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A Multiple Approach Combined with Portable Electronic Nose for Assessment of Post-harvest Sapota Contamination by Foodborne Pathogens
Food and Bioprocess Technology ( IF 5.3 ) Pub Date : 2020-06-15 , DOI: 10.1007/s11947-020-02473-2
Madeshwari Ezhilan , Noel Nesakumar , K. Jayanth Babu , C. S. Srinandan , John Bosco Balaguru Rayappan

Sapota (Manilkara Zapota) belongs to the Sapotaceae family and is one of the sweetest, delicious, and popular tropical fruits with high nutritional benefits. Due to its high water content (78%), it is easily damaged, which in turn reduces its shelf life. Also, significant post-harvest losses occur due to insufficient storage, poor handling, and transportation conditions, which also increase the microbial or pathogenic contamination. In this context, we have deployed an electronic nose for the first time to rapidly discriminate fresh sapota from bacterial contaminated sapota samples. The electronic nose response was varied from 0.1 to 2.9 V for sapota samples studied from day 1 to day 6. Fourier transform infrared spectra showed a gradual increase in the intensities of OH and H-O-H peaks at 3320 cm−1 and 1636 cm−1for these samples. Also, Staphylococcus bacteria were detected for the same set of sapota samples. Headspace GC-MS results of day 2, day 4, and day 6 sapota samples revealed the increasing trend in bacterial volatiles, namely ethanol, acetaldehyde, and isopropyl alcohol. The sensor data were provided as input to principal component analysis (PCA), Ward’s method analysis, and response surface methodology (RSM) for distinguishing fresh, half-contaminated, and fully contaminated sapota samples.

Graphical Abstract



中文翻译:

多种方法结合便携式电子鼻评估食源性病原菌对收获后沙波菌的污染

Sapota(Manilkara Zapota)属于Sapotaceae家族,是最甜,美味和受欢迎的热带水果之一,具有很高的营养价值。由于其含水量高(78%),很容易损坏,从而降低了其保质期。同样,由于存储不足,处理不当和运输条件的原因,收获后的损失也很大,这也增加了微生物或病原体的污染。在这种情况下,我们首次部署了电子鼻,以快速将新鲜番茄酱与细菌污染的番茄酱样本区分开。从第1天到第6天研究的sapota样品的电子鼻响应在0.1到2.9 V之间变化。傅立叶变换红外光谱显示在3320 cm处OH和HOH峰强度逐渐增加这些样品的-1和1636 cm -1。同样,检测到同一组腐烂样品的葡萄球菌。第2天,第4天和第6天顶空样品的顶空GC-MS结果表明,细菌挥发物,即乙醇,乙醛和异丙醇的增加趋势。提供传感器数据作为主成分分析(PCA),沃德方法分析和响应面方法(RSM)的输入,以区分新鲜,半污染和完全污染的番茄酱样品。

图形概要

更新日期:2020-06-18
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