WO2018156000A1 - Method for measuring meat quality based on infrared light absorption contrast - Google Patents

Method for measuring meat quality based on infrared light absorption contrast Download PDF

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Publication number
WO2018156000A1
WO2018156000A1 PCT/MX2017/000016 MX2017000016W WO2018156000A1 WO 2018156000 A1 WO2018156000 A1 WO 2018156000A1 MX 2017000016 W MX2017000016 W MX 2017000016W WO 2018156000 A1 WO2018156000 A1 WO 2018156000A1
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tissue
cut
present
absorption
classification
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PCT/MX2017/000016
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Spanish (es)
French (fr)
Inventor
Pedro Gabriel GONZÁLEZ ESTRADA
Martín Gustavo VÁZQUEZ PALMA
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Gonzalez Estrada Pedro Gabriel
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Priority to PCT/MX2017/000016 priority Critical patent/WO2018156000A1/en
Publication of WO2018156000A1 publication Critical patent/WO2018156000A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G01N33/12—Meat; Fish

Definitions

  • the present invention has its preponderant field of application in the detection and measurement of quality of cuts of cattle carcasses, specifically by the impiementation of methods and systems with artificial vision.
  • CN102156128 describes a system and method of intelligent classification of meat quality in artificial vision comprising a positioning platform for the beef carcass, a dark room, a camera for capturing images and a classification module for Meat quality
  • This technique is used for digital image processing of the cross section of the beef carcass and serves to analyze three indices of meat quality corresponding to marbling, fat color and red color in an effective area of Ribeye.
  • the invention AU2013264002 provides a method of standard determination of meat color classification that allows the determination of detailed meat color classification, comprising a camera for image capture and a beef color sorter .
  • the method comprises the following stages: taking an image of the Ribeye cross section of the bovine; marble extraction in the Ribeye region and marble classification.
  • US Patent No. 7123685 refers to the continuous determination of the fat content of meat where a conveyor belt is used to examine the meat when it is moving towards a radiation source that serves as a method of fat analysis.
  • Figure 1 is an outline of the key activities for the Method of Meat Quality Measurement through Tissue Identification and Spot Discrimination in 2D Images of the present invention.
  • Figure 2 is a diagram of the curves of the Absorption Coefficient behavior of different types of molecules present in biological tissues through the infrared spectrum, from 900 nm to 1300 nm.
  • the method starts after a cut of the cattle carcass is obtained [101].
  • the first capture is made with illumination and IR reception filter at 900 nm [102] from where, from the darkest areas, an estimate of the location of connective tissue [103] present in said cut will be obtained.
  • a capture will be made with illumination and IR filtration of wavelength 1170 nm [104], followed by 1210 nm [105]; from a contrast in these last two captures, the areas where adipose tissue is contained are determined [108].
  • the areas of each composition are calculated to obtain an average marbling percentage [107] of the cut of the canal.
  • FIG. 2 shows the behavior of the optical absorption curves in the infrared spectrum for different molecules present in a channel cut.
  • Muscle tissue contains water, elastin, collagen and other molecules.
  • the Elastin and Collagen curve is taken into account and for the fatty tissue the Lipid curve. It is illustrated that in the 900 nm wavelength the absorption of lipids and water is close to zero, so they are seen more clearly unlike the connective tissue that is observed darker. In the wavelength with a value of 1170 nm there is a very close absorption value for all types of molecules; If starting from 1170 nm to 1210 nm, the adipose tissue can be differentiated from the other tissues with greater contrast.

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  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Food Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Pathology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Medicinal Chemistry (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The present invention describes a method for classifying and measuring meat quality by identifying muscular, connective and adipose tissues and discriminating superficial fat stains and blood stains present. The method involves generating 2D images with illumination and filtering at various specific wavelengths within the infrared spectrum. Monochromatic segmentation algorithms based on dynamic thresholding are used to segment each type of tissue before obtaining the percentages (marbling) of the content thereof.

