SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort.

BACKGROUND: Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. METHODS: We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models. RESULTS: Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season. CONCLUSIONS: Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity.

Risk of SARS-CoV-2 reinfection during multiple Omicron variant waves in the UK general population.

SARS-CoV-2 reinfections increased substantially after Omicron variants emerged. Large-scale community-based comparisons across multiple Omicron waves of reinfection characteristics, risk factors, and protection afforded by previous infection and vaccination, are limited. Here we studied ~45,000 reinfections from the UK's national COVID-19 Infection Survey and quantified the risk of reinfection in multiple waves, including those driven by BA.1, BA.2, BA.4/5, and BQ.1/CH.1.1/XBB.1.5 variants. Reinfections were associated with lower viral load and lower percentages of self-reporting symptoms compared with first infections. Across multiple Omicron waves, estimated protection against reinfection was significantly higher in those previously infected with more recent than earlier variants, even at the same time from previous infection. Estimated protection against Omicron reinfections decreased over time from the most recent infection if this was the previous or penultimate variant (generally within the preceding year). Those 14-180 days after receiving their most recent vaccination had a lower risk of reinfection than those >180 days from their most recent vaccination. Reinfection risk was independently higher in those aged 30-45 years, and with either low or high viral load in their most recent previous infection. Overall, the risk of Omicron reinfection is high, but with lower severity than first infections; both viral evolution and waning immunity are independently associated with reinfection.

Milk recording data indicates the importance of fertility, including age at first calving, on the progression of first lactation cows to second lactation.

Dairy farmers do not recoup the rearing costs incurred from birth to first calving until second lactation but varying proportions of first lactation cows are removed from the herd before second calving. Herein, we used milk recording data to examine the outcomes and performance of first lactation cows to gain insight into farmer decisions to keep or remove them from the herd. An InterHerd+ dataset derived from 500 milk recording dairy herds in UK was used to examine first lactation cows which calved in 2020. Of 29,128 first lactation cows that calved in 2020, 82.6% remained within the herd and re-calved, 4.9% conceived but exited the herd before re-calving, 6.0% were served but exited the herd after failing to conceive and 6.6% exited the herd without being served. The fertility data on these cows support the logical conclusion that farmers retain cows that are served and conceive sooner, possibly in order to keep within a broadly seasonal calving pattern. Cows which were served but not conceived had a median AFC 16-20 days greater than the median AFC for those that conceived. Farmers may also be retaining cows with relatively high milk yields and lower somatic cell counts, or these parameters may be an indicator of a range of attributes affecting the farmer's decision. The data also suggest that farmers are rearing more replacements than required, because over one third of the cows removed in first lactation are never served, and 70% of these are sold within 120 days post-partum. These cows had a significantly older median age at first calving of 818 days, but their early removal without serving suggests there is an oversupply of replacements forcing farmers to dispose of these cows early in lactation. In order to develop a deeper understanding of herd turnover and replacement, future work could examine cow removals in lactation 2 onwards.

LAP-MALDI MS Profiling and Identification of Potential Biomarkers for the Detection of Bovine Tuberculosis.

Detecting bovine tuberculosis (bTB) primarily relies on the tuberculin skin test, requiring two separate animal handling events with a period of incubation time (normally 3 days) between them. Here, we present the use of liquid atmospheric pressure (LAP)-MALDI for the identification of bTB infection, employing a three-class prediction model that was obtained by supervised linear discriminant analysis (LDA) and tested with bovine mastitis samples as disease-positive controls. Noninvasive collection of nasal swabs was used to collect samples, which were subsequently subjected to a short (<4 h) sample preparation method. Cross-validation of the three-class LDA model from the processed nasal swabs provided a sensitivity of 75.0% and specificity of 90.1%, with an overall classification accuracy of 85.7%. These values are comparable to those for the skin test, showing that LAP-MALDI MS has the potential to provide an alternative single-visit diagnostic platform that can detect bTB within the same day of sampling.

Modelling norovirus dynamics within oysters emphasises potential food safety issues associated with current testing & depuration protocols.

