| Summary: | The quality monitoring process for sago often relies on traditional lab instruments, seen as complex, expensive, and time-consuming. To tackle such issues, this project, therefore, aims to develop an efficient sago quality estimator based on hyperspectral imaging (HSI) with multivariate analysis. The newly proposed Adaptive 1D-ConvNet architecture developed one of the best-performing models, achieving Rp2 of 0.9410 to 0.9981 and RPD of 4.11 to 32.08. In conclusion, the HSI combined with multivariate analysis proved effective as a rapid, reliable, and cost-effective sago quality estimator.
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