Description

MÉTODO DE MEDICIÓN DE CALIDAD DE CARNE BASADO EN  MEAT METHOD OF MEAT QUALITY BASED ON
CONTRASTE DE ABSORCIÓN DE LUZ INFRARROJA  INFRARED LIGHT ABSORPTION CONTRAST
CAMPO TÉCNICO DE LA INVENCIÓN TECHNICAL FIELD OF THE INVENTION
La presente invención tiene su campo de aplicación preponderante en la detección y medición de calidad de cortes de canales de ganado, específicamente mediante la impiementación de métodos y sistemas con visión artificial. The present invention has its preponderant field of application in the detection and measurement of quality of cuts of cattle carcasses, specifically by the impiementation of methods and systems with artificial vision.
ANTECEDENTES DE LA INVENCIÓN Las consideraciones básicas para la clasificación de carne de vaca es evaluar las características asociadas con su palatabilidad y del deseo relativo esperado de la carne en un corte expresado en términos estandarizados de calidad. Esta situación da pie a que se generen tecnologías que aportan diversas herramientas para tal fin. Una característica deseable en este tipo de procesos es el monitoreo de variables que permiten conocer la calidad del proceso de ¡as plantas de sacrificio, mismas que son reflejadas particularmente en la acidez, temperatura interna, color y porcentajes de contenido (marmoleo) de la canal. Tradicionalmente, la clasificación de carnes se basa en la experiencia de personas largamente entrenadas para tal fin. Sin embargo, debido a la subjetividad implícita de este tipo de evaluaciones ¡a tendencia en esta industria apunta a utilizar tecnologías que conlleven a una situación en que exista más exactitud y consistencia, pues una variación pequeña en puntos porcentuales podrá determinar un mercado y costo distinto del producto. Dando respuesta a la tendencia de las industrias por optar por sistemas modernos como tecnología de visión artificial, métodos de procesamiento electrónico, y otras, ¡a presente invención tiene como objetivo reclamar un método de medición de calidad, en el cual se detalla como un método de clasificación de carne con base en tecnología breve descripción de patentes actuales en relación al tema, con el fin de resaltar la actividad inventiva de ía presente invención. La Patente No. CN102156128 describe un sistema y un método de clasificación inteligente de calidad de la carne en visión artificial que comprende una plataforma de colocación para la canal de la res, un cuarto oscuro, una cámara para capturar imágenes y un módulo de clasificación de calidad de la carne. Esta técnica se utiliza para procesamiento de imagen digital de la sección transversal de la canal de res y sirve para análisis de tres índices de calidad de la carne correspondientes a marmoleado, color de la grasa y el color rojo en un área efectiva de Ribeye. Así mismo, la invención AU2013264002 proporciona un método de determinación estándar de clasificación del color de la carne que permite la determinación de la clasificación detallada del color de la carne, comprendiendo una cámara para la captura de imágenes y un clasificador de color de carne de ternera. BACKGROUND OF THE INVENTION The basic considerations for the classification of beef is to evaluate the characteristics associated with its palatability and the expected relative desire of the meat in a cut expressed in standardized terms of quality. This situation leads to the generation of technologies that provide various tools for this purpose. A desirable characteristic in this type of process is the monitoring of variables that allow to know the quality of the process of the slaughter plants, which are particularly reflected in the acidity, internal temperature, color and content percentages (marbling) of the canal . Traditionally, the classification of meats is based on the experience of people long trained for this purpose. However, due to the implicit subjectivity of this type of evaluation, the trend in this industry aims to use technologies that lead to a situation where there is more accuracy and consistency, since a small variation in percentage points may determine a different market and cost of the product. Responding to the tendency of industries to opt for modern systems such as artificial vision technology, electronic processing methods, and others, the present invention aims to claim a quality measurement method, which is detailed as a method of meat classification