Machine Olfaction Research Articles

Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research.

Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds. MOA is available for Windows, Mac and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-by-step guide and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes. Supplementary data are available at Bioinformatics online.

E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices

ABSTRACT Consumer demands for fresh foods with desired sensory properties. Lack of easy, cheap, and nondestructive methods to control the quality of fruit juices is one of the main challenges in the beverage industry. The present study has examined two types of natural and industrial juices. Machine olfaction with 9 metal oxide semiconductor (MOS) sensors was used for the experiments. Sensor response patterns were analyzed using artificial neural networks (ANN). PCR method was used to evaluate the discrimination power of the sensors. According to the results, the discrimination power of the sensors used in the electronic nose sensor array was determined, three of which were removed due to improper response power. The proposed method can be used to select the minimum and most effective number of MOS gas sensors to construct an electronic nose system to control the olfactory quality of different foods. The use of a minimum number of sensors reduces the cost of constructing an electronic nose system, decreases the volume of the processor data input, and consequently increases the classification accuracy. Therefore, the MOS-based electronic nose combined with the ANN can be an effective and highly efficient tool in the fast and nondestructive classification of pure and industrial fruit juices.

Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement.

Practical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-hand measurement, the comprehensive development of a measurement system including hardware, measurement protocols, and data analysis is necessary. In this study, we developed palm-size wireless odor measurement devices equipped with Membrane-type Surface stress Sensors (MSS) and investigated the effect of measurement protocols and feature selection on odor identification. By using the device, we measured vapors of liquids as odor samples through the free-hand measurement in different protocols. From the measurement data obtained with these protocols, datasets of transfer function ratios (TFRs) were created and analyzed by clustering and machine learning classification. It has been revealed that TFRs in the low-frequency range below 1 Hz notably contributed to vapor identification because the frequency components in that range reflect the dynamics of the detection mechanism of MSS. We also showed the optimal measurement protocol for accurate classification. This study has shown a guideline of the free-hand measurement and will contribute to the practical implementation of machine olfaction in society.

Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model.

For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.

Inkjet Production of Innovative Metal Oxide Sensors for e-Nose Systems

In recent years, electronic noses have been successfully adopted for odour detection and characterization in different fields, as environmental monitoring, safety, food quality control and clinical diagnosis [1-4]. However, industrial applications are still limited [5]. One of the major criticalities associated to e-nose adoption is sensor drift, which consists in small temporal variations of the sensor response exposed to the same analyte under identical conditions [6]. Indeed, signal variations over time result in a decrease of sensor sensitivity and selectivity that affects measurement reproducibility and limits the translation of e-nose laboratory results to industrial applications [5]. For the purpose of improving e-nose stability over time, many researchers started working on the development of drift mitigation algorithms to counteract drift effects and make sensor responses comparable over time and many different methods have been proposed in the scientific literature [6]. This paper proposes an alternative approach to the problem, which involves the development of an innovative technique for the production of Metal Oxide Sensors MOS, based on inkjet print. This technology is particularly suitable for microfabrication applications requiring high patterning precision and controlled deposition of functional materials. In particular, both ZnO sol-gel solutions and ZnO nanoparticles suspensions were developed and subsequently printed on alumina substrates. Inks physical properties were tailored to allow efficient jetting from a Dimatix materials printer. Annealing conditions were optimized and active films with different morphological and crystallographic characteristics obtained from the printed layers. Innovative sensors performance was tested by means of specific tests with a reference compound and evaluated in terms of efficiency towards reference compound:ε = (R0 - R)/R0 where R0 is the resistance at the beginning of the measurement and R the minimum resistance value recorder during the analysis of the reference compound. Innovative sensors were tested for a period of 9 months and n-butanol was used as reference compound. For the entire period of testing, sensor efficiencies toward n-butanol were above 90%, highlighting very good sensibility of inkjet sensors towards the reference compound and great stability of responses over time. These results proved the feasibility of the adoption of inkjet print for the production of MOS sensors, encouraging future investigations in this field. 1. Kiani, S.; Minaei, S.; Ghasemi-Varnamkhasti, M. Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review. Journal of Applied Research on Medicinal and Aromatic Plants 2016, 3, 1-9.2. Bax, C.; Taverna, G.; Eusebio, L.; Sironi, S.; Grizzi, F.; Guazzoni, G.; Capelli, L. Innovative diagnostic methods for early prostate cancer detection through urine analysis: A review. Cancers (Basel) 2018, 10, 123.3. Capelli, L.; Sironi, S.; Del Rosso, R. Electronic noses for environmental monitoring applications. Sensors (Basel, Switzerland) 2014, 14, 19979-20007.4. Gliszczyńska-Świgło, A.; Chmielewski, J. Electronic nose as a tool for monitoring the authenticity of food. A review. Food Analytical Methods 2017, 10, 1800-1816.5. Marco, S.; Gutierrez-Galvez, A. Signal and data processing for machine olfaction and chemical sensing: A review. IEEE Sensors Journal 2012, 12, 3189-3214.6. Di Carlo, S., Falasconi, M. Drift correction methods for gas chemical sensors. In Artifical olfaction systems: Techniques and challenges, InTech, Ed. New York, USA, 2012; pp 305-326.

