## 4 Discussion
[PERSON] and [PERSON] [44] used partial least squares spectroscopy and linear discriminant analysis (PLS-LDA) with R\({}^{2}\) = 0.96 to obtain comparable results. [PERSON] et al. [50] used partial least squares spectroscopy in combination with linear discriminant analysis (PLS-LDA) to obtain comparable results (R\({}^{2}\) = 0.96), which agreed with the results presented here. [PERSON] et al. [51] used an e-nose to analyze the quality changes in peanuts without skins and peanut kernels during storage. According to PLSR statistical analysis, the characteristics of shelled peanuts and peanut kernels were strongly correlated with acidity and peroxide content, respectively. [PERSON] et al. [52] applied three statistical methods, including PLSR, PCR, and MLR, to create separate regression models for sugars and carbohydrates. They reported that the accuracy of predicting carbohydrate levels in all cultivars was somewhat better than the accuracy of predicting sugar levels. [PERSON] et al. [53] used an electronic nose technique with PLS, PCR, and MLR models to predict the quality of sugarcane syrup based on the purity and the percentage of refined sugar. They reported that the accuracy of the PLS method for this purpose was 77 and 71%, respectively, and that the accuracy of the PCR method was 72% and 68%, respectively. Furthermore, the accuracy of the PLS method was 82% and 74%, respectively. [PERSON] et al. [54] predicted the amount of linalool content in volatiles of tea olive (_Osmanthus fragrans_) using an electronic nose. The R\({}^{2}\) for PCR and MLR methods was reported as 0.736 and 0.895, respectively, and the RMSE for each was reported as 3.98 and 10.10, respectively. Other studies have examined the correlation between e-nose analysis signatures, associated with VOC emissions from various parts of vegetable plant species, and the occurrence of specific chemical components that could be useful information for various applications. These studies have in common the use of VOC-based e-nose data (in combination with specific statistical models) to derive useful chemical information about plant-based products and fresh produce sold in commercial markets. [PERSON] et al. [55] used a MOS electronic nose to classify and discriminate between sweet peppers vs. hot peppers based on differences in VOC emissions detected by the e-nose instrument. They predicted the amount of capsaicin in two types of sweet pepper and hot pepper using MLR, PCR and PLSR statistical models with high accuracy. [PERSON] et al. [56] applied an electronic nose device to diagnose brown rot and ring rot diseases in potato. Other research has focused on the use of PLSR, PCR, and MLR statistical modelling methods in combination with e-nose devices to evaluate chemical constituents of plant products, including the sugar content of potatoes [57], chemical classification of black heart in potatoes [50], prediction of rapeseed quality and purity [58], shelf life of wheat bread [59], qualitative analysis of plant-derived edible oil oxidation [60], effects of the roasting process on pyridine content in coffee beans [61], effects of flour fruit-fiber supplementation on the VOC profile of bread [62], and prediction of post-harvest storage time and indicators of spoilage in peanuts [63]. Finding new ways to improve e-nose performance through the selection of targeted and more appropriate sensors and sensor arrays, pattern-recognition algorithms, and data analysis methods significantly widens the range of gas-sensing capabilities applicable to agriculture. Developing electronic noses for narrower, more specific industrial applications, such as in agriculture technology, allows e-nose manufacturers to build smaller and cheaper portable instruments that provide greater utility, efficiency, and effectiveness for more specialized agricultural applications [15].