Food waste disposal and utilization in the United States: A spatial cost benefit analysis
Not Available
Badgett, Alex (ORCID:0000000291697098)↗
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Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis
Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.
59 BASIC BIOLOGICAL SCIENCES↗
Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index
Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.
42 ENGINEERING↗
Precursor Analysis Report: JBS Foods Ransomware Attack 2021
The JBS Foods 2021 Ransomware Attack Precursor Analysis Report leverages publicly available information about the JBS ransomware cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. In late May 2021, one of the world’s largest meat producers, JBS Foods, announced they had fallen victim to a worldwide ransomware attack, later found to be REvil ransomware. In the United States alone, JBS Foods accounts for nearly 25% of beef and roughly 20% of pork production. Adversaries initially launched a Distributed Denial of Service (DDoS) attack on the company’s Information Technology (IT) networks in Australia, but the attack impacted operations in Brazil, Canada, and the United States, as well. The attack caused plant operations in all four countries to shut down for at least one day. All nine of the U.S. meatpacking plants temporarily shut down because of the attack. The adversaries initially demanded a $\$$22 million ransom for the company’s data, but later negotiated the ransom down to $\$$11 million even after JBS Foods restored most of their systems. JBS Foods eventually paid the $\$$11 Million for reassurance from the adversaries that none of their customers’ data would be compromised in the future. Despite its short duration, the attack still caused large stocks of meat to spoil. The incident also underscored how adversaries can simultaneously compromise and move laterally through global subsidiaries of an organization. Researchers and analysts identified 22 unique techniques (in a sequence of 21 steps) utilized during the attack with a total of 361 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Sixteen of the identified techniques used during the JBS Foods cyber attack were precursors to the triggering event. Analysis identified 308 observables associated with these precursor techniques, 163 of which were assessed to have an increased likelihood of being perceived in the 75 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
A Quantum Dot Nanobiosensor for Rapid Detection of Botulinum Neurotoxin Serotype E
Botulinum neurotoxins (BoNTs) are potent toxins produced by Clostridium bacteria that are responsible for the illness botulism and are listed as bioterrorism agents. BoNT serotype E (BoNT/E) is one of four BoNT serotypes that cause human botulism and is the second most frequent cause of foodborne botulism. Rapid detection and discrimination of BoNT serotypes implicated in human disease are critical for ensuring timely treatment of patients and identifying sources of toxins, but there have been few reported detection methods for BoNT/E and even fewer methods usable for BoNT serotyping. We report a nanobiosensor based on Forster resonance energy transfer (FRET) between semiconductor nanocrystals (quantum dots, QDs) and dark quencher-labeled peptide probes to detect biologically active BoNT/E in aqueous media. The peptide probes contain a specific cleavage site for active BoNT/E. QD photoluminescence, which changes intensity due to FRET when the peptide probe is cleaved, was used to indicate toxin presence and quantity. The detection of a BoNT/E light chain (LcE) and holotoxin was observed within 3 h. The limits of detection were 0.02 and 2 ng/mL for LcE and holotoxin, respectively. The nanobiosensor shows good specificity toward the target in tests with nontarget BoNT serotypes. The high sensitivity, simple operation, short detection time, and ability to be used in parallel with probes developed for other BoNT serotypes indicate that the nanobiosensor will be useful for rapid BoNT/E detection and serotype discrimination in food analysis.
