Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging

Abstract

In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (Rp2) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with Rp2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries.