Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.

Volatile flavour identification and odour complexity of radix Angelicae sinensis by electronic nose, integrated gas chromatography-mass spectrometry/olfactometry and comprehensive two-dimensional gas chromatography-time-of-flight-mass spectrometry

INTRODUCTION

Radix Angelicae sinensis (Danggui, DG) is known as one of the typical traditional Chinese medicines. DG material consists of a variety of volatile substances, polysaccharides, organic acids, ceramides, amino acids, vitamins, microelements, among others, and thus has been used for medicinal and edible purposes in a long history. The fragrance is of importance to assessing the DG material quality.

OBJECTIVES

This study was to determine volatile flavour compositions of DG materials and to reveal the odour complexity.

MATERIAL AND METHODS

Electronic nose (E-nose), integrated gas chromatography-mass spectrometry/olfactometry (GC-MS/O) and comprehensive two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC × GC-TOF-MS), combined with solid-phase micro-extraction (SPME), were mainly used to address the flavour complexity of DG materials.

RESULTS

Using the E-nose sensor responses, a total of 105 batches of DG samples cultivated in six provinces of China were categorised according to their odour differentiations, and a principal component analysis (PCA) model was established for evaluating the sample quality through a combination of Hotelling's T² and Q-residual values in a statistical quantitative sense. By the GC-MS/O and GC × GC-TOF-MS analyses, 196 volatile flavour compounds were identified, 51 odour-active areas discerned and 39 odourants determined. It was terpenes and aromatics of the flavour compounds that mainly contributed to the odour attributes of DG herb.

CONCLUSION

The SPME-GC × GC-TOF-MS method was the first time employed to analyse the volatile flavours of DG materials, and thus made a breakthrough in determining 196 flavour compounds, much more than those in any previous report. The work also made a significant step forward to link the flavour compositions and odour complexity of radix Angelicae sinensis by E-nose and GC-MS/O techniques. It not only provided a statistical PCA model that did not depend on any predetermined compositions or sensory properties for, but also a comprehensive insight into the quality evaluation of DG materials.

Chemical Composition Analysis and Discrimination of Essential Oils of Artemisia Argyi Folium from Different Germplasm Resources Based on Electronic Nose and GC/MS Combined with Chemometrics

In this study, the electronic nose and GC/MS were used to analyze the chemical components of essential oils from different germplasm resources of Artemisia argyi Folium (A. argyi), in order to quickly identify essential oils of A. argyi from different germplasm resources and clarify the differences among different A. argyi samples. The essential oils of A. argyi were extracted by steam distillation. This article describes for the first time that electronic nose combined with chemometrics can distinguish the essential oils of A. argyi from different germplasm, which proves the reliability and potential of this technology. GC/MS was used to identify 134 volatile components from the essential oil of A. argyi. The main bioactive components were cineole, thujarone, artemisia ketone, β-caryophyllene, (-)-4-terpinol, 3,3,6-trimethyl-1,5-heptadien-4-ol, (-)-α-thujone, camphor, borneol. In addition, the results of principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that there were significant differences in the essential oils of A. argyi from different germplasm resources, terpenes, alcohols and ketones played an important role in identifying the essential oils of A. argyi from different germplasm resources. This indicates that electronic nose and GC/MS combined with chemometrics can be used as reliable techniques to identify different germplasm resources of A. argyi, and provide certain reference value for quality evaluation, selection of high-quality varieties and rational development of resources of A. argyi.

Variations in volatile flavour compounds in Crataegi fructus roasting revealed by E-nose and HS-GC-MS

Introduction

Crataegi fructus (CF) is an edible and medicinal functional food used worldwide that enhances digestion if consumed in the roasted form. The odour of CF, as a measure of processing degree during roasting, significantly changes. However, the changes remain unclear, but are worth exploring.

Methods

Herein, the variations in volatile flavour compounds due to CF roasting were investigated using an electronic nose (E-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS).

Results

A total of 54 components were identified by GC-MS. Aldehydes, ketones, esters, and furans showed the most significant changes. The Maillard reaction, Strecker degradation, and fatty acid oxidation and degradation are the main reactions that occur during roasting. The results of grey relational analysis (GRA) showed that 25 volatile compounds were closely related to odour (r > 0.9). Finally, 9 volatile components [relative odour activity value, (ROAV) ≥ 1] were confirmed as key substances causing odour changes.

