Research Project: Assessment of Quality Attributes of Poultry Products, Grain, Seed, Nuts, and Feed

Location: Quality and Safety Assessment Research Unit

2025 Annual Report

Objectives
1. Assess the intrinsic properties of myopathic chicken that alter quality and processing attributes in meat and enable commercially-viable processing strategies to limit myopathy impact. 1.A. Identify the mechanisms by which the physical and chemical properties of myopathic broiler muscles influence quality and processing attributes of meat products. 1.B. Evaluate processing and formulation strategies to minimize the negative impact of broiler muscle myopathies on the technological, compositional, and sensory properties of meat products. 2. Develop nondestructive, rapid imaging technologies to enable commercial measurement of quality characteristics and defects in poultry meat and eggs. 2.A. Develop a high-speed imaging technology for detecting and sorting poultry muscle myopathies and meat quality defects. 2.B. Utilize sensor fusion to enhance the ability of imaging technology to simultaneously assess multiple quality attributes and defects in poultry meat. 2.C. Develop imaging technology for rapid assessment of egg quality and defects. 3. Develop rapid, nondestructive microwave sensors to enable commercial measurement of quality parameters in grain, seed, nuts, and feed. 3.A. Enable distributed networks of microwave sensors for real-time monitoring of moisture content in grain, seed, and nut storage facilities. 3.B. Enable on-the-trailer multiparameter microwave sensors for nondestructive and instantaneous grading and monitoring of drying nuts. 3.C. Enable microwave sensors for simultaneous and nondestructive determination of moisture content and water activity of peanuts, almonds, and other nuts.

Approach
Poultry meat, egg, grain, seed, nut and feed commodity values depend upon quality. Research on poultry meat quality defects will focus on underlying mechanisms, utilization methods and rapid detection/sorting systems for quality defects. To determine how woody breast (WB) affects postmortem changes in breast muscle, trials will measure WB muscle deboning response, rigor mortis development and postmortem energy metabolism. Low field time-domain nuclear magnetic resonance techniques will be used to assess how muscle water properties influence WB meat quality during aging, cooking, freezing and marination. Impact of spaghetti meat (SM) on breast meat quality, composition, and functionality will be measured. Effects of SM on processing and quality in further-processed products (ground meat patties, fresh sausages and hotdogs) will be measured. A machine vision WB detection technology will be expanded to integrate the side-view imaging component into a system for both detection and sorting. Image acquisition and processing will be enhanced to match commercial processing line speeds and system will be tested on breast meat from a range of broiler varieties and sizes. To simultaneously assess multiple quality traits associated with WB and white striping, sensor fusion techniques will be evaluated. Multiple sensors measuring 2D and 3D shape morphology, spatial texture, muscle rigidity, color, and spectral data will be evaluated via independent trials. Once best sensing modalities are determined, sensor fusion algorithms will be developed and tested. For measuring egg quality, a modified-pressure imaging system to detect hairline cracks will be modified to grade table eggs for air-cell depth and yolk shadow using new machine vision algorithms. System will be redesigned for online operation and applied to detect cracked eggs in hatcheries. Microwave sensors for quality assessment of grains, seeds, nuts, and feeds will be developed. Microwave sensors in a distributed network will be developed for real-time nondestructive monitoring of moisture content in storage facilities for peanuts, almonds, wheat, corn, soybean and corn-soybean meal. Following laboratory testing in an eighth-scale drying bin equipped with multiple sensors, sensor networks will be tested in commercial grain and nut facilities. Microwave sensors will be developed to assess multiple attributes (moisture, bulk density, meat content and foreign material) before and during drying of peanuts, almonds, pecans and pistachios. After calibration with static samples, sensors will be tested in a quarter-scale nut drying system. Microwave sensors will be developed to simultaneously measure moisture content and water activity of in-shell peanuts, almonds and other nuts. A dielectric database will be collected with lab grade instrumentation. Following selection of optimal frequencies, prototype sensors will be assembled, calibrated and tested. By seeking to understand quality attributes, investigating utilization methods and developing rapid assessment tools, this project takes a multifaceted approach to provide information and technologies for producing and marketing high quality commodities.

