Browsing by Author "Anastasiadi, Maria"
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Item Open Access Application of Spatial Offset Raman Spectroscopy (SORS) and machine learning for sugar syrup adulteration detection in UK Honey(MDPI , 2024-07-31) Shehata, Mennatullah; Dodd, Sophie; Mosca, Sara; Matousek, Pavel; Parmar, Bhavna; Kevei, Zoltan; Anastasiadi, MariaHoney authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.Item Open Access Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools(Elsevier, 2020-06-03) Manthou, Evanthia; Lago, Sergio-Llaneza; Dagres, Evaggelos; Lianou, Alexandra; Tsakanikas, Panagiοtis; Panagou, Efstathios Z.; Anastasiadi, Maria; Mohareb, Fady; Nychas, George-John E.Recently, rapid, non-invasive analytical methods relying on vibrational spectroscopy and hyper/multispectral imaging, are increasingly gaining popularity in food science. Although such instruments offer a promising alternative to the conventional methods, the analysis of generated data demands complex multidisciplinary approaches based on data analytics tools utilization. Therefore, the objective of this work was to (i) assess the predictive power of different analytical platforms (sensors) coupled with machine learning algorithms in evaluating quality of ready-to-eat (RTE) pineapple (Ananas comosus) and (ii) explore the potentials of The Unscrambler software and the online machine-learning ranking platform, SorfML, in developing the predictive models required by such instruments to assess quality indices. Pineapple samples were stored at 4, 8, 12 °C and dynamic temperatures and were subjected to microbiological (total mesophilic microbial populations, TVC) and sensory analysis (colour, odour, texture) with parallel acquisition of spectral data. Fourier-transform infrared, fluorescence (FLUO) and visible sensors, as well as Videometer instrument were used. For TVC, almost all the combinations of sensors and Partial-least squares regression (PLSR) algorithm from both analytics tools reached values of root mean square error of prediction (RMSE) up to 0.63 log CFU/g, as well as the highest coefficient of determination values (R2). Moreover, Linear Support Vector Machine (SVM Linear) combined with each one of the sensors reached similar performance. For odour, FLUO sensor achieved the highest overall performance, when combined with Partial-least squares discriminant analysis (PLSDA) in both platforms with accuracy close to 85%, but also with values of sensitivity and specificity above 85%. The SVM Linear and MSI combination also achieved similar performance. On the other hand, all models developed for colour and texture showed poor prediction performance. Overall, the use of both analytics tools, resulted in similar trends concerning the feasibility of the different analytical platforms and algorithms on quality evaluation of RTE pineapple.Item Open Access Biochemical profile of heritage and modern apple cultivars and application of machine learning methods to predict usage, age, and harvest season(American Chemical Society, 2017-06-02) Anastasiadi, Maria; Mohareb, Fady R.; Redfern, Sally P.; Berry, Mark; Simmonds, Monique; Terry, Leon AThe present study represents the first major attempt to characterise the biochemical profile in different tissues of a large selection of apple cultivars sourced from the UK’s National Fruit Collection comprising dessert, ornamental, cider and culinary apples. Furthermore, advanced Machine Learning methods were applied with the objective to identify whether the phenolic and sugar composition of an apple cultivar could be used as a biomarker fingerprint to differentiate between heritage and mainstream commercial cultivars as well as govern the separation among primary usage groups and harvest season. Prediction accuracy > 90% was achieved with Random Forest for all three models. The results highlighted the extraordinary phytochemical potency and unique profile of some heritage, cider and ornamental apple cultivars, especially in comparison to more mainstream apple cultivars. Therefore, these findings could guide future cultivar selection on the basis of health-promoting phytochemical content.Item Open Access A comprehensive study of factors affecting postharvest disorder development in celery(Elsevier, 2020-11-05) Anastasiadi, Maria; Falagán, Natalia; Rossi, Simone; Terry, Leon AFresh-cut celery is an economically important crop, susceptible to postharvest disorders such as browning at cut ends, which can compromise quality and affect freshness perception. The study herein represents the most comprehensive