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    Distinguishing plant and milk proteins and their interactions in hybrid cheese using confocal Raman microscopy with machine learning
    (Elsevier Limited, United Kingdom, 2026-01-01) Lu D; McGoverin C; Roy D; Acevedo-Fani A; Singh H; Waterland M; Zheng Y; Ye A
    The increasing demand for plant-based alternatives to milk protein has led to the development of hybrid processed cheese analogues (HPCAs) combining plant proteins and casein. However, their complex microstructure and molecular interactions remain poorly understood. This study integrated confocal Raman spectroscopy with advanced machine learning for high-resolution spatial mapping and molecular characterization of HPCAs containing mung bean protein isolate (MPI) or hemp protein isolate (HPI) with casein. This integration helped distinguish between protein sources and elucidate structural changes. The addition of casein changed the HPI structure, promoting structural disorder, disulfide bond rearrangement, and a sharp decrease in the tyrosine doublet ratio from 4.5 in HPI100 to 1.2 in HPI50. Conversely, casein interaction with MPI led to microstructural segregation and changes of β-sheet content (from 53 % in MPI100 to 20 % in MPI30). This integrated method represents a powerful tool for analysing protein structure and interactions in complex food systems.
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    On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining
    (MDPI (Basel, Switzerland), 2022-01-07) Ramaswami G; Susnjak T; Mathrani A; Cowling, M; Jha, M
    Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.