botulinum neurotoxin E↗
Enzymatic Laser-Induced Graphene Biosensor for Electrochemical Sensing of the Herbicide Glyphosate
Glyphosate is a globally applied herbicide yet it has been relatively undetectable in-field samples outside of gold-standard techniques. Its presumed nontoxicity toward humans has been contested by the International Agency for Research on Cancer, while it has been detected in farmers’ urine, surface waters and crop residues. Rapid, on-site detection of glyphosate is hindered by lack of field-deployable and easy-to-use sensors that circumvent sample transportation to limited laboratories that possess the equipment needed for detection. Herein, the flavoenzyme, glycine oxidase, immobilized on platinum-decorated laser-induced graphene (LIG) is used for selective detection of glyphosate as it is a substrate for GlyOx. The LIG platform provides a scaffold for enzyme attachment while maintaining the electronic and surface properties of graphene. The sensor exhibits a linear range of 10–260 µm, detection limit of 3.03 µm, and sensitivity of 0.991 nA µm -1 . The sensor shows minimal interference from the commonly used herbicides and insecticides: atrazine, 2,4-dichlorophenoxyacetic acid, dicamba, parathion-methyl, paraoxon-methyl, malathion, chlorpyrifos, thiamethoxam, clothianidin, and imidacloprid. Sensor function is further tested in complex river water and crop residue fluids, which validate this platform as a scalable, direct-write, and selective method of glyphosate detection for herbicide mapping and food analysis.
47 OTHER INSTRUMENTATION↗
An outbreak of renal failure in asian dogs due to s-triazine adulteration of pet food raw material: Analysis of unique, green kidney stones formed as a result
Renoliths were removed at necropsy from dogs that had died from acute kidney injury in Asia in 2004 and submitted to our laboratories for analysis including elemental composition, mass spectrometry, and microprobe analysis. The presence of a mixed s-triazine matrix comprising melamine, cyanuric acid, and ammelide, but no detectable ammeline, was found in the stone samples we analyzed. The unusual and unique green coloration of these stones was determined to be due to the presence of biliverdin. The occurrence of these green stones distinguished the 2004 incident from another incident in 2007 in the USA and other reported cases. The presence of crystals was reported in renal tubules and collecting ducts in both outbreaks, but no stones were reported in the 2007 incident. Here, this difference suggested a variation in the disease process caused by mixed s- triazine ingestion. Careful monitoring of food additives is warranted to prevent future problems in animals and humans.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics
This data supports the conclusions found in climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics. Included here are (i) the binarized geolocation vectors used for exhaustive vector comparisons, (ii) the resulting climatic networks, (iii) the results of applying Markov clustering to the climatic networks, and (iv) the results of applying Correlation-of-Correlations (cor-cor) to the climatic networks. The set of binarized geolocation vectors that are used as inputs for the Combinatorial Metrics library (CoMet) are of the form comet-UUUUUxVVVVV-XXXX-YYYY.shuffled.tped where UUUUU is the number of vectors, VVVVV is the length of each vector, XXXX is the starting year, and YYYY is the ending year. Each line corresponds to a geolocation vector of binary elements A (i.e., 0) and T (i.e., 1). The set of climatic networks that are used for downstream network analysis are of the form network-U-way-XXXX-YYYY.parsed.txt where U is the order of the comparison (2-way or 3-way), XXXX is the starting year, and YYYY is the ending year. Each line corresponds to an edge linking two geolocations (defined by latitude and longitude) with its corresponding edge weight (i.e., DUO score). The set of cluster results are of the form clusters-U-way-XXXX-YYYY-thresh-VVVV-inflation-WWW.clustered.txt where U is the order of the comparison (2-way or 3-way), XXXX is the starting year, YYYY is the ending year, VVVV is the similarity threshold, and WWW is the Markov clustering inflation rate. Each line corresponds to a single cluster and is composed of a number of corresponding geolocations (defined by latitude and longitude). The set of cor-cor results are of the form corcor-U-way-XXXX-YYYY.cumulative.txt where U is the order of the comparison (2-way or 3-way), XXXX is the starting year, and YYYY is the ending year. Each line corresponds to a single geolocation with it's corresponding cor-cor value.
