# US 12,007,324 B2 · Systems and methods for pH sensing in fluids

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- Number: US 12,007,324 B2
- Document ID: US12007324B2
- Status: Granted
- Kind: B2
- Application number: 17/860,428
- Filed: 2022-07-08
- Published: 2023-01-12
- Granted: 2024-06-11
- Earliest claimed: 2021-07-08
- Assignees: University of Maryland, College Park
- Applicants: University of Maryland, College Park
- Inventors: Yang Tao; Anjana Hevaganinge; Chiao-Yi Wang; Dongyi Wang; Mohamed Amr Ali
- Web page: https://patentgrep.com/patent/US12007324B2

## CPC

- G01N 21/3577: for analysing liquids, e.g. polluted water
- G01J 3/0264: {Electrical interface; User interface}
- G01J 3/108: {for measurement in the infrared range}
- G01J 3/2823: {Imaging spectrometer}
- G01N 21/359: using near infrared light
- G01J 2003/2826: {Multispectral imaging, e.g. filter imaging}
- G01J 2003/283: {computer-interfaced}
- G01J 2003/2833: {and memorised spectra collection}
- G01J 2003/2836: {Programming unit, i.e. source and date processing}
- G01J 2003/284: {Spectral construction}
- G01J 2003/2843: {Processing for eliminating interfering spectra}
- G01N 21/05: Flow-through cuvettes (G01N21/09 takes precedence; handling fluid samples G01N1/10)
- G01N 2201/126: Microprocessor processing
- G01N 2201/1296: using neural networks
- 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)
- G01J: MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY (light sources F21, H01J, H01K, H05B; investigating properties of materials by optical means G01N)

## Abstract

A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

## Claims

### Claim 1

A non-contact system for sensing of pH, the system comprising:
a hyperspectral imaging device configured to capture a hyperspectral image of a fluid;
a flow cell configured to enable the capturing of a hyperspectral image of a fluid;
a processor; and
a memory including instructions stored thereon, which, when executed by the processor, cause the system to:
generate a hyperspectral image of the fluid in the flow cell;
generate a spectral signal based on the hyperspectral image;
provide the spectral signal as an input to a machine learning network; and
predict by the machine learning network a pH of the fluid.

### Claim 2 (depends on 1)

The system of claim 1, further comprising a pump configured to pump a cell media into the flow cell.

### Claim 3 (depends on 1)

The system of claim 1, further comprising a cell media filter configured to filter the fluid prior to the media being flowed into the flow cell.

### Claim 4 (depends on 1)

The system of claim 1, wherein the machine learning network includes a convolutional neural network.

### Claim 5 (depends on 1)

The system of claim 1, wherein the flow cell includes a transparent window configured for imaging the fluid.

### Claim 6 (depends on 1)

The system of claim 1, wherein the instructions, when executed by the processor, further cause the system to preprocess the spectral signal to reduce noise, before providing the spectral signal to the machine learning network.

### Claim 7 (depends on 1)

The system of claim 1, wherein the spectral signal includes transmittance readings, and
wherein the instructions, when executed by the processor, further cause the system to convert the transmittance readings into absorbance readings.

### Claim 8 (depends on 7)

The system of claim 7, wherein the transmittance readings are converted into absorbance readings using A(x,y,λ)=−log10(R(x,y,λ)) (R(x,y,λ)) is the hyperspectral cubic image, and A(x,y,λ) is absorbance.

### Claim 9 (depends on 7)

The system of claim 7, wherein the instructions, when executed by the processor, further cause the system to filter in an XY image plane to reduce noise in the absorbance readings.

### Claim 10 (depends on 9)

The system of claim 9, wherein the instructions, when executed by the processor, further cause the system to perform z-score based masking to further remove background and noisy pixels from the absorbance readings.

### Claim 11

A computer-implemented method for sensing pH, the method comprising:
capturing a hyperspectral image of a fluid within a flow cell by a hyperspectral imaging device;
generating a spectral signal based on the hyperspectral image captured;
providing the spectral signal as an input to a machine learning network; and
predicting by the machine learning network a pH of the fluid.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, further comprising pumping a filtered fluid into the flow cell by a pump.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, further comprising filtering the fluid by a cell media filter prior to the flowing of the fluid through the flow cell.

### Claim 14 (depends on 11)

The computer-implemented method of claim 11, wherein the machine learning network includes a convolutional neural network.

### Claim 15 (depends on 11)

The computer-implemented method of claim 11, wherein the hyperspectral image is captured within a transparent window of the flow cell.

### Claim 16 (depends on 11)

The computer-implemented method of claim 11, further comprising preprocessing the spectral signal to reduce noise, before providing the spectral signal to the machine learning network.

### Claim 17 (depends on 11)

The computer-implemented method of claim 11, further comprising converting transmittance readings from the spectral signal to absorbance readings.

### Claim 18 (depends on 17)

The computer-implemented method of claim 17, further comprising filtering the absorbance readings in an XY image plane to reduce noise in the absorbance readings.

### Claim 19 (depends on 18)

The computer-implemented method of claim 18, further comprising performing z-score based masking to further remove background and noisy pixels from the absorbance readings.

### Claim 20 (depends on 11)

The computer-implemented method of claim 11, wherein the hyperspectral imaging device is a short-wave infrared hyperspectral imaging device.

### Claim 21

A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to perform a computer-implemented method for hyperspectral imaging comprising:
generating a hyperspectral image of a fluid within a flow cell by a hyperspectral imaging device;
generating a spectral signal based on the hyperspectral image captured;
providing the spectral signal as an input to a machine learning network; and
predicting by the machine learning network a pH of the fluid.