Norovirus is a significant global cause of viral gastroenteritis, with raw oyster consumption often linked to such outbreaks due to their filter-feeding in harvest waters. National water quality and depuration/relaying times are often classified using Escherichia coli, a poor proxy for norovirus levels in shellfish. The current norovirus assay is limited to only the digestive tracts of oysters, meaning the total norovirus load of an oyster may differ from reported results. These limitations motivated this work, building upon previous modelling by the authors, and considers the sequestration of norovirus into observed and cryptic (unobservable) compartments within each oyster. Results show that total norovirus levels in shellfish batches exhibit distinct peaks during the early depuration stages, with each peak's magnitude dependent on the proportion of cryptic norovirus. These results are supported by depuration trial data and other studies, where viral levels often exhibit multiphase decays. This work's significant result is that any future norovirus legislation needs to consider not only the harvest site's water classification but also the total viral load present in oysters entering the market. We show that 62 h of depuration should be undertaken before any norovirus testing is conducted on oyster samples, being the time required for cryptic viral loads to have transited into the digestive tracts where they can be detected by current assay, or have exited the oyster.

The aquaculture disease network model (AquaNet-Mod): A simulation model to evaluate disease spread and controls for the salmonid industry in England and Wales.

Infectious disease causes significant mortality in wild and farmed systems, threatening biodiversity, conservation and animal welfare, as well as food security. To mitigate impacts and inform policy, tools such as mathematical models and computer simulations are valuable for predicting the potential spread and impact of disease. This paper describes the development of the Aquaculture Disease Network Model, AquaNet-Mod, and demonstrates its application to evaluating disease epidemics and the efficacy of control, using a Viral Haemorrhagic Septicaemia (VHS) case study. AquaNet-Mod is a data-driven, stochastic, state-transition model. Disease spread can occur via four different mechanisms, i) live fish movement, ii) river based, iii) short distance mechanical and iv) distance independent mechanical. Sites transit between three disease states: susceptible, clinically infected and subclinically infected. Disease spread can be interrupted by the application of disease mitigation measures and controls such as contact tracing, culling, fallowing and surveillance. Results from a VHS case study highlight the potential for VHS to spread to 96% of sites over a 10 year time horizon if no disease controls are applied. Epidemiological impact is significantly reduced when live fish movement restrictions are placed on the most connected sites and further still, when disease controls, representative of current disease control policy in England and Wales, are applied. The importance of specific disease control measures, particularly contact tracing and disease detection rate, are also highlighted. The merit of this model for evaluation of disease spread and the efficacy of controls, in the context of policy, along with potential for further application and development of the model, for example to include economic parameters, is discussed.

SARS-CoV-2 spike protein reduces burst activities in neurons measured by micro-electrode arrays.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) caused a large-scale global pandemic between 2020 and 2022. Despite efforts to understand its biological and pathogenic mechanisms, the viral impact on the neurological systems remains unclear. The main goal of this study was to quantify the neurological phenotypes induced by the SARS-CoV-2 spike protein in neurons, as measured by in-vitro multiwell micro-electrode arrays (MEAs). Materials and methods: The authors extracted the whole-brain neurons from the newborn P1 mice and plated them on multiwell MEAs and administered purified recombinant spike proteins (both S1 and S2 subunits) from the SARS-CoV-2 virus. The signals from the MEAs were transmitted from an amplifier to a high-performance computer for recording and analysis using an in-house developed algorithm to quantify neuronal phenotypes. Results: Primary among the phenotypic features analyzed, we discovered that neuronal treatment with spike 1 protein (S1) protein from SARS-CoV-2 decreased the mean burst numbers observed on each electrode, an effect that could be rescued with an anti-S1 antibody. Conversely, this mean burst number decrease was not observed with spike 2 protein (S2) treatment. Finally, our data strongly suggest that the receptor binding domain of S1 is responsible for the reduction in neuronal burst activity. Conclusion: Overall, our results strongly indicate that spike proteins may play an important role in altering neuronal phenotypes, specifically the burst patterns, when neurons are exposed during early development.

Protection against SARS-CoV-2 Omicron BA.4/5 variant following booster vaccination or breakthrough infection in the UK.

Following primary SARS-CoV-2 vaccination, whether boosters or breakthrough infections provide greater protection against SARS-CoV-2 infection is incompletely understood. Here we investigated SARS-CoV-2 antibody correlates of protection against new Omicron BA.4/5 (re-)infections and anti-spike IgG antibody trajectories after a third/booster vaccination or breakthrough infection following second vaccination in 154,149 adults ≥18 y from the United Kingdom general population. Higher antibody levels were associated with increased protection against Omicron BA.4/5 infection and breakthrough infections were associated with higher levels of protection at any given antibody level than boosters. Breakthrough infections generated similar antibody levels to boosters, and the subsequent antibody declines were slightly slower than after boosters. Together our findings show breakthrough infection provides longer-lasting protection against further infections than booster vaccinations. Our findings, considered alongside the risks of severe infection and long-term consequences of infection, have important implications for vaccine policy.

