Development of high-speed high resolution magnetic resonance imaging for food process control

DEVELOPMENT OF HIGH-SPEED HIGH RESOLUTION MAGNETIC RESONANCE IMAGING FOR FOOD PROCESS CONTROL

Sponsoring Institution

National Institute of Food and Agriculture

Project Status

COMPLETE

Funding Source

NRI COMPETITIVE GRANT

Reporting Frequency

Annual

Accession No.

0211253

Grant No.

2008-35503-18665

Cumulative Award Amt.

(N/A)

Proposal No.

2007-02632

Multistate No.

(N/A)

Project Start Date

Dec 1, 2007

Project End Date

Nov 30, 2011

Grant Year

2008

Program Code

[71.1]- (N/A)

Recipient Organization
UNIVERSITY OF CALIFORNIA, DAVIS
410 MRAK HALL
DAVIS,CA 95616-8671

Performing Department
FOOD SCIENCE AND TECHNOLOGY

Non Technical Summary
The fruit industry is very concerned about maintaining the quality of their products and being able to successfully compete in the global fruit business. The ability to assess internal quality is critical to the fruit industry to increase profit, reduce operating costs and improve the marketability and competitiveness of the industry. The purpose of this study is to develop a cost effective sensor for simultaneously measuring multiple internal fruit quality factors.

Animal Health Component

40%

Research Effort Categories

Basic

40%

Applied

40%

Developmental

20%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
501	0999	2020	40%
501	1099	2020	10%
501	1199	2020	10%
501	1499	2020	40%

Knowledge Area
501 - New and Improved Food Processing Technologies;

Subject Of Investigation
1499 - Vegetables, general/other; 1099 - Tropical/subtropical fruit, general/other; 0999 - Citrus, general/other; 1199 - Deciduous and small fruits, general/other;

Field Of Science
2020 - Engineering;

Keywords

magnetic resonance imaging

fruit internal quality

Goals / Objectives
This project will develop a nondestructive fruit internal quality sensor based on magnetic resonance imaging. To achieve this goal, magnetic resonance imaging techniques will need to be optimized to measure fruit quality at packing line speeds of 10 to 12 fruit per second. Data analysis based on a combination of standard image processing techniques and chemometrics will be developed to classify fruit internal quality based on multiple quality factors in real-time.

Project Methods
Magnetic resonance imaging will be used to acquire 3-dimensional data sets of individual fruit using procedures that can process 10 to 12 fruit per second. The first approach will be using a convey-stop-convey method where the image data is acquired while the fruit is motionless. This imaging data will be analyzed using both standard imaging processing techniques and chemometrics to quantify the quality of each fruit. The next approach if this is successful will be to acquire MR imaging data while the fruit are in motion. The speed will be set at a low value and increased. The data acquired under motion will be of a reduced quality however we believe from our previous work that it should be possible to achieve rates of motion around 0.5 to 1 m/s. Motion correction algorithms will be used to minimize image artifacts. Chemometrics is the discipline that uses multivariate statistical methods to 1) design or select optimal chemical measurement procedures and 2) provide maximum chemical information from a measurement in an efficient way. Using the chemometric technique of principal components analysis (PCA), one can determine which parts of the MR spectrum are most important in differentiating among pixels of different intensities in the image. This technique can be applied to groups of pixels (usually regions of interest encompassing different structures in the object being imaged) to provide information on how areas in the interior of the object differ in terms of their MR properties. Using the chemometric regression technique of partial least squares (PLS), one can also use a learning set of images of objects in different classes to classify future test images. The image learning set is regressed onto a set of logical indices (e.g. an images index for a certain class would be 1 if the image belongs to the class or 0 if the image does not belong to the class), and the resulting multivariate regression model is used to predict the class membership of new images.

