Prior work has shown that smartphone spirometry can effectively measure lung function using the phone's built-in microphone and could one day play a critical role in making spirometry more usable, accessible, and cost-effective. Although traditional spirometry is performed with the guidance of a medical expert, smartphone spirometry lacks the ability to provide the patient feedback or guarantee the quality of a patient's spirometry efforts. Smartphone spirometry is particularly susceptible to poorly performed efforts because any sounds in the environment (e.g., a person's voice) or mistakes in the effort (e.g., coughs or short breaths) can invalidate the results. We introduce two approaches to analyze and estimate the quality of smartphone spirometry efforts. A gradient boosting model achieves 98.2% precision and 86.6% recall identifying invalid efforts when given expert tuned audio features, while a Gated-Convolutional Recurrent Neural Network achieves 98.3% precision and 88.0% recall and automatically develops patterns from a Mel-spectrogram, a more general audio feature.