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NE1008: Assuring Fruit and Vegetable Product Quality and Safety Through the Handling and Marketing Chain

Duration:
October 01, 2002 to September 30, 2007
Administrative Advisor(s):
Steve Goodwin (MAS)
NIFA Reps:
D. Ramkishan Rao

Statement of Issue(s) and Justification:

The goal of this project is to develop and improve rapid and non-destructive methods and technology for assessing, retaining, and assuring quality, safety, and integrity of fruits and vegetables through the marketing chain.

Annually, fruits and vegetables generate 25 billion dollars in farm income (Anon, 2001). They account for approximately 25% of the cash receipts of all crops grown in the United States while occupying only about 2.6% of the acreage devoted to cropland (Nichols, 1996). Furthermore, fruits and vegetables are an essential part of a healthy diet. The health benefits of compounds such as antioxidants and flavenoids, found in abundance in fresh fruits and vegetables, are becoming increasingly apparent.

As fruits and vegetables make their way from the producer to the consumer, they are sorted, stored, transported, processed, and packaged. With each step, their economic value increases. Annual sales of fresh produce alone exceed $95 billion (www.Fresh-Cuts.org). It is essential that the quality of this valuable resource be maintained. This, in brief, is the major focus of this proposal.

Although the system that delivers fruits and vegetables to the consumer works well, it is still vulnerable to the influences of population pressures, global competition, outbreaks of food-borne illness, and labor shortages. Furthermore, there are still opportunities for improving the efficiency of the system. While estimates of losses vary, it is not uncommon for the produce departments in grocery stores to experience losses of 10% because their produce spoils, contains undetected defects, or deteriorates in quality before it can be sold. Even if only 5% of the fresh produce were lost, the annual economic value of those losses would be nearly $5 billion.

Control of pathogens, resulting in food-borne illness and now the threat of bio-terrorism, is a critical issue that needs to be addressed. The fresh-cut industry, producing products that are not subject to conventional pasteurization methods, has been expanding rapidly. This industry accounts for 10-12 billion dollars in sales on an annual basis, about 10% of the total fresh produce market. New techniques are needed that rapidly identify the presence of pathogens on produce, as well as techniques to eliminate these pathogens.

NE-179 multi-state researchers have helped to improve existing technologies, such as color sorting. They are also developing new quality detection technologies such as: machine vision for the detection of bruises, surface defects and insect presence; x-ray scanning and magnetic resonance sensing for the detection of internal defects; and near infrared detection of soluble solids content (related to sweetness and quality). Accomplishments have included development of a color chart for improved color sorting of cherries, improvement of specifications for lighting of grading stations, and development of a device for orientation of apples grown in the eastern United States. (Note: Apples grown in the eastern United States cannot be oriented effectively using equipment routinely used for apples grown on the west coast.) Sharing of data, such as physical properties of fruits and vegetables, is also being promoted through the development of an Internet web site where links to data and reports issued by participating stations can be easily accessed.

Several NE-179 projects have involved cooperation among stations. For example, Cornell (New York) and the Appalachian Fruit Research Station (West Virginia) worked together in the development of the orientation device for eastern apples. They presently have a cooperative project on bruise detection by means of a combination of visible and near infrared radiation (multi-spectral imaging). The Fruit Research Station also evaluated detection of internal defects in apples by sponsoring and providing apples for an x-ray detection study at the University of Georgia and a magnetic resonance study at Purdue University (Indiana).

The project has sponsored two international conferences that brought together researchers working in the area of quality sorting. The first was conducted in Spokane, Washington in 1993 and the second in Orlando, Florida in 1996. Papers and discussion summaries from both of these meetings were published in proceedings. Interaction with processors, packers and growers was facilitated by means of tours held in conjunction with these meetings.

NE-179 also promotes the interaction of researchers with growers, processors and packers. Much of this interaction occurs during tours held in conjunction with each annual meeting. Researchers are able to visit facilities and dialogue with managers and others who are aware of the current concerns of the industry. Industry consultants, representatives of commodity groups and equipment manufacturers, and scientists from state and federal laboratories have attended NE-179 conferences and annual meetings. Their participation allows them to share their ideas and perspectives. Another example of NE-179s interaction with industry is the 1995 survey of apple packers and processors in which members conducted for the purpose of evaluating their satisfaction with current sorting systems and their needs for the future. The responses of 25 fresh pack firms and 7 processors were summarized and made available to NE-179 representatives so that they could gain a better understanding of the needs and concerns of the apple packers and processors.

NE-179 is a vital link between the fruit and vegetable industry and researchers who are developing machines and systems for assessing and retaining the quality of these commodities. NE-179 is accomplishing this by means of tours of processing and packing facilities, discussions with processors and packers held during the tours, industry surveys, sponsorship of international conferences, and the publication of the proceedings of these conferences. NE-179 fosters cooperation among participating stations as demonstrated by several joint research projects and the sharing of test samples among stations. They continue to address concerns of the industry by evaluating techniques and systems that can be used to improve product quality, reduce waste, and maintain or expand the United States share in a global market.

Related, Current, and Previous Work:

The literature contains information about the use of rapid sensing techniques and technologies for detection of internal quality constituents and internal defects. The proposed research will expand upon the research literature in these areas, both in terms of development of new sensing techniques and technologies as well as the application of existing sensing techniques and technologies to new types of produce.

Work was started on the design of a PCR-based biosensor to detect Salmonella in alfalfa sprout irrigation water (Jackson et al., 2001). A small thermal cycler was designed using a thermoelectric unit and an embedded controller. Amplified DNA was measured in a specially designed optical head using fluorescent markers and laser excitation with emphasis on semi- microfluidic components because of the need to test larger sample volumes. A real-time PCR for Salmonella was developed to work in the sensor. Currently, control of the thermal cycler is being optimized and methods of integration with the optical sensor are being considered. Calibration will be done with different serovars of Salmonella in distilled water and sprout irrigation water.

Technological developments and changes in user needs requires periodic reassessment of sensors for measuring fruit and vegetable quality. A list is needed of quality attributes that users would like to measure if suitable sensors were available. Especially valuable would be an understanding of the impacts expected at other business links of a food distribution chain if new sensors are implemented at any link. Simulation models and games of postharvest operations would help managers improve understanding of interactions within the chains.

The laser puff firmness detector developed at Georgia can rapidly measure firmness of fresh produce without direct contact. Expanded use is expected if procedures are simplified and on-line applications are made feasible.

Research on electrolyzed (EO) water at Georgia started about 4 years ago and focused on finding safe, effective, economic, and practical means of controlling food-borne pathogens as food moves from the farm, through postharvest operations, and onto the table at home. Research findings (Kim et al., 2000; Park et al., 2001a,b) indicated that EO water can be used as a non-thermal method to inactive food-borne pathogens.

The U. of Georgia has studied feasibilitiy of using x-ray for internal disease detection for the past 12 years. Watercore in apples, bruises in apples occurring prior or during harvest and diseases in onions can be determined with 90%+ accuracy and with less than 10% false positives. Work is in process to couple the internal inspection process with various optical indicators through cooperation with commercial firms.

In cooperation with personnel from the CA station, a limited number of green coffee bean samples were separated by geographic place of culture using NIR spectroscopy (unpublished). Numerous samples from various origins need to be scanned to develop a robust technique.

Several studies have demonstrated that proton magnetic resonance (1H-MR) can be used for measurement of quality and composition of fruits, vegetables, and nuts. (Ni and Eads, 1992, 1993ab; Tollner and Hung, 1995; McCarty et al., 1995; Zion et al., 1995). Chen et al., (1989) used a high-field (85.35 MHz) spectrometer to obtain magnetic resonance images of a variety of fruits and vegetables. When images were examined, the location of seeds or pits were detected, and also the presence of voids, worm damage, bruises, and dry regions. Jung et al. (1998) reported that low-field 1H-MR could be used to detect watercore and internal browning in apples. However, low-field 1H-MR would only be useful for quality sorting of fruits and vegetables if internal defects can be detected rapidly, as fruit is moving on a belt or stopped instantaneously. Chen et al. (1996) demonstrated that a relatively high-field 1H-MR sensor (85.35 MHz), in which Fourier transform of the signal was possible, could measure oil content of avocados moving on a specially designed conveyor belt at speeds up to 250 mm/s. Similar experiments should be conducted with a low-field MR sensor to determine whether the low-field techniques used successfully for stationary fruit will work on moving fruit.

Currently, blueberry growers are averse to using the less persistent, environmentally safe insecticides because the risk is high (i.e., if a detectable level of maggots is present then the processor could reject the entire crop from a given field). If infested blueberries could be removed by a combination of technologies and equipment based on near infrared spectroscopy (NIRS), then novel less contaminating control tactics might be adopted by growers. In 1996, Ridgway and Chambers reported on methods using reflectance mode NIRS to detect external and internal insect infestation of wheat. They were able to detect insect protein and/or chitin at low levels (0.01% by weight) as well as moisture differences in the infested wheat samples as compared to control, non-infested samples. Ridgway and Chambers (1998) used NIRS, in conjunction with spectral subtraction methods, to detect insects inside wheat kernels. Ridgway and Chambers (1999) furthered their previous detection work by examining which particular wavelengths are crucial for detection of infestation of wheat and found two wavelengths (982 and 1014 nm) were key for identification, making an in-process-line system more efficient.

For the past two crop seasons the ME station has collaborated with an entomologist in experiments funded by Wild Blueberry Commission of Maine that show infestation detection in blueberries via NIRS. The entomologist led artificial infestation experiments to obtain a higher percentage (approximately 30 %) of infested berries than can be expected by chance under normal field conditions. Preliminary NIRS work performed during the 2001 season showed evidence of wavelength ranges that allow detection of maggot infestation in blueberries in the range of 600-1400 nm, similar to data findings by other researchers studying infestation detection (Ridgway and Chambers 1996; Dowell et al. 1998; Dowell et al. 2000).

Other researchers successfully developed a spectral image database to use with discriminate analysis and/or neural networks to characterize quality attributes for various agricultural commodities (Baker et al. 1999; Carlini et al. 2000; Tarkosova and Copikova 2000; and Thygesen et al. 2001). Others have been able to develop in-process-line equipment using spectral image discriminate analysis as a foundation (Dowell et al. 1999; Osborne and Kunnemeyer 1999; Ridgway and Chambers 1999; and Walsh et al. 2000). Based on this information, we believe there are data treatment and analysis methods that will yield a small number of wavelengths (focus wavelengths) to concentrate discriminate analysis.

Studies (Lu, 2001; Lu et al., 2000) show that NIRS gives good predictions of the sugar content (or soluble solids) of apples and cherries. Relatively good predictions on firmness were also obtained for cherries, but the results for apples are not satisfactory. The different firmness predictions between apples and cherries could be due to the fact that cherry fruit are softer than apples and, thus, are easier for light to penetrate and scatter in the fruit tissue. Absorption and scattering are the two basic phenomena as light interacts with the fruit. Absorption is closely associated with the concentration of certain chemical constituents, such as sugar, moisture, protein, and chlorophyll. On the other hand, scattering properties offer insight into the composition, density, and tissue structure (Birth, 1986). NIRS only provides approximate quantifications of light absorption in a sample through measurement of either reflectance or transmittance. If both absorption and scattering properties can be measured and separated, more information about the fruit will be obtained, which can lead to improved predictions on fruit quality attributes. Therefore, we propose a new sensing technique using imaging spectroscopy (or hyperspectral imaging) to determine the absorption and scattering properties of apple fruit and relate them to fruit firmness, sugar content, and/or acid.

Hyperspectral imaging is a relatively new technique that combines the features of imaging and spectroscopy to obtain both spectral and spatial information from an object. A NIR hyperspectral imaging system was developed to detect both new and old bruises on apples. The system was able to detect up to 94% new and old bruises on apples. The spectral region between 1000 nm and 1340 nm was most appropriate for detecting apple bruises. The optimal number of spectral bands was between 20 and 40 with the corresponding wavelength resolution between 8 nm and 17 nm.

Current practice in the sweet potato industry relies on manual grading of roots as they proceed down a packing line. Thirty to 50 workers scan sweet potatoes and remove those with size discrepancies, disease, deformities, injuries, and abrasions. Automated grading systems have not been adopted due to low throughput capacity, marginal performance, and high cost. Recent advances in biometrics and machine vision technology promise to mitigate these shortcomings. Low cost digital imaging systems coupled with sophisticated pattern recognition algorithms can quickly identify low grade sweet potato roots and trigger a separation mechanism. Visual spectra from the camera can be combined with information from other sensors such as NIR to improve system performance.

Multiple images taken from different locations permit a 3-D model of the object to be constructed in memory with distortions of perspective removed. Fuzzy discriminators and ANN can be trained to distinguish between grades based upon different criteria.

Deck and Stikeleather (1994) evaluated the performance of a semi-automated sorting system for sweet potatoes which employed a video camera with a touchscreen monitor to improve worker safety. More than 200 million pounds of blueberries are sorted annually in the US based upon visual observation by humans on inspection lines or with equipment that measures surface color. Sensing of maturity in blueberries using optical density measurements has been used by researchers. Surface color will not distinguish between ripe vs. overripe berries. Light transmitted through a just ripe berry appears red and shifts to a dark blue as the berry becomes overripe. The transmitted light wavelength is closely related to anthocyanin pigments, sugars, acids, and ultimately to fresh shipping quality.

For a large sample of berries with various levels of maturity, spectrographs of individual berries were obtained statically and while the berries were moving across a light source. Each berry was chemically assayed for soluble solids (SS), pH, and titratable acidity (Acids). Based upon this data set, proprietary image processing algorithms were developed to classify the berry spectrograph to its SS/Acids ratio. Preliminary results were encouraging. Future work will focus on applying this classification method to multiple lanes of moving fruit that will be imaged using a single area scan digital camera. Real time digital signal processors will be used to extract image intensity and wavelength features. An ANN will fuse extracted wavelength features into a final classification of under ripe (green), ripe or overripe. Final prototype integration will be achieved utilizing current commercial sorting berry transport equipment from SBIR Phase II funding.

Postharvest handling and storage for perishable commodities work includes research into how temperature, humidity, and mix of atmospheric gases can affect the shelf life and appearance of sweet potatoes. Methods (both pre- and postharvest) that might positively effect storage life and the appearance of sweet potatoes are being determined. This work included field studies, design and testing of several harvesting aids and particularly development of a computerized method for determining levels of skin adhesion. Roots with high skin adhesion resist the damage of abrasion found in handling.

Visible and NIR spectral imaging has detected over 20 surface defects on over 11 cultivars of eastern US apples (Upchurch et al. 1994; Throop et al 1995, Aneshansley et al 1997, Throop et al 1997). Current and proposed work will continue visible and NIR spectral imaging for internal defects (worm holes, water core and other internal defects) (Throop et al. 1994a, 1994b; Upchurch et al 1997) and will include collaborations with Zedic Industries and the USDA Appalachian Fruit Research Center in implementing new X-ray imaging technologies to detect internal defects. A high speed (5 apples/sec) inspection station using both visible/near infrared spectral imaging has been developed and evaluated. The same collaboration is in the initial phases of developing a system to inspect internal defects on a high speed handling system using x-rays.