attempt to date to determine the factors that mediate celery postharvest disorders and uncover the physiological and biochemical mechanisms involved. Three main experiments were conducted over two years, covering the early and late Spanish season and the late UK season. The aim of the experiments was to study: a) the effect of seasonal variation and horticultural maturity on shelf-life (20 °C) of fresh-cut celery; b) the effect of postharvest application of 1-methylcyclopropene (1-MCP) and continuous ethylene supplementation on browning and pithiness development during cold storage (5 °C); and c) the effect of preharvest deficit irrigation on the quality of fresh-cut celery during shelf-life (20 °C). Lesser horticultural maturity increased browning and pithiness with browning severity being positively correlated with chlorogenic acid concentrations in celery cut-ends. Ethylene supplementation accelerated the metabolic activity of celery, leading to early senescence. We found that 1-MCP suppressed respiration rate and delayed browning. Deficit irrigation promoted browning, which coincided with a rapid increase in abscisic acid and its main catabolite phaseic acid during storage. Mild deficit irrigation promoted the increase of chlorogenic acid after 6 d of storage, while severe deficit irrigation did not show this increase. These findings will help growers and retailers standardise industry practices ensuring uniform quality and better shelf-life estimations.Item Open Access CRAMER: A lightweight, highly customisable web-based genome browser supporting multiple visualisation instances(Oxford University Press, 2020-02-28) Anastasiadi, Maria; Bragin, E.; Biojoux, P.; Ahamed, A.; Burgin, Josephine; de Castro Cogle, K.; Llaneza-Lago, S.; Muvunyi, R.; Scislak, M.; Aktan, I.; Molitor, Corentin; Kurowski, Tomasz J.; Mohareb, Fady R.In recent years the ability to generate genomic data has increased dramatically along with the demand for easily personalised and customisable genome browsers for effective visualisation of diverse types of data. Despite the large number of web-based genome browsers available nowadays, none of the existing tools provide means for creating multiple visualisation instances without manual set up on the deployment server side. The Cranfield Genome Browser (CRAMER) is an open-source, lightweight and highly customisable web application for interactive visualisation of genomic data. Once deployed, CRAMER supports seamless creation of multiple visualisation instances in parallel while allowing users to control and customise multiple tracks. The application is deployed on a Node.js server and is supported by a MongoDB database which stored all customisations made by the users allowing quick navigation between instances. Currently, the browser supports visualising a large number of file formats for genome annotation, variant calling, reads coverage and gene expression. Additionally, the browser supports direct Javascript coding for personalised tracks, providing a whole new level of customisation both functionally and visually. Tracks can be added via direct file upload or processed in real-time via links to files stored remotely on an FTP repository. Furthermore, additional tracks can be added by users via simple drag and drop to an existing visualisation instance.Item Open Access A critical review of conventional and emerging technologies for the detection of contaminants, allergens and adulterants in plant-based milk alternatives(Elsevier, 2025) Karimi, Zahra; Campbell, Katrina; Kevei, Zoltan; Patriarca, Andrea; Koidis, Anastasios; Anastasiadi, MariaThe increasing popularity of plant-based milk alternatives (PBMAs) necessitates effective safety and authentication measures to ensure food product integrity and maintain consumer trust. This review aims to offer a comprehensive overview of potential contaminants, allergens, and adulterants in PBMAs, and the analytical methodologies employed for their detection and quantitation. It details the advantages and limitations of widely employed testing techniques, such as chromatography, spectroscopy, immunoassays and PCR. In addition, it explores recent advancements in portable detection methods based on novel technologies such as CRISPR and biosensor systems that offer new opportunities for rapid and precise analysis. Despite these technological innovations, important challenges remain, particularly in optimizing sample preparation protocols and improving DNA-based methods efficiency. The integration of multiple detection strategies and the development of rapid, cost-effective