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative.

Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistant strains, reducing long-term disease control. When resistance is qualitative and complete-i.e. resistant strains are unaffected by the chemical and resistance requires only a single genetic change-using the lowest possible dose ensuring sufficient control is well known as the optimal resistance management strategy. However, partial resistance (where resistant strains are still partially suppressed by the fungicide) and quantitative resistance (where a range of resistant strains are present) remain ill-understood. Here, we use a model of quantitative fungicide resistance (parametrized for the economically important fungal pathogen Zymoseptoria tritici) which handles qualitative partial resistance as a special case. Although low doses are optimal for resistance management, we show that for some model parametrizations the resistance management benefit does not outweigh the improvement in control from increasing doses. This holds for both qualitative partial resistance and quantitative resistance. Via a machine learning approach (a gradient-boosted trees model combined with Shapley values to facilitate interpretability), we interpret the effect of parameters controlling pathogen mutation and characterising the fungicide, in addition to the time scale of interest.

Modelling quantitative fungicide resistance and breakdown of resistant cultivars: Designing integrated disease management strategies for Septoria of winter wheat.

Plant pathogens respond to selection pressures exerted by disease management strategies. This can lead to fungicide resistance and/or the breakdown of disease-resistant cultivars, each of which significantly threaten food security. Both fungicide resistance and cultivar breakdown can be characterised as qualitative or quantitative. Qualitative (monogenic) resistance/breakdown involves a step change in the characteristics of the pathogen population with respect to disease control, often caused by a single genetic change. Quantitative (polygenic) resistance/breakdown instead involves multiple genetic changes, each causing a smaller shift in pathogen characteristics, leading to a gradual alteration in the effectiveness of disease control over time. Although resistance/breakdown to many fungicides/cultivars currently in use is quantitative, the overwhelming majority of modelling studies focus on the much simpler case of qualitative resistance. Further, those very few models of quantitative resistance/breakdown which do exist are not fitted to field data. Here we present a model of quantitative resistance/breakdown applied to Zymoseptoria tritici, which causes Septoria leaf blotch, the most prevalent disease of wheat worldwide. Our model is fitted to data from field trials in the UK and Denmark. For fungicide resistance, we show that the optimal disease management strategy depends on the timescale of interest. Greater numbers of fungicide applications per year lead to greater selection for resistant strains, although over short timescales this can be oset by the increased control oered by more sprays. However, over longer timescales higher yields are attained using fewer fungicide applications per year. Deployment of disease-resistant cultivars is not only a valuable disease management strategy, but also oers the secondary benefit of protecting fungicide effectiveness by delaying the development of fungicide resistance. However, disease-resistant cultivars themselves erode over time. We show how an integrated disease management strategy with frequent replacement of disease-resistant cultivars can give a large improvement in fungicide durability and yields.

Optimal Resistance Management for Mixtures of High-Risk Fungicides: Robustness to the Initial Frequency of Resistance and Pathogen Sexual Reproduction.

There is a strong consensus that selection for fungicide resistant pathogen strains can be most effectively limited by using applications of mixtures of fungicides designed to balance disease control against selection. However, how to do this in practice is not entirely characterized. Previous work indicates optimal mixtures of pairs of fungicides which are both at a high risk of resistance can be constructed using pairs of doses that select equally for both single resistant strains in the first year of application. What has not been addressed thus far is the important real-world case in which the initial levels of resistance to each fungicide differ, for example because the chemicals have been available for different lengths of time. We show how recommendations based on equal selection in the first year can be suboptimal in this case. We introduce a simple alternative approach, based on equalizing the frequencies of single resistant strains in the year that achieving acceptable levels of control is predicted to become impossible. We show that this strategy is robust to changes in parameters controlling pathogen epidemiology and fungicide efficacy. We develop our recommendation using a preexisting, parameterized model of Zymoseptoria tritici (the pathogen causing Septoria leaf blotch on wheat), which exemplifies the range of plant pathogens that predominantly spread clonally, but for which sexual reproduction forms an important component of the life cycle. We show that pathogen sexual reproduction can influence the rate at which fungicide resistance develops but does not qualitatively affect our optimal resistance management recommendation. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The Invention of the Laser.

