Karlshøj Kristian, Nielsen Per V, Larsen Thomas O
Center for Microbial Biotechnology, BioCentrum-DTU, Building 221, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
J Agric Food Chem. 2007 May 30;55(11):4289-98. doi: 10.1021/jf070134x. Epub 2007 Apr 26.
Classification models for Penicillium expansum spoilage of apples and prediction models for patulin concentration in apples usable for apple juice production were made on the basis of electronic nose (e-nose) analysis correlated to HPLC quantification of patulin. A total of 15 Golden Delicious and 4 Jonagold apples were surface sterilized and divided into three groups per variety. The Golden Delicious group consisted of five apples each. Group 1 was untreated control, group 2 was surface inoculated with P. expansum, and group 3 was inoculated in the core with P. expansum. The apples were incubated at 25 degrees C for 10 days. E-nose analysis was performed daily. At day 10 the Golden Delicious apples were individually processed for apple juice production. During apple juice production the mash and juice were analyzed by e-nose, and samples were taken for patulin analysis by HPLC. The volatile metabolite profile was obtained by collection of volatile metabolites, on tubes containing Tenax TA, overnight between the 9th and 10th days of incubation and subsequent analysis of the collected compounds by GC-MS. Prediction models using partial least-squares, with high correlation, for prediction of patulin concentration in shredded apples as well as apple juice were successfully created. It was also shown that it is possible to classify P. expansum spoilage in apples correctly on the basis of soft independent modeling of class analogy classification of e-nose analysis data. To the authors' knowledge this is the first report of a regression model between e-nose data and mycotoxin content in which actual concentrations are reported. This implies that it is possible to predict mycotoxin production and concentration by e-nose analysis.
基于与棒曲霉素的高效液相色谱法定量相关的电子鼻分析，建立了苹果青霉腐烂的分类模型以及可用于苹果汁生产的苹果中棒曲霉素浓度的预测模型。总共15个金冠苹果和4个乔纳金苹果进行了表面消毒，每个品种分为三组。金冠苹果组每组有五个苹果。第1组为未处理的对照组，第2组在表面接种扩展青霉，第3组在果核中接种扩展青霉。将苹果在25摄氏度下孵育10天。每天进行电子鼻分析。在第10天，将金冠苹果单独加工用于生产苹果汁。在苹果汁生产过程中，对果泥和果汁进行电子鼻分析，并采集样品用高效液相色谱法进行棒曲霉素分析。挥发性代谢物谱通过在含有Tenax TA的管上收集挥发性代谢物获得，在孵育的第9天和第10天之间过夜，随后通过气相色谱 - 质谱联用仪对收集的化合物进行分析。成功创建了使用偏最小二乘法且具有高相关性的预测模型，用于预测切碎苹果以及苹果汁中棒曲霉素的浓度。研究还表明，基于电子鼻分析数据的类类比分类的软独立建模，可以正确地对苹果中的扩展青霉腐烂进行分类。据作者所知，这是首次报道电子鼻数据与霉菌毒素含量之间的回归模型，并报告了实际浓度。这意味着通过电子鼻分析可以预测霉菌毒素的产生和浓度。