Progress Report
Under Objective 1 researchers in Athens, Georgia identified mechanisms by which physical and chemical properties of myopathic broiler muscles influence quality and processing attributes of meat products (Sub-objective 1A). Impact of spaghetti meat (SM) myopathy on breast meat composition, oxidative status, protein degradation, functionality, and quality during postmortem storage was characterized. Studies determined wooden breast (WB) myopathy influences early postmortem muscle metabolism and rigor mortis as they relate to deboning time and meat quality. Low-field nuclear magnetic resonance (LF-NMR) was used to delineate how myowater properties influence chicken meat quality early postmortem, throughout extended postmortem refrigerated storage, with different rates of freezing/thawing, and during cooking. Additionally, LF-NMR studies identified changes in myowater properties that impact meat quality during marination with different levels of salt and phosphate, at different deboning times, and the effects of breast myopathies. Relationships between LF-NMR measurements and standard measures of meat water-holding capacity, texture, and product yield were established. Initial trials to determine the relationship between myowater properties and sensory attributes were conducted. Collaborative research with the University of Georgia (UGA) was conducted to determine the influence of broiler diets (types and levels of antioxidants, fats, proteins, and amino acids) and growth rate on meat quality, composition, and muscle myopathy development. Hundreds of differentially abundant proteins were identified in white striping (WS) meat using a straightforward proteomics method based on mass-spectrometry (MS). Differential metabolites in WB and/or SM fillets were identified by MS-based comparative metabolomics. Differential proteins, metabolites, and lipids in SM fillets were identified using MS-based multi-omics analyses. Differentially expressed genes in spatial locations throughout the breast muscle were identified using massively parallel, next generation RNA sequencing. Bioinformatic pathway analyses using various tools, including Gene Ontology, KEGG, protein-protein interactions, co-expression analyses, etc. provided critical insights into factors and pathways causing breast meat defects. Branched chain amino acid metabolism, antioxidant production, lipid metabolism, and energy metabolism were identified as altered biological functions in breast myopathies. Interestingly, commonalities and differences between WB and SM in both underlying etiologies and meat quality traits were identified as target pathways to mitigate and eliminate breast myopathies in broilers via nutritional, genetic, or systemic approaches. Differential abundances of several factors were confirmed using target verification studies with biochemical analyses. Trials to characterize the effects of the WB myopathy on mitochondria function in various tissues and metabolic pathways in muscle were conducted. Research was conducted to evaluate processing and formulation strategies to minimize the negative impact of broiler muscle myopathies on the technological, compositional, and sensory properties of meat products (Sub-objective 1B). Collaborative research with UGA was completed to develop processing and formulation parameters for utilizing SM in various types of further-processed products (patties, breakfast sausage, nuggets, and frankfurters). Studies were completed to determine the potential for utilizing WB meat in blade tenderized, marinated, sous-vide, and frankfurter type products. Trials were also conducted to determine the potential for using hydrolysis to generate functional protein ingredients from WB meat. Under Objective 2 research was conducted to develop a high-speed imaging technology for detecting and sorting breast myopathies and meat quality defects (Sub-objective 2A). Hyperspectral and color imaging techniques were developed for detection of WS in images. A real-time side-view machine vision system prototype was developed and patented for image acquisition and processing (rate of 200 images/sec) of breast fillets on a conveyor. Shape-based features, including fillet bending and thickness, were analyzed using the patented side-view technology, which allowed for WB detection (>95% accuracy). The machine vision system was tested at conveyor speeds from 10-260 ft/min, utilizing real-time application software to analyze fillet bending dynamics for WB detection. In comparison, advanced X-ray machines in the poultry industry can operate at speeds of approximately 200 ft/min. The machine vision system was integrated with commercial-grade reject equipment to automate WB fillet removal. Research using 3D imaging determined that average fillet thickness is a key WB indicator. Using artificial