# Mexican Bee Honey Identification Using Sugar Crystals' Image Histograms
## 3 Results and Discussion
### Analyzing and Identifying All Honey Samples  
Once the methodology's repeatability was proved and validated, it was applied to the rest of the honey samples, which had different floral origins and unknown crystallization times. From Section 3.1, it was proved that using a single excitation wavelength is enough to identify a group of honey, as they never overlap (see Figure 4). For this reason, the excitation wavelength at 940 nm was applied in this analysis, as it gives the highest intensity signal among all of the available wavelengths. All of the samples were directly recorded in the multiphotonic microscope, and their histograms were retrieved. The behavior of these gray-level histograms shows a random tendency, and, as with those of the previous section, it is impossible to identify the honey with them. Nevertheless, when the method is applied, they are well identified and separated by their corresponding floral origin, as Figure 7 shows.  
Observing Figure 7, along the PC1 axis, it is possible to see a tendency that classifies the honey samples according to their crystallization speed. It was observed during the experiments that red powder puff (RP) and coffee (CF) honey crystallize faster than mangrove (MG) and avocado (AV). It is also possible to observe that, along the PC1 axis, honey from orange (OG) and citrus (CB) are almost at the same position (gray-dotted oval). The  
Figure 6: SHG grouping of an unknown storage condition from a honey set with the same floral origin, applying 20 different excitation wavelengths.  
Figure 7: Gray-level histogram SHG grouping of all of the honey samples with different floral origins, crystallization times, and storage conditions.  
latter is remarkable, as orange honey is produced with orange tree flowers, while citrus honey has a mixture of orange, lemon, mandarin, and other citrus trees. Mint (MT) and mesquite (MQ) honey have similar crystallization speeds and appear close in this graph. The data from Figure 7 do not require a previous chemical analysis or preparation, yet they show a clear identification/separation from all of the analyzed samples using their sugar crystals for this purpose. A similar result is reported in reference [20], where the sugar composition and crystallization of nine honey samples was used to classify them using PCA due to their pollen variables and sugar ratios. Stingless bees' honey from four regions of Mexico's southern state (Campeche) were classified in reference [33]. They used melisospalynological analysis with PCA to identify each honey sample. However, the geographical region was smaller than the one we describe for Apis Mellifera in Table 1. PCA was also used in the chemical characterization to determine the markers for particular honey samples from Greece and other countries (see reference [34]). Here, 39 honey samples were studied, and the numerical analysis PCA identified potential unique honey markers due to their differences. This same response was observed in Figure 7, but only using a physical parameter, such as the sugar crystals, which are particular for each honey sample. Further study will be directed in this line when related floral origins and crystallization speeds from different samples are analyzed with this method to determine if they can be identified.