Biosensor technologies are being developed that can be applied to a variety of food safety issues. There is work on rapid multi-analyte biosensors, micro-cell lysis system, integrated optoelectronic microsystems for biological agents that can be used to assure food safety (Baeumner et al 1996, 1998, 2001). This work is aimed at biological warfare agents, Dengue virus serotypes, and also food pathogens such as C. parvum and E. coli which are important for food safety. Spectroscopic studies similar to those used for surface defects could also be used for food safety issues.

We have developed a variety of handling and orienting systems for the processing of inspecting apples for surface defects with the Appalachian Research Center (Throop et al. 2001, Patent 08/491,805 (1996), Patent 08/735,511 (1996). These techniques can and are being refined and expanded in order to inspect apples for other types of quality issues at high speed.

Although much work has been done with Universal Testing Machines on the texture of fruits and vegetables, Dynamic Mechanical Analyzers (DMA) are unique in that rheological data can be obtained during continuous heating or cooling of fruits and vegetables, as well as under isothermal conditions. Earlier we studied the effects of heating on the modulus of Russet Burbank and Yukon Gold potato discs. During the next period, we will conduct studies on apples, grapes, and other fruits and vegetables with regard to the role of composition on the storage modulus.

The loss of fruit firmness during storage is caused by the action of hydrolytic enzymes on the cell wall polysaccharides. Recent studies indicate that beta-galactosidase is the enzyme most likely to be responsible for apple softening. Research results show an inverse relationship between the beta-galactosidase activity and apple firmness. Further research is needed to study the effects of cultivar and crop year on fruit firmness and beta-galactosidase activity. The enzyme will be purified and characterized.

Machine vision was used to evaluate quality features of apples (Tao et al. 1995a; Heinemann et al. 1995) potatoes (Deck et al. 1995; Tao et al. 1995b; Heinemann et al. 1996), and mushrooms (Heinemann et al., 1994). Later work included development of a measurement procedure for sugars in juices, detection of cholesterol, tenderness of meat, microorganisms of fruit surfaces, and evaluation of the quality of honey.

NIR, MIR, FIR, UV-vis, and florescence spectroscopy have been used to measure composition, contamination, molecular structure, color, texture, and other physical properties of food and agricultural materials. Most of the past applications of spectroscopy in food and agricultural systems focused on NIR, and UV-vis spectroscopy. MIR spectra using fourier transform infrared (FTIR) spectroscopy has been used to study the functional chemical groups in a variety of food systems (Van de Voort, 1993; Chen et al., 1998). With the advent of photoacoustic spectroscopy (PAS) analysis of complex systems is possible (Yang and Irudayaraj, 1998).

Applications of ultrasound include study of emulsions, fruit freshness, meat tenderness, etc. (Henning et al., 1994; Gestrelius, 1994; Mizrach et al., 1994). Despite advances of ultrasonic sensing, only a fraction of its potential has so far been exploited and adopted in food manufacturing.

Fundamental understanding of the physics of impact damage in fruits and vegetables is being established. Instrumentation for measuring key parameters is being developed at the research level. Basic requirements for conditioning of several key commodities are being established. The next steps include development of condition systems and monitoring instrumentation for commercial use.

Fundamental microwave and RF energy relationships for controlling navel orange worm, Indian meal moths, codling moths while maintaining fruit and nut quality are being established. Future work will extend this knowledge to include fruit flies, scale-up the protocol for insect control in in-shell walnuts for industrial implementation, expand research to include almonds and pistachios, and continue protocol development to control insects in tree fruits, especially cherries and apples.

CRIS search results show several research projects doing work in this area. However, most of these are projects with investigators that are participants in this NE-179 project. In addition to those, here is a summary of projects in related areas with non-NE-179 participants:

Project VAK-2000-00295 is an SBIR project investigating combining machine vision with electronic and optical components to assess on-line non-destructive apple firmness. Project CALK-9800290 investigated NIR transmittance for measuring internal composition of several types of fruit such as sugar concentration in mandarins. Project FLA-LAL-03646 is assessing sensor techniques applicable to non-destructive determination of internal quality of fresh citrus fruit and correlating sensor measurements with standard quality indices of fresh citrus. Project COLK-9603703 is an SBIR project to produce a high performance multi-spectral processor that will evaluate color distribution and a number of defect types in agricultural products and will be evaluated using apples. Project CA-D-XXX-5909-SG investigated the use of NMR for evaluating internal quality factors of fruits and vegetables. Project 1270-44000-005-03T developed non-destructive sensing technology to determine the quality of fruits using imaging and NIR spectrophotometry techniques. Project 5325-44000-003-01T investigated line-scanning x-ray for the detection of defects in apples, such as larvae holes, water core, core rotting, and internal browning.

Other areas less directly related to the NE-179 objectives are more numerous, and some samples are cited here. Project MER-1999-03621 is evaluating the feasibility of an impedance-based ethylene sensor for the determination of fruit quality and storage life. Project RI-00-1999-03656 developed a biosensor for food pathogens such as and compared it to standard piezoelectric and fluorescent immunosensors. Project MDR-9702102 is investigating use of biosensor for detection of S. Aureus enterotoxin in foods. Broader systems approaches to safety for apple juice processing are found in project 1935-41420-007-00D and for fresh cut products in project LAB03470.