intelligence (AI), the side-view system was integrated with 3D imaging to improve characterization of WB fillet shapes. Research was conducted to refine high-speed imaging for WB detection. While the side-view system was effective for single fillets, a top-down view 3D machine vision technique using a single camera and AI technology was explored to increase throughput and detect multiple fillets simultaneously. This method enhanced WB detection accuracy and simplified the system design. The top-down 3D imaging technology utilizes advanced AI methods to improve processing and analysis of 3D point cloud data from imperfect, noisy measurements, resulting in more complete 3D shape representations of breast fillets in motion. Research was conducted to develop methods to objectively characterize the WB condition through advancements in sensor fusion techniques to identify and integrate several promising sensing modalities (Sub-objective 2B). Optical coherence tomography (OCT), hyperspectral imaging, and robotic compression force (CF) sensing were evaluated. OCT enabled the extraction of muscle microstructure features for subsurface tissue mapping, resulting in high classification accuracy, while CF sensing research mapped spatial hardness distributions for robotic tactile sensing mimicking human palpation. A wearable tactile sensing glove was developed to mimic human WB assessment for obtaining scientific data from human palpation. Hyperspectral imaging was identified as an effective method for analyzing surface composition and fat content. Research on sensor fusion (OCT + hyperspectral imaging) demonstrated the feasibility of objectively detecting WB features through system calibration for spatial co-registration of data and analysis using machine learning. Under Objective 3 research was conducted to enable distributed networks of microwave sensors for real-time monitoring of moisture content in grain, seed, and nut storage facilities (Sub-objective 3A). A 1/4th scale peanut drying system and 1/8th scale grain drying system were equipped with individual microwave sensors and multiple pairs of antennas for laboratory testing of distributed sensor networks in grains, seeds, and nuts at different temperatures, moisture contents, and moisture uniformity. Sensors were tested in the laboratory and at a peanut buying point. Sensors detected varying regions of moisture content and drying was monitored remotely in real-time (12-sec resolution). Real-time measurements of dielectric properties were used to simultaneously determine moisture content, bulk density, shrinkage, mass reduction, and bed height for grains and seeds during drying. Research was conducted to enable on-the-trailer multiparameter microwave sensors for nondestructive and instantaneous grading and monitoring of drying nuts (Sub-objective 3B). For clean and unclean peanut pods, dielectric properties at 2-18 GHZ were analyzed to identify calibration equations for bulk density and in-shell kernel moisture content. A Neural Network method was developed to determine foreign material content. Portable sensor prototypes were assembled with off-the-shelf components, calibrated, and tested in the laboratory for real-time determination of all three parameters. Microcontrollers in portable sensors were upgraded to Raspberry Pi single-board computers and custom software was developed to facilitate real-time measurements, data storage, analysis, and transmission. Sensors were tested in grading rooms at commercial buying points as benchtop instruments. For real-time monitoring of drying, three portable sensing systems integrating temperature and humidity sensors, were built, calibrated in the laboratory and tested on semitrailers at a buying point. Over 5,000 data points were collected in which in-shell kernel moisture content, bulk density, temperature, and RH were determined instantaneously and nondestructively. Errors were <0.6% for in-shell moisture and <0.02 g/cm3 for bulk density. Research was conducted to enable microwave sensors for simultaneous and nondestructive determination of moisture content and water activity of in-shell peanuts, almonds and other nuts from measurement of their dielectric properties at a single microwave frequency (Sub-objective 3C). Dielectric properties of shelled and unshelled peanuts, almonds, pistachios, and walnuts were measured in free space at 2-18 GHz, 5-45oC, and water activity ranges of interest. Several sensor designs were developed, calibrated, and successfully tested in the laboratory and field. Sensor data collected in the field agreed with laboratory grade instruments. Through collaborations with a company, commercial grade sensors for in-shell peanut kernel moisture content were produced and field tested at seven peanut buying points over two harvest seasons with outstanding results.