Progress 12/01/07 to 11/30/11

Outputs
OUTPUTS: During this project magnetic resonance imaging was optimized to measure the internal quality of fruit. Most significant outcomes include the correlation of spectral information to quality parameters in tomatoes, pomegranates, citrus and apples. These internal quality factors include maturity, degree of bruising, defect identification and suitability for specific end use. Research results have been presented annually at the Institute of Food Technologists Annual meetings, to commodity boards and to companies that have visited the laboratory. PARTICIPANTS: A postdoctoral scholar (S. Garcia) and a Ph.D. candidate in Food Science and Technology (L. Zhang) worked on this project during the last three years. We have worked collaboratively with the Center for Process Analytical Chemistry at the University of Washington on data analysis for fruit. A Ph.D. student completed a thesis on this project (R.R. Milczarek) in June of 2009. Three undergraduate students assisted with the research during the tomato season (K. Kostlan, M. Chui and C. Pinamonte). During the 2009, 2010 and 2011 tomato seasons researchers from ConAgra Foods, Heinz were involved. Researchers from PomWonderful assisted in data acquisition and by providing samples of pomegranate fruit. TARGET AUDIENCES: Presented seminars to tomato, citrus, and pomegranate processors and one on one discussions with processor companies to transmit information. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
Multivariate image analysis of tomato magnetic resonance images proved to be effective for predicting the conductivity score (proportional to cell membrane permeability) of pericarp tissue in tomatoes, maturity of tomatoes, defect type, and peel-ability. Magnetic resonance imaging of citrus fruit has also shown this technique to be extremely effective at measuring internal defects like freeze damage at speeds compatible with in-line packing house application. Detection of internal fruit quality parameters for pomegranate has been demonstrated for Brix, Brix/acid ratio and alternaria infection.

Publications

Milczarek, R.R., McCarthy, M.J. 2011. Prediction of Processing Tomator Peeling Outcomes. J. of Food Processing and Preservation, 35(5):631-638.
Zhang, L, McCarthy, M.J. 2011. Measurement and evaluation of tomato maturity using magnetic resonance imaging. Postharvest Biology and Technology, 67:37-43.
Zhang, L, McCarthy, M.J. 2011. Black heart characterization and detection in pomegranate using NMR relaxometry and MR imaging. Postharvest Biology and Technology, 67:96-101.
McCarthy, M.J., S.P. Garcia, S. Kim, and R.R. Milczarek. 2011. Quality Measurements Using Nuclear Magnetic Resonance and Magnetic Resonance Imaging. In: Emerging Technologies for Evaluating Food Quality and Food Safety. S. Kang and Y. Cho (eds). Taylor and Francis.

Progress 12/01/09 to 11/30/10

Outputs
OUTPUTS: Magnetic resonance imaging has been used to acquire data on citrus fruit, pomegranates and tomatoes. For citrus fruit magnetic resonance imaging data was acquired to determine if dryness could be detected. Data on pomegranates was acquired to build correlations for detection of alternaria infection and Brix/acid ratio. Tomato fruit data was acquired to determine if maturity stage could be added to models of tomato quality. Data in all cases has been partially analyzed by multivariate image analysis. Dissemination of this information is beginning and abstracts have been submitted for the Institute of Food Technologists Annual Meeting (IFT 2011) and IV Postharvest Unlimited. PARTICIPANTS: Working on the project this past year has been Lu Zhang a Ph.D. candidate at UC Davis. We have collaborated with ConAgra Foods, Heinz, PomWonderful and Paramount Citrus during the last year. All companies have assisted us in obtaining representative samples of fruit for analysis. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Multivariate image analysis of tomato magnetic resonance images proved to be effective for predicting maturity of tomatoes. Magnetic resonance imaging of citrus fruit has also shown this technique to be promising for detecting dryness in citrus fruit. Magnetic resonance imaging and relaxation time data has been shown to be effective at detecting alternaria infection in pomegranates.