In this chapter, I describe a scientific rivalry at Columbia University's physics department in the days of the 1950s before and when the laser invented, and the race to build a laser eventually won by a scientist in California in 1960. It tracks the arc from Charles Townes' success in amplifying microwaves with a device he called the maser, to graduate student Gordon Gould's realization of how shorter and more powerful light waves could also be amplified. The chapter describes Gould's notebook, written in 1957, diagramming an optically pumped laser. In the same notebook, he coined the word laser, an acronym for light amplification by stimulated emission of radiation, and suggested possible uses for the coherent light beams he imagined. The chapter also covers Gould's wish to patent and profit from his invention, his mistaken belief that he had to build a laser to receive a patent, and the flirtation with radical politics that barred him from the laboratory where he worked once the government classified the laser project that it was funding. Townes and his colleague and brother-in-law Arthur Schawlow, both eventual Physics Nobelists, meanwhile filed an application and received a patent for what they called an optical maser. Theodore Maiman at Hughes Laboratories built the first working laser in 1960, as the chapter describes. It concludes with the results of Gould's long battle to win basic laser patents and his eventual success.

Leaping Forwards, Bouncing Forwards, or Just Bouncing Back: Resilience in Environmental Public Agencies Through after the Austerity Decade.

The resilience of public environmental agencies is an important but broadly under-researched discourse. This paper addresses this lacuna by drawing on a three-part typology of resilience from organizational studies and applying it to the English natural environment agency, Natural England, following a decade of public sector agency de-funding under the aegis of austerity. The research question was explored qualitatively through eleven semi-structured interviews with the senior management team of Natural England during the summer of 2020. The findings suggest that public agency multi-functionality equate to heterogenous resilience across agency functions; that generally agency resilience (as a function of capacities) is poor with consequences upon good governance; and that they are broadly poorly positioned for the aftermath of Covid-19. The findings speak directly to the regulatory and organizational literatures with public administration by evidencing the complex realities of understanding resiliencies in large multi-functional public environmental agencies.

Impact of climate-driven temperature increase on inland aquaculture: Application to land-based production of common carp (Cyprinus carpio L.).

Climate change will expose the food-producing sector to a range of challenges. Inland aquaculture farms are particularly vulnerable, due to the difficulty in changing their location, and therefore require specific tools to predict the influence of direct and indirect effects on production, environment and economic feasibility. The objective of our study was to apply a simple set of models to produce a set of growth, risk and suitability maps for stakeholders within the common carp sector in Poland, to assist decision-making under two different scenarios of climate change: a moderate situation (RCP 4.5) and an extreme situation (RCP 8.5). We used present (2000-2019) and future projections (2080-2099) for water surface temperature based on land surface temperature data from regionally downscaled climate models to draw maps to: (i) show optimal temperature conditions for carp growth, (ii) assess risk of disease outbreak caused by three important common carp pathogens: Cyprinid herpesvirus 3 (CyHV-3), carp oedema virus (CEV) and spring viremia of carp (SVCV) and (iii) predict potential suitability changes of carp farming in Poland. The study identified areas with the most and least favourable temperature conditions for carp growth, as well as those areas with the highest/lowest number of days with suitable temperatures for virus infection. These suitability maps showed the combined effect of direct and indirect effects of climate change projections under RCP 8.5 and RCP 4.5 scenarios. The approach applied herein will be of use worldwide for analysing the risks of temperature increase to land-based aquaculture, and the results presented are important for carp farmers in Poland and elsewhere, industry in general, and government stakeholders, to understand the direct and indirect effects of climate change on the triple bottom line of people, planet, and profit.

LAP-MALDI MS coupled with machine learning: an ambient mass spectrometry approach for high-throughput diagnostics.

Large-scale population screening for early and accurate detection of disease is a key objective for future diagnostics. Ideally, diagnostic tests that achieve this goal are also cost-effective, fast and easily adaptable to new diseases with the potential of multiplexing. Mass spectrometry (MS), particularly MALDI MS profiling, has been explored for many years in disease diagnostics, most successfully in clinical microbiology but less in early detection of diseases. Here, we present liquid atmospheric pressure (LAP)-MALDI MS profiling as a rapid, large-scale and cost-effective platform for disease analysis. Using this new platform, two different types of tests exemplify its potential in early disease diagnosis and response to therapy. First, it is shown that LAP-MALDI MS profiling detects bovine mastitis two days before its clinical manifestation with a sensitivity of up to 70% and a specificity of up to 100%. This highly accurate, pre-symptomatic detection is demonstrated by using a large set of milk samples collected weekly over six months from approximately 500 dairy cows. Second, the potential of LAP-MALDI MS in antimicrobial resistance (AMR) detection is shown by employing the same mass spectrometric setup and similarly simple sample preparation as for the early detection of mastitis.

Biosecurity and the ornamental fish trade: A stakeholder perspective in England.

The freshwater and marine ornamental fish industry is a primary route of hazard introduction and emergence, including aquatic animal diseases and non-native species. Prevention measures are key to reducing the risk of hazard incursion and establishment, but there is currently little understanding of the biosecurity practices and hazard responses implemented at post-border stages of the ornamental fish supply chain. This study addresses this knowledge gap, using questionnaires to collate information on actual biosecurity behaviours and hazard responses practised by ornamental fish retailers and hobbyist communities in England. Actual behaviours varied considerably within retailers and hobbyists, suggesting that reliance on preventative practices by individuals in the post-border stages of the ornamental fish supply chain is likely to be ineffective in minimizing the risk of hazard incursion and establishment. Resources should be allocated towards improving and enforcing robust pre- and at-border control measures, such as risk-based surveillance of ornamental fish imports at border controls. In addition, these findings should be used to implement targeted awareness-raising campaigns and help create directed training on biosecurity practices for individuals involved in the post-border stages of the ornamental supply chain.

Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission.

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vector's own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding-as well as potential effects of infection on vector population density-on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics.

Estimated Dissemination Ratio-A Practical Alternative to the Reproduction Number for Infectious Diseases.

Policymakers require consistent and accessible tools to monitor the progress of an epidemic and the impact of control measures in real time. One such measure is the Estimated Dissemination Ratio (EDR), a straightforward, easily replicable, and robust measure of the trajectory of an outbreak that has been used for many years in the control of infectious disease in livestock. It is simple to calculate and explain. Its calculation and use are discussed below together with examples from the current COVID-19 outbreak in the UK. These applications illustrate that EDR can demonstrate changes in transmission rate before they may be clear from the epidemic curve. Thus, EDR can provide an early warning that an epidemic is resuming growth, allowing earlier intervention. A conceptual comparison between EDR and the commonly used reproduction number is also provided.

Understanding drivers of wild oyster population persistence.

Persistence of wild Pacific oyster, Magallana gigas, also known as Crassostrea gigas, has been increasingly reported across Northern European waters in recent years. While reproduction is inhibited by cold waters, recent warm summer temperature has increased the frequency of spawning events. Although correlation between the increasing abundance of Pacific oyster reefs in Northern European waters and climate change is documented, persistence of wild populations may also be influenced by external recruitment from farmed populations and other wild oyster populations, as well as on competition for resources with aquaculture sites. Our understanding of the combined impact of the spawning frequency, external recruitment, and competition on wild population persistence is limited. This study applied an age-structured model, based on ordinary differential equations, to describe an oyster population under discrete temperature-related dynamics. The impact of more frequent spawning events, external recruitment, and changes in carrying capacity on Pacific oyster density were simulated and compared under theoretical scenarios and two case studies in Southern England. Results indicate that long term persistence of wild oyster populations towards carrying capacity requires a high frequency of spawning events but that in the absence of spawning, external recruitment from farmed populations and other wild oyster populations may act to prevent extinction and increase population density. However, external recruitment sources may be in competition with the wild population so that external recruitment is associated with a reduction in wild population density. The implications of model results are discussed in the context of wild oyster population management.

Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine learning.

Growing interest in food quality and traceability by regulators as well as consumers demands advances in more rapid, versatile and cost-effective analytical methods. Milk, as most food matrices, is a heterogeneous mixture composed of metabolites, lipids and proteins. One of the major challenges is to have simultaneous, quantitative detection (profiling) of this panel of biomolecules to gather valuable information for assessing food quality, traceability and safety. Here, for milk analysis, atmospheric pressure matrix-assisted laser desorption/ionization employing homogenous liquid sample droplets was used on a Q-TOF mass analyzer. This method has the capability to produce multiply charged proteinaceous ions as well as highly informative profiles of singly charged lipids/metabolites. In two examples, this method is coupled with user-friendly machine-learning software. First, rapid speciation of milk (cow, goat, sheep and camel) is demonstrated with 100% classification accuracy. Second, the detection of cow milk as adulterant in goat milk is shown at concentrations as low as 5% with 92.5% sensitivity and 94.5% specificity.