Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

Donis-González, Irwin R. and Valero Ubierna, Constantino and Momin, Md Abdul and Kaur, Amanjot and Slaughter, David (2020). Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes. "Agronomy", v. 10 (n. 1); p. 148. ISSN 2073-4395. https://doi.org/10.3390/agronomy10010148.

Description

Title: Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes
Author/s:
  • Donis-González, Irwin R.
  • Valero Ubierna, Constantino
  • Momin, Md Abdul
  • Kaur, Amanjot
  • Slaughter, David
Item Type: Article
Título de Revista/Publicación: Agronomy
Date: January 2020
ISSN: 2073-4395
Volume: 10
Subjects:
Freetext Keywords: grape; peach; dry matter; total soluble solids; NIR spectroscopy; partial least-square regressio
Faculty: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Department: Ingeniería Agroforestal
UPM's Research Group: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA
Creative Commons Licenses: None

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Abstract

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data

More information

Item ID: 66811
DC Identifier: http://oa.upm.es/66811/
OAI Identifier: oai:oa.upm.es:66811
DOI: 10.3390/agronomy10010148
Official URL: https://doi.org/10.3390/agronomy10010148
Deposited by: Profesor Constantino Valero Ubierna
Deposited on: 21 Apr 2021 13:14
Last Modified: 21 Apr 2021 13:34
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