# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 3 Results and Discussion  
### Characteristic Band Selection and Sensor Design  
#### 3.1.1 NIR Spectra Analysis and Characteristic Band Determination  
For the effective determination of moisture content in rice grain samples, it might be necessary to reduce the full spectra from 950-1650 nm to a few characteristic bands according to the curvilinear trend and water absorption peaks. The raw NIR spectra of the 90 rice grain samples with different moisture content obtained in Section 2.2 are shown in Figure 1.  
According to Figure 1, both ends of the spectra showed obvious noise caused by the decreasing efficiency of the diffraction grating towards the edges. Beyond that, there are two distinct absorption peaks in the NIR spectra of rice grain ranging from 1100-1250 nm and 1350-1550 nm. More specifically, the spectral valley appeared around 1450 nm (first overtone of O-H stretching) [20; 31].  
To explore the model performance of 950-1650 nm, 1100-1250 nm, 1350-1550 nm and 1450 nm, respectively, chemometric analysis (PLS, CARS, SLR) was applied to model and analyze the spectral data. In addition, the SPXY (sample set partitioning based on joint x-y distance) method was used to divide the rice grain samples into two parts, including 60 samples in calibration set and 30 samples in the prediction set. Also, the leave-one-out method was used for cross validation. Table 1 summarizes the statistics description of moisture content in 90 rice grain samples and the model performance of those three bands is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Set** & **Number** & **Min (\%)** & **Max (\%)** & **Mean (\%)** & **SD a (\%)** \\ \hline
**Calibration set** & 60 & 10.46\% & 27.88\% & 20.26\% & 5.81\% \\
**Prediction set** & 30 & 10.27\% & 25.98\% & 19.18\% & 5.34\% \\
**Total** & 90 & 10.27\% & 27.88\% & 19.9\% & 5.62\% \\ \hline \hline \end{tabular}  
* SD (standard deviation).  
\end{table}
Table 1: Statistical description of the moisture content in rice grain samples.  
Figure 1: The NIR spectra of rice grain with different moisture content.  
Figure 2 and Table 2 show the model results of 950-1650 nm, 1100-1250 nm,1350-1550 nm and 1450 nm of rice grain modeled by PLS, CARS and SLR. As a result, the 950-1650 nm, containing the most comprehensive information, achieved the best prediction effects (\(R_{p}^{2}=0.970\) for PLS and \(R_{p}^{2}=0.977\) for CARS), while the prediction accuracy for 1100-1250 nm was the worst (\(R_{p}^{2}=0.844\) for PLS and \(R_{p}^{2}=0.883\) for CARS). For 1350-1550 nm, better prediction accuracy (\(R_{p}^{2}=0.923\) for PLS and \(R_{p}^{2}=0.917\) for CARS) was obtained than for 1100-1250 nm, indicating that 1350-1550 nm was more sensitive to rice grain moisture in the NIR region. Based on Figure 1, 1450 nm was determined as the optimal wavelength. Therefore, we established a regression model based on the near-infrared spectral absorption intensity of the 1450 nm band and rice grain moisture shown in Figure 3. Compared with the results by [PERSON] et al. [19] and [PERSON] et al. [20], the prediction effects were greatly increased in our research. The results showed that there was a good regression between the near-infrared spectral absorption intensity of the 1450 nm band and the accuracy of rice grain moisture (\(R_{p}^{2}=0.832\)), indicating that the strong compression of \(X\) to one dimension in 1450 nm did not noticeably influence the prediction accuracy. Therefore, 1450 nm could be determined as the characteristic waveband for developing the rapid-detection sensor, with similar results being reported by other authors [21; 32]. In summary, the LED light-emitting diode with 1450 nm wavelength was chosen as the light source for the detection sensor in this paper, the emission wavelength of which was also in the range of 1350-1550 nm.