Storage quality assessment of shelled peanuts using non-destructive electronic nose combined with fuzzy logic approach

The storage quality of shelled peanuts during storage were assessed using hybrid electronic nose (e-nose)-fuzzy logic approach, beyond conventional tests. Fuzzy logic was used to rank and screen best responsive MOS sensors (total 18) to detect global rancid odors from aged peanuts. Using e-nose data, an odor index (OI) was estimated and correlated with chemical rancidity indices (peroxide value (PV) and acid value (AV)). Multiple linear regressions (MLR) were used to predict the storage time and rancidity indices of peanuts using response data of fuzzified sensors. Fuzzy interpretation identified four sensors which best classified aged and deliberately rancid peanuts using principal component and hierarchical cluster analysis. E-nose data closely predicted the storage time of peanuts relative to chemical rancidity indices (R2, 0.993; RMSE, 3.31 vs. R2, 0.985; RMSE, 4.57) (p > 0.05). In addition, it predicted the rancidity indices with accuracy (PV: R2 = 0.995, RMSE = 0.29; AV: R2 = 0.989, RMSE = 0.19). OI of peanuts was highly correlated with PV (0.99) and AV (0.96) and estimated their discard time (basis threshold PV = O2 at 10 mmol kg−1) as 99 d (e-nose) vs. 97 d (conventional tests). The presented approach could be adopted as non-destructive alternative to conventional tests to assure post-harvest quality of shelled peanuts at agro-industrial settings.