analytical tools are critical steps towards enhancing both industry compliance and consumer confidence. Furthermore, green analytical methods — such as solvent-free extraction, AI-driven spectroscopy, and sustainable sample preparation techniques — pave the way toward eco-friendly and more efficient PBMA safety testing.Item Open Access Data "Detection of sugar syrup adulteration in honey using DNA barcoding"(Cranfield University, 2024-08-01) Dodd, Sophie; Anastasiadi, Maria; Karimi, Zahra; Koidis, AnastasiosHoney is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1-30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests. The datasets provided are the raw data from qPCR tests and HPLC analysis.Item Open Access Data for the paper Biochemical profile of heritage and modern apple cultivars and application of machine learning methods to predict usage, age, and harvest season(Cranfield University, 2017-06-05 09:32) Anastasiadi, Maria; Terry, Leon; Redfern, Sally; Mohareb, Fady; Berry, MarkThis dataset contains the quantitative data used for statistical analysis and predictive modelling in the paper entitled "Biochemical Profile of Heritage and Modern Apple Cultivars and Application of Machine Learning Methods to Predict Usage, Age, and Harvest Season". Specifically it contains concentration of phenolic compounds per Fresh weight in the whole apples as well as sugars and organic acids. In addition the phenolic content of individual tissues (peel, flesh, seeds) is uploaded.Item Open Access Data underpinning "Seasonal and Temporal Changes during Storage Affect Quality Attributes of Green Asparagus"(Cranfield University, 2019-09-19 18:33) Anastasiadi, Maria; Collings, Emma R.; Terry, Leon; Shivembe, Allan; Qian, BinghuaThis dataset contains the data used for statistical analysis and predictive modelling in the paper entitled "Seasonal and Temporal Changes during Storage Affect Quality Attributes of Green Asparagus". Specifically it contains physiological and biochemical changes in asparagus spears from two different cultivars during shelf-life captured over the course of the harvest season, as well as during cold storage and subsequent shelf-life for three different cultivars. Physiological changes include moisture loss, respiration rate, cutting energy, stiffness, objective colour. Biochemical data include individual sugars, ascorbic acid and abscisic acid and its catabolites. Also the data capture spatial differences along the asparagus spears (apical and basal regions).Item Open Access Dataset for "Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey"(Cranfield University, 2024-07-31) Anastasiadi, MariaHoney authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. The aim of this study was to develop non-invasive sensor methods coupled with multivariate data analysis for de-tecting the type and percentage of exogenous sugar adulteration in UK honeys. For this purpose, we employed through-container Spatial Offset Raman Spectroscopy (SORS) on 17 different types of natural honeys produced in the UK over the course of a season and the same honey samples spiked with rice and sugar beet syrups at levels 10%, 20%, 30%, 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprint using different algorithms, namely PLS-DA, XGBoost and Random Forest with the aim to detect the level of adulteration per type of sugar syrup. The best performing algorithm for classification was Random Forest with only 1% of the pure honeys misclassified as adulterated and < 3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, we collected SORS spectra from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica Cinerea) produced in the UK and cor-responding subsamples spiked with high fructose sugar cane syrup and performed exploratory data analysis with PCA and classification with Random Forest which both showed a clear sepa-ration between pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in field with potential application at all stages of the supply chain.Item Open Access Detection of sugar syrup adulteration in UK honey using DNA barcoding(Elsevier, 2025-01-01) Dodd, Sophie; Kevei, Zoltan; Karimi, Zahra; Parmar, Bhavna; Franklin, David; Koidis, Anastasios; Anastasiadi, MariaHoney is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1 – 30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests.Item Open Access The first comprehensive chemical profiling of Vachellia gummifera (Willd.) Kyal. & Boatwr., a plant with medicinal value(Wiley, 2024-06-01) Kisiriko, Musa; Noleto-Dias, Clarice; Bitchagno, Gabin T. M.; Naboulsi, Imane; Anastasiadi, Maria; Terry, Leon