# Distinguishing Bovine Fecal Matter on Spinach Leaves Using Field Spectroscopy
## 3 Results and Discussion  
The Savitzky-Golay first derivative transformation normalization technique was applied to the spectral data and then principal component analysis (PCA) was used to assess if the three sample types can be distinguished from one another. Figure 2 shows the PCA scores plot for all samples over the five test days using the full spectrum recorded, i.e., 368-1048 nm. All the soil on leaf samples are clearly separated from the fecal on leaf and uncontaminated leaf samples along the first principal component (PC1) axis. The fecal on leaf and uncontaminated leaf samples are clearly grouped together; however, they are separated by both PC1 and PC2. PC1, PC2, and PC3 represented 61%, 28% and 4% of the total variation explained by the model, respectively. It was decided to crop the spectral range to 600-800 nm to focus on important chlorophyll response wavelengths, including the reported strong chlorophyll \(a\) light absorption range from 680 to 725 nm [11]. Figure 3 shows the PCA scores plot using this spectra range for all samples over the five test days; PC1, PC2, and PC3 explained 69%, 24% and 3% of the variation in this model. In this PCA model, the soil on leaf samples are clearly distinguished from both fecal matter on leaf and uncontaminated leaf along the PC1 axis, and the fecal matter on leaf and uncontaminated leaf are clearly distinguished along the PC2 axis. As can be seen in Figure 4, the loading plot for this model, PC1 is heavily loaded near 756 and 764 nm, and PC2 has high loading values near 712 nm. The loadings plot shows the relationships between the original variables and the first two principal components in the PCA model. Both PCA models show that the three sample types are clearly distinguished into well-defined groups on the PC1 versus PC2 scores plots. Figure 2: Principal component analysis scores plot of showing PC1 (principal component 1) vs. PC2 using spectra from 368 to 1048 nm. \(\bullet\) and F, fecal matter on leaf; \(+\) and S, soil on leaf; o and L, uncontaminated leaf. To validate the technology and technique as a potential system to predict sample type, a SIMCA calibration model was developed using a training data set consisting of the spectral data recorded over the first four testing days. This model was subsequently used to predict the sample types from a test set, which consisted of the remaining fifth testing day's spectra. The spectral range of 600 to 800 nm was used for this validation. The fecal matter on leaf, soil on leaf, and uncontaminated leaf PCA models used five, four, and four principal components, respectively. The test showed that the SIMCA procedure correctly classified 100% of the 54 new samples at the 5% significance level, based on statistical tests performed on the sample-to-model distances. Compared to PCA, the waveband ratio is a statistically simpler technique, and it takes into consideration the slope changes observed between 688 and 710 nm among the different sample types (Figure 5). It was observed that both the fecal matter on leaf and uncontaminated leaf samples had positive slopes from 688 to 710 nm, while the soil on leaf had negative slopes. The uncontaminated leaf samples had higher positive slopes than the fecal on leaf samples. To validate this method, the ratio of  
Figure 4: Loadings plot for principal component model developed using all 270 samples over the five test days in the range of 600 to 800 nm. PC1 (——) and PC2 (——) explain 69% and 24% of the variation in the model, respectively. Figure 3: Principal component analysis scores plot of showing PC1 (principal component 1) vs. PC2 using spectra from 600 to 800 nm. \(\bullet\) and F, fecal matter on leaf; + and S, soil on leaf; o and L, uncontaminated leaf. 710:688 nm was calculated for all 270 samples. As can be seen in Table 3, the range of values for this ratio for each sample type does not overlap; therefore threshold values can be applied to distinguish the different sample types from the ratio of 710:688 nm.