# US 2019/0317079 A1 · System and method for volatile organic compound detection

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- Number: US 2019/0317079 A1
- Document ID: US20190317079A1
- Status: Published application
- Kind: A1
- Application number: 16/161,458
- Filed: 2018-10-16
- Published: 2019-10-17
- Earliest claimed: 2017-10-16
- Assignees: Sightline Innovation Inc.
- Applicants: Sightline Innovation Inc.
- Inventors: Wallace TRENHOLM; Mark ALEXIUK; Hieu DANG; Siavash MALEKTAJI
- Web page: https://patentgrep.com/patent/US20190317079A1

## CPC

- G01N 21/783: {for analysing gases}
- G01N 21/31: Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry {(G01N21/72 takes precedence)}
- G01N 33/52: Use of compounds or compositions for colorimetric, spectrophotometric or fluorometric investigation, e.g. use of reagent paper {and including single- and multilayer analytical elements (immunological elements G01N33/54386; involving labelled immunochemicals G01N33/58; for haemoglobin or occult blood G01N33/72)}
- G06F 18/24143: {Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0499: Feedforward networks
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06N 3/091: Active learning
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G06T 7/0002: {Inspection of images, e.g. flaw detection}
- G06T 7/0012: {Biomedical image inspection}
- G06V 10/48: by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation
- G06V 10/56: relating to colour
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods}
- G06V 10/82: using neural networks
- G16C 20/20: Identification of molecular entities, parts thereof or of chemical compositions
- G16C 20/70: Machine learning, data mining or chemometrics
- G01N 2021/7793: {Sensor comprising plural indicators}
- G06T 2207/10024: Color image
- G06T 2207/20224: Image subtraction
- G06T 2207/30072: Microarray; Biochip, DNA array; Well plate
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE

## Abstract

A system and method for identifying an analyte based on the presence of at least one volatile organic compound (“VOC”) in the analyte. The method includes: receiving image data from a sensor array, the sensor array having been exposed to the analyte, the sensor array including at least one sensor configured to respond to the presence of the at least one VOC in the analyte; processing the image data to derive one or more input image features; and using a trained machine learning classification technique, detecting the at least one VOC and classifying the analyte based on the one or more input image features, the machine learning classification technique trained using one or more reference images of known analytes.

## Claims

### Claim 1

A method of identifying an analyte based on the presence of at least one volatile organic compound (“VOC”) in the analyte, comprising:
receiving image data from a sensor array after the sensor array has been exposed to the analyte, the sensor array comprising at least one sensor configured to respond to the presence of the at least one VOC in the analyte;
processing the image data to derive one or more input image features; and
using a trained machine learning classification technique, detecting the at least one VOC and classifying the analyte based on the one or more input image features, the machine learning classification technique trained using one or more reference images of known analytes.

### Claim 2 (depends on 1)

The method of claim 1, wherein the sensor array comprises a colorimetric sensor array of a plurality of colorimetric sensors, each colorimetric sensor changing in color or in color intensity when exposed to the VOC present in the analyte.

### Claim 3 (depends on 2)

The method of claim 2, wherein receiving the image data comprises repeatedly receiving image data of the sensor array at a series of time intervals.

### Claim 4 (depends on 3)

The method of claim 3, wherein processing the image data to derive the one or more input image features comprises comparing image data in each of the images in the image series to the one of the reference images to generate comparison image data comprising color differences, the input image features comprising the comparison image data.

### Claim 5 (depends on 4)

The method of claim 4, wherein, for each image in the image series, processing the image data comprises:
performing edge detection on the image data;
performing a Hough circle transform on the image data;
detecting an image rotation angle; and
where there is a previous image in the image series, aligning the image with the previous image.

### Claim 6 (depends on 2)

The method of claim 2, wherein processing the image data comprises applying adaptive thresholding to produce a binarized image of the image data.

### Claim 7 (depends on 6)

The method of claim 6, wherein processing the image data further comprises performing a Hough Circle Transform to detect a blob circle on the binarized image and to segment color blobs on the binarized image.

### Claim 8 (depends on 7)

The method of claim 7, wherein processing the image data further comprises predicting missing a missing color blob using geometric interpolation.

### Claim 9 (depends on 2)

The method of claim 2, wherein the one or more input image features comprise color features comprising at least one of global mean, global mode, inner mean, and inner mode.

### Claim 10 (depends on 2)

The method of claim 2, wherein the one or more input image feature comprises textural features comprising at least one of co-occurrence matrix, angular second moment, contrast, correlation, entropy, Hellinger Distance, and Hausdorff Distance.

### Claim 11 (depends on 2)

The method of claim 2, wherein the machine learning classification technique comprises one of support vector machines, stacked auto encoders, multi-layer perceptrons, recurrent neural networks, or deep learning neural networks.

### Claim 12

A system for identifying an analyte based on the presence of at least one volatile organic compound (“VOC”) in the analyte, the system in communication with an image acquisition device, the system comprising one or more processors in communication with a memory, the one or more processors configured to execute:
an image processing module to:
receive image data from a sensor array on the image acquisition device after the sensor array has been exposed to the analyte, the sensor array comprising at least one sensor configured to respond to the presence of the at least one VOC in the analyte; and
process the image data to derive one or more input image features; and
a classification module to, using a trained machine learning classification technique, classify the analyte based on the one or more input image features, the machine learning classification technique trained using one or more reference images of known analytes.

### Claim 13 (depends on 12)

The system of claim 12, wherein the sensor array comprises a colorimetric sensor array of a plurality of colorimetric sensors, each colorimetric sensor changing in color or in color intensity when exposed to the VOC present in the analyte.

### Claim 14 (depends on 13)

The system of claim 13, wherein receiving the image data comprises repeatedly receiving image data of the sensor array at a series of time intervals.

### Claim 15 (depends on 14)

The system of claim 14, wherein processing the image data to derive the one or more input image features comprises comparing image data in each of the images in the image series to the one of the reference images to generate comparison image data comprising color differences, the input image features comprising the comparison image data.

### Claim 16 (depends on 13)

The system of claim 13, wherein processing the image data comprises applying adaptive thresholding to produce a binarized image of the image data.

### Claim 17 (depends on 16)

The system of claim 16, wherein processing the image data further comprises performing a Hough Circle Transform to detect a blob circle on the binarized image and to segment color blobs on the binarized image.

### Claim 18 (depends on 13)

The system of claim 13, wherein the one or more input image features comprise color features comprising at least one of global mean, global mode, inner mean, and inner mode.

### Claim 19 (depends on 13)

The system of claim 13, wherein the one or more input image features comprise textural features comprising at least one of co-occurrence matrix, angular second moment, contrast, correlation, entropy, Hellinger Distance, and Hausdorff Distance.

### Claim 20 (depends on 13)

The system of claim 13, wherein the machine learning classification technique comprises one of support vector machines, stacked auto encoders, multi-layer perceptrons, recurrent neural networks, or deep learning neural networks.