TY - GEN
T1 - Rapid Detection of Cadmium Concentration in Beche-de-mer Using Hyperspectral Imaging Technology and Deep Neural Networks Regression Technique
AU - Arianto, Sigit
AU - Saputro, Adhi Harmoko
AU - Ernawati, Teni
AU - Isnaeni, Isnaeni
AU - Imawan, Cuk
AU - Djuhana, Dede
N1 - Funding Information:
ACKNOWLEDGMENT We would like to thank the Ministry of Science and Technology of Republic of Indonesia for their support in doing this research by giving generous funding through Insinas Research Funding. Also we acknowledge the support from Research Center for Physics – National Research and Innovation Agency and Bio-Imaging Physics lab at Physics Department of Mathematics and Natural Sciences Faculty at the University of Indonesia, Depok, Indonesia in facilitating the research.
Funding Information:
We would like to thank the Ministry of Science and Technology of Republic of Indonesia for their support in doing this research by giving generous funding through Insinas Research Funding. Also we acknowledge the support from Research Center for Physics National Research and Innovation Agency and Bio-Imaging Physics lab at Physics Department of Mathematics and Natural Sciences Faculty at the University of Indonesia, Depok, Indonesia in facilitating the research.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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 (Rp) of 0.93, and root-mean-square-error prediction (RMSEP) of 9.21.
AB - 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 (Rp) of 0.93, and root-mean-square-error prediction (RMSEP) of 9.21.
KW - Beche-de-mere
KW - Cadmium
KW - Deep neural networks
KW - Hyperspectral
KW - PLSR
KW - SVM Regression
UR - http://www.scopus.com/inward/record.url?scp=85124548627&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE53823.2021.9655773
DO - 10.1109/ICITISEE53823.2021.9655773
M3 - Conference contribution
AN - SCOPUS:85124548627
T3 - Proceedings - 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Science and Artificial Intelligence Technologies for Global Challenges During Pandemic Era, ICITISEE 2021
SP - 30
EP - 34
BT - Proceedings - 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021
Y2 - 24 November 2021 through 25 November 2021
ER -