title: E-nose based rapid prediction of early mouldy grain using probabilistic neural networks.
abstract: In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction.
authors.affiliation: a School of Information Engineering; Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province; Zhejiang A & F University ; Linan , China.
authors.forename: Xiaoguo
authors.initials: X
authors.lastname: Ying
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Guohua
authors.initials: G
authors.lastname: Hui
authors.forename: Jun
authors.initials: J
authors.lastname: Fu
mesh_terms: D018554:Avena / Q000737:chemistry / Q000382:microbiology; D015374:Biosensing Techniques / Q000295:instrumentation* / Q000379:methods; D002523:Edible Grain / Q000737:chemistry* / Q000382:microbiology; D062609:Electronic Nose; D005504:Food Analysis / Q000295:instrumentation* / Q000379:methods; D005658:Fungi / Q000737:chemistry*; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012275:Oryza / Q000737:chemistry / Q000382:microbiology; D027805:Phaseolus / Q000737:chemistry / Q000382:microbiology; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity
keywords: early mouldy grain; electronic nose; non-linear; probabilistic neural network; rapid prediction