A.; Sobeh, Mansour; Beale, Michael H.; Ward, Jane L.Vachellia gummifera (Willd.) Kyal. & Boatwr. is a medicinal plant endemic to Morocco that has no documented studies on its chemical composition. In this study, the chemical composition of the water/methanol (4 : 1) extracts of air-dried leaf and stem samples of Moroccan V. gummifera was determined using UHPLC-MS and NMR. In total, over 100 metabolites were identified in our study. Pinitol was the major compound in both the leaf and stem extracts, being significantly more abundant in the former. Asparagine and 3-hydroxyheteroendrin were the second most abundant compounds in the stem and leaf extracts, respectively, though both compounds were present in each tissue. The other compounds included flavonoids based on quercetin, and phenolic derivatives. Eucomic acid, only identified in the stems and was the major aromatic compound distinguishing the leaf and stem profiles. Quercetin 3-O-(6′′-O-malonyl)-β-D-glucopyranoside was identified as the major flavonoid in the leaves but was also present in the stems. Other malonylated derivatives that were all flavonol glycosides based on myricetin, kaempferol, and isorhamnetin in addition to quercetin were also identified. This is the first report of eucomic acid and malonylated compounds in Vachellia species. This report provides valuable insights into the chemotaxonomic significance of the Vachellia genus.Item Open Access Fusion vs. Isolation: evaluating the performance of multi-sensor integration for meat spoilage prediction(MDPI, 2025-05-01) Heffer, Samuel; Anastasiadi, Maria; Nychas, George-John; Mohareb, FadyHigh-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for the estimation of sample quality and safety through monitoring of key physical attributes. However, the developed predictive models often show varying degrees of accuracy, depending on food type, storage conditions, sensor platform, and sample sizes. This work explores various fusion approaches for potential predictive enhancement, through the summation of information gathered from different observational spaces: infrared spectroscopy is supplemented with multispectral imaging for the prediction of chicken and beef spoilage through the estimation of bacterial counts in differing environmental conditions. For most circumstances, at least one of the fusion methodologies outperformed single-sensor models in prediction accuracy. Improvement in aerobic, vacuum, and mixed aerobic/vacuum chicken spoilage scenarios was observed, with performance enhanced by up to 15%. The improved cross-batch performance of these models proves an enhanced model robustness using the presented multi-sensor fusion approach. The batch-based results were corroborated with a repeated nested cross-validation approach, to give an out-of-sample generalised view of model performance across the whole dataset. Overall, this work suggests potential avenues for performance improvements in real-world, minimally invasive food monitoring scenarios.Item Open Access Genetic and physiological responses to heat stress in Brassica napus(Frontiers, 2022-04-05) Kourani, Mariam; Mohareb, Fady; Rezwan, Faisal I.; Anastasiadi, Maria; Hammond, John P.Given the current rise in global temperatures, heat stress has become a major abiotic challenge affecting the growth and development of various crops and reducing their productivity. Brassica napus, the second largest source of vegetable oil worldwide, experiences a drastic reduction in seed yield and quality in response to heat. This review outlines the latest research that explores the genetic and physiological impact of heat stress on different developmental stages of B. napus with a special attention to the reproductive stages of floral progression, organogenesis, and post flowering. Several studies have shown that extreme temperature fluctuations during these crucial periods have detrimental effects on the plant and often leading to impaired growth and reduced seed production. The underlying mechanisms of heat stress adaptations and associated key regulatory genes are discussed. Furthermore, an overview and the implications of the polyploidy nature of B. napus and the regulatory role of alternative splicing in forming a priming-induced heat-stress memory are presented. New insights into the dynamics of epigenetic modifications during heat stress are discussed. Interestingly, while such studies are scarce in B. napus, opposite trends in expression of key genetic and epigenetic components have been identified in different species and in cultivars within the same