Near Infrared Hyperspectral Imaging for Predicting Quality of Dehydrated Ginger

Abstract

The quality of any food product processed from fruit and vegetables can vary depending mainly on the quality of raw material and their processing. Near infrared hyperspectral imaging (NIR-HSI) has been shown to be a reliable and effective method of online monitoring of food products and was therefore tested on dehydrated ginger. The quality parameters of the dehydrated ginger assessed were hardness and total soluble solids (TSS). The models for hardness and TSS were established using partial least square regression (PLSR). Spectral pretreatments were tested in order to get better precision of the models. The accuracy of the prediction models for hardness was achieved correlation coefficient of prediction (Rp) of 0.79 and root mean square error of prediction (RMSEP) of 3.13 N and for TSS was Rp= 0.82 and RMSEP= 2.25%. Results showed that NIR-HSI has the possibility for determining hardness and TSS of dehydrated ginger non-destructively and could possibly to be used as part of the production process for online grading in dehydration factories.