Objectives

  1. Define and measure the physical, mechanical, optical, and other properties of fruits and vegetables and their functional relationships to quality, and establish a database of these properties. (CA, HI, NY-C, GA, ARS-MI, ME, MI, NC, NY-G, WA)
  2. Develop, evaluate and apply rapid non-destructive sensor technology for quantitative measurement of fruit and vegetable quality. (CA, MI, ARS-MI, PA, IN, NY-C, GA, NC, MD, WA, HI, ME)
  3. Develop, evaluate, and apply rapid sensing technologies to assure food safety including bio-security, purity, and integrity of produce. (CA, PA, MI, NY-C, GA, WA)
  4. Integrate sensor technologies with handling and storage systems to retain post harvest quality in fruits and vegetables. (NY-C, GA, NC, CA, HI, PA, WA)

Methods:


Objective 1

CA

Cooperating station: HI. Spectrophotometric instrumentation will be used to determine the optical properties of fruits and vegetables related to quality. Gas chromatography will be used to measure the aromatic properties of defective fruits.

GA

Cooperating stations: All NE-179 participants. Surveys will be conducted of current and potential users of sensors for fresh fruits and vegetables to determine the engineering properties that provide the most useful results for commercially important quality attributes. Studies will be conducted for determining changes that could improve the output of existing and new instruments. User friendly software for calculating standard quantities such as modulus of elasticity will be developed. The software will be tested and made available on suitable web sites.

HI

Cooperating station: ME. Samples of green beans will be collected from growers throughout Hawaii. Whole green beans will be scanned with Foss NIRS 6500 spectrometer and Ocean Optics 2000. The spectra will be stored in open standard format with geographic place of cultivation. Commercial calibrations for NIR will be applied and checked with HPLC chemical composition. PLS will be used as needed to extend calibrations.

ME

Cooperating stations: MI, ARS-MI, NC. Once berry stems have been subjected to caging with flies for one week, the berry stems are removed and put into a container and into a laboratory at room temperature to allow for maggot development. Berries will be selected from the containers, sized, and the basic berry information will form the basis of the spectral database. Individual berries will be scanned on the transverse and calyx axis and the spectra recorded electronically for later analysis. They will be scanned with either the NIR equipment at ME station (600-1000 nm) or NIR equipment at USDA-MI station (500-1700 nm range). After all scans are performed berries will be individually dissected under a light microscope to determine maggot presence.

ARSMI

We will develop a hyper- and/or multi-spectral imaging technique for nondestructive measurement of apples and other fruits in the visible and near-infrared spectral region. Mathematical models and computer algorithms will be developed to relate the absorption and scattering properties to the quality attributes (firmness, sugar content, acid) of fruit. Numerical techniques will be used to develop simulation models to understand the interaction of light with the fruit and its relation to the physical and chemical properties of fruit.

NY-C

Electrically alterable spectral filters in the visible and near-infrared regions allow for collection of spectral images of physical properties of quality parameters. Low intensity x-ray imaging using x-ray film and commercial x-ray electronic sensors will be use to examine internal physical properties for quality parameters.

NYG

Rheological properties of fruits and vegetables will be determined with a dynamic mechanical analyzer (Model 2980, TA Instruments, New Castle, DE) and the role of different salts and temperature will be examined. The firmness of fruits and vegetables will be measured with a McCormick penetrometer or a Lloyd texture measuring instrument. The activity of beta-galactosidase will be analyzed with nitrophenyl beta-galactopyranoside as substrate. The enzyme will be purified and its properties will be determined.

WA

The fundamental basis for fruit and vegetable conditioning to improve bruise threshold will be developed by 2003 through development of instrumentation and techniques to measure bruise threshold and tissue elastic modulus directly in whole specimens, and through tissue failure property measurement.

Objective 2

CA

Rapid optical sensors (e.g., diode array spectrometers) and models will be developed for the assessment of internal quality of intact produce. MRI, electronic nose, impact, and ultrasound sensors for defect detection in intact produce will be assessed.

GA

The laser air-puff non-destructive food firmness detector will be improved to enable on-line applications by designing a dual nozzle around the laser displacement sensor to increase its range. Modulus of elasticity values calculated from laser puff measurements will be compared with values from a universal testing machine for a wide range of fresh fruits and vegetables. Holder designs will be developed for on-line applications.

X-ray line scan equipment will be positioned in an onion packing house. Images will be captured and existing algorithms will continue to be evaluated. Work with a local manufacturer will help to integrate the x-ray and existing optical modalities to provide a highly accurate inspection machines for selected commodities including onions.

