## Materials and Methods
### Data Preprocessing, Sample Partition, and Cross-Validation  
Multiple scattering correction (MSC) is probably the most widely used preprocessing method in NIRS; it was originally proposed by [PERSON] et al. [29]. The idea of MSC is to remove those defects or flaws caused by scattering effects from the original data matrix before developing models. The theoretical basis for MSC stems from the fact that the wavelength dependence of scattered light is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number in** & **Sensor** & **Object Substances for Sensing** & **Threshold Value** \\
**Array** & **Name** & **Object Substances for Sensing** & **(mL-m\({}^{-3}\))** \\ \hline R1 & W1C & Aromatics & 10 \\ R2 & W5S & Nitrogen oxides & 1 \\ R3 & W3C & Ammonia and aromatic molecules & 10 \\ R4 & W6S & Hydrogen & 100 \\ R5 & W5C & Methane, propane and aliphatic Nonpolar molecules & 1 \\ R6 & W1S & Broad methane & 100 \\ R7 & W1W & Sulfur-containing organics & 1 \\ R8 & W2S & Broad alcohols & 100 \\ R9 & W2W & Aromatics, sulfur-and chlorine-containing organics & 1 \\ R10 & W3S & Methane and aliphatics & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The response features of the sensor array. Figure 2: Schematic diagram of the Fourier transform NIR (FT-NIR) system in diffuse reflection measurement mode with the integrating sphere. different from that of light based on compound absorption. Thus, data from multiple wavelengths can be used to distinguish between the absorption of light and the scattering of light. The derivative preprocessing has the ability to eliminate additivity and multiplicative effects in the spectra and has been used in the spectral analysis for over a decade. The first-order derivative removes only the baseline, while the second-order derivative removes both the baseline and the linear trend. The derivative method proposed by [PERSON] and [PERSON] [30] is probably one of the most popular NIR data preprocessing methods, using smoothing that does not overly reduce the signal-to-noise ratio of the corrected spectra. They popularized a method of numerical derivation of a vector by fitting a polynomial to a symmetrical window of the raw data in order to find the derivative value at the center point. When calculating the parameters of this polynomial, it is easy to find the derivative value of any order of the function by analysis and then use that value as the estimated value of the derivative at that central point, repeat this step, and apply it to all other points in the spectra. The number of points (window size) used to calculate the polynomial and the number of orders to fit the polynomial are specified in advance. Mean centering subtracts the mean value of the response at each wavelength point in all samples by the spectral response value. Since the response value at each wavelength point is subtracted from the mean value of that column, the data in each row after averaging represents the difference between that sample and the mean sample of the original data. This study uses two sample partition methods: the Kennard-Stone [31] method and iterative random partition of samples. The former one selects a subset of samples from the original dataset, which provides uniform coverage of the original dataset and includes samples on the dataset boundaries. The method initially uses geometric distance to find the two samples furthest apart in the original data set. To add another sample to the selected subset, the method selects from the remaining samples the sample that is furthest from the selected sample in the geometric distance. The distance separating a candidate sample from a selected subset refers to the geometric distance between the candidate sample and its closest selected subset. Then, add that candidate sample and repeat the process until the specified required number of samples is added to the selected subset. In fact, this will generate a very evenly distributed space of selected sample points on the dataset.