MX2022013127A - Pruebas de volatiles de productos agricolas para predecir la calidad usando aprendizaje automatico. - Google Patents

Pruebas de volatiles de productos agricolas para predecir la calidad usando aprendizaje automatico.

Info

Publication number
MX2022013127A
MX2022013127A MX2022013127A MX2022013127A MX2022013127A MX 2022013127 A MX2022013127 A MX 2022013127A MX 2022013127 A MX2022013127 A MX 2022013127A MX 2022013127 A MX2022013127 A MX 2022013127A MX 2022013127 A MX2022013127 A MX 2022013127A
Authority
MX
Mexico
Prior art keywords
food items
volatiles
quality characteristics
machine learning
quality
Prior art date
Application number
MX2022013127A
Other languages
English (en)
Inventor
Taylor Hayward
Elaine Kirschke
Allison Ferranti
Zoe Friedberg
Matthew Lee
Original Assignee
Apeel Tech Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Apeel Tech Inc filed Critical Apeel Tech Inc
Publication of MX2022013127A publication Critical patent/MX2022013127A/es

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N7/00—Analysing materials by measuring the pressure or volume of a gas or vapour
    • G01N7/14—Analysing materials by measuring the pressure or volume of a gas or vapour by allowing the material to emit a gas or vapour, e.g. water vapour, and measuring a pressure or volume difference
    • G01N7/18—Analysing materials by measuring the pressure or volume of a gas or vapour by allowing the material to emit a gas or vapour, e.g. water vapour, and measuring a pressure or volume difference by allowing the material to react
    • 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
    • G01N33/025—Fruits or vegetables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/02—Devices for withdrawing samples
    • G01N1/22—Devices for withdrawing samples in the gaseous state
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/04—Preparation or injection of sample to be analysed
    • G01N30/06—Preparation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N7/00—Analysing materials by measuring the pressure or volume of a gas or vapour
    • G01N7/02—Analysing materials by measuring the pressure or volume of a gas or vapour by absorption, adsorption, or combustion of components and measurement of the change in pressure or volume of the remainder
    • G01N7/04—Analysing materials by measuring the pressure or volume of a gas or vapour by absorption, adsorption, or combustion of components and measurement of the change in pressure or volume of the remainder by absorption or adsorption alone
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/04—Preparation or injection of sample to be analysed
    • G01N30/06—Preparation
    • G01N2030/062—Preparation extracting sample from raw material
    • 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
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0011—Sample conditioning
    • 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
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
    • G01N33/0047—Organic compounds

Landscapes

  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Business, Economics & Management (AREA)
  • Food Science & Technology (AREA)
  • Economics (AREA)
  • Medicinal Chemistry (AREA)
  • Operations Research (AREA)
  • Accounting & Taxation (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Strategic Management (AREA)
  • Quality & Reliability (AREA)
  • Marketing (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Theoretical Computer Science (AREA)
  • Combustion & Propulsion (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Sampling And Sample Adjustment (AREA)
  • General Preparation And Processing Of Foods (AREA)
  • Seasonings (AREA)
  • Catching Or Destruction (AREA)
  • Investigating Or Analyzing Materials By The Use Of Fluid Adsorption Or Reactions (AREA)
  • Preparation Of Fruits And Vegetables (AREA)

Abstract

Esta divulgación se dirige a sistemas y métodos para evaluar características de calidad de artículos alimentarios basándose en el análisis de los volátiles desprendidos por los mismos. Las características de calidad pueden incluir presencia de infección, fase de maduración, aroma, sabor y olor. La determinación de características de calidad puede ser ventajosa para hacer modificaciones en la cadena de suministro que optimizan la calidad y reducen el desperdicio de alimentos. Se puede colocar un tubo que tiene un material sorbente en un entorno que contiene los artículos alimentarios. Los volátiles desprendidos por los artículos alimentarios pueden acumularse en el material sorbente. Un sistema informático puede recibir los datos de concentración y de presencia de volátiles y puede aplicar un modelo de aprendizaje automático a los datos para determinar características de calidad de los artículos alimentarios. El modelo se puede entrenar usando observaciones humanas de características de calidad, información de cadena de suministro histórica y datos de volátiles procesados asociados con otros artículos alimentarios, en donde los otros artículos alimentarios son del mismo tipo que los artículos alimentarios.
MX2022013127A 2020-04-27 2021-04-27 Pruebas de volatiles de productos agricolas para predecir la calidad usando aprendizaje automatico. MX2022013127A (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202063016074P 2020-04-27 2020-04-27
PCT/US2021/029417 WO2021222261A1 (en) 2020-04-27 2021-04-27 Testing of agricultural products volatiles to predict quality using machine learning

Publications (1)

Publication Number Publication Date
MX2022013127A true MX2022013127A (es) 2022-11-10

Family

ID=76305982

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2022013127A MX2022013127A (es) 2020-04-27 2021-04-27 Pruebas de volatiles de productos agricolas para predecir la calidad usando aprendizaje automatico.