54 ENVIRONMENTAL SCIENCES↗
Exploring the working range of automated standard dilution analysis of nutrient elements in foods by inductively coupled plasma optical emission spectrometry
Inductively coupled plasma optical emission spectrometry (ICP-OES) is an important tool for measuring nutrient elements in food. ICP-OES methods typically determine analytical concentrations using external standard calibration but can be susceptible to matrix effects. The method of standard additions does not suffer from matrix effects but is time consuming and labor intensive. Automated standard dilution analysis (SDA) allows for online matrix matched calibration without preparing individual standard additions for each sample matrix. This approach may solve both time and matrix issues and has been described in the literature as an attractive alternative to standard additions. The working range of the method for nutrient elements, however, is an understudied feature of SDA that may be a potential drawback to routine analysis of foods. We evaluated automated SDA performance through the analysis of 10 reference materials and four fortified (i.e., spiked) foods spanning the AOAC food triangle. We evaluated the working range, accuracy, and precision for analyses of nutrient elements in foods. Accepted accuracy (80–120% recovery) was achieved for 10 nutrient elements, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, and Zn, when the analytical solution concentration to standard concentration ratio was less than 10. This equates to a working range for each element spanning at least two orders of magnitude. Removing outliers, Z scores (n = 95) ranged from –1.8 to 0.88, and the average recovery (n = 85) from fortification experiments was 97 ± 12% (2σ). Therefore, automated SDA applied to ICP-OES may be used for nutrient elemental analyses in samples with difficult matrices such as foods.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics
Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37x10 18 operations/sec) and one of the largest (168.7x10 21 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.
59 BASIC BIOLOGICAL SCIENCES↗
Enhanced detection of volatile organic compounds (VOCs) by caffeine modified carbon nanotube junctions
Detection of vapor phase analytes and gas molecules is primarily essential for numerous applications including medical diagnostic, environmental monitoring, chemical threat detection, food quality control, and chemical warfare agents. Here, we developed Caffeine functionalized carbon nanotubes (CNTs) random network based vapor quantum resistive sensors (vQRSs) for volatile organic compounds (VOCs) detection. The localized transmission electron microscopy (TEM) image and energy dispersive X-ray spectroscopy (EDS) confirm migration of caffeine to CNT–CNT junction (CNTj). We have recorded more than one order of magnitude higher sensitivity and unique selectivity towards selective VOCs (e.g., acetone, toluene, ethanol, methanol, etc.) with short response time (few seconds) for caffeine modified CNT junction (Caf-CNTj) vQRSs. Molecular dynamics (MD) calculations suggest that the sensitivity of the sensor is diffusion limited, with an advective contribution for ethanol. Furthermore, the designed Caf-CNTj sensors can potentially be important candidate to be a part of electronic nose used for various application such as breath analysis and food quality monitoring.
36 MATERIALS SCIENCE↗
Remarkably coherent population structure for a dominant Antarctic Chlorobium species
Background: In Antarctica, summer sunlight enables phototrophic microorganisms to drive primary production, thereby “feeding” ecosystems to enable their persistence through the long, dark winter months. In Ace Lake, a stratified marine-derived system in the Vestfold Hills of East Antarctica, a Chlorobium species of green sulphur bacteria (GSB) is the dominant phototroph, although its seasonal abundance changes more than 100-fold. Here, we analysed 413 Gb of Antarctic metagenome data including 59 Chlorobium metagenome-assembled genomes (MAGs) from Ace Lake and nearby stratified marine basins to determine how genome variation and population structure across a 7-year period impacted ecosystem function. Results: A single species, Candidatus Chlorobium antarcticum (most similar to Chlorobium phaeovibrioides DSM265) prevails in all three aquatic systems and harbours very little genomic variation (≥ 99% average nucleotide identity). A notable feature of variation that did exist related to the genomic capacity to biosynthesize cobalamin. The abundance of phylotypes with this capacity changed seasonally ~ 2-fold, consistent with the population balancing the value of a bolstered photosynthetic capacity in summer against an energetic cost in winter. The very high GSB concentration (> 10 8 cells ml –1 in Ace Lake) and seasonal cycle of cell lysis likely make Ca. Chlorobium antarcticum a major provider of cobalamin to the food web. Analysis of Ca. Chlorobium antarcticum viruses revealed the species to be infected by generalist (rather than specialist) viruses with a broad host range (e.g., infecting Gammaproteobacteria) that were present in diverse Antarctic lakes. The marked seasonal decrease in Ca. Chlorobium antarcticum abundance may restrict specialist viruses from establishing effective lifecycles, whereas generalist viruses may augment their proliferation using other hosts. Conclusion: The factors shaping Antarctic microbial communities are gradually being defined. In addition to the cold, the annual variation in sunlight hours dictates which phototrophic species can grow and the extent to which they contribute to ecosystem processes. The Chlorobium population studied was inferred to provide cobalamin, in addition to carbon, nitrogen, hydrogen, and sulphur cycling, as critical ecosystem services. The specific Antarctic environmental factors and major ecosystem benefits afforded by this GSB likely explain why such a coherent population structure has developed in this Chlorobium species.
