研究目的
To predict the moisture content in dried sea cucumber (Beche-de-mer) using hyperspectral imaging system the wavelength range from 400 to 1000 nm.
研究成果
The proposed system can predict the moisture content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability, offering a fast, non-destructive, and environmentally-friendly alternative to conventional methods.
研究不足
The study focuses on the moisture content prediction of dried sea cucumber using hyperspectral imaging, which may not account for all factors affecting quality. The method requires specific equipment and may not be easily scalable for all production environments.
1：Experimental Design and Method Selection:
The research employs a hyperspectral imaging technique in reflectance mode for moisture content prediction. The PLSR algorithm is used for model prediction.
2：Sample Selection and Data Sources:
192 samples of dried sea cucumbers from four regions in Indonesia were used. Moisture content was measured using a gravimetric method as a reference.
3：List of Experimental Equipment and Materials:
Hyperspectral camera system, two 150 W halogen lamps, Teflon table, motors, and a personal computer.
4：Experimental Procedures and Operational Workflow:
Samples were scanned using a line scanning method. Image correction, segmentation, and feature extraction were performed. The PLSR algorithm was applied for regression analysis.
5：Data Analysis Methods:
The performance of the prediction system was evaluated using root mean square errors and correlation coefficient.
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