Accomplishments
1. Novel insight into factors associated with chicken meat quality defect. In fast-growing modern broiler chickens, meat quality defects caused by the wooden breast (WB) myopathy result in significant product downgrades and discards. Unfortunately, the specific triggers that cause WB development in the breast muscle are unknown. Most research to date has focused solely on alterations that occur in the affected breast muscle. Using a more holistic approach, ARS researchers in Athens, Georgia, found that mitochondria function and energy production pathways are altered throughout various body tissues and organs in broiler chickens exhibiting the WB myopathy. These findings suggest for the first time that gut health is altered and may play a role in WB development in broilers.

2. Functional ingredients from chicken meat. Protein hydrolysis is a process by which proteins can be broken down into small peptides and free amino acids using enzymes, acids, or alkalis. These products can exhibit bioactivities, high digestibility, and potential therapeutic and functional effects. ARS researchers in Athens, Georgia, utilized enzymatic hydrolysis to demonstrate that both normal and inferior quality chicken breast meat can be used to produce hydrolysate products with high protein contents and antioxidant capacities. This suggests the poultry industry can potentially enhance the utilization and value of meat waste and low-quality meat by using hydrolysis methods to produce high quality functional protein ingredients with health-promoting benefits for human and animal nutrition.

3. Biomolecules and pathways associated with chicken breast myopathies using multi-omics analyses. Broiler chicken meat is the most consumed meat in the US with approximately 95 lbs per capita each year. Wooden breast, white striping, and spaghetti meat myopathies pose serious problems to the poultry industry. Myopathies have been estimated to cost the US industry ~$200 million per year. ARS researchers in Athens, Georgia, used various -omics methods (e.g., transcriptomics, proteomics, metabolomics, lipidomics) to identify critical regulatory molecules and specific biological pathways altered in broiler breast tissue with myopathies. Findings from metabolomics and biochemical assays identified for the first time metabolic markers and altered physiological pathways in wooden breast meat. From independent proteomics studies, common and unique alterations were discovered in white striping compared to normal and wooden breast meat. Findings from RNA-sequencing identified for the first time spatial differences in gene expression throughout the breast fillet muscle. The markers and pathways identified provide critical integrative insights into previously unknown mechanisms that underly chicken breast myopathy development. Identified physiological pathways and associated molecules can potentially be utilized to develop dietary supplementation strategies to mitigate and eliminate breast myopathies in broilers.

4. Wearable tactile sensor glove for characterizing chicken meat quality defect. Progress in developing technologies for real-time sorting of chicken breast fillets with the wooden breast (WB) defect has been slowed by a lack of standardized objective measures for accurately assessing the distinct hardened texture of the raw meat. ARS researchers in Athens, Georgia, developed a wearable tactile sensing glove equipped with multiple force sensors to replicate the way human experts assess the WB condition in chicken breast fillets by touch. The glove was designed to capture tactile data during manual palpation of chicken fillets, focusing on measuring distribution and patterns of compression force and pressure. The advanced data provided by this sensing glove will serve as the foundation for designing a robotic "digital palpation" system capable of mimicking expert touch-based evaluations. Ultimately, such a system will enable automated, consistent, and high-throughput WB detection in commercial poultry processing environments with a robot.

5. Sensor fusion technique for objectively assessing chicken meat quality defect. Myopathies in broiler breast muscles alter the structure and composition of the tissue and lead to significant meat quality defects. Unfortunately, commonly used methods for assessing these myopathies commercially or for research purposes are subjective visual and tactile evaluations. While microscopic techniques can be used to verify the presence of these myopathies more objectively, these techniques are time consuming, destructive, and prone to sampling errors. ARS researchers in Athens, Georgia, developed an advanced sensor fusion technique that combines optical coherence tomography and hyperspectral imaging to analyze skinless, boneless chicken breast meat with the wooden breast (WB) condition. Optical coherence tomography provides high-resolution images of muscle microstructure up to 1 mm deep, while hyperspectral imaging assesses surface fat content and meat quality. To ensure accurate spatial alignment, a calibration method was developed to co-register the optical coherence tomography and hyperspectral image data, allowing detailed analysis of the same area. This approach enhances the reliability of WB assessment by providing a comprehensive view of tissue structure and composition, advancing non-destructive, objective meat quality evaluation in poultry.

6. Commercialization of a microwave moisture sensor for in-shell peanut kernel moisture content. The official meter currently used to measure peanut kernel moisture during grading requires that the peanuts be shelled. As a result, moisture measurements are typically done at the end of the grading process. Having peanut kernel moisture content at the beginning of the grading process could provide a 60% time savings. ARS researchers in Athens, Georgia, developed and patented a prototype microwave sensor system where the kernel moisture content can be determined without having to shell the peanuts. The technology was licensed and a company was secured to produce the sensors commercially. The commercial benchtop sensors have been successfully used by inspectors at peanut buying points over the last two peanut harvest seasons. Other players in the peanut industry, including growers and shellers, have shown interest in adopting this novel technology in their processes. Routine use of this technology in the peanut grading, during drying, and while in storage is expected to improve peanut quality, limit waste, and significantly reduce labor and energy costs.

