Hyperspectral image-based method for predicting growth of rot funguses

A technology of hyperspectral image and spectral image, applied in the field of non-destructive technology for rapid detection and monitoring of food quality and safety

Inactive Publication Date: 2015-01-21
NANJING AGRICULTURAL UNIVERSITY
View PDF6 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

After retrieval, the invention patent "Detection system and method for the total number of bacteria in livestock meat (CN103257109A)" applied in 2013 discloses an automatic detection device system and method for the total number of bacteria in fresh livestock meat using hyperspectral images, but it does not involve the detection of spoilage in fruits. Dynamic Growth Prediction of Fungi

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Hyperspectral image-based method for predicting growth of rot funguses
  • Hyperspectral image-based method for predicting growth of rot funguses
  • Hyperspectral image-based method for predicting growth of rot funguses

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] A hyperspectral image technology method for predicting the growth of Pseudomonas aeruginosa, the specific implementation is as follows:

[0046] 1 Materials and methods

[0047]Botrytis cinerea, Rhizopus stolonifer, and Colletotrichumacutatum were provided by the Laboratory of Food Science and Technology College of Nanjing Agricultural University.

[0048] The medium is potato agar medium, which consists of 5g of potato powder, 20g of glucose, 5g of NaCl, 15g of agar, 0.1g of chloramphenicol, 1000mL of water, pH5.8-6.2; the volume of medium contained in each petri dish is 20± 2mL, the medium thickness is 2.5±0.5mm.

[0049] The strain culture method is as follows: prepare 30 sterilized plates, pour 20mL of the same sterilized medium on it, and inoculate the three kinds of fungi by plane streaking after cooling, 10 plates for each type of bacteria, and cultivate them. The thermostat is 25°C and the relative humidity is 85%. After culturing the strains for 5 days, they...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

PropertyMeasurementUnit
reflectanceaaaaaaaaaa
Login to view more

Abstract

The invention provides a hyperspectrum-based method for creating a growth curve of rot funguses in a fruit, and belongs to nondestructive technology of food quality safety quick detection and monitoring. Hyperspectral images of the funguses at different growth stages can be acquired respectively through a hyperspectral imager, so as to analyze difference between images of different types of funguses at different stages and a spectrum, extracting corresponding images and corresponding spectrum characteristic parameters, and respectively creating the growth models of three types of funguses. Compared with the fungus growth conditions obtained through the conventional microbe growth detection manner, the related coefficient ranges from 0.88 to 0.96. The method provides a new concept and a new technology for growth detection of microbes in the foods, can be used for creating a fungus growth curve more conveniently and faster, and can be used for detecting, monitoring and controlling fruit rot fungus diseases.

Description

technical field [0001] The invention is a method for predicting the growth of spoilage fungi in fruits, such as botrytis cinerea, rhizopus stoloniferus and anthracnose, by hyperspectral image technology, and belongs to the non-destructive technology for rapid detection and monitoring of food quality and safety. Background technique [0002] Rot caused by spoilage fungi during postharvest transportation and storage of fruits can cause huge economic losses and pose a health hazard to consumers. Postharvest diseases caused by fungi mainly include gray mold, rhizopus, anthracnose, etc., and their pathogenic bacteria are: Botrytis cinerea, rhizopus stolonifer, and anthracnose. Botrytis cinerea can damage flowers and fruits, and a layer of gray plaques will grow on the diseased part. After individual fruits get sick, it will cause the surrounding whole fruits to get sick. Rhizopus stolonifera survives in the air, in soil, and on the surfaces of various implements, infecting damag...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/25
Inventor 潘磊庆孙晔屠康顾欣哲胡鹏程韦莹莹张伟
Owner NANJING AGRICULTURAL UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products