Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms?

Abstract

Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose) response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose.

Characterization of protein changes and development of flavor components induced by thermal modulation during the cooking of chicken meat

“…Electronic nose techniques can be utilized to sense and distinguish volatile compounds to provide information on flavor constituents (Peris & Escudergilabert, ). Principal component analysis (PCA) is a typical data analysis method used for e‐nose data, which converts the original data from high dimensions into low dimensions without much loss of information (Zou et al, ). Theoretically, the high variance in the cumulative contribution rate depicted a more efficient treatment (Verma & Suthar, ).…”

“…is a typical data analysis method used for e-nose data, which converts the original data from high dimensions into low dimensions without much loss of information (Zou et al, 2015). Theoretically, the high variance in the cumulative contribution rate depicted a more efficient treatment (Verma & Suthar, 2018).…”

Phytochemical analysis, anticancer screening and antimicrobial activity of Ballota undulata Fresen (Benth.) from Al- Baha region Saudi Arabia

“…Literature surveys suggest the important role played by plant-based drugs in treating infectious diseases (Khalil et al, 2009). Due to the diversity and complexity of natural phenolic compounds, it is dif icult to characterize every compound present in the crude extract to elucidate its structure (Zou et al, 2015), qualitative estimation for some phenolic and lavonoids compounds for different successive extract of B. undulata was observed at Table 8 by HPLC. The chloroform extract contains kaempferol (Figure 7), the ethyl acetate extract contains caffeic acid, kaempferol, rutin and quercetin (Figure 8) and the methanolic extract contain chlorogenic acid and Caffeic acid (Figure 9).…”

Classification and Identification of Industrial Gases Based on Electronic Nose Technology

“…Fortunately, advanced gas detection technologies, such as the portable electronic nose (E-nose), have become widely available to tackle the issues. Some researchers have applied E-nose technology to inspect the quality of food, including fruits [11,12], meat [13,14], rice [15,16], tea [17], cigarettes [18], Chinese herbal medicines [19], and others plant crops [20]. The latest reports show that an E-nose can be applied to the process of harmful gas detection [21,22,23], the medical industry [24,25], and space applications [26,27,28].…”