High-Dimensional Time Series Feature Extraction for Low-Cost Machine Olfaction

The complexity of airborne odors offers interesting challenges and opportunities for chemical detection and identification. Biological olfactory systems have evolved to extract information from spatiotemporally complex odor plumes, but many engineered electronic noses use only coarse time features while neglecting valuable transient fluctuations. In this paper, we use the TruffleBot , our low-cost e-nose platform, to dynamically ‘sniff’ odors while collecting multidimensional chemical, pressure and temperature time series. By extracting high-dimensional time series features (TSF) from a diverse set of relatively low-bandwidth sensor signals, we can identify subtle differences in odor concentration and composition. We use this approach to perform a variety of classification experiments, including the discrimination of three similar beers at >98% accuracy. Additionally, we demonstrate that time series features can be aggregated and applied to improve concentration estimation of ethanol by a factor of three, to the limit of our experimental calibration error.

Towards Gas Sensors for Aquatic Chemical Sensing Inspired By the Star-Nosed Mole

Aquatic chemical sensors, as any other surface submerged in water, are subject to biofouling, by bacteria and algae that quickly form biofilms on the submerged surface [1]. In the case of sensors, accumulated biofilms skew the chemical measurements until the sensors are cleaned and recalibrated. A number of mechanical and chemical solutions have been proposed to scrape off or kill the biofilms, but they can be sensor-specific, costly, or have adverse effects on the environment [2]. The process of biofouling happens over the span of days, making long-term chemical measurements labor-intensive and oftentimes unfeasible. The star-nosed mole, with its bizarre nose geometry, presents a way to circumvent the issue of biofouling. These semi-aquatic moles exhale and inhale bubbles rapidly underwater. When the bubbles contact a target of interest, molecules are collected on the bubble surface and brought back into the nose. In this fashion, its olfactory receptors never contact the water and the mole can use its terrestrial nose to track prey at the bottom of swamps [3]. The appendages that make up the star on their nose stabilize the bubbles, allowing them to consistently blow larger bubbles without the bubble pinching off and floating away [4]. We develop an underwater electronic nose, inspired by the star-nosed mole’s underwater sniffing. A motor pushes and pulls on a syringe to form and contract a 0.5 mL bubble underwater at 1 Hz. A 3D printed star, inspired by the mole’s nose geometry, stabilizes the bubble and prevents pinch-off [4]. The 6 fins of the star, separated by 30-degree angles, are slanted downward at a 35-degree angle to optimally stabilize the largest volume bubble. The air from the bubble is pushed and pulled past an array of metal oxide sensors. As a proof of concept, this underwater e-nose is used in an on-going experiment to detect the presence or absence of sparkler fragments at the bottom of a water sample. In the event of the bubble pinching off, inhaled water shorts out the sensor array. At the same time, if the volume of dead space between the star and the sensor array is greater than the volume of the bubble, tests show that the e-nose will be incapable of detecting changes in the underwater environment. Thus, stabilizing the bubble during the sniff is key in designing this underwater e-nose. This prototype e-nose marks the expansion of machine olfaction to underwater applications. Figure 1. Underwater bubble sniffing can allow gas sensors to be used underwater. A) The star-nosed mole sniffs bubbles to pick up scents underwater. Photo courtesy of K. Catania. B) Mimicking this behavior could allow gas sensors to detect chemicals without directly exposing the sensor to its environment. C) A motor and syringe sniff underwater across a metal oxide sensor to form an underwater e-nose. [1] G. Xu, W. Shen, and X. Wang, “Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey,” Sensors, vol. 14, no. 9, pp. 16932–16954, Sep. 2014. [2] L. Delauney, C. Compère, and M. Lehaitre, “Biofouling protection for marine environmental sensors,” Ocean Sci, vol. 6, no. 2, pp. 503–511, May 2010. [3] K. C. Catania, “Olfaction: Underwater ‘sniffing’ by semi-aquatic mammals,” Nature, vol. 444, no. 7122, p. 1024, Dec. 2006. [4] A. B. Lee and D. L. Hu, “Bubble stabilization by the star-nosed mole,” Phys. Rev. Fluids, vol. 3, no. 12, 2018. Figure 1

