# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 3 Results
### RGO Sensors Enabled Accurate VOC Classification Using Machine Learning  
The sensing response curves (Figure 5a) from each sensor were used to calculate two different features for pattern recognition analysis: \(R_{norm}\) at t \(\sim\) 1000 s and the integral area under the curve (_Area_) from gas injection to t \(\sim\) 1000 s. Four different sensors in the sensor array were tested with 5 ppm of acetone, isopropanol, and ammonia, and 17 ppm of carbon monoxide, which are within the concentration ranges of disease breath (Table 1), and six data points were collected for each gas analyte. PCA plots are shown in Figure 7. PC 1, PC2, and PC 3 explained 88.83%, 7.48%, and 2.35% of the variance, respectively. It should be noted that, before the dimension reduction, z-score normalization was conducted to remove the scale differences between different features. Figure 5: (**a**) Transient responses of RGO–MnTPP, –ZnTPP, –FeTPP, and –CoTPP were plotted when subjected to 5 ppm of ammonia. (**b**) A radar plot shows the sensing responses of the RGO–metalloporphyrin-sensing array for acetone, isopropanol, ammonia, and carbon monoxide. Figure 6: Sensing responses of the RGO–metalloporphyrin-sensor array were plotted as a function of different concentrations of (**a**) ammonia, (**b**) acetone, and (**c**) isopropanol. After the sensing data visualization, three different ML algorithms (LR, SVM, and ANN) were applied for gas classification. The classification performance results of the LR, SVM, and ANN using 3-fold cross-validation are summarized in Table 2. All ML models showed great performance for distinguishing ammonia, with accuracies and an F1-score both of 1.0. This is due to the overall high sensitivity of the RGO-metalloporphyrin sensors to ammonia, as mentioned in Section 3.2. The classification performance for the other three gas analytes was varied among different classification methods. In general, the SVM and ANN models outperformed the LR model. The SVM distinguished acetone better than the others with an accuracy and F1-score both of 1.0, although it is expected that its performance will drop when more data closer to boundary conditions will be collected. The SVM and ANN models showed comparable performance for isopropanol and carbon monoxide detection with an F1-score > 0.83. Since the number of datasets was limited, the F1-score classification performance was evaluated on bootstrapped samples to improve the robustness of the classification models [54]. First, the dataset was randomly split into 3 folds. Then, 1000 training datasets of equal sizes (\(n=16\)) were generated through sampling by replacement. A classification model was built from each training dataset with F1-score results summarized in Figure S2. Consistent results with the 3-fold cross-validation can be found; for example, SVM performed better than the other two classification methods for acetone detection, with a mean F1-score of 1. All three classification models were able to successfully classify ammonia with a mean F1-score of 1. For isopropanol and carbon monoxide detection, SVM showed great performance with a mean F1-score of 1, but a considerable variation can be seen. RGO Sensors with ML Algorithms Can Discriminate the Healthy and Unhealthy Samples with 91.7% Accuracy  
An ML-based chronic kidney disease classifier was derived for discriminating healthy and unhealthy groups. First, ammonia concentrations were generated for 334 healthy and 334 unhealthy samples given the mean and SD of normal distributions (healthy: mean = 0.356 ppm, SD = 0.300; unhealthy: mean = 3.863 ppm, SD = 2.6855).