HI

Response surface methodology will be used to establish optimal geometry for vacuum seeder to singulate and present green bean coffee to fiber-optics

IN

Cooperating stations: NY-C. A conveyor system powered by a stepper motor that can carry whole fruits and vegetables with diameters less than 8.0 cm through its low field (1150 Gauss, 5.35 MHz) Magnetic Resonance sensor has been developed. Components and computer software will be developed that will control the conveyor system, allowing fruits to be either stopped momentarily in the center of the MR coil or moved through the coil at a specified velocity. The software will be capable of actuating the MR sensor so that a signal can be acquired from the fruit or vegetable as it travels through the sensor. Once the system is functioning properly, signals will be gathered from fruit traveling through the sensor at various velocities, ranging from 0 (a momentary stop) to 250 mm/s. These tests will establish the maximum permissible sample velocity and maximum permissible sample misalignment relative to the center of the coil. Then the system will be used to test healthy onions and onions with internal defects such as interbacterial damage. The onions will be obtained from cooperating scientists at either Cornell University or Colorado State University. Several pulse sequences will be used to acquire signals from healthy and diseased onions and the effects of the defects on the signals obtained from the onions will be determined. Noticeable differences have already been noted in onions placed by hand in the sensor. However, the amount of time required to acquire the signal must be reduced so that differences can be detected on fruit that is moving (or stopped instantaneously). If success is achieved with onions, additional tests will be conducted on defects in other fruits, such as internal browning of apples or frost damage to oranges.

ME

Cooperating stations: MI. Individual spectra will be imported into GRAMS/AI software (Thermo Galactic Industries Corporation, Salem, NH) and standard spectral image files will be created from the raw scan data files. Averaged spectra from infested and non-infested blueberries will be subtracted in order to examine the difference spectra for potential wavelength segments that would indicate differences that can be attributed to the presence of maggot infestation in the blueberry and this information will be added to the spectral database. Then a training data set will be created and used to do basic partial least squares and multivariate discriminate analysis. A suite of regression and analysis techniques will be used in sequential fashion to determine which method yields the best detection scheme with the goal being minimizing the number of focus wavelengths needed for an accurate discriminate model. Once a small number of focus wavelengths have been identified, we will continue our analysis by using these bandwidths as the discriminating variables between non-infested and infested fruits.

MI

Cooperating station: ARS-MI. An information search of companies commercially marketing electronic sorting equipment will be performed. Technique/technology/methodology used and commodities and their quality characteristic being addressed/measured will be documented. Personal contacts with company representatives/engineers will be made. Where possible/feasible, tests/evaluations will be conducted to substantiate performance. Information, findings, and results will be documented and reported.

Cooperating stations: All NE-179 participants. Database of fruit and vegetable quality characteristics versus technology and specific parameters capable of detecting such characteristics will be an extension of the commercial industry evaluation (above) and will involve combining both commercial and research-based information to develop a database/library of specific electronic measurements versus commodity quality characteristic. Collection of this information will be via our own group member findings and compilation of other reported finding.

Cooperating stations: ARS-MI, ME. Development of fruit tissue quality characteristic sensing systems will require implementation of spectroradiometer and multi- and hyperspectral imaging instrumentation for identification of spectral bands capable of providing valued contrast between desirable and undesirable tissue and other fruit quality characteristics. Structuring lighting, filtering and imaging components to obtain chlorophyll fluorescence images. Coupling spectral image data with fluorescence image data through neural networks and statistical classifiers for fruit characteristic (including insect presence) classification. Identification, evaluation or development of dedicated instrumentation for prototype development.

ARSMI

Cooperating stations: MI. We will apply hyper- and multi-spectral imaging to develop a novel sensing technique for nondestructive measurement of fruit internal quality (firmness, sugar content, acid). Calibration procedures will be developed and integrated into the sensing system. Research will be conducted to develop a low cost, portable NIR sensor for measuring quality of apple fruit.

NC

Cooperating stations: ME, ARS-MI. Industry cooperators in NC, MA, WA, ME. Digital images of area spectrographs of transmitted light form fresh blueberries at various stages of physiological ripeness will be classified with a neural network trained on the individual berry's physiological ripeness parameters of soluble solids and acidity.

NY-C

Specialty optics and filters will be used to obtain 3 or 4 spectral images of the same site simultaneously through a common aperture onto the camera sensor. High speed computational processing allows multiple images in each spectrum to be combined to give a complete spectral surface of the apple or other oriented object. Low intensity x-rays and electronic detectors will be for used for rapid collection of internal characteristics of fruits and vegetables.

PA

Spectroscopy Method: Experiments will be conducted using the FTS-6000 spectrometer. Instrumental parameters will be optimized ans spectra of liquid products will be obtained using Attenuated Total Reflection accessory and the solids and surfaces will be evaluated by Photoacoustic spectroscopy. The Spectra will be qualitatively characterized and quantitatively analyzed for food quality estimates by multivariate statistics. Ultrasound Experiments: The instrument will be optimized for measurements and the transducer variations will be accounted for and calibrated. The sample will be analyzed by pulse-echo or transmission method and the spectra analyzed using neural networks.

WA

Fruit and vegetable condition assessment technology by 2004 will be based on methods developed under objective 1 which will be refined for commercial use.

