Project : USDA ARS

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Research Project: Advancement of Sensing Technologies for Food Safety and Security Applications

Location: Environmental Microbial & Food Safety Laboratory

2023 Annual Report

Accomplishments
1. A hyperspectral plant health monitoring system for space crop production. Plant monitoring in growth chambers onboard the International Space Station is currently conducted by estimating growth rates based on photographic analysis of daily increments in leaf areas. This limited approach cannot detect plant stresses or nutrient deficiencies that usually occur days before the leaves manifest any visible changes. In a collaborative project between scientists at USDA ARS, Beltsville, Maryland, and at NASA, Kennedy Space Center (KSC), Florida, a compact and automated hyperspectral imaging system was developed and installed at the KSC to monitor plant health for space crop production under controlled environments. The prototype system can collect both hyperspectral reflectance and fluorescence images in the visible and near-infrared region within a single imaging cycle, which can provide rich spectral and spatial information for possible early detection of abiotic stresses and diseases for pick-and-eat salad crops. In a preliminary study on Dragoon lettuce, the system showed potential for detecting drought stress before visible symptoms and leaf size differences were evident, using a machine learning method with the spectral reflectance data of the lettuce. The method would benefit NASA’s space crop production and other fresh produce production in controlled-environment agriculture in enhancing quality and safety of fruits and vegetables by reducing crop losses due to stress and disease and enabling earlier interventions to mitigate problems.

2. A multimodal optical sensing system for automated and intelligent food safety inspection. Commercial integrated spectroscopy systems are usually bulky and not flexible for testing various food and agricultural products. There exists a lack of compact sensing devices and methods for quick and routine analysis of the chemical and biological content of food samples. As an extension of previous ARS-developed macro-scale Raman technologies, ARS scientists at Beltsville, Maryland, developed a new transportable multimodal (transmission, color, fluorescence, and Raman) optical sensing system with embedded artificial intelligence capabilities for automated and intelligent food safety inspection. By using machine vision and motion control techniques, the system can conduct automated Raman spectral acquisition for a variety of sample types presented in customized well plates or in Petri dishes (from food materials to bacterial colonies grown on media). Interesting targets within the sample materials can be identified and labeled using real-time image and spectral processing and machine learning functions integrated into the in-house developed software. The system shows promise for use by food safety regulatory agencies as an initial screening tool for quick species identification of common foodborne bacteria. Compact and easily transportable, the prototype is suitable for field and on-site food safety inspection in potential regulatory and industrial applications.

3. Dual-modality IR and Raman in situ spectral measurements distinctly identify seven individual, biologically essential, lipids at room temperature. Commercially available fish oil products are mixtures of lipids, all of which contain the same redundant cis-polyunsaturated fatty acids structure. Spectral verification of specific lipid identities is possible but requires distinguishing spectral wavenumbers specific to individual lipid compounds—fingerprint-like markers—from redundant nonselective wavenumbers. ARS scientists in Beltsville, Maryland, determined that, surprisingly, infrared (IR) wavenumbers unique to seven lipids are different from the wavenumber markers in the Raman domain for those same lipids. Using dual-modality measurements–both IR and Raman markers together—can facilitate and enhance the identification of lipids for verification purposes, for example, by readily distinguishing between cis-polyunsaturated fatty acids with an even number of double bonds from those with an odd number of double bonds, such as the omega-3 fatty acid DHA (with six double bonds) and the omega-3 fatty acid EPA (with five double bonds) that each exhibit a unique spectral signature. This approach enables verifying the identity of lipids supplements, such as the omega-3 fatty acids that are often added to poultry feed to enhance egg quality.

4. A rapid spectroscopic technique for detecting veterinary drug residues on meat surfaces. Current methods to detect veterinary drug residues require a strict sample collection and processing protocol, the use of very expensive analytical instrumentation, and a high technical level of expertise to both operate the equipment and to interpret the results. ARS scientists at Beltsville, Maryland, have developed macro-scale Raman imaging and spectroscopy technologies and methodologies for research addressing food integrity concerns arising from adulteration or contamination. A 785-nm point-scan Raman system was used in the development of a detection method for drug residues including salbutamol, clenbuterol, and ractopamine. The method detects the spectral fingerprint of the residue compounds after they are first absorbed onto gold nanoparticles. These spectroscopic methods, once developed, are less complicated to run and more user-friendly than conventional methods, and can produce practical analytical results in real-time, which is useful to those raising healthy animals for safe human consumption.

