## 3 Results and Discussion
### Spectral Analysis
#### 3.2.1 Classification Based on Data of Maize Kernels with Germ Up  
Samples of the Petri dish with kernels orientated with germ up were used, a total of 144 samples made up the data set, with 18 samples of each of the eight treatments. Ninety-six of 144 kernels were selected by using the Kennard-Stone (KS) algorithm as the calibration set, while the other 48 kernels were used as the validation set. Two smooth methods, SGS and MAS, were used firstly to reduce random noise of raw data. In addition, to get rid of the scattering effects caused by different surface roughness and kernel shape, three scatter correction methods, NMZ, SNV and MSC, were applied individually. Discrimination performance of different pretreatment methods and corresponding combinations were shown in Table 1. The performance of SGS and MAS was similar, whether or not they combined with other methods. Results of NMZ were the worst and accuracies of validation (72.92%) were much lower than that of calibration (100%). This might be caused by introducing only the maximum and minimum values of the data set in the algorithm. MAS combined with SNV had the highest classified accuracies of 97.92% and 91.67% for calibration and validation data sets, respectively. The corresponding confusion matrix with characteristics (i.e., sensitivity, specificity, and overall accuracy) was shown in Table 2. Generally, the results were promising with the precision, sensitivity and specificity substantially above 80%, except for low sensitivity (57.14%) for DC in validation, which might be caused by less total sample numbers of the DC group and a large proportion of the number of validation sets compared with the number of calibration sets for the DC group. Moreover, the D\({}_{1-2}\) group showed the highest separability, which was illogical. The reason may be related to water content in maize sample kernels. In the experiment, kernels were immersed in water to create a humid environment for mold growth and dipped into the spore suspension for inoculation. A portion of water in kernels evaporated rapidly during the initial period after inoculation, which caused a relatively larger difference in water content between the D\({}_{1-2}\) group and other groups (the DC group was not dipped into the spore suspension). The influence of water content could be verified in Figure 8 (loading line plot for PC\({}_{4}\)-PC\({}_{6}\)), where several predominant peaks and valleys at 1935 nm (associated with water) were shown. Misclassification might be caused by the size, texture and shape of germ of the samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Pretreatment** & **MAS** & **SGS** &
\begin{tabular}{c} **MAS +** \\ **NMZ** \\ \end{tabular} & **NMSE** & **MAS +** & **SGS +** & **MAS +** & **SGS +** \\
**Methods** & & & **NMZ** & **NMZ** & **SNV** & **SNV** & **MSC** & **MSC** \\ \hline PCs & 8 & 7 & 9 & 9 & **11** & 9 & 6 & 6 \\ Calibration (\%) & 89.58 & 88.54 & 100 & 100 & **97.92** & 97.92 & 82.29 & 84.38 \\ Validation (\%) & 87.50 & 87.50 & 72.92 & 72.92 & **91.67** & 87.50 & 81.25 & 83.33 \\ \hline MAS moving average smoothing (width = 7); SGS: Savitzky-Golay smoothing (width = 7); NMZ: normalization; & SNV: standard normal variate; MSC: multiple scatter correction. & & & & \\ \end{tabular}
\end{table}
Table 1: Comparison of classification models with different pretreatment methods based on kernel samples orientated with germ up.  
Figure 7: Average spectra of the germ region of all kernels under 8 different treatments.