based on technology brief description of current patents in relation to the subject, in order to highlight The inventive activity of the present invention. Patent No. CN102156128 describes a system and method of intelligent classification of meat quality in artificial vision comprising a positioning platform for the beef carcass, a dark room, a camera for capturing images and a classification module for Meat quality This technique is used for digital image processing of the cross section of the beef carcass and serves to analyze three indices of meat quality corresponding to marbling, fat color and red color in an effective area of Ribeye. Likewise, the invention AU2013264002 provides a method of standard determination of meat color classification that allows the determination of detailed meat color classification, comprising a camera for image capture and a beef color sorter .
Entre la búsqueda se encuentra un aparato y un método para predecir la suavidad de la carne, que permite la identificación de carne tierna e incluye la inserción de una o más cuchillas de punta plana en una muestra de carne para la medición de un valor como el estrés, la fuerza o la energía de corte y cálculo de factor de suavidad de los mismos basados en un límite de suavidad (US8225645). Similarmente, se encuentra la patente CN1026081 18 que describe un dispositivo portátil de adquisición de imágenes del sistema de clasificación de calidad de carne basada en tecnología de visión artificial integrada por una carcasa, un reflector y una cámara industrial, el cual tiene el objetivo de capturar una imagen para después ser procesada. Por otro lado la invención CN101706445 describe un método y un aparato de clasificación de marmoleado de carne de vaca. El método comprende las siguientes etapas: toma de una imagen de la sección transversal de Ribeye del bovino; extracción de marmoleado en región del Ribeye y clasificación del marmoleado. La Patente Estadounidense No. 7123685 se refiere a la determinación continua del contenido de grasa de la carne en donde una cinta transportadora se utiliza para la examinar la carne cuando esta avanza hacia una fuente de radiación que sirve como método de análisis de la grasa. Among the search is an apparatus and a method to predict the smoothness of the meat, which allows the identification of tender meat and includes the insertion of one or more blades with a flat tip in a meat sample for the measurement of a value such as stress, the force or the energy of cut and calculation of factor of smoothness of the same based on a limit of smoothness (US8225645). Similarly, there is the CN1026081 18 patent which describes a portable image acquisition device of the meat quality classification system based on artificial vision technology composed of a housing, a reflector and an industrial camera, which aims to capture An image to be processed later. On the other hand, the invention CN101706445 describes a method and an apparatus for classifying beef marbling. The method comprises the following stages: taking an image of the Ribeye cross section of the bovine; marble extraction in the Ribeye region and marble classification. US Patent No. 7123685 refers to the continuous determination of the fat content of meat where a conveyor belt is used to examine the meat when it is moving towards a radiation source that serves as a method of fat analysis.
Como se menciona anteriormente, ninguna de las patentes considera un proble ma muy común en la clasificación automática de carnes que consiste en tomar manchas de grasa (causadas por la operación de corte) y sangre como elementos profundos, siendo que deberían discriminarse al tratarse de estar solamente presentes en la superficie. A través de la generación de imágenes en distintas longitudes de onda en el espectro IR se ha podido atacar este y otros retos en esta práctica. As mentioned above, none of the patents consider a very common problem in the automatic classification of meats that consists of taking grease stains (caused by the cutting operation) and blood as deep elements, since they should be discriminated against when they are only present on the surface. Through the generation of images at different wavelengths in the IR spectrum it has been possible to attack this and other challenges in this practice.
DESCRIPCION DETALLADA DE LA INVENCIÓN DETAILED DESCRIPTION OF THE INVENTION