Discussion

This study not only achieves the objectification of odour evaluation during food processing, but also verifies the applicability and similarity of the E-nose and HS-GC-MS.

Comparative Analysis of Acanthopanacis Cortex and Periplocae Cortex Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis

Chinese Herbal Medicines (CHMs) can be identified by experts according to their odors. However, the identification of these medicines is subjective and requires long-term experience. The samples of Acanthopanacis Cortex and Periplocae Cortex used were dried cortexes, which are often confused in the market due to their similar appearance, but their chemical composition and odor are different. The clinical use of the two herbs is different, but the phenomenon of being confused with each other often occurs. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two species for fast and robust discrimination, in order to provide a scientific basis for avoiding confusion and misuse in the process of production, circulation and clinical use. In this study, the odor and volatile components of these two medicinal materials were detected by the E-nose and by gas chromatography-mass spectrometry (GC-MS), respectively. An E-nose combined with pattern analysis methods such as principal component analysis (PCA) and partial least squares (PLS) was used to discriminate the cortex samples. The E-nose was used to determine the odors of the samples and enable rapid differentiation of Acanthopanacis Cortex and Periplocae Cortex. GC-MS was utilized to reveal the differences between the volatile constituents of Acanthopanacis Cortex and Periplocae Cortex. In all, 82 components including 9 co-contained components were extracted by chromatographic peak integration and matching, and 24 constituents could be used as chemical markers to distinguish these two species. The E-nose detection technology is able to discriminate between Acanthopanacis Cortex and Periplocae Cortex, with GC-MS providing support to determine the material basis of the E-nose sensors' response. The proposed method is rapid, simple, eco-friendly and can successfully differentiate these two medicinal materials by their odors. It can be applied to quality control links such as online detection, and also provide reference for the establishment of other rapid detection methods. The further development and utilization of this technology is conducive to the further supervision of the quality of CHMs and the healthy development of the industry.

Exploration on Varying Patterns of Morphological Features and Quality of Armeniacae Semen Amarum in Rancid Process Based on Colorimeter, Electronic Nose, and GC/MS Coupled With Human Panel

In recent years, the domestic and international trade volumes of Chinese medicinal materials (CMMs) keep increasing. By the end of 2019, the total amount of exported CMMs reached as high as US $1.137 billion, while imported was US $2.155 billion. A stable and controllable quality system of CMMs apparently becomes the most important issue, which needs multifaceted collaboration from harvesting CMMs at a proper season to storing CMMs at a proper temperature. However, due to imperfect storage conditions, different kinds of deteriorations are prone to occur, for instance, get moldy or rancid, which not only causes a huge waste of CMM resources but also poses a great threat to clinical medication safety and public health. The key issue is to quickly and accurately distinguish deteriorated CMM samples so as to avoid consuming low-quality or even harmful CMMs. However, some attention has been paid to study the changing quality of deteriorated CMMs and a suitable method for identifying them. In this study, as a medicine and food material which easily becomes rancid, armeniacae semen amarum (ASA) was chosen as a research objective, and experimental ASA samples of different rancidness degrees were collected. Then, various kinds of analytical methods and technologies were applied to explore the changing rules of ASA quality and figure out the key indicators for the quality evaluation of ASA in the rancid process, including the human panel, colorimeter, electronic nose, and GC/MS. This study aims to analyze the correlation between the external morphological features and the inner chemical compounds, to find out the specific components from "quantitative change" to "qualitative change" in the process of "getting rancid," and to discover the dynamic changes in the aforementioned key indicators at different stages of rancidness. The results showed since ASA samples began to get rancid with the extension of storage time, morphological features, namely, surface color and smell, changed significantly, and the degree of rancidness further deepened at the same time. Based on macroscopic identification accomplished via the human panel, ASA samples with varying degrees of rancidness were divided into four groups. The result of colorimeter analysis was in agreement with that of the human panel, as well as the determination of the amygdalin content and peroxide value. Moreover, there were obvious differences in the amygdalin content and peroxide value among ASA samples with different rancidness degrees. With a higher degree of rancidness, the content of amygdalin decreased, while the peroxide value increased significantly. The rancidness degree of ASA has a negative correlation with the amygdalin content and a positive correlation with the peroxide value. The newly discovered nonanal and 2-bromopropiophenone in rancid ASA samples may be the key components of "rancidity smell," and these two components would be the exclusive components that trigger "quantitative change" to "qualitative change" in the process of rancidness of ASA. This study sheds light on studying the internal mechanism of "rancidness" of CMMs and provides an important basis for the effective storage and safe medication of easy-to-get rancid herbs, and it also plays an important foundation for the establishment of a stable and controllable quality system for CMMs.

Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods

Crataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.

Influence of the aromatic surface on the capacity of adsorption of VOCs by magnetite supported organic-inorganic hybrids

It has been recently evidenced that hybrid magnetic nanomaterials based on perylene diimide (PDI) dopamine and iron oxide nanoparticles are useful for the adsorption and determination of volatile organic compounds (VOCs). However, NDI compounds are expensive and difficult to handle compared to smaller size diimides. Therefore, in this manuscript a combined experimental and theoretical investigation is reported including the analysis of the effect of changing the aromatic surface on the ability of these magnetite supported organic-inorganic hybrid nanoparticles (NPs) to adsorb several aromatic and non-aromatic VOCs. In particular, two new hybrid Fe3O4NPs are synthesized and characterized where the size of organic PDI dopamine linker is progressively reduced to naphthalene diimide (NDI) and pyromellitic diimide (PMDI). These materials were utilized to fill two sorbent tubes in series. Thermal desorption (TD) combined with capillary gas chromatography (GC)/flame detector (FID) was used to analyze both front and back tubes. Adsorption values (defined as % VOCs found in the front tube) were determined for a series of VOCs. The binding energies (DFT-D3 calculations) of VOC-Fe3O4NP complexes were also computed to correlate the electron-accepting ability of the arylene diimide (PDI, NDI or PMDI) with the adsorption capacity of the different tubes. The prepared hybrids can be easily separated magnetically and showed great reusability.

Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal

Volatile organic compounds (VOCs) are natural markers useful in rapid assessment of adverse changes occurring in biological material. The use of an electronic nose seems to be a good, fast, and cheap method to determine particular VOCs. This paper presents a new method determination for VOCs and their concentration based on three sensorgram parameters: maximum of normalized sensor response, reaction time, and cleaning time measured from the end of the test to the half value of the maximum of normalized sensor response. The novelty of the method consists in the use for the first time of two parameters: reaction time and cleaning time measured from the end of the test to the half value of the maximum of normalized sensor response. The VOC sensorgrams at different VOC concentrations (26 to 3,842 ppm) were measured by an electronic nose Food Volatile Compound Analyzer (Agrinose) equipped with eight metal oxide semiconductor sensors dedicated to detect different gases. In the present studies, only six sensors that best respond to the VOCs were used. The highest responses to VOCs were obtained for two sensors-TGS2602 and AS-MLV-P2. The results showed that the dependence between the sensorgram parameters on VOC concentration was well described by a logarithmic curve in the whole range of concentrations. Detailed analysis revealed that the cleaning time increases with an increase in the number of carbon atoms in aliphatic molecules. The principal component analysis (PCA) was used to verify the utility of the new three parameters method in VOCs differentiation. The PCA analysis of these parameters showed that maximum of the normalized sensor response alone, which has been used for identification of particular VOCs so far, could not be regarded as a good parameter used for this purpose. Application of all the three parameters gave the best results in VOC identification. The research indicates that the use of three parameters of a volatile compound instead of only one parameter can allow precise determination of substances. Moreover, the results indicate that the analyzed parameters depend on the chemical structure of VOCs.

Discrimination of Two Cultivars of Alpinia Officinarum Hance Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Chemometrics

Background: Alpinia officinarum Hance is both an herbal medicine and a condiment, and generally has different cultivars such as Zhutou galangal and Fengwo galangal. The appearance of these A. officinarum cultivars is similar, but their chemical composition and quality are different. It is therefore important to discriminate between different A. officinarum plants to ensure the consistency of the efficacy of the medicine. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two cultivars for fast and robust discrimination. Methods: Odor and volatile components of all A. officinarum samples were detected by the E-nose and gas chromatography-mass spectrometry (GC-MS), respectively. The E-nose sensors and GC-MS data were analyzed respectively by principal component analysis (PCA), the correlation between E-nose sensors and GC-MS data were analyzed by partial least squares (PLS). Results: It was found that Zhutou galangal and Fengwo galangal can be discriminated by combining the E-nose with PCA, and the E-nose sensors S2, S6, S7, S9 were important sensors for distinguishing different cultivars of A. officinarum. A total of 56 volatile components of A. officinarum were identified by the GC-MS analysis, and the composition and content of the volatile components from the two different A. officinarum cultivars were different, in particular the relative contents of 1,8-cineole and α-farnesene. The classification result by PCA analysis based on GC-MS data was consistent with the E-nose results. The PLS analysis demonstrated that the volatile terpene, alcohol and ester components primarily interacted with the sensors S2 and S7, indicating that particular E-nose sensors were highly correlated with some aroma constituents. Conclusions: Combined with advanced chemometrics, the E-nose detection technology can discriminate two cultivars of A. officinarum, with GC-MS providing support to determine the material basis of the E-nose sensors’ response.

