Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy

This study aims to apply near infrared technology as a fast, simultaneous and non-destructive method for quality assessment on intact mango fruit in form of total soluble solids (TSS) and vitamin C. Absorbance spectra of 186 intact mango fruits with four different cultivars were acquired and recorde...

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Main Authors: Kusumiyati, Agus Arip Munawar, Diding Suhandy
Format: Article
Language:English
Published: AIMS Press 2021-01-01
Series:AIMS Agriculture and Food
Subjects:
Online Access:http://www.aimspress.com/article/doi/10.3934/agrfood.2021011?viewType=HTML
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spelling doaj-daf58044d7e24325b90451bbaf2251b32021-05-21T02:27:43ZengAIMS PressAIMS Agriculture and Food2471-20862021-01-016117218410.3934/agrfood.2021011Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopyKusumiyati0Agus Arip Munawar1Diding Suhandy21. Department of Agrotechnology, Padjadjaran University, Bandung-Indonesia2. Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh-Indonesia3. Department of Agricultural Engineering, Lampung University Bandar Lampung-IndonesiaThis study aims to apply near infrared technology as a fast, simultaneous and non-destructive method for quality assessment on intact mango fruit in form of total soluble solids (TSS) and vitamin C. Absorbance spectra of 186 intact mango fruits with four different cultivars were acquired and recorded in wavelength ranging from 1000–2500 nm. Spectra data were enhanced and corrected using three different methods namely moving average smoothing (MAS), extended multiplicative scatter correction (EMSC) and standard normal variate (SNV). In addition, they were divided into two datasets namely calibration (n = 143) and prediction (n = 43) datasets consisting all four mango cultivars. The models used to predict TSS and vitamin C were developed using partial least square regression (PLSR). Prediction performance were quantified using correlation coefficient (r), root mean square error (RMSE), ratio prediction to deviation (RPD) and range to error ratio (RER) indexes. The results showed that the best prediction models for TSS and vitamin C were achieved when the models were constructed using EMSC correction approach with r = 0.86, RMSE = 1.67 Brix, RPD = 2.34 and RER = 9.72 for TSS. Meanwhile, for vitamin C, r = 0.86, RMSE = 6.84 mg·100g−1, RPD = 2.00 and RER = 8.87. From this study, it was concluded that near infrared technology combined with proper spectra enhancement method may be applied as a rapid, simultaneous and contactless method for quality assessment on intact mangoes.http://www.aimspress.com/article/doi/10.3934/agrfood.2021011?viewType=HTMLnirsmango fruitsinfrarednon-invasiveagriculture
collection DOAJ
language English
format Article
sources DOAJ
author Kusumiyati
Agus Arip Munawar
Diding Suhandy
spellingShingle Kusumiyati
Agus Arip Munawar
Diding Suhandy
Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
AIMS Agriculture and Food
nirs
mango fruits
infrared
non-invasive
agriculture
author_facet Kusumiyati
Agus Arip Munawar
Diding Suhandy
author_sort Kusumiyati
title Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
title_short Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
title_full Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
title_fullStr Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
title_full_unstemmed Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
title_sort fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy
publisher AIMS Press
series AIMS Agriculture and Food
issn 2471-2086
publishDate 2021-01-01
description This study aims to apply near infrared technology as a fast, simultaneous and non-destructive method for quality assessment on intact mango fruit in form of total soluble solids (TSS) and vitamin C. Absorbance spectra of 186 intact mango fruits with four different cultivars were acquired and recorded in wavelength ranging from 1000–2500 nm. Spectra data were enhanced and corrected using three different methods namely moving average smoothing (MAS), extended multiplicative scatter correction (EMSC) and standard normal variate (SNV). In addition, they were divided into two datasets namely calibration (n = 143) and prediction (n = 43) datasets consisting all four mango cultivars. The models used to predict TSS and vitamin C were developed using partial least square regression (PLSR). Prediction performance were quantified using correlation coefficient (r), root mean square error (RMSE), ratio prediction to deviation (RPD) and range to error ratio (RER) indexes. The results showed that the best prediction models for TSS and vitamin C were achieved when the models were constructed using EMSC correction approach with r = 0.86, RMSE = 1.67 Brix, RPD = 2.34 and RER = 9.72 for TSS. Meanwhile, for vitamin C, r = 0.86, RMSE = 6.84 mg·100g−1, RPD = 2.00 and RER = 8.87. From this study, it was concluded that near infrared technology combined with proper spectra enhancement method may be applied as a rapid, simultaneous and contactless method for quality assessment on intact mangoes.
topic nirs
mango fruits
infrared
non-invasive
agriculture
url http://www.aimspress.com/article/doi/10.3934/agrfood.2021011?viewType=HTML
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AT didingsuhandy fastsimultaneousandcontactlessassessmentofintactmangofruitbymeansofnearinfraredspectroscopy
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