59 BASIC BIOLOGICAL SCIENCES↗
Antimicrobial resistance and genomic characterization of Salmonella Dublin isolates in cattle from the United States
Salmonella enterica subspecies enterica serotype Dublin is a host-adapted serotype in cattle, associated with enteritis and systemic disease. The primary clinical manifestation of Salmonella Dublin infection in cattle, especially calves, is respiratory disease. While rare in humans, it can cause severe illness, including bacteremia, with hospitalization and death. In the United States, S . Dublin has become one of the most multidrug-resistant serotypes. The objective of this study was to characterize S . Dublin isolates from sick cattle by analyzing phenotypic and genotypic antimicrobial resistance (AMR) profiles, the presence of plasmids, and phylogenetic relationships. S . Dublin isolates (n = 140) were selected from submissions to the NVSL for Salmonella serotyping (2014–2017) from 21 states. Isolates were tested for susceptibility against 14 class-representative antimicrobial drugs. Resistance profiles were determined using the ABRicate with Resfinder and NCBI databases, AMRFinder and PointFinder. Plasmids were detected using ABRicate with PlasmidFinder. Phylogeny was determined using vSNP. We found 98% of the isolates were resistant to more than 4 antimicrobials. Only 1 isolate was pan-susceptible and had no predicted AMR genes. All S . Dublin isolates were susceptible to azithromycin and meropenem. They showed 96% resistance to sulfonamides, 97% to tetracyclines, 95% to aminoglycosides and 85% to beta-lactams. The most common AMR genes were: sulf2 and tetA (98.6%), aph(6)-Id (97.9%), aph(3’’)-Ib, (97.1%), floR (94.3%), and blaCMY-2 (85.7%). All quinolone resistant isolates presented mutations in gyr A. Ten plasmid types were identified among all isolates with IncA/C2, IncX1, and IncFII(S) being the most frequent. The S . Dublin isolates show low genomic genetic diversity. This study provided antimicrobial susceptibility and genomic insight into S . Dublin clinical isolates from cattle in the U.S. Further sequence analysis integrating food and human origin S . Dublin isolates may provide valuable insight on increased virulence observed in humans.
60 APPLIED LIFE SCIENCES↗
Microbiomes of commercially-available pine nuts and sesame seeds
Metagenomic analysis of food is becoming more routine and can provide important information pertaining to the shelf life potential and the safety of these products. However, less information is available on the microbiomes associated with low water activity foods. Pine nuts and sesame seeds, and food products which contain these ingredients, have been associated with recalls due to contamination with bacterial foodborne pathogens. The objective of this study was to identify the microbial community of pine nuts and sesame seeds using targeted 16S rRNA sequencing technology. Ten different brands of each seed type were assessed, and core microbiomes were determined. A total of 21 and 16 unique taxa with proportional abundances >1% in at least one brand were identified in the pine nuts and sesame seeds, respectively. Members of the core pine nut microbiome included the genera Alishewanella, Aminivibrio, Mycoplasma, Streptococcus, and unassigned OTUs in the families of Desulfobacteraceae and Xanthomonadaceae. For sesame seeds, the core microbiome included Aminivibrio, Chryseolina, Okibacterium, and unassigned OTUs in the family Flavobacteriaceae. The microbiomes of these seeds revealed that these products are dominated by environmental bacterial genera commonly isolated from soil, water, and plants; bacterial genera containing species known as commensal organisms were also identified. Understanding these microbiomes can aid in the risk assessment of these products by identifying food spoilage potential and community members which may co-enrich with foodborne bacterial pathogens.
