Browsing by Author "Mosca, Sara"

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    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, Maria
    Honey 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.
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    Data for feasibility study on using combined tomography and spectroscopy techniques to evaluate the physical and chemical characteristics of organo-mineral fertilisers
    (Cranfield University, 2025-05-16) Sakrabani, Ruben; Mosca, Sara; Liptak, Alexander; Burca, Genoveva
    Fertilisers play a key role in agriculture, providing key nutrients needed by crops to ensure a secure food supply. However, with increasing prices and rising environmental concerns, there is a growing need to rely on alternative and sustainable fertiliser sources, introducing the opportunity to use organic amendments to formulate organo-mineral fertilisers (OMF). Despite their environmental advantages, the inherent variability in composition of organic amendments within OMF poses a challenge for their standardisation. This study aims to use OMF derived from anaerobic digestate and coupled with carbon capture technologies to analyse for its physical characteristics and chemical composition using neutron computed tomography (NCT), X-ray computed tomography (XCT) and Raman spectroscopy (RS). This work represents the first attempt to utilise a combination of imaging techniques to investigate on OMF and demonstrates their feasibility for measuring the variability between individual samples. This is a proof-of-concept study which shows that combining NCT and XCT can provide images on how uniformly packed each OMF pellet are. The use of RS is to characterise OMF is more challenging largely due to the high fluorescence background arising from its matrix. This study needs to be further developed to enable image-based analysis using machine learning algorithms to determine characteristics of large batches of OMF.