Publications

No publications reported this period

Progress 12/01/08 to 11/30/09

Outputs
OUTPUTS: Magnetic resonance imaging has been used to acquire data sets on Roma processing tomatoes. The data have been partially analyzed to correlate magnetic resonance imaging spectral information to quality parameters bruise damage, maturity, defect type and peelability. Research results have been presented at the Institute of Food Technologists Annual meeting in June, 2009, the American Society of Agricultural and Biological Engineers Annual Meeting in June 2009 and to industrial tomato processors that have visited the laboratory. PARTICIPANTS: A Ph.D. student completed a thesis on this project (R.R. Milczarek) in June of 2009. During the last year a new Ph.D. student in Food Science began working on the project (L. Zhang) and three undergraduate students assisted with the research during the tomato season (K. Kostlan, M. Chui and C. Pinamonte). During the 2009 tomato season researchers from ConAgra Foods and USDA Western Regional Lab participated in the project. TARGET AUDIENCES: Presented seminars to tomato processors and one on one discussions with processor companies to transmit information. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Multivariate image analysis of tomato magnetic resonance images proved to be effective for predicting the conductivity score (proportional to cell membrane permeability) of pericarp tissue in tomatoes, maturity of tomatoes, defect type, and peel-ability. Magnetic resonance imaging of citrus fruit has also shown this technique to be extremely effective at measuring internal defects like freeze damage at speeds compatible with in-line packing house application.

Publications

McCarthy, M.J. 2009. Rapid Agricultural Product Quality Measurements Using Magnetic Resonance Based Sensors. In: Proceedings of the International Symposium 2009 Jeonju International Symposium on Fermented Foods, Jeonju Korea pp. 51-62
Milczarek, R.R., M.E. Saltveit, T.C. Garvey and M.J. McCarthy. 2009. Assessment of tomato pericarp mechanical damage using multivariate analysis of magnetic resonance images. Postharvest Biology and Technology 52:189-195.

Progress 12/01/07 to 11/30/08

Outputs
OUTPUTS: Magnetic resonance imaging has been used to acquire data on citrus fruit and tomatoes. Citrus fruit data was analyzed to build correlations between magnetic resonance imaging data and presence of seeds, presence and amount of freeze damage, Brix/acid ratio and other structural features. Tomato fruit data were acquired to build correlations from whole harvested tomatoes to peel-ability and internal quality. Data in both cases have been partially analyzed using chemometric procedures including multivariate image analysis, principal component analysis and partial least squares analysis. Dissemination of information is just beginning and is included in the publications listed below, and abstracts for presentations have been submitted for the Institute of Food Technologists Annual Meeting/Food Expo and the American Society of Agricultural and Biological Engineers Annual International Meeting. PARTICIPANTS: A Ph.D. candidate in Biological Systems Engineering (R.R. Milczarek) and a visiting scholar Prof. S.M. Kim (Chonbuk University, Republic of Korea) worked on this project during the last year. We worked collaboratively with researchers from Paramount Citrus Association (Delano, CA), ConAgra Research (Davis, CA) and Aspect AI (Davis, CA). The project provided graduate training for R. Milczarek and nondestructive inspection training for Prof. S. Kim, Dr. T. C. Garvey (ConAgra), and T. Consolascio (Paramount Citrus). Aspect AI personnel were trained in modern fruit sorting techniques/conveyor systems and learned about use of chemometric approaches to data analysis. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Multivariate image analysis of tomato magnetic resonance images proved to be effective for predicting the conductivity score (proportional to cell membrane permeability) of pericarp tissue in tomatoes. The conductivity score of tomato pericarp promises to be a strong indicator of peel-ability, hence magnetic resonance imaging will be an effective in-line tool for tomato peel-ability assessment. Magnetic resonance imaging of citrus fruit has also shown this technique to be extremely effective at measuring internal defects like freeze damage at speeds compatible with in-line packing house application.

Publications

Kim, S.M., Milczarek, R.R. and M.J. McCarthy. 2008. Fast Detection of Seeds and Freeze Damage of Mandarines Using Magnetic Resonance Imaging. Modern Physics Letters B, 22(11):941-946.
Milczarek, R.R. and M.J. McCarthy. 2009. Low Field MR Sensors for Fruit Inspection. In: S.L. Codd and J.D. Seymour (eds). Magnetic Resonance Microscopy: Spatially Resolved NMR Techniques and Applications, Wiley-VCH, Darmstadt, pp. 289-301.