# A Laser-Based Method for the Detection of Honey Adulteration
## 3 Results
### Dimensionality Reduction and Classification of LIBS Spectra for Adulteration Detection  
For the visualization of the level of similarity of the collected LIBS spectra, multidimensional scaling was used (Figure 2). As can be seen, some honey samples are clearly distinguished from the others, forming a large cluster, while the rest of the samples are grouped in a second cluster that contains honey, adulterated honey, and glucose samples. The mixing of the honey samples with the adulterated ones is due to the fact that they were used to make the adulterated ones, thus exhibiting similar spectral characteristics. Moreover, some adulterated samples were found placed near to the glucose ones, again indicating their similarities, because the high glucose content adulterated honey samples have very similar spectral characteristics with those of glucose samples. Next, the training and test set of the first approach (see Samples section, Section 2.1) were used for the construction of the predictive model by means of the LDA algorithm. It must be noted that except for the construction of a predictive model, LDA is capable of visualization of multidimensional data (see also Data Analysis section, Section 2.3), and the corresponding plot is presented in Figure 3a. As can be seen, all of the classes (i.e., honey, adulterated honey, and glucose syrup samples) are clearly distinguishable between them, indicating the differences between the samples. The obtained classification accuracy determined via 10-fold cross-validation was (95.6 \(\pm\) 1.2)% and the test accuracy obtained via external validation was 91.8%. For a more detailed insight of the classification results, the corresponding confusion matrix is presented in Figure 3b. As can be seen, from the 200 honey spectra, only five were misclassified as adulterated honey, and also, from the 400 adulterated honey spectra, 38 were misclassified as honey and two as glucose syrup spectra, while all of the 50 glucose syrup spectra were correctly predicted. It should be noted that, in the case of the adulterated honey spectra that were misclassified as honey, they belonged to samples with relatively low percentage of adulteration (i.e., 10% and 20% (\(w/w\))), while the two misclassified spectra that were predicted as glucose belonged to the 90% (\(w/w\)) adulterated sample. Figure 2: Multidimensional scaling (MDS) of the LIBS spectra. Next, the capabilities of the constructed predictive model were assessed using only the spectral lines of Ca, Na, and K (see Figure 1d), and, more specifically, by using the spectral regions 392-399 nm, 587-592 nm, and 765-772 nm. The results obtained using the LDA algorithm are shown in Figure 3c. As can be seen, some of the glucose syrup samples are slightly overlapping with the adulterated honey samples, as well as with some honey samples. The overlapping honey samples belong to the samples that were used to prepare the adulterated honey samples, while the rest of the honey samples are clearly distinguishable. The obtained classification and test accuracies were found to be (91.2 \(\pm\) 2)% and 96.2%, respectively. Then, for more detailed overview of the constructed model's performance, the confusion matrix is presented in Figure 3d, as before. In this case, a better algorithmic behavior was observed, since only 20 adulterated samples' spectra (19 as honey and one as glucose syrup spectra) of the total 300 were misclassified, while just one glucose syrup spectrum was predicted as an adulterated one, and none of the honey spectra were falsely predicted. Despite the fact that the accuracies are slightly lower than using the whole LIBS spectra, these results are quite impressive because they indicate that honey adulteration can be detected equally effectively by using only specific emissions (Ca, Na, and K) with great success. Figure 3: **(a)** LDA canonical variable plot employing the LIBS spectra. **(b)** Confusion matrix showing the prediction of the test samples using the LDA algorithm employing the LIBS spectra.