Correlation between macroscopic characteristics and tissue-specific chemical profiling of the root of Salvia miltiorrhiza

BACKGROUND

Macroscopic identification has been widely used as a convenient method for herbal authentication and quality assessment. However, sensory evaluation heavily relied on personal experience and lacked enough evidence-based validations.

PURPOSE

We aim to reveal the correlation between macroscopic characteristics and tissue-specific chemical composition of the root of Salvia miltiorrhiza (SMR), and then develop a rapid method for quality assessment.

METHODS

Thirty-two batches of SMR were collected and evaluated. The outer-surface color and diameter as the representative tissue features of SMR were selected as the macroscopic indexes. SMR were then divided into three parts along transverse section as outer bark, middle part and central part, to explore the spatial distribution of chemicals. Outer-surface color information was converted into RGB values, while the diameter data were expressed by mean distance, respectively. Thirteen major components including eight salvianolic acids and five tanshinones in each part were determined by liquid chromatography tandem mass spectrometry. Finally, several mathematical models were established and optimized to evaluate the correlation between outer-surface color, size and chemical distribution.

RESULT

All five tanshinones mainly distributed in the outer bark while salvianolic acids were averagely existed among three parts. Correlational studies revealed that the surface color depth was significantly and positively correlated with tanshinone contents in the outer bark, while the size showed poor correlation in any chemicals. A color-oriented model was thus developed for the prediction of tanshinone contents in SMR, and a 9 × 9 standard color chart was created for easily use.

CONCLUSION

This study contributes an alternative method for macroscopic features-based quality evaluation of herbs, and also complements some scientific data for traditional knowledge.

Large-scale analysis of small RNAs derived from traditional Chinese herbs in human tissues

Plant-derived microRNAs have recently been reported to function in human blood and tissues. Controversy was immediately raised due to possible contamination and the lack of large sample sizes. Here, we report thousands of unique small RNAs derived from traditional Chinese medicine (TCM) herbs found in human blood cells and mouse lung tissues using a large-scale analysis. We extracted small RNAs from decoctions of 10 TCM plants (Ban Zhi Lian, Chai Hu, Chuan Xin Lian, Di Ding Zi Jin, Huang Qin, Jin Yin Hua, Lian Qiao, Pu Gong Ying, Xia Ku Cao, and Yu Xing Cao) and obtained millions of RNA sequences from each herb. We also obtained RNA-Seq data from the blood cells of humans who consumed herbal decoctions and from the lung tissues of mice administered RNAs from herbal decoctions via oral gavage. We identified thousands of unique small RNA sequences in human blood cells and mouse lung tissues. Some of these identified small RNAs from Chuan Xin Lian and Hong Jing Tian could be mapped to the genomes of the herbs, confirming their TCM plant origin. Small RNAs derived from herbs regulate mammalian gene expression in a sequence-specific manner, and thus are a superior novel class of herbal drug components that hold great potential as oral gene-targeted therapeutics, highlighting the important role of herbgenomics in their development.

An Electronic Nose Based Method for the Discrimination of Weathered Petroleum-Derived Products

In recent years pollution due to hydrocarbon spills has increased markedly as a result of the numerous advances in technologies and industrial processes. Anthropogenic activities (accidental or illegal) are responsible for most of these incidents. In some cases, the spills are not detected at the moment they occur and the contaminants are subjected to different degradation phenomena that may change the chemical composition of the hydrocarbon over time. An incorrect or ineffective identification of the spill could lead to significant consequences, bearing in mind that most spills are hazardous to the environment. In the present work the capacity of the analytical technique based on the Electronic Nose (eNose) combined with chemometrics in the identification and discrimination of different weathered petroleum-derived products (PDPs) was studied. Different volumes (40 μL and 80 μL) of PDPs (gasoline, diesel, and paraffin) were poured onto different supports (wood, cork, paper, and cotton sheet) and subjected to a natural weathering process by evaporation for one month. The porosity of the support was also studied. The application of linear discriminant analysis allowed the full discrimination of the samples according to the presence/absence of PDP and a 97.7% of correct discrimination of the different PDPs regardless of the weathering time, support or volume used. The results show that the system is capable of detecting and discriminating the presence of petroleum-derived products in any of the situations studied.