7. Development of a distributed network of microwave sensors for commodity assessment. Real-time monitoring of quality parameters such as moisture content is increasingly needed during post-harvest processing of grains and nuts. It is important to monitor such parameters throughout a large bed of stored or drying product versus at a single point to avoid non-representative measurements and inaccuracies. ARS researchers in Athens, Georgia, developed a distributed network microwave sensor system capable of measuring real-time moisture content of various commodities at multiple locations within semitrailers, silos, or warehouses during drying or storage. Data from the network of sensors can be viewed remotely via a Wi-Fi enabled device such as a smartphone, laptop or tablet. The real-time capabilities of this system will allow for more efficient drying, storage, and processing practices to be implemented in the post-harvest handling of commodities. Implementation would improve the quality of the products since more data would be available for decision making. Operators could minimize spoilage, as well as over- and under-drying. Costs associated with propane and energy use for drying would be reduced, cutting costs for farmers. Consumers would benefit from more consistent product quality.

8. Method for characterizing peanut smut in single in-shell peanuts. Peanut smut is a devastating disease affecting peanut production and exports in Argentina; however, the possibility remains that it could spread to other countries. Currently, hand shelling individual peanut pods and visual inspection is the only method for identifying diseased pods. An automated screening methodology for assessing peanut smut and internal abnormalities is needed by graders and inspectors to increase the control of peanut smut. As part of an ARS collaborative project, researchers in Athens, Georgia, developed a sensing methodology and software to identify internal abnormalities in individual peanut pods of different varieties (Runner, Spanish, Valenica, and Virginia) from measurements of their dielectric properties within a resonant cavity. AI methods were used to establish classification and resulting models were embedded in the custom software and performed with 95% accuracy for detecting internal abnormalities in in-shell peanuts. This research provides a mechanism to nondestructively identify peanuts with abnormalities before possible contamination during production. Identification and removal of such peanuts ensures that producers receive the best rate for their peanuts, and that consumers can be assured that they receive high-quality, disease-free peanuts.

9. Machine learning (ML)-based model for predicting moisture content in multiple grains and seeds. Microwave moisture sensors for instantaneous and nondestructive determination of moisture content in grains and seeds typically require individual calibrations for each type of commodity which is tedious and time-consuming. A single ML-based model was developed by ARS researchers in Athens, Georgia, for determining moisture content in individual grains and seeds after training the model with dielectric properties measured for multiple grain and seed samples. This AI/ML-based unified method for determining moisture content in grains and seeds drastically simplifies the calibration process of microwave moisture sensors while ensuring better repeatability and accuracy. This model makes possible the development of an easy to use, highly accurate, universal microwave moisture meter that can be used on multiple types of commodities. Consequently, widespread use of this novel technology will result in better quality products and less waste with higher returns for growers and producers.

