Predicting organoleptic scores of sub-ppm flavour notesPart 2.† Computational analysis and results

Pearce, Timothy Charles; Gardner, Julian W.

doi:10.1039/a804019b

Cited by 22 publications

(21 citation statements)

References 6 publications

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“…Throughout this paper, standard pre-processing metrics were used to extract information from the time-dependent sensor-response profiles. These metrics, which include difference, fractional, relative, array normalization difference, array normalization fractional, array normalization relative, sensor normalization difference, sensor normalization fractional, and sensor normalization relative, are well documented [37,38], and are not discussed further. The type of metric employed in each situation will be identified where necessary.…”

Section: Resultsmentioning

confidence: 99%

“…3). These unique patterns, or sensor fingerprints, are usually used to discriminate between odors by use of cross-reactive chemical sensor arrays (or electronic noses) [17,22,37,38,39,40,41,42,43,44]. We now demonstrate several new ways in which sensor odor-response patterns can be used to "fingerprint" each sensor type within a random array.…”

Section: Homogeneous Single-sensor Arraysmentioning

confidence: 95%

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Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

et al. 2002

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In this paper automatic sensor identification of sensor classes within a high-density randomized array, without a priori knowledge of sensor locations, is demonstrated. Two different fluorescence-based sensor types, with hundreds of replicates each, were randomly distributed into an optical imaging fiber array platform. The sensor element types were vapor-sensitive microspheres with the environmentally-sensitive fluorescent dye Nile Red adsorbed on their surface. Nile Red undergoes spectral changes when exposed to different microenvironmental polarity conditions, e.g. microsphere surface polarity or odor exposure. These reproducible sensor spectral changes, or sensor-response profiles, enable sensors within a randomized array to be grouped into categories by optical decoding methods. Two computational decoding methods (supervised and unsupervised) are introduced; equal classification rates were achieved for both. By comparing sensor responses from a randomized array with those obtained from known (control) arrays, 587 sensors were correctly classified with 99.32% accuracy. Although both methods were equally effective, the unsupervised method, which uses sensor response changes to odor exposure, is a better decoding model for the vapor-sensitive arrays studied, because it relies only on the odor-response profiles. Another decoding technique employed the emission spectra of the sensors and is more applicable to other types of multiplexed fluorescence-based arrays and assays. The sensor-decoding techniques are compared to demonstrate that sensors within high-density optical chemosensor arrays can be positionally-registered, or decoded, with no additional overhead in time or expense other than collecting the sensor-response profiles.

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Section: Resultsmentioning

confidence: 99%

Section: Homogeneous Single-sensor Arraysmentioning

confidence: 95%

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

et al. 2002

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“…We will use the quasi-ML estimator, described below, to perform inference using quantized observations. Effectively, the quasi-ML estimator assumes that the noise at individual sensor nodes has an added component σ 2 Q I, with the total covariance being (compare with (13))…”

Section: Quantized Observationsmentioning

confidence: 99%

Cramér-Rao Bounds for Polynomial Signal Estimation Using Sensors With AR(1) Drift

Kar

Varshney

Palaniswami

2012

IEEE Trans. Signal Process.

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We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively studied in the literature, the loss of estimation performance due to systematic errors like drift have rarely been looked into. In this paper, we derive closed-form Fisher Information matrix and subsequently Cramér-Rao bounds (upto reasonable approximation) for the estimation accuracy of drift-corrupted signals. We assume a polynomial time-series as the representative signal and an autoregressive process model for the drift. When the Markov parameter for drift ρ < 1, we show that the firstorder effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. For ρ = 1, i.e., when the drift is non-stationary, we show that the constant part of a signal can only be estimated inconsistently (nonzero asymptotic variance). Practical usage of the results are demonstrated through the analysis of 1) networks with multiple sensors and 2) bandwidth limited networks communicating only quantized observations.

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“…Pearce et al [20] used a linear fit to compensate for base-line drift in measurements with an electronic nose on beer over a period of 12 days. This would be a mathematical model rather than a physical, but could still be useful in situations where the environment causes drift in the sensors, but does not change much over time.…”

Section: Modeling Of Sensor Behaviormentioning

confidence: 99%

Drift Compensation, Standards, and Calibration Methods

Holmberg

Artursson²

2002

Handbook of Machine Olfaction

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In Webster's Seventh New Collegiate Dictionary, drift is defined as "a gradual change in any quantitative characteristic that is supposed to remain constant". Thus, a drifting chemical sensor does not give exactly the same response even if it is exposed to exactly the same environment for a long time. Drift is a common problem for all chemical sensors, and thus needs to be considered as soon as measurements are made for a long period of time.First in this chapter, possible reasons for drift will be discussed. A distinction is made between drift in the sensors, and drift in the measurement system. After this, typical features of drift as seen in the measurements will be shown. These features include gradual increase or decrease, and jumps in the responses. At the end, many different methods for reducing the effects of drift will be described. These drift reduction methods try to compensate for the changes in sensor performance using mathematical models and thus maintaining the gas identification capability of the electronic nose. Many different methods have been applied for different situations. It is impossible to compare all the methods since each one of them makes some assumptions of how the measurements are made and/or how the drift is manifested. Not all examples discussed are for measurements with electronic noses, but the concepts may easily be transferred also to such applications. The purpose of describing all the methods is to show some possible ways of reasoning when dealing with a data-set from drifting sensors.13.1

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Predicting organoleptic scores of sub-ppm flavour notesPart 2.† Computational analysis and results

Cited by 22 publications

References 6 publications

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays

Cramér-Rao Bounds for Polynomial Signal Estimation Using Sensors With AR(1) Drift

Drift Compensation, Standards, and Calibration Methods

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