Country Status (7)

Country Link
US (1) US20210333185A1 (es)
EP (1) EP4143566A1 (es)
JP (1) JP2023522312A (es)
CN (1) CN115485551A (es)
IL (1) IL297185A (es)
MX (1) MX2022013127A (es)
WO (1) WO2021222261A1 (es)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021252369A1 (en) 2020-06-07 2021-12-16 Comestaag Llc Selectively treating plant items
WO2023129702A1 (en) * 2021-12-30 2023-07-06 Apeel Technology, Inc. Machine learning-based assessment of food item quality
CN114517889B (zh) * 2022-01-25 2024-01-23 佛山绿色发展创新研究院 一种用于实现氢气质量在线检测的控制方法及加氢系统
CN114740075A (zh) * 2022-02-24 2022-07-12 广东美味鲜调味食品有限公司 一种基于hs-ptr-tof-ms快速确定酱油发酵阶段及特征香气的方法
WO2023196477A1 (en) * 2022-04-06 2023-10-12 Apeel Technology, Inc. Ultraviolet light and machine learning-based assessment of food item quality

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11448632B2 (en) * 2018-03-19 2022-09-20 Walmart Apollo, Llc System and method for the determination of produce shelf life
CN110161194A (zh) * 2019-05-29 2019-08-23 中北大学 一种基于气味信息bp神经模糊识别的水果鲜度识别方法、装置及系统

Also Published As

Publication number Publication date
EP4143566A1 (en) 2023-03-08
US20210333185A1 (en) 2021-10-28
CN115485551A (zh) 2022-12-16
IL297185A (en) 2022-12-01
JP2023522312A (ja) 2023-05-30
WO2021222261A1 (en) 2021-11-04

Similar Documents

Publication Publication Date Title
MX2022013127A (es) Pruebas de volatiles de productos agricolas para predecir la calidad usando aprendizaje automatico.
García et al. Quality and shelf-life prediction for retail fresh hake (Merluccius merluccius)
Diao et al. Meta-analysis of D-values of proteolytic Clostridium botulinum and its surrogate strain Clostridium sporogenes PA 3679
Tsironi et al. Effect of processing parameters on water activity and shelf life of osmotically dehydrated fish filets
Ghollasi-Mood et al. Microbial and chemical spoilage of chicken meat during storage at isothermal and fluctuation temperature under aerobic conditions
Górska-Horczyczak et al. Chromatographic fingerprints supported by artificial neural network for differentiation of fresh and frozen pork
Chulayo et al. Research on pre-slaughter stress and meat quality: A review of challenges faced under practical conditions
Olea-Popelka et al. Relative effectiveness of Irish factories in the surveillance of slaughtered cattle for visible lesions of tuberculosis, 2005-2007
Bertrand et al. Dietary fat intake and risk of non-Hodgkin lymphoma in 2 large prospective cohorts
Bozzo et al. Consumer attitudes towards animal welfare and their willingness to pay
Tarlak et al. Predictive modelling for the growth kinetics of Pseudomonas spp. on button mushroom (Agaricus bisporus) under isothermal and non-isothermal conditions
Teleken et al. Mathematical modeling of microbial growth in milk
Kim et al. Monitoring of microbial contaminants of beef, pork, and chicken in HACCP implemented meat processing plants of Korea
Gill et al. Recovery of bacteria from poultry carcasses by rinsing, swabbing or excision of skin
Lee et al. Predictive model for the growth kinetics of Staphylococcus aureus in raw pork developed using Integrated Pathogen Modeling Program (IPMP) 2013
Ishihara et al. Effects of climatic elements on Salmonella contamination in broiler chicken meat in Japan
Møretrø et al. Evaluation of ATP bioluminescence‐based methods for hygienic assessment in fish industry
Oliveira e Silva et al. The use of photography to perform an online consumer test on the freshness of chicken breast and the extension of shelf life
Poirier et al. Large-scale multivariate dataset on the characterization of microbiota diversity, microbial growth dynamics, metabolic spoilage volatilome and sensorial profiles of two industrially produced meat products subjected to changes in lactate concentration and packaging atmosphere
Lean et al. Effects of hormonal growth promotants on beef quality: a meta-analysis
Rodríguez-Hernández et al. Geographical origin, curing plant and commercial category discrimination of cured Iberian hams through volatilome analysis at industry level
Lashley et al. Evaluation of methods to estimate understory fruit biomass
Kandil et al. Identification of S. aureus and E. coli from dairy products intended for human consumption
Brizio et al. Chilled broiler carcasses: A study on the prevalence of Salmonella, Listeria and Campylobacter
Conrad et al. Diet sustainability analyses can be improved with updates to the Food Commodity Intake Database