evaluation of an artificial olfactory system for grain quality discrimination
电子鼻系统用于谷物品质评价
s. balasubramanian, s. panigrahi,, b. kottapalli, c.e. wolf-hall
department of agricultural & biosystems engineering, north dakota state university (ndsu), usa
department of agricultural & biosystems engineering, ndsu, 1221 albrecht blvd., po box: 5626, fargo, nd 58105, usa
cdepartment of veterinary & microbiological sciences, ndsu, usa
ddepartment of veterinary & microbiological sciences, ndsu, usa
received 25 december 2005; received in revised form 19 december 2006; accepted 21 december 2006
abstract
a commercially available cyranose-320tm conducting polymer-based electronic nose system was used to analyze the headspace from stored barley samples. three types of barley samples were analyzed, namely, clean barley, naturally fusarium infected barley and fusarium inoculated clean barley. the barley samples were stored at moisture contents of 13, 18, 20 and 25 g of water/100 g sample. the raw signals obtained from the electronic nose system were pre-processed by various signal-processing techniques to extract area-based features. principal component analysis was subsequently performed on the processed signals to further reduce the dimensionalities. classification models using linear (lda) and quadratic discriminant analyses (qda) were developed using the extracted features. the performance of the developed models was validated using leave-1-out cross validation and bootstrapping method. the models classified the barley samples stored into two groups based on the ergosterol content, i.e., ‘‘acceptable’’ (ergosterol content o3.0 mg/g) and‘‘unacceptable’’ (ergosterol content x3.0 mg/g). overall, the total maximum classification accuracy obtained was 86.8% by both lda and qda when leave-1-out cross-validation was used. by bootstrapping validation the maximum total classification accuracy obtained was 86.4% and 86.1% respectively, by qda and lda. the study proves that there is potential in using an electronic nose system for indicating mold spoilage in stored grains, and necessitates future studies in this direction.
@2007 swiss society of food science and technology. published by elsevier ltd. all rights reserved.
使用cyranose-320导电聚合物基电子鼻系统分析了储存的大麦样品。分析了三种大麦样品,即干净的大麦、自然镰刀菌感染的大麦和镰刀菌接种干净的大麦。大麦样品以13、18、20和25 /100 g样品的含水量储存。这个从电子鼻系统获得的原始信号通过各种信号处理技术进行预处理,以提取基于面积的信号。特征。随后对处理后的信号进行主成分分析,以进一步减小尺寸。利用提取的特征建立了基于线性(lda)和二次判别分析(qda)的分类模型。这个利用左1出交叉验证和自举方法对所开发模型的性能进行了验证。分类的模型大麦样品根据麦角固醇含量分为两组,即“可接受”(麦角固醇含量o3.0 mg/g)和“不可接受”(麦角固醇含量x3.0 mg/g)。总体而言,两个lda获得的大分类准确率为86.8%。使用“1-out”交叉验证时的qda。通过引导验证,获得大的总分类精度 qda和lda分别为86.4%和86.1%。研究证明,使用电子鼻系统指出储存颗粒中的霉菌变质,这一方向的研究势在必行。
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