Hyperspectral imaging to measure apricot attributes during storage

Abstract

The fruit industry needs rapid and non-destructive techniques to evaluate the quality of the products in the field and during the post-harvest phase. The soluble solids content (SSC), in terms of °Brix, and the flesh firmness (FF) are typical parameters used to measure fruit quality and maturity state. Hyperspectral imaging (HSI) is a powerful technique that combines image analysis and infrared spectroscopy. This study aimed to evaluate the potential of the application of the Vis/NIR push-broom hyperspectral imaging (400 to 1000 nm) to predict the firmness and the °Brix in apricots (180 samples) during storage (11 days). Partial least squares (PLS) and artificial neural networks (ANN) were used to develop predictive models. For the PLS, R2 values (test set) up to 0.85 (RMSEP=1.64 N) and 0.72 (RMSEP=0.51 °Brix) were obtained for the FF and SSC, respectively. Concerning the ANN, the best results in the test set were achieved for the FF (R2=0.85, RMSEP=1.50 N). The study showed the potential of the HSI technique as a non-destructive tool for measuring apricot quality even along the whole supply chain.

Keywords

• Apricot
• storage
• hyperspectral imaging
• artificial neural networks
• partial least squares.