detection of apple defects using an electronic nose and znose
电子鼻技术用于苹果质量缺陷检测
changying li, phd student, pennsylvania state university, 249 agricultural engineering
building, university park, pa, 16802. cul140@psu.edu
paul heinemann, professor, pennsylvania state university, 249 agricultural engineering
building, university park, pa, 16802
joseph irudayaraj, associate professor, purdue university, 225 s. university st., west
lafayette, in 47907
devin peterson, assistant professor, pennsylvania state university, 215 borland laboratory,
university park, pa, 16802
written for presentation at the
2005 asae annual international meeting
sponsored by asae
tampa convention center
tampa, florida
17 - 20 july 2005
abstract. apple defects and spoilage not only reduce commodity economic value, but cause food safety concerns as well. it is essential for fruit quality assurance and safety to rapidly detect fruit physical damage and spoilage. this article presents the application of an electronic nose (cyranose 320) and znose to the development of a nondestructive, rapid and cost effective system for the detection of defects of apples. the key compounds associated with apple aroma were identified and the “smellprints” of these key compounds were established by the electronic nose and znose. healthy and damaged apples were kept in 2l glass jars for 6 hours for preconcentration before measuring. principal component analysis (pca) models were developed based on the enose and znose data. maholanobis distance was applied for discriminant analysis. experiments showed that the enose and znose are both capable of detecting the volatile differences between healthy apples and damaged apples. after five days deterioration, the correct classification rate for the enose was 83.3%, and for the znose was 100%. after seven days, the correct classification rate was 100% for both instruments. for the next stage, a non-linear model and sensor fusion technique will be developed.
苹果的缺陷和变质不仅降低了商品的经济价值，也引起了食品安全的担忧。快速检测水果的物理损伤和变质是保证水果质量和安全的关键。本文介绍了电子鼻(cyranose320)和znose在开发无损、快速、经济有效的苹果缺陷检测系统中的应用。通过电子鼻和znose，鉴定了与苹果香气有关的关键化合物，并建立了这些关键化合物的“气味图谱”。健康和受损的苹果在2升玻璃罐中保存6小时，在测量前进行预浓缩。基于enose和znose数据建立了主成分分析(pca)模型。采用maholanobis距离进行判别分析。实验表明，该酶和氧化锌均能检测健康苹果和受损苹果的挥发性差异。经过5天的变质处理后，enose的正确分类率为83.3%，znose的正确分类率为100%。7天后，两种仪器的正确分类率为100%。下一阶段将开发非线性模型和传感器融合技术。