, Herlina
Abdul Rahim, Ruzairi
author_facet Kim, Seng Chia
Abdul Rahim, Herlina
Abdul Rahim, Ruzairi
author_sort Kim, Seng Chia
title Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
title_short Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
title_full Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
title_fullStr Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
title_full_unstemmed Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
title_sort prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network
publisher Elsevier
publishDate 2012
url http://eprints.utm.my/id/eprint/33470/
http://dx.doi.org/10.1016/j.biosystemseng.2012.07.003
_version_ 1643649340722380800
score 13.211869

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