Development of non-destructive method for the assessment of storage quality of small onion (A. ascalonicum)

Abstract

The storage qualities of small onion (A. ascalonicum) during storage was assessed using hybrid electronic nose (e-nose)–fuzzy logic approach, beyond conventional tests. Fuzzy logic was used to rank and screen best responsive metal-oxide semiconductors (MOS) sensors (total 18) to detect global volatile odors from small onion. Using e-nose data, an odor index (OI) was estimated and correlated with the aroma, flavor, and tearing effect of onions. Multiple linear regressions (MLR) were used to predict the storage time and sulfur indices of onion using response data of sensors. Fuzzy interpretation identified four sensors which best classified aged and deliberately aged onion using principal component and hierarchical cluster analysis. E-nose data closely predicted the storage time of onion relative to chemical indices (p > 0.05). In addition, it predicted the change in sulfur indices with accuracy (R 2 = 0.995). E-nose data closely predicted the storage time of onions relative to order indices (R 2 , 0.993; RMSE, 3.31 vs. R 2 , 0.985; RMSE, 4.57) (p > 0.05). In addition, it predicted the sulfur indices with accuracy (R 2 = 0.995, RMSE = 0.29). Order Indices (OI) of onions was highly correlated with aroma and color. Their estimated discard time was calculated by 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 small onion at agro-industrial settings.