Quantitative texture measurement apparatus and method

• US 10,598,648 B2
• Filed: 09/21/2017
• Issued: 03/24/2020
• Est. Priority Date: 09/24/2015
• Status: Active Grant

• Current Assignee: Frito-Lay North America Incorporated (PepsiCo Incorporated)
• Original Assignee: Frito-Lay North America Incorporated (PepsiCo Incorporated)
• Inventors: Bai, Ou, Bourg, Jr., Wilfred Marcellien, Fagan, Scott, Michel-Sanchez, Enrique, Mirza, Shahmeer Ali, Richardson, Scott G., Shao, Chen C.
• Primary Examiner(s): Caputo, Lisa M
• Assistant Examiner(s): Devito, Alex T
• Application Number:
  US15/711,592
• Publication Number:
  US 20180011069A1
• Time in Patent Office: 915 Days
• CPC Class Codes: G01B 11/30 for measuring roughness or irregularity of surfaces
• CPC Class Codes: G01B 17/08 for measuring roughness or irregularity of surfaces
• CPC Class Codes: G01H 9/008 by using ultrasonic waves measuring position using ultrasonic waves G01S15/02
• CPC Class Codes: G01N 2291/02827 Elastic parameters, strength or force
• CPC Class Codes: G01N 2291/02845 Humidity, wetness
• CPC Class Codes: G01N 2291/0289 Internal structure, e.g. defects, grain size, texture
• CPC Class Codes: G01N 29/12 by measuring frequency or resonance of acoustic waves measuring frequency or resonant frequency of mechanical vibrations or acoustic waves in general G01H1/06, G01H3/04, G01H13/00; acoustic resonators G10K11/04; vibration or shock testing of structures G01M7/00
• CPC Class Codes: G01N 29/2418 using optoacoustic interaction with the material, e.g. laser radiation, photoacoustics photoacoustic cells G01N21/1702; measuring characteristics of vibrations by using radiation-sensitive means G01H9/00; acousto-optical conversion techniques for short-range imaging G01S15/8965; sound-producing devices using laser bundle G10K15/046
• CPC Class Codes: G01N 33/10 Starch-containing substances, e.g. dough

Abstract

A non-destructive measurement apparatus and method for quantitatively measuring texture of a food snack is disclosed. The apparatus includes a laser generating tool, an ultrasound excitation device, an acoustic capturing device, an ultrasound capturing device and a data processing unit. The laser generating tool and the ultrasound excitation tool direct energy towards a food snack placed on a surface and produce an acoustic signal and an ultrasound signal. The data processing unit further comprises a digital signal processing module that processes the received acoustic signal and ultrasound signal. A statistical processing module further filters the acoustic signal from the data processing unit and generates a quantitative acoustic model for texture attributes such as hardness and fracturability. The quantitative model is correlated with a qualitative texture measurement from a descriptive expert panel. Texture of food snacks are quantitatively measured with the quantitative acoustic model.

Claims

1. An apparatus for quantitative non-destructive texture attribute measurement of a food product comprising:

a housing;

a laser generator attached to said housing;

an acoustic capturing device proximally located to said housing;

an ultrasound excitation device proximally located to said housing;

an ultrasound capturing device proximally positioned with respect to said acoustic capturing device;

a data processing unit in communication with at least said acoustic capturing device and at least said ultrasound capturing device;

wherein a laser from said laser generator is directed to strike said food product, thereby producing an acoustic signal to be detected by said acoustic capturing device; and

an ultrasound wave from said ultrasound excitation device is directed to strike said food product, thereby producing an ultrasound signal to be detected by said ultrasound capturing device;

wherein said data processing unit is configured to quantitatively measure said texture attribute of said food product based on input from said acoustic capturing device and input from said ultrasound capturing device;

wherein said texture attribute is selected from a group consisting of;

hardness, fracturability, tooth-pack, crispiness, roughness of mass, residual greasiness, surface roughness, and surface oiliness.

2. The apparatus of claim 1, wherein said ultrasound excitation device is a non-contact transducer.

3. The apparatus of claim 1, wherein said ultrasound excitation device is a contact transducer.

4. The apparatus of claim 1, wherein said food product is placed on a stationary surface when said laser or said ultrasound wave strikes said food product.

5. The apparatus of claim 1, wherein said food product is passing within said housing when said laser or said ultrasound wave strikes said food product.

6. The apparatus of claim 1, wherein said ultrasound capturing device is configured to capture frequencies in said ultrasound signal;

said frequencies range from 0.1 MHz to 6 MHz.

