Environment and Agrifood

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https://dspace.lib.cranfield.ac.uk/handle/1826/20716

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Browsing Environment and Agrifood by Author "Anastasiadi, Maria"

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    Data "Detection of sugar syrup adulteration in honey using DNA barcoding"
    (Cranfield University, 2024-08-01) Dodd, Sophie; Anastasiadi, Maria; Karimi, Zahra; Koidis, Anastasios
    Honey 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.
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    ItemOpen 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, Mark
    This 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.
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    ItemOpen 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, Binghua
    This 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).
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    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, Maria
    Honey 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.
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    ItemOpen 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.
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    Underpinning data "A comprehensive study of factors affecting postharvest disorder development in celery ".
    (Cranfield University, 2020-10-14 12:47) Anastasiadi, Maria; Falagan Sama, Natalia; Rossi, Simone; Terry, Leon
    This dataset contains the data used for statistical analysis in the paper entitled "A comprehensive study of factors affecting postharvest disorder development in celery". Specifically it contains physiological and biochemical changes affecting quality, during shelf-life in celery plants from three different horticultural maturity stages and harvested in different seasons from Spain and the UK. Part of the samples were subjected to different postharvest treatments: 1-MCP application for 24 h followed by cold storage storage under continuous air, continuous ethylene application, and continuous air application (control). In addition, this dataset contain data recording the physiological and biochemical changes occurring during shelf-life in celery plants grown under deficit irrigation and harvested at different horticultural maturity stages. Physiological changes recorded include, respiration rate, objective colour, dry matter changes, browning development and pithiness development. Biochemical data include individual sugars, abscisic acid and its catabolites and chlorogenic acid. Also the data capture spatial differences along the celery plants (apical and basal regions).
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    Underpinning data 'Investigating the role of abscisic acid and its catabolites on senescence processes in green asparagus under controlled atmosphere (CA) storage regimes'
    (Cranfield University, 2022-03-25 10:47) Anastasiadi, Maria; Collings, Emma R.; Terry, Leon A.
    This dataset contains the data used for statistical analysis and predictive modelling in the paper entitled "Revisiting CA Protocols and Their Effect on Hormonal Flux in Asparagus". Specifically it contains physiological and biochemical changes in asparagus spears from three different cultivars during cold storage under different CA and DCA regimes and subsequent shelf-life. Physiological changes include moisture loss, respiration rate, cutting energy, objective colour. Biochemical data include individual sugars, abscisic acid and its catabolites. Also the data capture spatial differences along the asparagus spears (apical and basal regions).