Development of a Piezoelectric-Based Odor Reproduction System

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction.

We have developed a spiking neural network (SNN) algorithm for signal restoration and identification based on principles extracted from the mammalian olfactory system and broadly applicable to input from arbitrary sensor arrays. For interpretability and development purposes, we here examine the properties of its initial feedforward projection. Like the full algorithm, this feedforward component is fully spike timing-based, and utilizes online learning based on local synaptic rules such as spike timing-dependent plasticity (STDP). Using an intermediate metric to assess the properties of this initial projection, the feedforward network exhibits high classification performance after few-shot learning without catastrophic forgetting, and includes a none of the above outcome to reflect classifier confidence. We demonstrate online learning performance using a publicly available machine olfaction dataset with challenges including relatively small training sets, variable stimulus concentrations, and 3 years of sensor drift.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins

The wide range of geographical cocoa production areas and the increasing consumption trend towards single origin products induced the necessity to verify and certify cocoa beans origin for quality assurance purposes. In this study cocoa beans of various origins were examined by machine olfaction and machine vision techniques. Fifty-nine fermented and dried Forastero cocoa beans from 23 different geographical origins (Africa, Americas, Southeast Asia) were investigated using Proton Transfer Reaction-Quadrupole interface-Time of Flight-Mass Spectrometry and Hyperspectral Imaging to elucidate the geographical information in the beans. The volatile and spectral fingerprints showed the same tendency in clustering samples from Africa separate from those from the Americas. High variability was observed for the Southeast Asian samples, which is most likely related to differences in fermentation. Machine olfaction and machine vision characterization provided a similar degree of separation but are complementary rapid techniques, which may be further developed for use in practical settings.

Electronic Taste and Smell: The Case for Performance Standards [Point of View

International standards have proven invaluable in the technology sector for developing functional and reliable products for the global marketplace. Standards provide performance criteria that technical engineers can use to design products to optimize the reliability and safety of new products. For example, standards have played a decisive role in the development of products associated with the senses of vision, audition, and touch. The design of products that perform automated “visual” tasks including unmanned vehicles, autonomous robots, optical tracking systems, and highway traffic monitoring devices has relied heavily on standards as well as technical regulations. Likewise, standards related to the sense of hearing have played a major role in the development of devices and systems that assist or mimic “audition” including cochlear implants, hearing aids, and voice and speech recognition systems. Standards related to the sense of touch have been seminal in the design of robotic arms and prosthetic hands. Unlike the senses of vision, audition, and touch, there are, however, no formal standards for electronic devices called e-noses and e-tongues that are designed to detect and evaluate odors and tastes. The purpose of this opinion piece is to give a brief background on the senses of smell and taste, to describe why standards for e-noses and e-tongues are needed, and to call for IEEE volunteers to participate and collaborate on technical standards development to ensure that machine olfaction and taste provide reliable and reproducible results that are comparable to human smell and taste.

The Odor Characterizations and Reproductions in Machine Olfactions: A Review.