Objective 3

CA

Sensors to detect bacterial pathogens in the irrigation and wash water of fresh and minimally processed fruits and vegetables will be designed. The first application will be to detect Salmonella in alfalfa sprout irrigation water. A real-time PCR has been developed and will be adapted to work in the sensor. Thermal cycling of the reaction cell will be done with a thermoelectric heater/cooler and embedded controller. Amplified DNA fragments will be measured using fluorescent markers and a laser-based optical sensor. An automated system to sample the water and concentrate the cells will be developed. The entire system will be mounted in a small waterproof enclosure and tested with several serovars in distilled water and sprout irrigation water to determine sensitivity and specificity. The potential for detection of other pathogens will be evaluated.

Opto-chemical techniques for detection of contaminants in produce will be developed and assessed.

GA

Electrolyzed (EO) water is generated by electrolysis of a dilute salt (NaCl) solution in an electrolysis chamber where anode and cathode electrodes are separated by a membrane. On the anode side, acidic EO water is generated and has strong bactericidal effect on most known pathogenic bacteria due to its low pH, high oxidation-reduction potential (about 1,100 mV), and the presence of hypochlorous acid (about 50 mg/L). Different produce inoculated with food borne pathogens will be treated with EO water to evaluate the efficacy of the treatment. Hyper spectral imaging is an imaging technique that combines aspects of conventional imaging with spectrophotometry and radiometry. Fresh produce before and after treatment will be evaluated using the hyper spectral imaging technique for detecting the pathogens.

MI

Multi-array biosensor for food safety and biosecurity: The biosensor platform is made of silicon (Si) wafer. An open channel is formed on the center of the Si wafer by the etching method, where the capture zone will be located. The capture zone is coated with primary antibody and situated between two electrodes. Electrical signal is monitored between the electrodes during use of the biosensor. Electrical signal is directly related to bacterial cell concentration. More than one capture zone will be designed, thus, different antibodies can be immobilized on the biosensor surface.

Sampling protocol will be developed according to the biosensor design and the type of food to be monitored. It is anticipated that protocols will differ between solid and liquid food and between fresh and processed food.

Fouling and shelf-life of the biosensor will be studied under different storage temperatures. Optimum storage time will be identified.

Cooperating station: PA. Validation of the pathogen sniffer (electronic nose): The pathogen sniffer (electronic nose) designed and developed at Michigan State University will be used in this study. The sniffer will be used to detect E. coli O157:H7 and Salmonella spp. in fresh fruits and vegetables. Fresh produce will be obtained from a local grocery store. Samples will be prepared, chopped, and added with nutrient broth. The sample will be spiked with E. coli or Salmonella stock culture and placed in the test tube for headspace gas measurements by the sniffer. An enrichment procedure will be developed appropriate to the organism and food group, such fruit or vegetable. Various enrichment broths will be evaluated for the most robust gas patterns. Sampling time will be varied. Chemical compounds from the emitted gases will be identified by gas chromatography/mass spectrometry and other appropriate methods. All culturing of the organisms and experimental procedures will be conducted in a biosafety level 2 microbiology laboratory at MSU. All biohazard waste will be disposed of according to MSU guidelines.

NY-C

Implementation of biosensors and spectral imaging will be used to measure physical properties associated with food safety parameters. Specialty optics and filters will be used to capture multispectral images of surfaces and particles on surfaces associated with food safety parameters. Rapid multi-analyte biosensors, a micro-cell lysis system and integrated optoelectronic microsystems will also be used to determine food safety parameters.

PA

Cooperating station: MI. Headspace samples from apples inoculated with surrogate E. Coli strains will be evaluated using a portable electronic nose (Cyranose 320) to determine the feasibility of early and rapid detection of pathogens in pre-juicing operations. Detection thresholds will be established.

WA

Technology to replace methyl bromide for insect eradication in fruits and nuts by 2005 will be developed through use of electromagnetic technologies to kill insects without harming those commodities.

Objective 4

GA

Cooperating stations: All NE-179 participants. Simulation models will be developed for predicting the impact of potential changes in sensing technologies on the operations of postharvest businesses handling fresh produce. Potential modeling programs will be evaluated for applications to fresh produce handling. Interactive interviewing methods will be used for determining decisions related to sensors. Existing data, equations, and other relationships will be used for developing models that predict differences in quality for changes in operations such as sorting. Laboratory studies will be conducted to validate the models. The models will be expanded to additional products. Changes in quality at postharvest businesses will be used to validate model predications.

HI

Cooperating stations: PA and CA. Response surface methodology will be used to establish optimal singulation, fiberoptics, and spectrum sensors for NIR and VIS to sort coffee for commercial grading.

NC

Once the neural network-based sorting algorithm is imbedded into a DSP (digital signal processor) for multiple lane operations, it will be integrated into an existing industry surface color sorting system.

NY-C

High speed conveyors with specialty orientation mechanisms will be used in conjunction x-ray imaging and spectral imaging equipment to provide high throughput of fruits and vegetables in inspection stations for separation of produce based upon quality measures.

WA

Bases for crop conditioning to minimize bruising by 2003 will be developed along with fundamental work under objective 1. Whole specimen and tissue elastic properties will be the basis of the main assessment tool.

