eprintid: 66811 rev_number: 18 eprint_status: archive userid: 2293 dir: disk0/00/06/68/11 datestamp: 2021-04-21 13:14:47 lastmod: 2021-04-21 13:34:22 status_changed: 2021-04-21 13:34:22 type: article metadata_visibility: show creators_name: Donis-González, Irwin R. creators_name: Valero Ubierna, Constantino creators_name: Momin, Md Abdul creators_name: Kaur, Amanjot creators_name: Slaughter, David creators_id: constantino.valero@upm.es title: Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes publisher: MDPI rights: none ispublished: pub subjects: agricultura subjects: electronica full_text_status: public keywords: grape; peach; dry matter; total soluble solids; NIR spectroscopy; partial least-square regressio note: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA 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 date_type: published date: 2020-01 publication: Agronomy volume: 10 number: 1 pagerange: 148 id_number: 10.3390/agronomy10010148 institution: Agronomica department: Ingenieria_2014 refereed: TRUE issn: 2073-4395 official_url: https://doi.org/10.3390/agronomy10010148 citation: 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 . document_url: http://oa.upm.es/66811/7/agronomy-10-00148-v2.pdf