Sensors, Vol. 24, Pages 174: A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries & rsquo; Pomological Traits
Sensors, Vol. 24, Pages 174: A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries’ Pomological Traits
Sensors doi: 10.3390/s24010174
Authors:
Tiziana Amoriello
Roberto Ciorba
Gaia Ruggiero
Monica Amoriello
Roberto Ciccoritti
Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF)...
Source: Sensors - Category: Biotechnology Authors: Tiziana Amoriello Roberto Ciorba Gaia Ruggiero Monica Amoriello Roberto Ciccoritti Tags: Article Source Type: research