Table 5.

Classifier metrics using nanoparticle trajectories to predict agarose gel concentration (0.4%, 0.6%, 0.8%, 1.0%, and 1.2%). From left to right: (1) sample size of training and test datasets after spatial checkerboard split, (2) pore obstruction model with parameters fitted using mean diffusion coefficients from each agarose concentration strata (3) pore obstruction model using locally averaged diffusion coefficients fitted using mean diffusion coefficients from each agarose concentration strata, (4) pore obstruction model with reduced resolution (0.4%, 0.8%, 1.2% agarose) using locally averaged diffusion coefficients fitted using mean diffusion coefficients from each agarose concentration strata, (5) neural network predictor trained with principal components from trajectory feature analysis using both individual and locally averaged geometric features, (6) neural network trained with principal components from trajectory feature analysis with reduced resolution (0.4%, 0.8%, 1.2% agarose)

	Sample size		Obstruct. model Overall	Obstruct. modela Overall	Obstruct. Modelb Overall	Obstruct. Modela,b Overall	Neural networkb		Neural Networka,b
	Training	Test					Training	Test	Training	Test
0.4%	44 643	45 388	0.409	0.420	0.623	0.712	0.910	0.695	0.992	0.834
0.6%	42 941	41 812	0.061	—	0.235	—	0.843	0.486	—	—
0.8%	46 913	45 652	0.164	0.316	0.121	0.228	0.805	0.329	0.974	0.569
1.0%	63 001	65 820	0.151	—	0.027	—	0.880	0.395	—	—
1.2%	122 390	121 818	0.304	0.365	0.502	0.524	0.972	0.702	0.996	0.837
Avg/tot	180 610	181 437	0.218	0.367	0.302	0.488	0.882	0.521	0.987	0.747

^aPredictor trained using only 0.4%, 0.8%, and 1.2% datasets (reduced resolution).

^bPredictions performed using locally averaged features.