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Article

Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment

1
Laboratory of Vegetable Crops, Department of Horticulture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Department of Surveying Engineering & Geoinformatics, International Hellenic University, 62124 Serres, Greece
3
Nubi Group Geoservices & Research Private Company, 54453 Thessaloniki, Greece
4
Precision Agriculture Laboratory, Department of Agro-Environmental Systems Management, Perrotis College, American Farm School, 57001 Thermi, Greece
5
Laboratory of Agricultural Engineering, Department of Hydraulics, Soil Science and Agricultural Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Federico Marini and Alessandra Biancolillo
Appl. Sci. 2021, 11(6), 2630; https://doi.org/10.3390/app11062630
Received: 20 February 2021 / Revised: 11 March 2021 / Accepted: 12 March 2021 / Published: 16 March 2021
(This article belongs to the Special Issue Application of Spectroscopy in Food Analysis: Volume II)
Background: Quality and safety of potato is both cultivar and postharvest management dependent. The precise assessment of freshness and cultivar are complex tasks requiring time-consuming, expensive, and destructive techniques. Method: Potatoes from three commercial cultivars were stored for 5 months at 5 °C. Color and chlorophyll fluorescence were recorded, Red-Green-Blue (R-G-B), Red-Green-Near infrared (R-G-NIR) and Red-Blue-Near infrared (R-B-NIR) digital images, as well as hyperspectral images were acquired both on the external periderm of the tuber and in the inner flesh part. Partial least square regression (PLSR) and discriminant analysis, combined with feature selection techniques were implemented, in order to assess the potato freshness and to classify them into the respective genotypes. Results: The PLSR analysis of visible/near infrared (Vis/NIR) spectra reflectance most reliably predicted potato freshness, with a cross-validated regression coefficient equal to 0.981 and 0.947, as determined by external or internal measurements, respectively. Variance inflation factor, variable importance scores, and genetic algorithms identified specific wavelength regions that mostly affected the accuracy of the model in terms of strongest regression and lowest collinearity and root mean cross validation error. Conclusions: Vis/NIR spectra reflectance data from the skin of the potato tubers may be reliably used in the assessment of postharvest storage life, as well as in the cultivar discrimination process. View Full-Text
Keywords: spectroscopy; chemometrics; partial least square analysis; color; chlorophyll fluorescence; digital imaging; postharvest; sorting; non-destructive spectroscopy; chemometrics; partial least square analysis; color; chlorophyll fluorescence; digital imaging; postharvest; sorting; non-destructive
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MDPI and ACS Style

Kasampalis, D.S.; Tsouvaltzis, P.; Ntouros, K.; Gertsis, A.; Moshou, D.; Siomos, A.S. Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment. Appl. Sci. 2021, 11, 2630. https://doi.org/10.3390/app11062630

AMA Style

Kasampalis DS, Tsouvaltzis P, Ntouros K, Gertsis A, Moshou D, Siomos AS. Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment. Applied Sciences. 2021; 11(6):2630. https://doi.org/10.3390/app11062630

Chicago/Turabian Style

Kasampalis, Dimitrios S., Pavlos Tsouvaltzis, Konstantinos Ntouros, Athanasios Gertsis, Dimitrios Moshou, and Anastasios S. Siomos. 2021. "Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment" Applied Sciences 11, no. 6: 2630. https://doi.org/10.3390/app11062630

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