species under various abiotic stresses, suggesting a complex role of these genes and their regulation in heat stress tolerance mechanisms. Additionally, omics-based studies are discussed with emphasis on the transcriptome, proteome and metabolome of B. napus, to gain a systems level understanding of how heat stress alters its yield and quality traits. The combination of omics approaches has revealed crucial interactions and regulatory networks taking part in the complex machinery of heat stress tolerance. We identify key knowledge gaps regarding the impact of heat stress on B. napus during its yield determining reproductive stages, where in-depth analysis of this subject is still needed. A deeper knowledge of heat stress response components and mechanisms in tissue specific models would serve as a stepping-stone to gaining insights into the regulation of thermotolerance that takes place in this important crop species and support future breeding of heat tolerant crops.Item Restricted An improved model for the analysis of combined antimicrobials: a replacement for the Chou‐Talalay combination index method(Wiley, 2017-10) Anastasiadi, Maria; Polizzi, Karen; Lambert, Ronald J. W.Aims To rationalise confusion in the literature concerning the analysis of combined antimicrobials, specifically to see if the combination index (CI) method of analysis was as rigorous as claimed. Methods & Results data from previous studies of the inhibition of Staphylococcus aureus by mixed antimicrobials were re-analysed using the CI method and a model which takes account of differences in the concentration exponents of individual antimicrobials. Conclusions The Chou-Talalay combination index method for the analysis of combined antimicrobials was found to be valid only in the specific cases where concentration exponents were equal. In these cases the CI method was found to be a function of the residuals of fitting the additive model to the observed data. Where concentration exponents were not equal the CI method was invalid, whereas the additive model took these differences into account. Significance and Impact of Study The CI method can be replaced wholly by the additive model described. The model allows simple regression to be used to analyse whole data sets and provides simple graphical output.Item Open Access Investigating the role of abscisic acid and its catabolites on senescence processes in green asparagus under controlled atmosphere (CA) storage regimes(Elsevier, 2022-03-14) Anastasiadi, Maria; Collings, Emma R.; Terry, Leon AAsparagus (Asparagus officinalis) is a highly perishable crop with a short postharvest life. Although some research has been done on the application of controlled atmosphere (CA), it has not been sufficiently explored and the underlying mechanisms controlling asparagus senescence processes are not well understood, restricting its potential for commercial application. The aim of this study was to investigate for the first time the link between abscisic acid (ABA) and ABA catabolites and senescence in asparagus stored under a range of different CA conditions. Two different set-ups were run in parallel; a traditional CA delivered by an International Controlled Atmosphere (ICA) system with continuous gas supply and LabPods™ fitted with sensors for real time monitoring of respiration rate (RR) and respiratory quotient (RQ) and able to retain established CA conditions with minimum gas supply requirements. The role of genetic variability was also studied by including two UK grown asparagus cultivars ‘Gijnlim’ and ‘Jaleo’ adapted for different climatic conditions. The results indicated that ABA and its catabolites were present in significantly higher concentrations in the air stored spears (control) compared to CA throughout storage, irrespective of cultivar, and were associated with accelerated senescence processes observed in control samples, such as textural changes indicative of spear toughening, discolouration, sugar depletion and asparagine accumulation. Furthermore, partial least squares regression (pls-r) applied for both cultivars, successfully differentiated samples based on O2 and CO2 concentrations and storage duration, both in cold storage and during shelf-life with the separation being driven primarily by ABA and its catabolites. Physiological and biochemical results indicated that all three CA conditions tested ([CA1] 2.5% O2, 3% CO2, [CA2] 2.5% O2, 6% CO2 and [CA3] 2.5% O2, 10% CO2) successfully retained quality parameters including texture, colour, moisture content and visual appearance longer compared to air (control); however, they