a preliminary study on detection of lung cancer cells based on volatile organic compounds sensing using electronic nose
基于电子鼻技术感应肺癌细胞挥发性有机物初步研究
reena thriumania*, amanina iymia jeffreea, ammar zakariaa, yumi zuhanis has-yun hasyimb, khaled mohamed helmyc, mohammad iqbal omara, abdul hamid adoma, ali yeon shakaffa, latifah munirah kamarudina
abstract
this paper proposes a preliminary investigation on the volatile production patterns generated from three sets of in-vitro cancerous cell samples of headspace that contains volatile organic compounds using the electronic nose system. a commercialized electronic nose consisting of 32 conducting polymer sensors (cyranose 320) is used to analyze the three classes of signals which are lung cancer cells grown in media, breast cancer cells grown in media and the blank media (without cells). neural network (pnn) based classification technique is applied to investigate the performance of an electronic nose (e-nose) system for cancerous lung cell classification.
本文利用电子鼻系统对三组含挥发性有机化合物的顶空癌细胞样品的挥发性生成模式进行了初步研究。由32个导电聚合物传感器(cyranose 320)组成的商业化电子鼻用于分析三种类型的信号，这些信号是在培养基中生长的肺癌细胞、在培养基中生长的乳腺癌细胞和空白培养基(没有细胞)。应用基于神经网络(pnn)的分类技术，研究了电子鼻(e鼻)系统对癌肺细胞分类的性能。
keywords: e-nose, volatile organic compounds (vocs), in-vitro, lung cancer cells, probabilistic neural network (pnn)
关键词：e-nose、vocs、体外、肺癌细胞、概率神经网络pnn
e-nose system was developed by mimic the human olfactory system using an array of chemical sensors that produce electrical signals and combined with a pattern recognition system. the adsorption of vocs on the sensor surface leads to changes in the physical properties of sensors such as conductivity, resistance or frequency. this specific change (signals) can be recorded and transformed into digital data which then can be computed based on chemometric analysis [3][11]. considering that, in this preliminary study, we have investigated the ability of cyranose 320 to detect lung cancer using lung cancer cultured cells. the a549 (lung cancer cell line) and mcf7 (breast cancer cell line) were cultured and the vocs released from these cells were sniffed using cyranose 320 (e-nose). the vocs patterns were analyzed using the pnn algorithm to investigate the effectiveness of cyranose 320 in distinguishing the lung cancer cells from control samples. there are three sub-sections under this chapter which are materials, instrumentation and chemometric analysis.
电子鼻系统是利用一系列产生电信号的化学传感器模拟人类嗅觉系统，并与模式识别系统相结合而开发的。voc在传感器表面的吸附导致传感器物理性能的变化，如电导率、电阻或频率。这种特定的变化(信号)可以被记录下来并转换成数字数据，然后根据化学计量学分析进行计算[3][11]。鉴于此，在这项初步研究中，我们研究了cyranose 320利用肺癌培养细胞检测肺癌的能力。培养a549(肺癌细胞系)和mcf7(乳腺癌细胞系)，并用cyranose 320(电子鼻)嗅探这些细胞释放的voc。利用pnn算法对vocs模式进行了分析，研究了cyranose 320对肺癌细胞与对照细胞的鉴别效果。本章分为三个小节，分别是材料、仪器和化学计量学分析。
pnn算法数据集属性、参数 4层结构pnn图