Review Publications
Liu, Q., Jingxin, S., Zhuang, H., Yoon, S.C., Bowker, B.C., Yang, Y., Bin, P. 2025. Prediction of raw meat texture and myopathic severity of broiler breast meat with the wooden breast condition by hyperspectral imaging. British Poultry Science. https://doi.org/10.1080/00071668.2025.2471450.
Choi, J., Shakeri, M., Kim, W., Kong, B.C., Bowker, B.C., Zhuang, H. 2024. Comparative metabolomic analysis of spaghetti meat and wooden breast in broiler chickens: unveiling similarities and dissimilarities. Frontiers in Physiology. 15:1456664. https://doi.org/10.3389/fphys.2024.1456664.
Zhou, H., Quach, A., Nair, M., Abasht, B., Kong, B.C., Bowker, B.C. 2025. Omics based technology application in poultry meat research. Poultry Science. 104(1):104643.
Choi, J., Shakeri, M., Bowker, B.C., Zhuang, H., Kong, B.C. 2025. Differentially abundant proteins, metabolites, and lipid molecules in spaghetti meat compared to normal chicken breast meat: Multiomics analysis. Poultry Science. 104(7):105165. https://doi.org/10.1016/j.psj.2025.105165.
Choi, J., Lee, J., Goo, D., Han, G., Choppa, V.S., Gudidoddi, S.R., Shakeri, M., Zhuang, H., Bowker, B.C., Kim, W.K., Kong, B.C. 2025. Spatial transcriptomic differences in the breast muscle of grower broilers at 21 and 28 days of age. Poultry Science. 104(6). https://doi.org/10.1016/j.psj.2025.105095.
Yoon, S.C., Ekramirad, N. 2024. Machine learning- assisted multispectral and hyperspectral imaging, editor(s): Jeong-Yeol Yoon, Chenxu Yu, Book Chapter, Machine Learning and Artificial Intelligence in Chemical and Biological Sensing, Elsevier Science, 2024, Pages 227-258, ISBN 9780443220012 https://doi.org/10.1016/B978-0-443-22001-2.00009-3
Guo, X., Jia, B., Zhang, H., Ni, X., Zhuang, H., Lu, Y., Wang, W. 2023. Evaluation of Aspergillus flavus growth and detection of aflatoxin B1 content on maize agar culture medium using Vis/NIR hyperspectral imaging. Agriculture. 13. Article 237. https://doi.org/10.3390/agriculture13020237.
So, J., Joe, S., Hwang, S., Yoon, S.C., Lee, S. 2022. Development of brown egg micro-crack detection system using modified pressure method. Journal of Agricultural Machinery Engineering. (2)1: 69-78. https://doi.org/10.12972/jame.20220008.
Zhuang, H., Rothrock Jr, M.J., Lawrence, K.C., Gamble, G.R., Bowker, B.C. 2024. Effect of in-package cold plasma treatment on poultry breast meat packaged in high CO2 atmosphere. Poultry Science. https://doi.org/10.1016/j.psj.2024.104085.
Shakeri, M., Choi, J., Kong, B.C., Zhuang, H., Bowker, B.C. 2024. Proteomic analysis suggests mitochondria disorders and cell death lead to spaghetti meat myopathy. Meat and Muscle Biology. 8(1): 18205, 1–7. https://doi.org/10.22175/mmb.18205.
Goo, D., Singh, A.K., Choi, J., Sharma, M.K., Paneru, D., Lee, J., Katha, H.R., Zhuang, H., Kong, B.C., Bowker, B.C., Kim, W. 2024. Different dietary branched-chain amino acid ratios, crude protein levels, and protein sources can affect the growth performance and meat yield in broilers. Poultry Science. 103(12):104313.
Bowker, B.C. 2013. Meat science and muscle biology symposium: In utero factors that influence postnatal muscle growth, carcass composition, and meat quality. Journal of Animal Science. 91(3):1417-1418.
Bowker, B.C., Zhuang, H. 2019. Detection of razor shear force differences in broiler breast meat due to the woody breast condition depends on measurement technique and meat state. Poultry Science. 98:6170–6176. https://doi.org/10.3382/ps/pez334.
Pang, B., Bowker, B.C., Yu, X., Sun, J., Zhuang, H. 2022. Evaluation of visible spectroscopy and low-1 field nuclear magnetic resonance techniques for predicting defects in broiler breast fillets. Food Control. https://doi.org/10.1016/j.foodcont.2022.109386.
Chatterjee, D., Sanchez Brambila, G., Bowker, B.C., Zhuang, H. 2019. Effect of tapioca flour on physicochemical properties and sensory descriptive profiles of chicken breast meat patties. Journal of Applied Poultry Research. 28:598–605. https://doi.org/10.3382/japr/pfy076.
Leblanc, A.P., Trabelsi, S., Rasheed, K., Miller, J. 2025. Machine learning algorithms for nondestructive sensing of moisture content in grain and seed. IEEE Instrumentation & Measurement Society. 4. https://doi.org/10.1109/ojim.2025.3568080.