did not completely suppress the development of ‘tip-breakdown’ (a physiological disorder also known as tip rot) towards the end of storage, which coincided with rising concentrations of phaseic acid indicating an activation of the abscisic biosynthetic and catabolic pathway. It can be concluded that CA conditions can delay senescence for at least 3-weeks (2 weeks cold storage and 1 week shelf-life), by lowering metabolic rate and respiratory quotient (RQ) within the spears compared to control, and through successfully regulating ABA biosynthetic and catabolic pathways.Item Open Access Modelling the effect of combined antimicrobials: A base model for multiple-hurdles(Elsevier, 2017-04-14) Anastasiadi, Maria; Lambert, R. J.Combining antimicrobials to reduce microbial growth and to combat the potential impact of antimicrobial resistance is an important subject both in foods and in pharmaceutics. Modelling of combined treatments designed to reduce or eliminate microbial contamination in foods (microbiological predictive modelling) has become commonplace. Two main reference models are used to analyse mixtures: the Bliss Independence and the Loewe reference models (LRM). By using optical density to analyse the growth of Aeromonas hydrophila, Cronobacter sakazakii and Escherichia coli in combined NaCl/NaCl (a mock combination experiment) and combined NaCl/KCl experiments, previous models for combined antimicrobials in foods, based on the Bliss approach, were shown to be inconsistent and that models based on the LRM more applicable. The LRM was shown, however, to be valid only in the specific cases where the concentration exponents of all components in a mixture were identical. This is assured for a mock combination experiment but not for a true mixture. This, essentially, invalidates the LRM as a general reference model. A new model, based on the LRM but allowing for mixed exponents, was used to analyse the combined inhibition data, and concluded that the NaCl/KCl system gave the additive effect expected from literature studies. This study suggests the need to revise current models used to analyse combined effects.Item Open Access Phenolics from medicinal and aromatic plants: characterisation and potential as biostimulants and bioprotectants(MDPI, 2021-10-20) Kisiriko, Musa; Anastasiadi, Maria; Terry, Leon A; Yasri, Abdelaziz; Beale, Michael Henry; Ward, Jane LouiseBiostimulants and bioprotectants are derived from natural sources and can enhance crop growth and protect crops from pests and pathogens, respectively. They have attracted much attention in the past few decades and contribute to a more sustainable and eco-friendly agricultural system. Despite not having been explored extensively, plant extracts and their component secondary metabolites, including phenolic compounds have been shown to have biostimulant effects on plants, including enhancement of growth attributes and yield, as well as bioprotectant effects, including antimicrobial, insecticidal, herbicidal and nematicidal effects. Medicinal and aromatic plants are widely distributed all over the world and are abundant sources of phenolic compounds. This paper reviews the characterisation of phenolic compounds and extracts from medicinal and aromatic plants, including a brief overview of their extraction, phytochemical screening and methods of analysis. The second part of the review highlights the potential for use of phenolic compounds and extracts as biostimulants and bioprotectants in agriculture as well as some of the challenges related to their use.Item Open Access Physiological and hormone data for the paper entitled 'Transcriptome and phytohormone changes associated with ethylene-induced onion bulb dormancy'(Cranfield University, 2020-06-15 17:29) del carmen Alamar Gavidia, Maria; Terry, Leon; Anastasiadi, Maria; Thompson, Andrew; Mohareb, Fady; Turnbull, Colin G. N. ; Lopez-Cobollo, Rosa M.; Bennett, Mark H.Underlying data for this onion bulb dormancy paper which includes: respiration rate, sprout elongation and sprout incidence; abscisic acid (ABA) and ABA-metabolites concentration; cytokinins concentration; and differentially expressed genes for ABA, ethylene and cytokinins pathways.Item Open Access Probabilistic modelling of the food matrix effects on curcuminoid’s in vitro oral bioaccessibility(MDPI, 2024-07-16) de Castro Cogle, Kevin; Kubo, Mirian T. K.; Merlier, Franck; Josse, Alexandra; Anastasiadi, Maria; Mohareb, Fady R.; Rossi, ClaireThe bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid’s bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices.