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Statistical modeling methods have been used for estimating difficult-to-measure quality variables by using easy-to-measure process variables. The estimation performance of a statistical model depends on the quality and quantity of data. To build a better model when the amount of the data is limited, joint-Y partial least squares (JY-PLS) was proposed. JY-PLS can concurrently use the data from the...
Calibration models have been widely used for estimating product quality or other key variables with near-infrared spectroscopy (NIRS), and it is important to select appropriate input variables (wavelengths) for building a highly accurate calibration model. A novel input variable selection method based on nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method,...
In process analytical technology (PAT), partial least squares (PLS) regression has been widely used to construct calibration models for near-infrared (NIR) spectroscopy. To construct a highly accurate calibration model, wavenumber selection is crucial. In the present work, an efficient wavenumber selection method especially for PLS is proposed. The proposed method is referred to as nearest correlation...
The individuality of production devices should be taken into account when statistical models are designed for parallelized devices. In the present work, a new clustering method, referred to as NC-spectral clustering, is proposed for discriminating the individuality of production devices. The key idea is to classify samples according to the differences of the correlation among measured variables, since...
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