59 BASIC BIOLOGICAL SCIENCES↗
Technoeconomic and Life Cycle Analysis of an Integrated Fermentation and Microbial Electrochemical Process for Volatile Fatty Acid Production from Food Waste
Techno-economic analysis (TEA) and life cycle assessment (LCA) were conducted for an integrated system designed for the production of volatile fatty acid (VFA) from food waste. The TEA estimated a production cost of $\$$3.12/kg VFA, and the LCA predicted negative greenhouse gas (GHG) emissions of -0.4 kg CO 2 e/kg VFA, driven primarily by diverting organic waste from landfills and avoiding methane emissions while producing valuable chemical products. Hotspot analysis showed arrested methanogenesis (AM) fermentation as the largest contributor to costs (37%) and environmental burden (47%), driven by high sodium hydroxide (NaOH) consumption. Distillation and microbial electrosynthesis (MES) units were the next-largest environmental contributors (28% and 18%). Major cost drivers also included residuals management (biosolids and wastewater) and the equipment and operating costs for AM, MES, and sonication pretreatment units. Although the new integrated system is environmentally benign, its costs and environmental impacts can be further reduced by integrating alternative energy sources, minimizing chemical and energy inputs through process optimization, and improving efficiency. In conclusion, this work highlighted the viability of waste-derived VFA production and provided a clear, data-driven strategy to accelerate the commercialization of waste valorization technology.
Carboxylic Acid Production↗
Experimental nitrogen and phosphorus enrichment stimulates multiple trophic levels of algal and detrital-based food webs: a global meta-analysis from streams and rivers
Anthropogenic increases in nitrogen (N) and phosphorus (P) concentrations can strongly influence the structure and function of ecosystems. Even though lotic ecosystems receive cumulative inputs of nutrients applied to and deposited on land, no comprehensive assessment has quantified nutrient-enrichment effects within streams and rivers. We conducted a meta-analysis of published studies that experimentally increased concentrations of N and/or P in streams and rivers to examine how enrichment alters ecosystem structure (state: primary producer and consumer biomass and abundance) and function (rate: primary production, leaf breakdown rates, metabolism) at multiple trophic levels (primary producer, microbial heterotroph, primary and secondary consumers, and integrated ecosystem). Our synthesis included 184 studies, 885 experiments, and 3497 biotic responses to nutrient enrichment. We documented widespread increases in organismal biomass and abundance (mean response = +48%) and rates of ecosystem processes (+54%) to enrichment across multiple trophic levels, with no large differences in responses among trophic levels or between autotrophic or heterotrophic food-web pathways. Responses to nutrient enrichment varied with the nutrient added (N, P, or both) depending on rate versus state variable and experiment type, and were greater in flume and whole-stream experiments than in experiments using nutrient-diffusing substrata. Generally, nutrient-enrichment effects also increased with water temperature and light, and decreased under elevated ambient concentrations of inorganic N and/or P. Overall, increased concentrations of N and/or P altered multiple food-web pathways and trophic levels in lotic ecosystems. Finally, our results indicate that preservation or restoration of biodiversity and ecosystem functions of streams and rivers requires management of nutrient inputs and consideration of multiple trophic pathways.
59 BASIC BIOLOGICAL SCIENCES↗
Retrospective analysis of dog study data from food and color additive petitions
Not Available
Flannery, Brenna M. (ORCID:0000000207695058)↗