Fig. 2 | Scientific Reports

Fig. 2

From: Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing

Fig. 2

Architecture of L-SLR. First, absorbance signal is converted to a polynomial basis (A), then masked based on weights from pretrained sparse linear regression model (C). Next, masked signal is passed into pretrained LDA model (E). LDA forms compressed latent space which is used to generate metabolite predictions via linear regression (F). Coefficients from LDA and SLR are then used to form weight matrix visuals (G). Models used in (C) and (E) are pretrained using a labeled dataset consisting of integer labels computed via Hilbert curve (B) that represent unique metabolite combinations.

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