Machine olfaction is a novel technology and has been developed for many years. The electronic nose with an array of gas sensors, a crucial application form of the machine olfaction, is capable of sensing not only odorous compounds, but also odorless chemicals. Because of its fast response, mobility and easy of use, the electronic nose has been applied to scientific and commercial uses such as environment monitoring and food processing inspection. Additionally, odor characterization and reproduction are the two novel parts of machine olfaction, which extend the field of machine olfaction. Odor characterization is the technique that characterizes odorants as some form of general odor information. At present, there have already been odor characterizations by means of the electronic nose. Odor reproduction is the technique that re-produces an odor by some form of general odor information and displays the odor by the olfactory display. It enhances the human ability of controlling odors just as the control of light and voice. In analogy to visual and auditory display technologies, is it possible that the olfactory display will be used in our daily life? There have already been some efforts toward odor reproduction and olfactory displays.

Predictive modeling for odor character of a chemical using machine learning combined with natural language processing.

Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the odor impression of a chemical from its physicochemical properties. Our predictive model utilizes nonlinear dimensionality reduction on mass spectra data and performs the clustering of descriptors by natural language processing. Sensory evaluation is widely used to measure human impressions to smell or taste by using verbal descriptors, such as “spicy” and “sweet”. However, as it requires significant amounts of time and human resources, a large-scale sensory evaluation test is difficult to perform. Our model successfully predicts a group of descriptors for a target chemical through a series of computer simulations. Although the training text data used in the language modeling is not specialized for olfaction, the experimental results show that our method is useful for analyzing sensory datasets. This is the first report to combine machine olfaction with natural language processing for odor character prediction.

Correcting Instrumental Variation and Time-Varying Drift Using Parallel and Serial Multitask Learning

When instruments and sensor systems are used to measure signals, the posterior distribution of test samples often drifts from that of the training ones, which invalidates the initially trained classification or regression models. This may be caused by instrumental variation, sensor aging, and environmental change. We introduce transfer-sample-based multitask learning (TMTL) to address this problem, with a special focus on applications in machine olfaction. Data collected with each device or in each time period define a domain. Transfer samples are the same group of samples measured in every domain. They are used by our method to share knowledge across domains. Two paradigms, parallel and serial transfer, are designed to deal with different types of drift. A dynamic model strategy is proposed to predict samples with known acquisition time. Experiments on three real-world data sets confirm the efficacy of the proposed methods. They achieve good accuracy compared with traditional feature-level drift correction algorithms and typical labeled-sample-based MTL methods, with few transfer samples needed. TMTL is a practical algorithm framework which can greatly enhance the robustness of sensor systems with complex drift.

Electronic nose system on the Zynq SoC platform

Electronic nose or machine olfaction are systems used for detection and identification of odorous compounds and gas mixtures. An electronic nose system is mainly made of two parts, the sensing part which takes the form of a single or a set of sensors and the processing part which takes the form of some pattern recognition algorithms. As an alternative solution to pure software or hardware implementation of the processing part of a gas identification system, this paper proposes a hardware/software co-design approach using the Zynq platform for the implementation of an electronic nose system based on principal component analysis as a dimensionality reduction technique and decision tree as a classification algorithm using two different sensors array, a 4 × 4 in-house fabricated sensor and a commercial one based on 7 Figaro sensors, for comparison purpose. The system was successfully trained and simulated in MATLAB environment prior to the implementation on the Zynq platform. Various scenarios were explored and discussed including the investigation of different combination of principal components as well as the utilization of drift compensation technique to improve the identification accuracy. High level synthesis was carried out on the proposed designs using different optimization directives including loop unrolling, array partitioning and pipelining. Hardware implementation results on the Zynq system on chip show that real-time performances can be achieved for proposed electronic nose systems using hardware/software co-design approach with a single ARM processor running at 667 MHz and the programmable logic running at 142 MHz. In addition, using the designed IP cores and for the best scenarios, a gas can be identified in 3.46 μs using the 4 × 4 sensor and 0.55 μs using the Figaro sensors. Furthermore, it has been noticed that the choice of the sensor array has an important impact on performances in terms of accuracy and processing time. Finally, it has been demonstrated that the programmable logic of the Zynq platform consumes much less power than the processing system.