Los detalles característicos de la presente invención, se muestran claramente en la siguiente descripción y en las figuras que se acompañan, las cuales se mencionan a manera de ejemplo, por lo que no deben considerarse como una limitante para dicha invención. The characteristic details of the present invention are clearly shown in the following description and in the accompanying figures, which are mentioned by way of example, and therefore should not be considered as a limitation for said invention.
Breve descripción de las figuras: Brief description of the figures:
La figura 1 es un esquema de las actividades clave para el Método de Medición de Calidad de Carne a través de Identificación de Tejidos y Discriminación de Manchas en Imágenes 2D de la presente invención. Figure 1 is an outline of the key activities for the Method of Meat Quality Measurement through Tissue Identification and Spot Discrimination in 2D Images of the present invention.
La figura 2 es un diagrama de las curvas d el comportamiento del Coeficiente de Absorción de distintos tipos de moiéculas presentes en tejidos biológicos a través del espectro infrarrojo, desde 900 nm hasta 1300 nm.  Figure 2 is a diagram of the curves of the Absorption Coefficient behavior of different types of molecules present in biological tissues through the infrared spectrum, from 900 nm to 1300 nm.
Como se indica en la Figura 1 , el método parte después de obtenerse un corte de la canal de ganado [101]. La primera captura se realiza con iluminación y filtro de recepción IR a 900 nm [102] de donde se obtendrá, a partir de las zonas más oscuras, una estimación de localización de tejido conectivo [103] presente en dicho corte. A continuación, se realizará una captura con iluminación y filtrado IR de longitud de onda 1170 nm [104], seguido por 1210 nm [105]; a partir de un contraste en estas últimas dos capturas se determinan las zonas donde se contiene tejido adiposo [108]. Para finalizar, a partir de la identificación de los distintos tipos de tejidos, se calculan las áreas de cada composición para obtener un porcentaje de marmoleo [107] promedio del corte de la canal. As indicated in Figure 1, the method starts after a cut of the cattle carcass is obtained [101]. The first capture is made with illumination and IR reception filter at 900 nm [102] from where, from the darkest areas, an estimate of the location of connective tissue [103] present in said cut will be obtained. Next, a capture will be made with illumination and IR filtration of wavelength 1170 nm [104], followed by 1210 nm [105]; from a contrast in these last two captures, the areas where adipose tissue is contained are determined [108]. Finally, from the identification of the different types of tissues, the areas of each composition are calculated to obtain an average marbling percentage [107] of the cut of the canal.
La figura 2 muestra el comportamiento de las curvas de absorción óptica en el espectro infrarrojo para distintas moléculas presentes en un corte de canal. El tejido musculoso contiene agua, elastina, colágeno y otras moléculas. Para identificar el tejido conectivo se toma en cuenta la curva de Elastina y Colágeno y para el tejido graso la curva para Lípidos. Se ilustra que en la longitud de onda de 900 nm la absorción de lípidos y agua es cercana a cero, por lo que se ven de manera más clara a diferencia del tejido conectivo que se observa más oscuro. En la longitud de onda con valor de 1170 nm se tiene un valor de absorción muy cercano para todos tos tipos de moléculas; si se parte desde 1170 nm a 1210 nm se podrá diferenciar el tejido adiposo de los demás tejidos con un mayor contraste. Figure 2 shows the behavior of the optical absorption curves in the infrared spectrum for different molecules present in a channel cut. Muscle tissue contains water, elastin, collagen and other molecules. To identify the connective tissue, the Elastin and Collagen curve is taken into account and for the fatty tissue the Lipid curve. It is illustrated that in the 900 nm wavelength the absorption of lipids and water is close to zero, so they are seen more clearly unlike the connective tissue that is observed darker. In the wavelength with a value of 1170 nm there is a very close absorption value for all types of molecules; If starting from 1170 nm to 1210 nm, the adipose tissue can be differentiated from the other tissues with greater contrast.

