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Dr. Zhiming Guo: Research advances on intelligent non-destructive detection of the internal quality and invisible defects of fruits and vegetables

Supported by National Natural Science Foundation of China (31501216),the National Key R&D Program of China (2017YFC1600802, 2018YFC1604401) and other national or ministry projects, guided by food nutrition, safety and health, our research focus on the technical requirements for healthy and sustainable development of fruit and vegetable industry, investigate fast, high accuracy, non-invasive and intelligent detection techniques and systems for fruits and vegetables.

1. The novel method of data mining for spectral information representation of fruit and vegetable internal quality. The optimization of characteristic variables for spectral information of fruits and vegetables is a combinatorial optimization problem. Ant colony optimization (ACO) with the characteristics of global, dispersion and probability selection are desirable for spectrum data mining. Therefore, this research pioneering applied the heuristic global search mechanism of ACO to select the characteristic spectra of quality information of food and agricultural products, and establish a robust and simplified quantitative analysis model of quality.

 

2. The new method of signal intensity correction for spectral imaging detection of fruits and vegetables. When fruits and vegetables are detected offline or online, the change of optical path will cause the fluctuation of spectral signal intensity, which affects the accuracy of measurement. A standardized correction method of spectral intensity is presented to eliminate the influence of distance change between sample and optical fiber sensor, and to solve the technical problems of poor detection adaptability caused by optical signal disturbance.



 

3. The creative approach of color compensation for spectral detection of fruit and vegetable quality. The spectral disturbance caused by color will lead to the shift of spectral absorption position, and then affect the accuracy of quality prediction model. A color compensation method for spectral detection of agricultural products quality is presented by this research. In order to improve the accuracy and stability of quality detection of agricultural products, color as a compensation factor can effectively improve the performance of quality detection.

 

4. The new method for detecting invisible defects in fruits and vegetables by NIR transmission spectroscopy. In order to simultaneous nondestructive on-line inspect edible quality and internal defect of apple, this work presents the development of an on-line detection prototype system using near infrared transmittance technology as a novel approach for on-line detect quality attributes without sample destructiveness. The on-line detection system was designed and developed to improve spectra signal quality, lower heat damage, reduce mechanical damage. The results show that the transmission spectral can obtain more comprehensive internal information of agricultural products. The present research provides the foundation for the future development of an automatic system based on transmittance spectroscopy which is extremely important from the economic point of view.

 

 

Zhiming Guo, Ph.D, Associate Professor, Master Supervisor.

Scientific Research Field:

a) Fast & nondestructive detection of food quality and safety

b) Optical sensing and automation for food quality evaluation

c) Internet of things (IOT) for food quality and safety

d) Advanced chemometrics methods

Emailguozhiming@ujs.edu.cn

Personal information: http://spswxy.ujs.edu.cn/info/1076/4171.htm

Welcome the enterprises, institute and university to cooperate joint research!

Welcome the talented persons who have relevant professional knowledge background or working experience in food, computer, machinery and control to join our research team. We will cooperate with each other sincerely and forge ahead to create a new chapter of food intelligent detection and processing technology.

 

Returns〗 Hits: Date:2018-12-24
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