Temperature Compensated Electronic Nose for Fruit Ripeness Determination Using Component Correction Principal Component Analysis

The study integrates a component correction algorithm on the electronic nose, intended for fruit ripeness determination, to counteract the tendency of the electrochemical metal oxide sensors to drift.The drift is any unwanted deviation of the chemoreceptor response from the true value leading to erroneous and inconsistent results.The study utilizes CCPCA -Component Correction Principal Component Analysis to approximate and remove the temperature drift component.In using this algorithm, the study theorizes that the drift acquired from the simulation of temperature drift without any sample (clean air) could generalize the drift pattern or structure in sampling Lacatan aroma samples.The PCA or Principal Component Analysis is utilized for 'dimensioanlity reduction' and 'data mining' that uncovers patterns unobserved on the multidimensional data structure.The cluster Silhouette is computed to quantitatively validate the intracluster cohesion and intercluster separation.Using PCA, the electronic nose distinguished 630 Lacatan aroma samples on the PCA Loadings -PC1 (94.65%) and PC2 (3%) and significantly improved the clustering upon removal of temperature drift.The cluster Silhouette is improved from 0.7903 to 0.8571 (unripe), 0.5358 to 0.6080 (ripe), and 0.7784 to 0.8357 (overripe).