7. The apparatus of claim 1, wherein ablation does not occur when said laser strikes said food product.

8. The apparatus of claim 1, wherein the laser generator is configured so that the laser imparts fluence within a range of 0.15 mJ/cm² to 700 mJ/mm² at the food product.

9. The apparatus of claim 1, wherein said food product is a starch based food snack.

10. The apparatus of claim 1, wherein said laser and said ultrasound wave are configured to strike said food product simultaneously.

11. The apparatus of claim 1, wherein said laser and said ultrasound wave are configured to strike said food product at different times.

12. The apparatus of claim 1, wherein said texture attribute is selected from a group consisting of:

hardness, fracturability, tooth-pack, crispiness, residual greasiness, and surface oiliness.

13. The apparatus of claim 1, wherein said food product remains intact after said strike from said laser generator or said ultrasound excitation device.

23. The apparatus of claim 1, wherein said texture attribute is selected from a group consisting of:

residual greasiness and surface oiliness.

24. The apparatus of claim 1, wherein said texture attribute is tooth-pack.

25. The apparatus of claim 1, wherein said texture attribute is hardness.

26. The apparatus of claim 1, wherein said texture attribute is fracturability.

14. A non-destructive photo acoustic quantitative method for measuring a texture attribute of a food product, said method comprises the steps of:

a) striking a surface of said food product with a laser, thereby generating an acoustic signal from said surface of said food product;

b) exciting said food product with an ultrasound excitation device, thereby generating an ultrasound signal from said food product;

c) capturing said acoustic signal with an acoustic capturing device;

d) capturing said ultrasound signal with an ultrasound capturing device;

e) sending said acoustic signal and said ultrasound signal to a data processing unit;

f) converting said acoustic signal and said ultrasound signal from a time domain to a frequency domain;

g) identifying relevant frequencies and their associated intensities; and

h) quantifying said texture attribute of said food product based on said relevant frequencies and said associated intensities;

wherein said texture attribute is selected from a group consisting of;

hardness, fracturability, tooth-pack, crispiness, roughness of mass, residual greasiness, surface roughness, and surface oiliness.

15. The method of claim 14 wherein said food product is moving on a production line during said striking step or said exciting step.

16. The method of claim 14 wherein the laser imparts fluence within a range of 0.15 mJ/cm² to 700 mJ/mm² at the food product.

17. The method of claim 14, wherein said food product is a starch based food snack.

18. The method of claim 14, wherein said striking step and said exciting step occur concurrently.

19. The method of claim 14, wherein said striking step and said exciting step occur non-concurrently.

20. The method of claim 14 wherein said texture attribute is selected from a group consisting of:

hardness, fracturability, tooth-pack, crispiness, residual greasiness, and surface oiliness.

27. The method of claim 14, wherein said texture attribute is selected from a group consisting of:

residual greasiness and surface oiliness.

28. The method of claim 14, wherein said texture attribute is tooth-pack.

29. The method of claim 14, wherein said texture attribute is hardness.

30. The method of claim 14, wherein said texture attribute is fracturability.

21. An apparatus for non-contact construction of a microstructure image of a layer of a food product, comprising:

a housing;

a laser generator attached to said housing;

said laser generator configured to generate a laser array;

at least one acoustic capturing device proximally located to said housing;

a data processing unit in communication with said at least one acoustic capturing device;

wherein a laser from said laser generator is directed to strike said food product at a plurality of locations, thereby producing an acoustic signal to be detected by said at least one acoustic capturing device;

wherein further said data processing unit is configured to construct said microstructure image of said layer of said food product based on said acoustic signal;

wherein the apparatus is configured to convert said acoustic signal from a time domain to a frequency domain;

wherein the apparatus is configured to identify relevant frequencies and their associated intensities; and

wherein the apparatus is configured to construct said microstructure image of said layer based on said relevant frequencies and said associated intensities.

22. A non-contact method for constructing a microstructure image of a layer of a food product, said method comprises the steps of:

a) striking a surface of said food product with a laser array, thereby generating an acoustic signal from said surface of said food product;

b) capturing said acoustic signal with at least one acoustic capturing device;

c) sending said acoustic signal to a data processing unit coupled to said at least one acoustic capturing device;

d) converting said acoustic signal from a time domain to a frequency domain;

e) identifying relevant frequencies and their associated intensities; and

f) constructing said microstructure image of said layer based on said relevant frequencies and said associated intensities.