## 4 Discussion  
The process and mechanism by which an e-nose device is capable of indirectly detecting differences in specific types and amounts of nutrients that have been taken up by the plant is important for conceptual understanding. When a nutrient such as nitrogen is first taken up by the plant, it is rapidly converted into more stable forms that can be further assimilated and used for specific plant physiological processes. None of these stable forms of nitrogen can be directly detected by an electronic nose device which only detects VOC emissions. However, the nutrients are utilized at varying levels, based on availability due to relative amounts of nutrient uptake by the plant from the soil, which directly affect the vegetative growth and metabolic processes of the plant within the leaves and fruits. As a result of biochemical effects of different nutrient levels on host metabolic pathways, the VOC emissions released from various plant parts accordingly change to reflect these differences in nutrient level effects on plant pathways that generate VOC emissions. An electronic-nose device is capable of directly detecting changes in plant VOC emissions resulting from effects of different nutrient levels on host physiology. In this way, a plant receiving no additional exogenous nutrients may be distinguished from a plant that has received additional nutrients by differences in their VOC emissions which reflects internal differences in plant metabolic pathways that give rise to uniquely different types and amounts of plant volatiles present in plant VOC emissions. The development of PCR and PLSR regression models were used to determine the effectiveness and error levels of each statistical model for indirectly estimating nutrient levels in fruits. These values were further used to describe the regression models using the MLR method. We found that the PCR and PLSR methods could accurately predict the estimated nitrogen content of cucumber fruits, following different graduated soil application rates, with an accuracy of about 90%, based on an optimal factor (X1) and  
Figure 9: MLR prediction models for phosphorus fertilizer based on optimal factors of (**a**) PCR model and (**b**) PLSR model. calibration error and a prediction of 0.046 and 0.047, respectively. For the measurement of potassium with the PCR method using four optimal factors (X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), and X\({}_{4}\)) and the PLSR method with two optimal factors (X\({}_{1}\) and X\({}_{2}\)), we obtained calibration and prediction errors equal to 0.013 and 0.0141, respectively, and an accuracy of about 83%. For the prediction of phosphorus content using PCR and PLSR methods with one optimal factor (X\({}_{1}\)) and calibration and prediction error, the predictive value was 0.021 and the accuracy was about 80.0%. We also determined that nitrogen could be estimated with high accuracy, using a combination of statistical prediction models and electronic-nose data based on the detection of cucumber fruit VOCs. The results suggest that the NPK content of cucumber fruit may be accurately estimated indirectly using MOS e-nose data based on VOCs emissions using appropriate regression models. The e-nose prediction of N content from the analysis of cucumber fruit VOC gases in the sample headspace was at a higher accuracy than for K or P. It was concluded that changes in cucumber fruit VOC emissions associated with nitrogen levels in cucumber fruits were better correlated with e-nose smellprint signatures than were fruit VOC emissions associated with changes in soil K or P levels detected by the electronic nose. [PERSON] [48] used PLS models to identify olive oil quality parameters from chemical data derived from an e-nose. The e-nose showed good performance in modelling the acidity parameter. Moreover, the correlation coefficients for a PLS model for acidity were 0.87 for calibration and 0.88 for validation sets. [PERSON] et al. [49] reported similar results using the PLSR method and an electronic nose for grapes, with an R\({}^{2}\) of 0.93.