Steric Perturbation of the Grob-like Final Step of Ethylene-Forming Enzyme Enables 3-Hydroxypropionate and Propylene Production.

Ethylene-forming enzyme (EFE) is an iron(II)-dependent dioxygenase that fragments 2-oxoglutarate (2OG) to ethylene (from C3 and C4) and 3 equivs of carbon dioxide (from C1, C2, and C5). This major ethylene-forming pathway requires l-arginine as the effector and competes with a minor pathway that merely decarboxylates 2OG to succinate as it oxidatively fragments l-arginine. We previously proposed that ethylene forms in a polar-concerted (Grob-like) fragmentation of a (2-carboxyethyl)carbonatoiron(II) intermediate, formed by the coupling of a C3-C5-derived propion-3-yl radical to a C1-derived carbonate coordinated to the Fe(III) cofactor. Replacement of one or both C4 hydrogens of 2OG by fluorine, methyl, or hydroxyl favored the elimination products 2-(F1-2/Me/OH)-3-hydroxypropionate and CO2 over the expected olefin or carbonyl products, implying strict stereoelectronic requirements in the final step, as is known for Grob fragmentations. Here, we substituted active-site residues expected to interact sterically with the proposed Grob intermediate, aiming to disrupt or enable the antiperiplanar disposition of the carboxylate electrofuge and carbonate nucleofuge required for concerted fragmentation. The bulk-increasing A198L substitution barely affects the first partition between the major and minor pathways but then, as intended, markedly diminishes ethylene production in favor of 3-hydroxypropionate. Conversely, the bulk-diminishing L206V substitution enables propylene formation from (4R)-methyl-2OG, presumably by allowing the otherwise sterically disfavored antiperiplanar conformation of the Grob intermediate bearing the extra methyl group. The results provide additional evidence for a polar-concerted ethylene-yielding step and thus for the proposed radical-polar crossover via substrate-radical coupling to the Fe(III)-coordinated carbonate.

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants.

Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf-stable food products. The conventional culture-based methods for microbial detection are time and labor-intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.

Rheological and sensory behaviour of rice flour dough: effect of selected additives in relation to dough flattening.

The handling of rice flour doughs in terms of sheeting, flattening and rolling is difficult due to the absence of gluten forming proteins; scope exists to improve these characteristics by incorporating appropriate additives during the preparation of rice doughs. Different levels of additives such as whey protein concentrate (WPC) (0-10 %), xanthan gum (0-5 %), sucrose (0-20 %) and salt (0-2 %) have been incorporated, and the rheological (small-deformation oscillation) as well as sensory characteristics have been determined, in addition to microstructural observations and finding inter-relationships. The second order polynomial can adequately explain the rheological parameters like storage modulus, loss modulus and complex viscosity (R = 0.863-0.889, p ≤ 0.01) while it is poor for phase angle (R = 0.659, p ≤ 0.01). Among these additives, xanthan gum imparts the strongest effect (significant at p ≤ 0.01) followed by whey protein concentrate. The effects of these additives are predominantly linear though quadratic effects are also significant in several cases. A cohesive microstructure with improved binding occurs with a high level (7.5 %) of WPC. It is concluded that a judicious selection of additives in appropriate levels can develop rice doughs that possess the desirable handling properties leading to preparation of products.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging.

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

Hyperspectral imaging through vacuum packaging for monitoring cheese biochemical transformation caused by Clostridium metabolism.

This study developed a novel method for monitoring cheese contamination with Clostridium spores non-invasively using hyperspectral imaging (HSI). The ability of HSI to quantify Clostridium metabolites was investigated with control cheese and cheese manufactured with milk contaminated with Clostridium tyrobutyricum, Clostridium butyricum and Clostridium sporogenes. Microbial count, HSI and SPME-GC-MS data were obtained over 10 weeks of storage. The developed method using HSI successfully quantified butyric acid (R2 = 0.91, RPD = 3.38) a major compound of Clostridium metabolism in cheese. This study creates a new venue to monitor the spatial and temporal development of late blowing defect (LBD) in cheese using fast and non-invasive measurement.

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification.

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ18O, δ2H, and δ34S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R2predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.

A critical review of social media research in sensory-consumer science.

The collection and analysis of digital data from social media is a rapidly growing methodology in sensory-consumer science, with a wide range of applications for research studying consumer attitudes, preferences, and sensory responses to food. The aim of this review article was to critically evaluate the potential of social media research in sensory-consumer science with a focus on advantages and disadvantages. This review began with an exploration into different sources of social media data and the process by which data from social media is collected, cleaned, and analyzed through natural language processing for sensory-consumer research. It then investigated in detail the differences between social media-based and conventional methodologies, in terms of context, sources of bias, the size of data sets, measurement differences, and ethics. Findings showed participant biases are more difficult to control using social media approaches, and precision is inferior to conventional methods. However, findings also showed social media methodologies may have other advantages including an increased ability to investigate trends over time and easier access to cross-cultural or global insights. Greater research in this space will identify when social media can best function as an alternative to conventional methods, and/or provide valuable complementary information.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics.

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Carbon intensity of global crude oil trading and market policy implications.

The energy mix transition has accelerated the need for more accurate emissions reporting throughout the petroleum supply chain. Despite increasing environmental regulations and pressure for emissions disclosure, the low resolution of existing carbon footprint assessment does not account for the complexity of crude oil trading. The lack of source crude traceability has led to poor visibility into the "well-to-refinery-entrance" carbon intensities at the level of granular pathways between producers and destination markets. Using high-fidelity datasets, optimization algorithms to facilitate supply chain traceability and bottom-up, physics-based emission estimators, we show that the variability in global "well-to-refinery-entrance" carbon intensities at the level of crude trade pathways is significant: 4.2-214.1 kg-CO2-equivalent/barrel with a volume-weighted average of 50.5 kg-CO2-equivalent/barrel. Coupled with oil supply forecasts under 1.5 °C scenarios up to 2050, this variability translates to additional CO2-equivalent savings of 1.5-6.1 Gigatons that could be realized solely by prioritizing low-carbon supply chain pathways without other capital-intensive mitigation measures.

Understanding variability in petroleum jet fuel life cycle greenhouse gas emissions to inform aviation decarbonization.

A pressing challenge facing the aviation industry is to aggressively reduce greenhouse gas emissions in the face of increasing demand for aviation fuels. Climate goals such as carbon-neutral growth from 2020 onwards require continuous improvements in technology, operations, infrastructure, and most importantly, reductions in aviation fuel life cycle emissions. The Carbon Offsetting Scheme for International Aviation of the International Civil Aviation Organization provides a global market-based measure to group all possible emissions reduction measures into a joint program. Using a bottom-up, engineering-based modeling approach, this study provides the first estimates of life cycle greenhouse gas emissions from petroleum jet fuel on regional and global scales. Here we show that not all petroleum jet fuels are the same as the country-level life cycle emissions of petroleum jet fuels range from 81.1 to 94.8 gCO2e MJ-1, with a global volume-weighted average of 88.7 gCO2e MJ-1. These findings provide a high-resolution baseline against which sustainable aviation fuel and other emissions reduction opportunities can be prioritized to achieve greater emissions reductions faster.

Air pollution impacts of COVID-19-related containment measures.

Responses to the COVID-19 outbreak resulted in one of the largest short-term decreases in anthropogenic emissions in modern history. To date, there has been no comprehensive assessment of the impact of lockdowns on air quality and human health. Using global satellite observations and ground measurements from 36 countries in Europe, North America, and East Asia, we find that lockdowns led to reductions in NO2 concentrations globally, resulting in ~32,000 avoided premature mortalities, including ~21,000 in China. However, we do not find corresponding reductions in PM2.5 and ozone globally. Using satellite measurements, we show that the disconnect between NO2 and ozone changes stems from local chemical regimes. The COVID-related lockdowns demonstrate the need for targeted air quality policies to reduce the global burden of air pollution, especially related to secondary pollutants.

Laser-induced breakdown spectroscopy (LIBS) for rapid analysis of ash, potassium and magnesium in gluten free flours.

Gluten free (GF) diets are prone to mineral deficiency, thus effective monitoring of the elemental composition of GF products is important to ensure a balanced micronutrient diet. The objective of this study was to test the potential of laser-induced breakdown spectroscopy (LIBS) analysis combined with chemometrics for at-line monitoring of ash, potassium and magnesium content of GF flours: tapioca, potato, maize, buckwheat, brown rice and a GF flour mixture. Concentrations of ash, potassium and magnesium were determined with reference methods and LIBS. PCA analysis was performed and presented the potential for discrimination of the six GF flours. For the quantification analysis PLSR models were developed; R2cal were 0.99 for magnesium and potassium and 0.97 for ash. The study revealed that LIBS combined with chemometrics is a convenient method to quantify concentrations of ash, potassium and magnesium and present the potential to classify different types of flours.

Quantification of copper content with laser induced breakdown spectroscopy as a potential indicator of offal adulteration in beef.

Laser induced breakdown spectroscopy (LIBS) is an emerging technique in the field of food analysis which provides various advantages such as minimal sample preparation, chemical free, rapid detection, provision of spatial information and portability. In this study, LIBS was employed for quantitative analysis of copper content in minced beef samples spiked with beef liver over three independent batches. Copper content was determined with graphite furnace atomic absorption spectroscopy (GFAAS) in order to obtain reference values for modelling. Partial least square regression (PLSR) was performed to build a calibration and validation model. A calibration model with a high Rcv2 of 0.85 and a RMSECV of 43.5ppm was obtained, confirming a good fit for the model. The validation model showed a good prediction accuracy with a high Rp2 of 0.85 and RMSEP of 36.8ppm. Moreover, on a further study to evaluate the spatial capabilities, LIBS was able to successfully map copper content within a pellet, indicating the suitability of LIBS to provide spatial information and therefore potential use on heterogeneous samples. Overall, it can be concluded that LIBS combined with chemometrics demonstrates potential as a quality monitoring tool for the meat processing industry.

Multipoint NIR spectroscopy for gross composition analysis of powdered infant formula under various motion conditions.

Powdered infant formula (PIF) is a worldwide, industrially produced, human milk substitute. Manufacture of PIF faces strict quality controls in order to ensure that the product meets all compositional requirements. Near-infrared (NIR) spectroscopy is a rapid, non-destructive and well-qualified technique for food quality assessments. The use of fibre-optic NIR sensors allows measuring in-line and at real-time, and can record spectra from different stages of the process. The non-contact character of fibre-optic sensors can be enhanced by fitting collimators, which allow operation at various distances. The system, based on a Fabry-Perot interferometer, records four spectra concurrently, rather than consecutively as in the "quasi-simultaneous" multipoint NIR systems. In the present study, a novel multipoint NIR spectroscopy system equipped with four fibre-optic probes with collimators was assessed to determine carbohydrate and protein contents of PIF samples under static and motion conditions (0.02, 0.15 and 0.30m/s) to simulate possible industrial scenarios. Best results were obtained under static conditions providing a R(2) of calibration of 0.95 and RMSEP values of 1.89%. Yet, considerably low values of RMSEP, for instance 2.70% at 0.15m/s, were provided with the in-motion predictions, demonstrating the system's potential for in/on-line applications at various levels of speed. The current work also evaluated the viability of using general off-line calibrations developed under static conditions for on/in-line applications subject to motion. To this end, calibrations in both modes were developed and compared. Best results were obtained with specific calibrations; however, reasonably accurate models were obtained with the general calibration. Furthermore, this work illustrated independency of the collimator-probe setup by characterizing PIF samples simultaneously recorded according to their carbohydrate content, even when measured under different conditions. Therefore, the improved multipoint NIR approach constitutes a potential in/on-line tool for quality evaluation of PIF over the manufacturing process.