Rapid Detection of Cadmium Concentration in Beche-de-mer Using Hyperspectral Imaging Technology and Deep Neural Networks Regression Technique

Abstract

In this research, cadmium concentrations contained in Beche-de-mere (BDM) due to pollution of heavy metals in the sea were determined using hyperspectral imaging (HSI) technology and deep neural networks regression method. This detection system was important for maintaining the quality and safety of BDM which was one of the most important export food commodities in Indonesia. Several BDM samples of sandfish type were obtained from local markets in Indonesia. BDM samples were put in the glass jars that contained solutions of CdCl2 with various concentrations from 0 ~ 5 mg/L for three days. After three days, samples were dried using drying oven for 5 days. Next, the samples were scanned three times for each sample by hyperspectral imaging camera to obtain their hyperspectral data. Finally, after the spectral data acquired, the cadmium concentrations of the samples were ready to be determined by using Absorption Atomic Spectroscopy (AAS). Four regions of interests (ROI) were selected from each sample, and with three times scanning for each sample, resulted in total of 96 ROIs. Spectral profile data of the ROIs showed that there were variations in spectral reflectance of the BDM related to the different concentrations of cadmium used in the treatment of samples. Savitzky-Golay (SG) filter was used to clean noises from the spectral data. The model prediction was developed using deep neural networks. The results were also compared with shallow neural networks, partial least squared regression (PLSR), and support vector machine regression (SVR) methods. The best-established model which was deep neural networks with SG filtering had coefficient of correlation (R<inf>p</inf>) of 0.93, and root-mean-square-error prediction (RMSEP) of 9.21.