Study of the Weathering Process of Gasoline by eNose

In a fire investigation the rapid detection of the presence of ignitable liquids like gasoline is of great importance as it allows appropriate treatment of the remains, the identification of prevention methods and detects the possible presence of an arsonist. In some cases, analysts cannot access the fire scene in the first few hours due to the dangers involved in the situation and, as a consequence, phenomena such as weathering start. Ignitable liquid weathering is an evaporation process that results in an increase in the abundance of non-volatile compounds relative to volatile compounds, and this process changes the chemical composition. In the present work, the weathering of samples of gasoline at different times (from 0 h to a month) has been studied using an electronic nose (eNose). The influence of the volume used (40 µL and 80 µL) and the type of support (cork, wood, paper and cotton sheet) has been studied. Chemometric tools have been used with the aim of ascertaining the weathering time for which the developed method is capable of detecting the presence of gasoline. The eNose was able to discriminate samples of weathered gasoline. The support used for the samples did not seem to have an influence on the detection and the system.

Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification

This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods for e-noses. However, these steps are complicated and empirical because there is no uniform rule for choosing appropriate methods from many different options. The main advantage of SSAE is that it can automatically learn features from the original sensor data without the steps of preprocessing and feature extraction; which can greatly simplify data processing procedures for e-noses. To identify different brands of Chinese liquors; an SSAE based multi-layer back propagation neural network (BPNN) is constructed. Seven kinds of strong-flavor Chinese liquors were selected for a self-designed e-nose to test the performance of the proposed method. Experimental results show that the proposed method outperforms the traditional methods.

The Use of Electrochemical Biosensors in Food Analysis

Rapid and accurate analysis of food produce is essential to screen for species that may cause significant health risks like bacteria, pesticides and other toxins. Considerable developments in analytical techniques and instrumentation, for example chromatography, have enabled the analyses and quantitation of these contaminants. However, these traditional technologies are constrained by high cost, delayed analysis times, expensive and laborious sample preparation stages and the need for highly-trained personnel. Therefore, emerging, alternative technologies, for example biosensors may provide viable alternatives. Rapid advances in electrochemical biosensors have enabled significant gains in quantitative detection and screening and show incredible potential as a means of countering such limitations. Apart from demonstrating high specificity towards the analyte, these biosensors also address the challenge of the multifactorial food industry of providing high analytical accuracy amidst complex food matrices, while also overcoming differing densities, pH and temperatures. This (public and Industry) demand for faster, reliable and cost-efficient analysis of food samples, has driven investment into biosensor design. Here, we discuss some of the recent work in this area and critique the role and contributions biosensors play in the food industry. We also appraise the challenges we believe biosensors need to overcome to become the industry standard.

The Regular Interaction Pattern among Odorants of the Same Type and Its Application in Odor Intensity Assessment

The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture’s odor intensity. This study investigated the odor interaction principle in odor mixtures of aldehydes and esters, respectively. Then, a modified vector model (MVM) was proposed and it successfully demonstrated the similarity of the odor interaction pattern among odorants of the same type. Based on the regular interaction pattern, unlike a determined empirical model only fit for a specific odor mixture in conventional approaches, the MVM distinctly simplified the odor intensity prediction of odor mixtures. Furthermore, the MVM also provided a way of directly converting constituents’ chemical concentrations to their mixture’s odor intensity. By combining the MVM with usual data-processing algorithm of e-nose, a new e-nose system was established for an odor intensity rating. Compared with instrumental analysis and human assessor, it exhibited accuracy well in both quantitative analysis (Pearson correlation coefficient was 0.999 for individual aldehydes (n = 12), 0.996 for their binary mixtures (n = 36) and 0.990 for their ternary mixtures (n = 60)) and odor intensity assessment (Pearson correlation coefficient was 0.980 for individual aldehydes (n = 15), 0.973 for their binary mixtures (n = 24), and 0.888 for their ternary mixtures (n = 25)). Thus, the observed regular interaction pattern is considered an important foundation for accelerating extensive application of olfactory evaluation in odor pollution monitoring.