1Department of Electrical Engineering, Faculty of Engineering and Maritime Technology, Universitas Maritim Raja Ali Haji, Indonesia
2Department of Fisheries Products Technology, Faculty of Marine Science and Fisheries, Universitas Maritim Raja Ali Haji, Indonesia
3Research Center for Food Technology and Processing, National Research and Innovation Agency, Indonesia
4 College of Oceanography and Ecological Science, Shanghai Ocean University, China
5 Geomatics Technology Program, Politeknik Negeri Batam, Indonesia
BibTex Citation Data :
@article{IK.IJMS67221, author = {Hollanda Kusuma and Tonny Suhendra and Aidil Ilhamdy and Carel Ilhami and Dwi Setyono and Muhammad Lubis}, title = {Spectral Characterization in Seaweed, Kappaphycus alvarezii, using AS7285x Spectroscopy Sensor Device}, journal = {ILMU KELAUTAN: Indonesian Journal of Marine Sciences}, volume = {30}, number = {2}, year = {2025}, keywords = {Multiple Linear Regression; Visible Light; Infrared Light; Instrument; Seaweed}, abstract = { This study explores the spectral characterization of seaweed, Kappaphycus alvarezii, using the SparkFun Triad Spectroscopy Sensor AS7265x to assess the relationship between water content and light intensity. This research aims to provide a foundation for non-destructive monitoring of post-harvest seaweed quality using spectral techniques. The SeaSpec device was constructed using an ESP32 microcontroller, a TFT display, and the AS7265x sensor. Seaweed samples were collected from the coastal area of Karimun Islands and subjected to a controlled drying process at 40°C to determine the water content in the seaweed. The spectral data were recorded across 18 channels in the visible and infrared spectra, highlighting distinct patterns that correlate with varying moisture levels. A multiple linear regression analysis was employed to determine the contributions of individual spectral channels to water content prediction, revealing that each channel has its own unique contribution to the model. Coefficient of determination (R²), percentage error (%), and percentage accuracy (%) were also used to assess model performance. The results indicated that higher water content corresponds to increased light intensity. The analysis indicated that the visible spectrum outperformed the infrared spectrum in predictive accuracy, with an R² value of 0.79 compared to 0.61 for the infrared spectrum. This indicates that the visible light spectrum is more effective in predicting water content in K. alvarezii. The findings underscore the potential of spectral analysis as a reliable method for assessing the physico-chemical properties of seaweeds, advancing the use of optical sensors in aquaculture and environmental monitoring while paving the way for future research. }, issn = {2406-7598}, pages = {183--191} doi = {10.14710/ik.ijms.30.2.183-191}, url = {https://ejournal.undip.ac.id/index.php/ijms/article/view/67221} }
Refworks Citation Data :
This study explores the spectral characterization of seaweed, Kappaphycus alvarezii, using the SparkFun Triad Spectroscopy Sensor AS7265x to assess the relationship between water content and light intensity. This research aims to provide a foundation for non-destructive monitoring of post-harvest seaweed quality using spectral techniques. The SeaSpec device was constructed using an ESP32 microcontroller, a TFT display, and the AS7265x sensor. Seaweed samples were collected from the coastal area of Karimun Islands and subjected to a controlled drying process at 40°C to determine the water content in the seaweed. The spectral data were recorded across 18 channels in the visible and infrared spectra, highlighting distinct patterns that correlate with varying moisture levels. A multiple linear regression analysis was employed to determine the contributions of individual spectral channels to water content prediction, revealing that each channel has its own unique contribution to the model. Coefficient of determination (R²), percentage error (%), and percentage accuracy (%) were also used to assess model performance. The results indicated that higher water content corresponds to increased light intensity. The analysis indicated that the visible spectrum outperformed the infrared spectrum in predictive accuracy, with an R² value of 0.79 compared to 0.61 for the infrared spectrum. This indicates that the visible light spectrum is more effective in predicting water content in K. alvarezii. The findings underscore the potential of spectral analysis as a reliable method for assessing the physico-chemical properties of seaweeds, advancing the use of optical sensors in aquaculture and environmental monitoring while paving the way for future research.
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