Recognition of harmful fungal species and quantitative detection of fungal contamination in peanuts based on electronic nose technology

Abstract: Current methods for fungi contamination determination in peanuts are usually labor-intensive and time-consuming. In this paper, a new method for rapid detection of the contamination by harmful fungi species in peanut kernels based on electronic nose (E-nose) technology was investigated. Peanut samples were firstly irradiated by Co-60 gamma radiation with a dose of 15 kGy to kill all fungi on or within kernels. After irradiation, clean and sterile peanuts were placed in moist chambers and inoculated with 5 different spore suspensions of aspergillus spp., which were A. flavus 3.17, A. flavus 3.395 0, A. parasiticus 3.395, A. parasiticus 3.012 4 and A. ochraceus 3.648 6, the former 3 of which were aflatoxin (AFT) producer, and the latter one was ochratoxin (OT) producer. Spore suspensions were prepared by blending the 7-day old colonies cultured on potato dextrose agar (PDA) with ultrapure sterilized water. Initial spore concentration was about 5 log (CFU/mL), and then 10 μL spore suspension was dropped onto individual peanut sample by a pipette. All infected samples were stored at 26 ℃ and 80% relative humidity (RH) for 9 d until all peanut samples were covered with a mass of fungi. Subsequently, the E-nose (Fox 3000, Alpha Mos) was used for the collection of volatile odor information from peanut samples stored for 0, 3, 6 and 9 d, respectively. Finally, response signals of 12 E-nose sensors were extracted by multivariate statistical analysis method. Qualitative and quantitative models for the determination of harmful fungi contamination in peanuts were established. The principal component analysis (PCA) results showed that peanut samples with different storage days could be successfully discriminated for different fungal infection levels. Loading analysis of E-nose sensors indicated that the sensors of T70/2, LY2/LG, P10/1, T30/1 were found to be more sensitive than other sensors. These sensors might play an important role in the discrimination of samples, which provided a reference for the development of special-purpose sensor systems for peanut samples in future. The changes in volatile compounds of infected peanut samples could be mainly attributed to oxynitride, hydrocarbon and aromatic compounds. For the classification of peanut samples with different infection levels, the correct rate of 100%(or approaching) was obtained by linear discriminant analysis (LDA) models. The results also verified the possibility of discriminating peanuts infection by different fungi species. In addition, good correlation between E-nose signals and colony forming units in peanut samples was obtained by partial least squares regression (PLSR) analysis models. The coefficient of determination for the prediction set (Rp2) and the root mean square error of prediction (RMSEP) for the prediction models were 0.814 5 and 0.244 0 lg (CFU/g), respectively. Both LDA and PLSR methods were proven to be effective in the discrimination/quantification of fungi contamination in peanuts. The results indicate that E-nose technology can be used as a feasible and reliable method for the determination of peanut quality during the storage, which can provide the theoretical reference for rapid detection of mold contamination during grain storage using volatile odor information.