Lewis, M.A., Trabelsi, S., Nelson, S.O. 2017. USING MICROWAVE SENSING TO INVESTIGATE KERNEL MOISTURE CONTENT AT THE FRONT AND BACK OF SEMITRAILERS DURING PEANUT DRYING. Applied Engineering in Agriculture. 33(5): 611-617. https://doi.org/10.13031/aea.12079.
Lewis, M.A., Trabelsi, S. 2020. Performance comparrison of three density-independent calibration functions for microwave moisture sensing in unshelled peanuts during drying. Applied Engineering in Agriculture. 36,5,667-672. https://doi.org/10.13031/aea.13703.
Lewis, M.A., Trabelsi, S. 2022. Comparison of Permittivity Between Traditional and High-oleic Runner-type Peanuts at Microwave Frequencies . Transactions of the ASABE. https://doi.org/10.13031/trans.14323.
Lewis, M.A., Trabelsi, S. 2025. Integrating microwave sensors within a distributed network to monitor peanut drying parameters in real-time. Applied Engineering in Agriculture. 41(3), 249-256. https://doi.org/10.13031/aea.16208.
Ozturk, S., Kong, F., Trabelsi, S., Singh, R.K. 2016. Dielectric properties of dried vegetable powders and their temperature profile during radio frequency heating. Journal of Food Engineering. 160: 91-100.
Sanchez Brambila, G.Y., Chatterjee, D., Bowker, B.C., Zhuang, H. 2017. Descriptive texture analyses of cooked patties made of chicken breast with the woody breast condition. Poultry Science. 96(9):3489-3494.
Trabelsi, S., Nelson, S.O., Lewis, M.A. 2010. Effects of “natural” water and “added” water on prediction of moisture content and bulk density of shelled corn from microwave dielectric properties. Journal of Microwave Power and Electromagnetic Energy. 44(2):72-80.
Julrat, S., Trabelsi, S. 2018. Measuring dielectric properties for sensing foreign material in peanuts. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2018.2882367.
Julrat, S., Trabelsi, S. 2022. Use of dielectric mixture equations for the characterization of uncleaned peanuts. Measurement: Food. https://doi.org/10.1016/j.meafoo.2022.100022.
Yoon, S.C., Shin, T., Lawrence, K.C., Jones, D.R. 2020. Development of Online Egg Grading Information Management System with Data Warehouse Technique. Applied Engineering in Agriculture. 36(4), pp.589-604..
Jiang, H., Yoon, S.C., Zhuang, H., Wang, W., Li, Y., Lu, C., Li, N. 2018. Non-destructive assessment of final color and pH attributes of broiler breast fillets using visible and near-infrared hyperspectral imaging: a preliminary study. Infrared Physics and Technology. https://doi.org/10.1016/j.infrared.2018.06.025.
Yang, L., Yan, W., Wang, H., Zhuang, H., Zhang, J. 2017. Shell thickness-dependent antibacterial activity and biocompatibility of gold@silver core–shell nanoparticles. RSC Advances. 7:11355-11361.
Jia, B., Wang, W., Yoon, S.C., Zhuang, H., Li, Y. 2018. Using a combination of spectral and textural data to measure water-holding capacity in fresh chicken breast fillets. Applied Sciences. 8(3), p.343.
Zhuang, H., Rothrock Jr, M.J., Hiett, K.L., Lawrence, K.C., Gamble, G.R., Bowker, B.C., Keener, K.M. 2019. In-package antimicrobial treatment of chicken breast meat with high voltage dielectric barrier discharge – Electric voltage effect. Journal of Applied Poultry Research. 28(4):801-807.
Xiao, S., Zhuang, H., Zhou, G., Zhang, J. 2018. Investigation of inhibition of lipid oxidation by L-carnosine using an oxidized-myoglobin-mediated washed fish muscle system. LWT - Food Science and Technology. 97:703-710.
Yang, Y., Zhuang, H., Yoon, S.C., Wang, W., Jiang, H., Jia, B. 2017. Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy. Food Chemistry. https://doi.org/10.1016/j.foodchem.2017.09.148.
Yang, Y., Zhuang, H., Yoon, S.C., Wang, W., Jiang, H., Jia, B., Li, C. 2018. Quality assessment of intact chicken breast fillets using factor analysis with Vis/NIR spectroscopy. Journal of Food Analytical Methods. https://doi.org/10.1007/s12161-017-1102-0.
Trabelsi, S., Lewis, M.A. 2025. Dielectric-based calibration algorithm for rapid and nondestructive determination of multiple quality attributes of in-shell nuts. IEEE Sensors Letters. 2(3). https://doi.org/10.1109/LSENS.2025.3539583.