5. Rapid assessment of fish freshness for multiple supply-chain nodes using multi-mode spectroscopy and fusion-based artificial intelligence. Fresh fish is a highly perishable product with more than 20% wasted at retail level every year. One reason for such waste is that early fish decay is not easily detectable by human senses. There is a need for sensing techniques that allow for onsite inspection of fish freshness in a rapid, cost-effective, and nondestructive manner. ARS scientists in Beltsville, Maryland, developed a multimode spectroscopy method for rapid assessment of fish freshness. Three types of spectral data (i.e., visible and near- infrared reflectance, short -wave infrared reflectance, and fluorescence) were collected from fish fillets of four species (i.e., farmed Atlantic salmon, wild coho salmon, Chinook salmon, and sablefish) over time as fillet conditions progressed from fresh to spoiled. A machine learning method using the fusion of the three spectral data types achieved 95% accuracy in classifying fresh and spoiled fish samples. The data analysis and classification methods developed in this research can be used to assist development of an easy-to-use handheld device to estimate remaining shelf life of the fish fillets, which could enable dynamic sales management and major reductions in waste for the seafood industry.

6. Citrus disease detection using convolutional neural network generated features and Softmax classifier on hyperspectral image data. Citrus diseases and peel blemishes can limit marketability of citrus crops and in some cases lead to shipping restrictions into certain regions. Proper and timely identification and control of citrus diseases can assure fruit quality and safety, improve production, and minimize economic losses. ARS scientists in Beltsville, Maryland, developed an AI-based hyperspectral imaging and classification method for identification of various diseased peel conditions on citrus fruit. Hyperspectral reflectance images were collected in the visible and near-infrared wavelength range from Ruby Red grapefruits with normal peels and with common peel diseases and defects, including canker, greasy spot, insect damage, melanose, scab, and wind scar. A classification accuracy of over 98% was achieved using a deep learning algorithm based on convolution neural network with the hyperspectral reflectance image data. Using this method could benefit the citrus industry and regulatory agencies (e.g., FDA and USDA APHIS) in helping to ensure and enforce quality and safety standards for citrus-related food and beverage products.

7. Nondestructive evaluation of packaged butter for adulteration based on spatially offset Raman spectroscopy coupled with FastICA. Butter is a dairy product that is prone to mixing with cheaper vegetable fat (e.g., margarine) in economically motivated adulteration. Traditional optical sensing techniques can be used for adulteration detection for unpackaged butter products. However, nondestructively authenticating packaged foods is challenging due to complicated interactions between light and packaging materials. ARS scientists in Beltsville, Maryland, employed a spatially offset Raman line-scan imaging technique using a point laser to nondestructively detect adulterated butter within its intact packaging. Animal butter was mixed with margarine in different ratios. Raman image and spectral data were acquired from the butter-margarine mixtures covered by original packaging sheets and plastic film. Analysis models were developed and successfully used to predict the adulteration content of the butter-margarine samples covered with different packaging materials. The detection method is useful for through-package safety and quality inspection of food materials. Use of the technique would benefit the food industry and regulatory agencies (e.g., FDA and USDA FSIS) in ensuring and enforcing safety and quality standards for packaged food products.

8. Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation. Nondestructive evaluation of safety and quality for packaged foods is a challenging task due to difficulties in acquiring optical signals from food samples through packaging materials. Spatially offset Raman spectroscopy (SORS) is a promising depth-profiling technique to tackle this problem. However, there is a lack of studies to evaluate the signal separation methods for the SORS technique. ARS scientists in Beltsville, Maryland, presented a method based on the line scan SORS and statistical replication Monte Carlo simulation to evaluate effectiveness of retrieving Raman signals from subsurface food samples. The simulation results were verified by three packaged foods (i.e., sugar in plastic jar, bagged rice, and boxed butter), in which fast independent component analysis method can effectively separate Raman signals from surface layer of the packaging materials and subsurface layer of the foods. The evaluation method can assist in developing and optimizing SORS-based methods for through-package safety and quality inspection of the foods and ingredients. The technique would benefit the regulatory agencies (e.g., FDA and USDA FSIS) and the food industry in enforcing standards of the safety and quality of the packaged food products.

U.S. DEPARTMENT OF AGRICULTURE

Environmental Microbial & Food Safety Laboratory: Beltsville, MD