Odor Recognition in Multiple E-Nose Systems With Cross-Domain Discriminative Subspace Learning

In this paper, we propose an odor recognition framework for multiple electronic noses (E-noses), machine olfaction odor perception systems. Straight to the point, the proposed transferring odor recognition model is called cross-domain discriminative subspace learning (CDSL). General odor recognition problems with E-nose are single domain oriented, that is, recognition algorithms are often modeled and tested on the same one domain data set (i.e., from only one E-nose system). Different from that, we focus on a more realistic scenario: the recognition model is trained on a prepared source domain data set from a master E-nose system A, but tested on another target domain data set from a slave system B or C with the same type of the master system A. The internal device parameter variance between master and slave systems often results in data distribution discrepancy between source domain and target domain, such that single-domain-based odor recognition model may not be adapted to another domain. Therefore, we propose domain-adaptation-based odor recognition for addressing the realistic recognition scenario across systems. Specifically, the proposed CDSL method consists of three merits: 1) an intraclass scatter minimizationand an interclass scatter maximization-based discriminative subspace learning is solved on source domain; 2) a data fidelity and preservation constraint of the subspace is imposed on target domain without distortion; and 3) a minipatch feature weighted domain distance is minimized for closely connecting the source and target domains. Experiments and comparisons on odor recognition tasks in multiple E-noses demonstrate the efficiency of the proposed method.

A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea

Tea industries enjoy a significant position in the socio-economic ladder for any demographics, especially in India who is the largest producer as well as consumer of the agro-product. While tea ranks only next to water in the pedigree of globally consumed beverages, the imperative fermentation stage in the processing of tea leaves is conventionally monitored through olfactory perception of tea tasters. Recent advances in the field of machine olfaction have witnessed the advent of electronic nose prototypes, which provide a scientific validation to the organoleptic estimations disseminated by the tasters. However, fermentation is a continuous process requiring constant monitoring whose successful completion relies heavily on identification of distinct aroma peaks emanated at optimum instants. Since the fermentation process is integral to the final quality, it is deemed beneficial if the optimum fermentation period can be predicted at an earlier stage. Such preemptive information can mitigate constant monitoring requirements and momentary concentration lapses. Recognizing the time series nature of the data generated during the fermentation process with an electronic nose prototype, we have implemented a recurrent Elman network to predict the optimum fermentation period for different black tea samples. The results showed that the prescribed network could predict the optimum period with confidence at the halfway of the process. The minimal error between the predicted and the actual fermentation period at the halfway point suggests that the proposed model can well be integrated with an electronic nose dedicated for monitoring the fermentation process.

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

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Fast Detection Method of Rice Storage Quality Based on Machine Olfaction

A fast detection method of rice storage quality was studied in this paper. The smell quality detection of stored rice was as a research object, and the odor data was collected by the gas sensor array. The method of principal component analysis was used for feature dimension reduction. Finally the BP network and stochastic resonance algorithm were used for classification respectively. The test results showed that the correct recognition rate was 93.33% by using the classification method of stochastic resonance. The machine olfaction system proposed in this paper can achieve the purpose of testing the rice storage quality non-destructively, rapidly and accurately, which provides a new way for food quality nondestructive testing research.

Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components

The of the quality of odors emitted from automobile cabin interiors is an important element for the design of vehicles that meet prospective customers' expectations. Extending our previous work on machine-versus-human odor assessment for intact automobile cabin interiors, in this paper, we evaluated odors generated from individual interior parts using a human panel and field asymmetric ion mobility spectrometry (FAIMS). We used image processing techniques to extract geometric features from FAIMS dispersion fields, and built the predictive models for three odor assessment parameters (intensity, irritation, and pleasantness) by means of partial least squares regression. The best feature set was chosen by backward sequential feature selection. Using k -fold cross validation, we achieved statistically significant correlation 0.95 between human panel measured and machine olfaction predicted odor assessment scores with a sample set of 48 interior automobile parts. These results, generated using the geometric image processing methods demonstrated herein, further support the feasibility of replacing a human panel by machine olfaction for the assessment of odor quality of interior automobile parts.