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

Kusumiyati; Agus Arip Munawar; Diding Suhandy; Kusumiyati; Agus Arip Munawar; Diding Suhandy

doi:10.3934/agrfood.2021011

AIMS Agriculture and Food

2021, Volume 6, Issue 1: 172-184. doi: 10.3934/agrfood.2021011

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Research article

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

1.
Department of Agrotechnology, Padjadjaran University, Bandung-Indonesia
2.
Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh-Indonesia
3.
Department of Agricultural Engineering, Lampung University Bandar Lampung-Indonesia

Received: 25 November 2020 Accepted: 21 December 2020 Published: 30 December 2020

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.
- NIRS,
- mango fruits,
- infrared,
- non-invasive,
- agriculture
Citation: Kusumiyati, Agus Arip Munawar, Diding Suhandy. Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy[J]. AIMS Agriculture and Food, 2021, 6(1): 172-184. doi: 10.3934/agrfood.2021011

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Abstract

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.

References

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