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Open AccessArticle

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

1
Biological and Agricultural Engineering Department, University of California-Davis, Davis, CA 95616, USA
2
Departamento Ingeniería Agroforestal, Universidad Politécnica de Madrid, 28040 Madrid, Spain
3
Department of Plant Sciences, University of California-Davis, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this manuscript.
Agronomy 2020, 10(1), 148; https://doi.org/10.3390/agronomy10010148
Received: 3 December 2019 / Revised: 14 January 2020 / Accepted: 16 January 2020 / Published: 19 January 2020
(This article belongs to the Special Issue Selected Papers from 10th Iberian Agroengineering Congress)
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. View Full-Text
Keywords: grape; peach; dry matter; total soluble solids; NIR spectroscopy; partial least-square regression grape; peach; dry matter; total soluble solids; NIR spectroscopy; partial least-square regression
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Donis-González, I.R.; Valero, C.; Momin, M.A.; Kaur, A.; C. Slaughter, D. Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes. Agronomy 2020, 10, 148.

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