基于仿生技术检测薰衣草气味的方法
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摘要
研究以嗅觉仿生技术对薰衣草的嗅觉信息进行鉴别,选择产自黑龙江佳木斯薰衣草(A)和新疆伊犁地区的法国蓝狭叶薰衣草(B)为研究对象,进行仿生嗅觉的薰衣草气味指纹图谱的研究。研究发现,采用多维传感器阵列能够获取表征样品化学组成特征的组成群体的分析谱图或图象;采用数学统计计量法、人工神经网络或支持向量化方法可分析所获取的图谱信息。研究结果发现,佳木斯薰衣草(A)中乙酸薰衣草酯(13.13%)、薰衣草醇(4.01%)、香叶醇(18.00%)、橙花醇(2.25%)的含量均高于法国蓝狭叶薰衣草(B)中4种成分的含量(8.25%, 1.14%,0.09%和0),薰衣草的气味更柔和一些,香味清爽,以花香为主。该技术能够对薰衣草味道进行客观描述,并给出具体的嗅觉量化值,可为产品的研发提供数据支持。
The olfactory information of lavender was identified by olfactory bionic technique. The lavender scent fingerprints of the lavender scent were obtained from the research of Heilongjiang Jiamusi lavender(A) and the French blue scented lavender(B) in Yili, Xinjiang. The study found that multi-dimensional sensor arrays could be used to obtain analytical spectra or images of the constituent populations that characterize the chemical composition of the samples. Mathematical statistical methods, artificial neural networks or support vectorization methods can be used to analyze the acquired map information.The results showed that the content of lavender acetate(13.13%), lavender(4.01%), geraniol(18.00%) and nerol(2.25%)in Jiamusi lavender(A) was higher than that of French blue lavender(B). The content of the four components was 8.25%,1.14%, 0.09% and 0, the scent of lavender was softer, and the fragrance was refreshing, mainly floral. This technology could objectively describe the taste of lavender and give specific olfactory quantification values, which had been listed as an important research direction in the world.
The olfactory information of lavender was identified by olfactory bionic technique. The lavender scent fingerprints of the lavender scent were obtained from the research of Heilongjiang Jiamusi lavender(A) and the French blue scented lavender(B) in Yili, Xinjiang. The study found that multi-dimensional sensor arrays could be used to obtain analytical spectra or images of the constituent populations that characterize the chemical composition of the samples. Mathematical statistical methods, artificial neural networks or support vectorization methods can be used to analyze the acquired map information.The results showed that the content of lavender acetate(13.13%), lavender(4.01%), geraniol(18.00%) and nerol(2.25%)in Jiamusi lavender(A) was higher than that of French blue lavender(B). The content of the four components was 8.25%,1.14%, 0.09% and 0, the scent of lavender was softer, and the fragrance was refreshing, mainly floral. This technology could objectively describe the taste of lavender and give specific olfactory quantification values, which had been listed as an important research direction in the world.
引文
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[2]唐军,符继红,童红,等.薰衣草品种的FTIR快速分析鉴别研究[J].计算机与应用化学, 2013, 30(6):655-658.
[3]秦臻,董琪,胡靓,等.仿生嗅觉与味觉传感技术及其应用的研究进展[J].中国生物医学工程学报, 2014, 33(5):609-619.
[4]王平,庄柳静,秦臻,等.仿生嗅觉和味觉传感技术的研究进展[J].中国科学院院刊, 2017, 32(12):1313-1321.
[5]童红.化学计量学在新疆伊犁薰衣草挥发性成分分析中的应用[D].乌鲁木齐:新疆大学, 2014.
[6]朱敏辉,尼加提·沙吾提,苏来曼·哈力克.维吾尔药薰衣草的生药学研究[J].中国现代中药, 2012, 14(11):18-20.
[7]石向群,龚志刚.薰衣草精油快速鉴别分析[J].安徽农业科学, 2012, 40(23):11817-11819.
[8]王静,王强,唐军,等.傅里叶变换红外光谱结合PCA及DPLS对薰衣草品种的识别[J].新疆农业科学, 2013, 50(4):614-619.
[9] LIU Q J, ZHANG F N, HU N, et al. Microelectrode recording of tissue neural oscillations for a bionic olfactory biosensor[J]. Journal of Bionic Engineering, 2012(9):494-500.
[10] LIU Y J, MENG Q H, QI P F, et al. Using spike-based bio-inspired olfactory model for data processing in electronic noses[J]. Ieee Sensors Journal, 2018(18):692-702.
[11] MACKAYSIM A. Development of a bionic nose[J]. Genetic Engineer and Biotechnologist, 1991(11):11-13.
[12] JING Y Q, MENG Q H, QI P F, et al. Signal processing inspired from the olfactory bulb for electronic noses[J].Measurement Science and Technology, 2017, 28:1-102.
[13] LING S C, GAO T Y, LIU J, et al. The fabrication of an olfactory receptor neuron chip based on planar multi-electrode arrayanditsodor-responseanalysis[J].Biosensors&Bioelectronics, 2010, 26:1124-1128.