*3.1. Numerical Simulation of Microfluidic Chip*

According to the set simulation conditions, it is necessary to simulate the parameters of the microfluidic chip to obtain a reasonable structure of the chip to obtain a good enrichment effect. First, the sub-bureau set the conditions to obtain the pressure map of the microfluidic chip in Figure 4A and the gas velocity map of Figure 4B in the microfluidic chip. In this study, the simulation of the channel width changed to obtain the best enrichment effect. The enrichment effect evaluation index is to release 100 particles, and the number of particles obtained in the corresponding enrichment area determines the enrichment efficiency. The number of particles can be counted in a specific area by selecting specific particles from the derived values in the simulation results of COMSOL multiphysics5.5, and the enrichment rate can be expressed as the percentage of the number of enriched particles to the total number of released particles.

**Figure 4.** Microfluidic chip simulation diagram: (**A**) microfluidic chip pressure map; (**B**) microfluidic chip velocity map.

According to the simulation condition setting, and after simulation with the width adjustment of the microfluidic chip, the enrichment rate of the microfluidic chip under different ratios of the channel width was obtained. Figure 5A,B show the effect of the W2/W1 ratio on the separation and enrichment effect of particles, taking 6 μm and 8 μm particles as examples. It can be seen from Figure 4A,B that when W1 = 700 μm, the enrichment effect of 6 μm and 8 μm particles is better, and when W2/W1 = 1.6, the enrichment effect of the first enrichment zone is the best, the enrichment rate of 6 μm is 93%, and the enrichment rate of 8 μm is 93%. The enrichment rate was 94%, and the channel widths were fixed at W1 = 700 μm and W2 = 1120 μm. It can be seen from Figure 5C,D

that when W3 = 400 μm, the enrichment effect of 3 μm and 5 μm particles is better, while W4/W3 is 1.1, the enrichment efficiency of 3 μm particles is 93%, and the enrichment rate of 5 μm particles is 94%; at this time the fixed channel widths were W3 = 400 μm and W4 = 440 μm.

**Figure 5.** Statistical graph of particle enrichment imitation rate: (**A**) 6 μm collection efficiency; (**B**) 8 μm collection efficiency; (**C**) 3 μm collection efficiency; (**D**) 5 μm collection efficiency.

According to the above simulation and analysis, the optimal enrichment parameters of 6 μm, 8 μm, 3 μm, and 5 μm were obtained, and the best enrichment effect was obtained by simulation according to the optimal parameters, as shown in Figure 6.

**Figure 6.** Statistical graph of particle enrichment imitation rate: (**A**) 6 μm collection effect; (**B**) 8 μm collection effect; (**C**) 3 μm collection effect; (**D**) 5 μm collection effect.

#### *3.2. Raman Analysis*

A total of 200 spectra of four fungi were collected in this study. Since the original spectral data have more spectral noise and high fluorescence background interference, effective spectral preprocessing is very important. Preprocessing the spectrum by selecting a reasonable spectral preprocessing method can effectively reduce spectral noise, retain useful information, simplify the modeling process, and improve the stability of the model. During the Raman spectrum acquisition process, the detector has a small probability of receiving various interference rays such as cosmic rays in the environment, forming sharp peaks in the spectrum, which affects the stability of the spectrum. Therefore, spectra with cosmic spikes need to be identified and eliminated before data analysis. This paper adopted SG smoothing and SNV to remove the influence of noise on the model. Among them, SG smoothing can eliminate the noise interference and uneven fluorescence intensity of the original Raman spectrum. As shown in Figure 7A, and SNV can construct an ideal spectrum by taking the average value of the spectrum, thereby eliminating the effect of particle scattering, as shown in Figure 7B. Baseline calibration is achieved by an iterative polynomial fitting method for baseline drift phenomena in the spectrum. The preprocessing steps are: (1) removing the cosmic spike Raman curve; (2) SG smoothing; (3) SNV correction; (4) baseline calibration.

**Figure 7.** Raman analysis: (**A**) SG-smoothed spectrum after removing cosmic spikes; (**B**) spectra processed by SNV; (**C**) average Raman spectra of four diseased spores.

It can be seen that the SNV greatly reduces the influence of baseline drift and noise on the spectra, while preserving the important spectral information of the fungus. Figure 7C shows the processed average spectra collected from four fungi to provide Raman fingerprints important for the identification of fungal cells. Since Raman scattering depends on the change in molecular polarizability during atomic vibrations, non-polar groups such as S–S, C–C, S–H, and N–N vibrations have strong corresponding signals in Raman, reflecting that various structural information of organic compounds has been obtained [29]. Diseased spores contain cell walls and abundant mRNA. The main components of cell walls are polysaccharides and a small number of proteins and lipids. Different spores contain different types of polysaccharides [30–32].

According to the existing research and experimental data, all characteristic spectral bands and spectral assignments of the four fungi are shown in Table 1. In all Raman spectra, the peaks at 493–497 cm−<sup>1</sup> and 1416 cm−<sup>1</sup> are characteristic peaks for galactomannan and chitin, which are important components of fungal cell walls [29,32]. The peak at

686–687 cm−<sup>1</sup> is attributed to Guanine, Thymine [29,33]. The peaks at 765cm<sup>−</sup>1–772 cm−<sup>1</sup> were assigned to (O-P-O) stretching RNA, respectively [33]. The peak at 984 cm<sup>−</sup>1–989 cm−<sup>1</sup> is attributed to C=C deformation, C–N stretching [12,29]. The peaks at 1065 cm−<sup>1</sup> and 1117 cm−<sup>1</sup> are galactomannan [29]. The peak at 1148 cm−<sup>1</sup> was attributed to C–O ring aromatic [12]. The peaks at 1200 cm−<sup>1</sup> and 1202 cm−<sup>1</sup> are Amide III (random) and Thymine [32,33]. The peak at 1328 cm−<sup>1</sup> is attributed to C–O Amide III (protein), C–H deformation [12]. The peak at 1570 cm<sup>−</sup>1–1577 cm−<sup>1</sup> is Adenine, Guanine (ring stretching) [12,32]. The Raman signals of diseased spores have common components, and there are also differences with their own characteristics. Therefore, the Raman fingerprints of the four fungi measured in this study provide a basis for species identification.

**Table 1.** Peak assignment of the average spectrogram of the four spores.


Combining Figure 7 and Table 2, it can be concluded that the Raman spectra of the four disease spores have some significant common characteristic peaks, which indicates that they contain many of the same components, and there are also some distinctive characteristic peaks unique to spores, indicating their unique composition. However, there were also some insignificant shared characteristic peaks and unique characteristic peaks, which also indicated some shared and unique compositions of diseased spores. Then, classification modeling of spores by only significant characteristic peaks led to inaccurate classification of spores, and it was necessary to find all characteristic peaks by training the algorithm on all bands of the Raman spectrum.

**Table 2.** Accuracy statistics of spore classification model.

