Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment

Applied Sciences

Konstantinos Ntouros¹  Dimitrios Moshou²  Pavlos Tsouvaltzis³  DimitriosS. Kasampalis³  AnastasiosS. Siomos³  Athanasios Gertsis⁴ 

[1] Department of Surveying Engineering & Geoinformatics, International Hellenic University, 62124 Serres, Greece;Laboratory of Agricultural Engineering, Department of Hydraulics, Soil Science and Agricultural Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Laboratory of Vegetable Crops, Department of Horticulture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Precision Agriculture Laboratory, Department of Agro-Environmental Systems Management, Perrotis College, American Farm School, 57001 Thermi, Greece;

关键词: spectroscopy;    chemometrics;    partial least square analysis;    color;    chlorophyll fluorescence;    digital imaging;   

DOI  :  10.3390/app11062630

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown