Hyperspectral imaging in the visible and near infrared spectral range (450–1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of ...brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and non-brined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957–1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450–960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSEp 1.9%, R2p 0.92) and cooked (RMSEp 2.6%, R2p 0.83) samples used the 957–1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed.
•Vis-NIR hyperspectral imaging is suitable for the assessment of brining of raw and cooked pork loins.•Chemometric models were developed to classify brined and non-brined pork samples and to predict brining salt concentration.•Spectral pre-treatments and variable selection improved performance of models developed.
Due to high perishability and poor distribution management, strawberry is one of the most frequently discarded fruits. The aim of this study was to develop a non-destructive system for accurate ...estimation of shelf-life of strawberries using hyperspectral imaging technology. Harvested strawberries were stored for 9 day at five different temperatures. Shelf-life was calculated based on subjective visual evaluation of appearance attributes (colour, shrivelling, and decay) using a rating scale. Hyperspectral images of strawberries were obtained during cold storage, using a novel handheld push broom line-scanning hyperspectral camera. The model developed by partial least square regression (PLSR) with selected spectra was used to predict appearance scores of strawberries with a coefficient of determination of prediction (R2p) of 0.97 and root mean square error of prediction (RMSEp) of 0.17. The appearance scores from the PLSR model were used to develop a model based on first-order kinetics and Arrhenius equations to predict the remaining shelf-life of the strawberries. The models predicted remaining shelf-life with R2p of 0.86 and RMSEp of 1.4 days. Prediction models were also developed for other quality attributes and biochemical properties (weight loss, ascorbic acid, and soluble solids). The results from this study support the development a non-destructive system for the accurate estimation of shelf-life of strawberries. A system that could potentially offer objective quality assessment as product moves through the supply chain, aiding decision making and facilitating proactive actions to be taken with the aim of minimising loss/waste; benefiting food supply chain stakeholders across the globe and supporting the drive for zero waste.
•Establishing a non-destructive system for shelf-life estimation of strawberries.•Remaining shelf-life of strawberries determined along the supply chain.•Novel system supports for reducing waste along strawberry supply chain.
Abstract Viviparity evolved ~115 times across squamate reptiles, facilitating the colonization of cold habitats, where oviparous species are scarce or absent. Whether the ecological opportunity ...furnished by such colonization reconfigures phenotypic diversity and accelerates evolution is unclear. We investigated the association between viviparity and patterns and rates of body size evolution in female Liolaemus lizards, the most species-rich tetrapod genus from temperate regions. Here, we discover that viviparous species evolve ~20% larger optimal body sizes than their oviparous relatives, but exhibit similar rates of body size evolution. Through a causal modeling approach, we find that viviparity indirectly influences body size evolution through shifts in thermal environment. Accordingly, the colonization of cold habitats favors larger body sizes in viviparous species, reconfiguring body size diversity in Liolaemus . The catalyzing influence of viviparity on phenotypic evolution arises because it unlocks access to otherwise inaccessible sources of ecological opportunity, an outcome potentially repeated across the tree of life.
There is a need to develop a rapid technique to provide real time information on the microbial load of meat along the supply chain. Hyperspectral imaging (HSI) is a rapid, non-destructive technique ...well suited to food analysis applications. In this study, HSI in both the visible and near infrared spectral ranges, and chemometrics were studied for prediction of the bacterial growth on beef Longissimus dorsi muscle (LD) under simulated normal (4 °C) and abuse (10 °C) storage conditions. Total viable count (TVC) prediction models were developed using partial least squares regression (PLS-R), spectral pre-treatments, band selection and data fusion methods. The best TVC prediction models developed for storage at 4 (RMSEp 0.58 log CFU/g, RPDp 4.13, R2p 0.96), 10 °C (RMSEp 0.97 log CFU/g, RPDp 3.28, R2p 0.94) or at either 4 or 10 °C (RMSEp 0.89 log CFU/g, RPDp 2.27, R2p 0.86) were developed using high-level data fusion of both spectral regions. The use of appropriate spectral pre-treatments and band selection methods was key for robust model development. This study demonstrated the potential of HSI and chemometrics for real time monitoring to predict microbial growth on LD along the meat supply chain.
•Microbial quality of beef stored under normal or abuse conditions can be predicted.•Spectral pre-treatments, band selection and data fusion methods are key for robust model development.•Hyperspectral imaging and chemometrics have potential for real-time monitoring of microbial quality.
Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require ...extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models' effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.
•NIR efficiently characterizes sorghum biomass rapidly and at high throughput.•Spectral pre-treatments and band selection enhanced PLSR model robustness.•Validations confirmed the developed models' effectiveness.•Sustainable nature of NIR supports environmentally friendly practices.
The aim of this study was to investigate the potential of a micro-electromechanical NIR spectrophotometer (NIR-MEMS) and visible (Vis)/NIR hyperspectral imaging (HSI) systems to predict the moisture ...content, antioxidant capacity (DPPH, FRAP) and total phenolic content (TPC) of treated ground peppercorns. Partial least squares (PLS) models were developed using spectra from peppercorns treated with hot-air, microwave and cold plasma. The spectra were acquired using three spectroscopy systems: NIR-MEMS (1350–1650 nm), Vis-NIR HSI (450–950 nm) and NIR HSI (957–1664 nm). Very good predictions of TPC (RPD > 3.6) were achieved using NIR-MEMS. The performance of models developed using Vis-NIR HSI and NIR HSI were good or very good for DPPH (RPD > 3.0), FRAP (RPD >2.9) and TPC (RPD > 3.8). This study demonstrated the potential of NIR-MEMS and Vis-NIR/NIR HSI to predict the moisture content, antioxidant capacity and total phenolic content of peppercorns. The spectroscopy technologies investigated are suitable for use as in-line PAT tools to facilitate improved process control and understanding during peppercorn processing.
•NIR MEMS and Vis/NIR hyperspectral imaging predict antioxidant capacity of peppercorns.•NIR MEMS and Vis/NIR hyperspectral imaging predict total phenolic content of peppercorns.•Band selection and spectral pre-treatments were key for robust prediction model development.•NIR MEMS and hyperspectral imaging may be employed as in-line PAT tools in peppercorn processing.
Background
Patients with severe asthma may present with characteristics representing overlapping phenotypes, making them eligible for more than one class of biologic. Our aim was to describe the ...profile of adult patients with severe asthma eligible for both anti‐IgE and anti‐IL5/5R and to compare the effectiveness of both classes of treatment in real life.
Methods
This was a prospective cohort study that included adult patients with severe asthma from 22 countries enrolled into the International Severe Asthma registry (ISAR) who were eligible for both anti‐IgE and anti‐IL5/5R. The effectiveness of anti‐IgE and anti‐IL5/5R was compared in a 1:1 matched cohort. Exacerbation rate was the primary effectiveness endpoint. Secondary endpoints included long‐term‐oral corticosteroid (LTOCS) use, asthma‐related emergency room (ER) attendance, and hospital admissions.
Results
In the matched analysis (n = 350/group), the mean annualized exacerbation rate decreased by 47.1% in the anti‐IL5/5R group and 38.7% in the anti‐IgE group. Patients treated with anti‐IL5/5R were less likely to experience a future exacerbation (adjusted IRR 0.76; 95% CI 0.64, 0.89; p < 0.001) and experienced a greater reduction in mean LTOCS dose than those treated with anti‐IgE (37.44% vs. 20.55% reduction; p = 0.023). There was some evidence to suggest that patients treated with anti‐IL5/5R experienced fewer asthma‐related hospitalizations (IRR 0.64; 95% CI 0.38, 1.08), but not ER visits (IRR 0.94, 95% CI 0.61, 1.43).
Conclusions
In real life, both anti‐IgE and anti‐IL5/5R improve asthma outcomes in patients eligible for both biologic classes; however, anti‐IL5/5R was superior in terms of reducing asthma exacerbations and LTOCS use.
The effectiveness of anti‐IgE and anti‐IL5/5R was compared in this prospective cohort study including patients with severe asthma enrolled in ISAR and eligible for both biologic classes. Both anti‐IgE and anti‐IL5/5R improved asthma outcomes; however, anti‐IL5/5R was superior in reducing asthma exacerbations and LTOCS use. These findings may be useful in assisting treatment decisions for patients with severe asthma.Abbreviations: Anti‐IgE, anti‐immunoglobulin E; Anti‐IL5/5R, anti‐interleukin 5/5R; CI, confidence interval; ER, emergency room; IRR, incidence rate ratio; ISAR, International Severe Asthma Registry; LTOCS, long‐term oral corticosteroid
Produced water is the largest liquid effluent in volume generated in petroleum production. It originates from natural wells or from water that was previously injected during the exploration process. ...The composition of produced water is complex, containing high salt concentration, emulsified oil, suspended solids, chemical additives used in the various stages of oil production, heavy metals, and other contaminants. Several technologies can be used in the treatment of produced water in order to meet the conditions specified in local legislations and the most used are phase separators, decanters, cyclones, and filters. The separation process mechanism of oil emulsions by coalescence in fibrous media has excellent results, though it is not fully understood and is frequently based on empirical, as well as on experimental, observations. This article presents a general overview on produced water, including origin, production, composition, environmental impact, treatment techniques, disposal, and legislation, as well as an updated discussion utilizing recent literature regarding the unit operation of coalescence: general aspects, kinetics, mechanisms, and factors that influence the coalescence process.
Jet fuel production is a key element in the aviation industry’s strategy to reduce operating costs and environmental impacts. Alternatives are required, and bioturbosine obtained from biomass can ...replace significant amounts of jet fuel. In this investigation, the properties of the production of bioturbosine from coconut oil and mixtures of B5, B10, B20, B1OO, bottom, and jet fuel were measured according to the ASTM standards. A transesterification reaction between coconut oil and methanol was carried out using ultrasound, resulting in 99.93% conversion of triglycerides into bioturbosine at room temperature for 10 min, with a 6:1 molar ratio of methanol and oil. The catalyst concentration was 1.0 g/100 g of oil, and purification was carried out without water using an ion-exchange resin to remove impurities. The results obtained for density and viscosity were within the regulations. The temperature of the clogging point for a cold filter in the mixtures was up to −30 °C. The average caloric values of mixtures B5, B10, and B20 were 45,042, 44,546, and 43,611 MJ/Kg, respectively. In a copper corrosion test, the result for all samples was class 1A. It is expected that the results of this research may influence the use of bioturbosine in the aviation industry.
This study investigated quality control of ground beef samples (n = 45) using both a guided microwave spectroscopy (GMS) system and a NIR-Hyperspectral imaging (NIR-HSI) system to mimic ...in-line/on-line measurement conditions. Partial least squares (PLS) regression models to predict fat and moisture content of ground beef samples were developed for both systems. The most informative spectral variables were selected by comparing the results of three different approaches (i.e. Martens’ uncertainty test, genetic algorithm and an ensemble Monte Carlo variable selection (EMCVS)) to improve the consistency of the prediction results and reduce data processing time to facilitate on-line application. PLS models developed using the most informative microwave spectral variables obtained using a modified EMCVS procedure resulted in coefficient of determination in cross validation (R2CV) of 0.93 and coefficient of determination in prediction (R2P) of 0.90 for calibration and prediction of fat content respectively. Corresponding values of root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) were between 2.13 and 2.18% w/w and 1.72–1.83%w/w respectively. For moisture content prediction, R2CV and R2P values were 0.89 and 0.82 with a RMSECV of 1.93% w/w and a RMSEP of 1.77% w/w respectively. Performance of PLS models developed on NIR-HSI information using EMCVS resulted in a R2P of 0.99 for both fat and moisture prediction with a minimum RMSEP of 0.73%w/w for fat content prediction and RMSEP of 0.64%w/w for moisture content prediction.
•The potential of both GMS and NIR-HSI systems was explored for determinations of fat and moisture content in ground beef.•Multivariate data analysis was used to predict fat and moisture content of ground beef samples by each technique.•The most informative spectral variables were selected using EMCVS to improve the consistent accuracy of prediction.
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