Measurement of Progress and Results:

Outputs:
Outcomes or projected Impacts:
Milestones:

(2003): Develop: commercial prototype system for damage-free fruit transportation; laser air-puff with laser co-axial w/air stream; low field magnetic resonance sensor conveyor system; "state of industry" report; surveys to select crops, identify important attributes; protocols using electrolyzed water for fresh produce safety; initial design of Si biosensor; commercial sensing technology report for quality characteristics sorting, insect presence.

Test: bacterial sensor prototype; commercial sorting equipment; automated apple inspection system at packing houses; on-line internal defect x-ray inspection.

Show feasibility of: transferring inspection technology to private sector; portable e-nose apple pathogen detection.

Select or identify: technologies for sensing freeze damaged oranges; simulation models for key decision points at each business link for growing/marketing fresh produce; basis for produce condition, storage assessment for minimizing impact damage susceptibility.

Complete Phase I SBIR project, submit Phase II proposal-ANN for blueberry spectral classification.

(2004): Develop: dimensional criteria for positioning fiber-optic sensor for on-line NIR spectra of whole green bean coffee; whole green bean coffee singulation equipment compatible w/fiber-optic sensors; relationships between laser output and distance to surface measured for nozzle designs; chemifluorescence for pathogen presence; on-line prototype x-ray inspection system;existing relationships between quality measurements and impacts; Si biosensor.

Test: automated bacterial sensor; internal produce quality linescanning approaches; orienting device modification for use with biosensors.

Determine: feasibility of modifying apple inspection system; appropriate biosensor technology-food safety issues; concentration thresholds-apple pathogens identification; internal spectral characteristics for produce quality, insect presence.

Integrate electronic chemical sensors into prototype "smart" e-nose detection device.

(2005): Develop: hyperspectral imaging system to detect fresh produce contamination; handling equipment for rapid speed testing with biosensor technology; e-nose fresh vegetable sniffing protocol; spectral VIS/NIR image database for produce quality, maggot-infested blueberry discrimination; methyl bromide replacement technology to eradicate insects from fruits & nuts; multi-array biosensor, validate design.

Evaluate: sensor with other bacterial pathogens; commercial linescan x-ray system; apple inspection station modifications for fresh pack; firmness values from laser air-puff with other measurements for selected produce; measurements that satisfy stakeholder requirements.

Test: two appropriate biosensor technologies; x-ray inspection system at field sites; simulation model for one crop using available inputs.

(2006): Develop: web based data and calculator; simulation models for other crops; e-nose fresh fruit sniffing protocol; e-nose optimum enrichment procedure; new sensing technique for measuring fruit optical properties; prototype sensing system for predicting fruit quality; prototype fresh pack inspection system; model & assess efficacy on discriminating maggot-infested blueberries with multivariate discriminate analysis & ANN; fruit optical properties database; electronics & optics for dedicated physical prototype instrumentation; new holder design to firmly hold products during evaluation, adopt for on-line sensing.

Evaluate: fruit motion effects on magnetic resonance signals, sensor ability to detect produce internal defects.

Obtain: stakeholder feedback on implementing new measurements.

Optimize: biosensor design, food sampling protocol development.

Adapt one biosensor technology for on-line use.

(2007): Develop: methods, equipment specs to integrate discriminate tech. into frozen blueberry processing line; new machine vision algorithms for increased & apple defect detection accuracy; dual NIR & MIR defect recognition imaging methods; prototype on-line biosensor technology; database of coffee whole green bean NIR spectra including place of origin; relationship between produce enzyme activity;integrated NIR spectra analysis, singulation equipt., color sensors for coffee.

Eval: collected spectra for commercially available chemical composition calibration; cultivar, crop year on B-galactosidase activity, firmness, other produce chemical constituents; prototype(s), potential for technology transfer; best broth composition that generates most robust e-nose gas signature; volatile compounds contributing to e-nose gas signature; biosensor shelf-life & economics; prototype fresh pack inspection system at selected field sites.

Disseminate: new laser air-puff unit for state-of-the-art quality sensing; linescan x-ray results; state-of-the-art on-line safety detection, treatment system; validated results of simulation models using trial shipments.

Isolate, purify,characterize B-galactosidases from different cultivars.

Projected Participation:

Include a completed Appendix E form

Outreach Plan:

Results of this research will be made available through several means: Refereed publications, conference papers and proceedings, project reports, on-line sources (web), and industry reports. Several participants have partial extension appointments and therefore will develop outreach materials through fact sheets and other extension publications.

Organization and Governance:

One person at each participating agency is designated, with approval of the agency director, as a voting member of the Technical Committee. Other persons at agencies are encouraged to participate as non-voting members of the Technical Committee.

An Executive Committee, consisting of the Chairman, Secretary, Industry Liaison, Member-at-Large, and Administrative Advisor will conduct the activities of the regional project between annual Technical Committee meetings. Membership on the Executive Committee is not limited to the official voting representatives. The Executive Committee is elected annually by voting members of the Technical Committee with the exception of the Industry Liaison. The Industry Liaison will be elected for a three year term, and will be responsible for informing industry representatives about yearly committee activities. Normally, a succession of offices from Member-at-Large to Secretary to Chairman is desirable, but is not established pro-forma. All members of the Executive Committee will be elected each year. No member shall hold the same office in consecutive years.

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