Table 1.

Comparison of material pixel classification accuracy and ROC AUC values for all algorithms applied to the XRD images of the water-PLA phantoms. Accuracy was computed as (% material pixels identified correctly) / (total number of material pixels), excluding air pixels from the metric. Accuracy and AUC values are reported for all phantom pixels (global) as well as those that are 3 mm from water-PLA boundaries (boundary). The commercial diffractometer measurements of water and PLA represent the 2 “database cases” provided to the cross-correlation and least squares algorithms, while the 95,885 cases represent the 60% of all material pixels that the SVM and SNN were trained on.

Classifier	# Database or Training Spectra	Global Accuracy	Global AUC	Boundary Accuracy	Boundary AUC
Cross-Correlation	2	96.48%	0.9941	89.32%	0.9683
Least Squares	2	96.48%	0.9941	89.32%	0.9682
Support Vector Machine	95,885	97.36%	0.9952	92.03%	0.9760
Shallow Neural Network	95,885	98.94%	0.9989	96.79%	0.9938