Claims

REIVINDICACIONES La presente invención reclama: 1. -Método de identificación de distintos tipos de tejidos biológicos y discriminación de manchas superficiaies de grasa y sangre en corte de canal de ganado, a través de generación de imágenes 2D con iluminación y filtrado a diferentes longitudes de onda específicas dentro del espectro infrarrojo (IR) y algoritmos de procesamiento de datos de colores monocromáticos, caracterizado por: a. - Sistema de iluminación IR compuesto por arreglo de Diodos Emisores de Luz (LEDs) Infrarroja en 3 longitudes de onda especificas con valor pico 900 nm, 1170 nm y 1210 nm, con misma potencia lumínica, b. - Sensor óptico IR en rango de 800 nm a 1300 nm, que integra mecanismo de intercambio de filtros ópticos con valor nominal de 900 nm, 1170 nm y 1210 nm. c. ~ Unidad de procesamiento recibe datos de imagen del sensor óptico IR y ejecuta grupo de algoritmos de segmentación monocromática por valor umbral dinámico de 8 bits que, debido a la naturaleza y geometría del corte de la canal, compensa el valor de límites característicos para cada clasificación con referencia del supuesto tejido muscular para distintos ángulos de captura de imagen. d. - Generación de información a través de dicho grupo de algoritmos referente a clasificación de tejido presente en corte de canal con exclusión de manchas superficiales con profundidad menor a 2 mm, basándose en coeficientes de reflectividad y absorción IR de cada tipo de tejido en longitudes de onda: i) 900 nm, donde se determina límite para identificar tejido conectivo al ser el de mayor absorción en esta longitud de onda, ii) 1170 nm, se reconocen secciones para contrastar con imagen obtenida a iii) 1210 nm, donde se identifica tejido adiposo al tener un valor pico y mayor diferencia en absorción para esta longitud de onda, e. - Algoritmo para cálculo de áreas de cada tejido identificado presente en corte de canal de ganado para obtención de porcentajes de contenido en músculo, grasa y tejido conectivo, también conocido como marmoleo. CLAIMS The present invention claims: 1. - Method of identification of different types of biological tissues and discrimination of superficial spots of fat and blood in cattle carcass section, through the generation of 2D images with illumination and filtering at different wavelengths specific within the infrared (IR) spectrum and monochromatic color data processing algorithms, characterized by: a. - IR lighting system composed by arrangement of Light Emitting Diodes (LEDs) Infrared in 3 specific wavelengths with a peak value 900 nm, 1170 nm and 1210 nm, with the same light output, b. - IR optical sensor in the range of 800 nm to 1300 nm, which integrates optical filter exchange mechanism with a nominal value of 900 nm, 1170 nm and 1210 nm. C. ~ Processing unit receives image data from the IR optical sensor and executes a group of 8-bit dynamic threshold segmentation algorithms that, due to the nature and geometry of the cut of the channel, compensates for the value of characteristic limits for each classification with reference to the supposed muscle tissue for different angles of image capture. d. - Generation of information through said group of algorithms regarding the classification of tissue present in the cut of the channel, excluding surface stains with depth less than 2 mm, based on reflectivity coefficients and IR absorption of each type of tissue at wavelengths : i) 900 nm, where the limit to identify connective tissue is determined to be the one with the highest absorption at this wavelength, ii) 1170 nm, sections are recognized to contrast with image obtained at iii) 1210 nm, where adipose tissue is identified having a peak value and greater difference in absorption for this wavelength, e. - Algorithm for calculation of areas of each identified tissue present in cattle carcass section to obtain percentages of muscle, fat and connective tissue content, also known as marbling.
1. - Método como el especificado en reivindicación 1 , donde el sistema de iluminación cuenta con filtros polarizadores tanto en la emisión como en la recepción para eliminar capturas de reflejos especulares en la superficie del corte de la canal. 1. - Method as specified in claim 1, wherein the lighting system has polarizing filters both in the emission and in the reception to eliminate captures of mirror reflections on the surface of the cut of the channel.
2. - Método como el especificado en reivindicación 2, donde la captura de imágenes y variación de longitud de onda IR se realiza en un espacio encerrado que evitar la entrada de ruido luminoso por fuentes externas de luz IR. 2. - Method as specified in claim 2, wherein the image capture and IR wavelength variation is performed in an enclosed space that prevents the entry of luminous noise by external sources of IR light.
3.- Método como el especificado en reivindicación 3, donde la segmentación de los distintos tipos de tejidos ejecutada por la unidad de procesamiento es complementada a través de la retroalimentación de un usuario mediante una pantalla táctil. 3. Method as specified in claim 3, wherein the segmentation of the different types of tissues executed by the processing unit is complemented by the feedback of a user through a touch screen.
PCT/MX2017/000016 2017-02-22 2017-02-22 Method for measuring meat quality based on infrared light absorption contrast WO2018156000A1 (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1603794A (en) * 2004-11-02 2005-04-06 江苏大学 Method and device for rapidly detecting tenderness of beef utilizing near infrared technology
WO2010081116A2 (en) * 2009-01-10 2010-07-15 Goldfinch Solutions, Llc System and method for analyzing properties of meat using multispectral imaging

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1603794A (en) * 2004-11-02 2005-04-06 江苏大学 Method and device for rapidly detecting tenderness of beef utilizing near infrared technology
WO2010081116A2 (en) * 2009-01-10 2010-07-15 Goldfinch Solutions, Llc System and method for analyzing properties of meat using multispectral imaging

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Qué es un filtro polarizador y para qué sirve.", 16 December 2015 (2015-12-16), XP055535774, Retrieved from the Internet <URL:http://365enfoques.com/accesorios-fotografia/filtro-polarizador-para-que-sirve> [retrieved on 20170925] *
CHENG-LUN TSAI ET AL.: "Near-infrared Absortion Property of Biological Soft Tissue Constituents", JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, vol. 1, 2001 *

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