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Research Article
Effect of Toasting on Quality and Sensory Attributes of Oat Flakes and Brazil Nuts Intended for Use in Functional Muesli
Maija Gertsone*
,
Asnate Kirse-Ozolina
Issue:
Volume 14, Issue 5, October 2026
Pages:
199-213
Received:
6 August 2026
Accepted:
14 August 2026
Published:
8 September 2026
Abstract: This study evaluated the effects of toasting temperature and time on the sensory characteristics, moisture content, lipid stability, total phenolic content, and antioxidant capacity of gluten-free oat flakes and Brazil nuts (Bertholletia excelsa) to determine optimal processing conditions. Oat flakes were toasted at 120.0, 150.0, and 180.0 ± 0.5°C, and Brazil nuts at 120.0, 130.0, 140.0, and 150.0 ± 0.5°C for 5, 10, 15, and 20 min. Toasting significantly affected sensory quality (p < 0.05). Based on all quality measurements and sensory attribute scores, the best overall results were obtained at 180.0 ± 0.5°C for 5 min for oat flakes and 140.0 ± 0.5°C for 10 min for Brazil nuts. Moisture content decreased with increasing temperature and time, reaching optimal levels of 3-5% for oat flakes and 0.5-0.8% for Brazil nuts. In Brazil nuts, peroxide values increased from 1.12 to 3.75 meq O2 kg⁻1 oil, indicating accelerated lipid oxidation under excessive heating. Toasting also increased total phenolic content from 0.070 to 0.448 mg GAE/g in oats and from 0.487 to 1.171 mg GAE/g in Brazil nuts, while enhancing DPPH and ABTS radical scavenging activities. Controlled toasting is therefore essential for producing functional muesli with high sensory quality, improved antioxidant properties, and good storage stability.
Abstract: This study evaluated the effects of toasting temperature and time on the sensory characteristics, moisture content, lipid stability, total phenolic content, and antioxidant capacity of gluten-free oat flakes and Brazil nuts (Bertholletia excelsa) to determine optimal processing conditions. Oat flakes were toasted at 120.0, 150.0, and 180.0 ± 0.5°C,...
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Research Article
Deepfakes and Information Literacy: Implications for the Emerging Role of Academic Librarians
Susan Nnadozie Umeozor*
Issue:
Volume 14, Issue 5, October 2026
Pages:
214-221
Received:
14 September 2026
Accepted:
27 September 2026
Published:
20 October 2026
Abstract: The emergence of deepfakes challenges the traditional assumptions underlying information literacy because authenticity can no longer be established through conventional evaluation of source, appearance and audiovisual evidence alone. Consequently, academic librarians must expand information-literacy instruction towards synthetic-media literacy, verification, provenance assessment and critical evaluation of AI-generated content. The paper addressed this issue by examining the challenges deepfakes pose to conventional information-literacy practice and the implications for the emerging role of academic librarians. It discussed how deepfakes undermine traditional authority cues, complicate source corroboration, make authentic information difficult to distinguish from synthetic content, and expose limitations in existing information-evaluation practices of users. The paper further considered the evolution of information-literacy practice from source-centered approaches of CRAAP test towards SIFT and lateral reading, and established that these approaches, although valuable, may not sufficiently address the authenticity problem created by synthetic media. It proposed an expanded information-literacy practice incorporating synthetic-media awareness, provenance tracing, verification, contextual analysis, triangulation, AI literacy and critical judgement. The paper concluded that academic librarians should be repositioned as mediators of trust, verification and critical information judgement within contemporary academic information environments through curriculum integration, institutional partnerships, verification-tool training and continuous professional development in AI and synthetic-media literacy.
Abstract: The emergence of deepfakes challenges the traditional assumptions underlying information literacy because authenticity can no longer be established through conventional evaluation of source, appearance and audiovisual evidence alone. Consequently, academic librarians must expand information-literacy instruction towards synthetic-media literacy, ver...
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Research Article
Regulating the Visible: the EU AI Act, Labour Governance, and the Limits of a Product-Risk Framework
Achi Iseko*
Issue:
Volume 14, Issue 5, October 2026
Pages:
222-237
Received:
6 October 2026
Accepted:
16 October 2026
Published:
31 October 2026
DOI:
10.11648/j.ijsts.20261405.13
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Abstract: The European Union Artificial Intelligence Act (Regulation (EU) 2024/1689) establishes a comprehensive legal framework for artificial intelligence (AI) governance. Organised around a risk-based classification of AI systems and their deployment contexts, the Act makes workers visible principally as persons affected by AI decisions, not as the distributed workforce whose labour, judgement and exposure produce AI systems in the first place. This paper presents a systematic regulatory audit of the Act using the Ethical Visibility Framework (EVF), a three-dimensional analytical framework for analysing material, epistemic and narrative dimensions of labour invisibility in AI production. Applying EVF indicators to the Act's operative articles, recitals, annexes and current implementing instruments, we find that the Act weakly recognises AI labour relations across all three dimensions: it governs the data artefact produced by annotation work without specifying the conditions under which that work is performed (material); it positions human oversight as deployer control over system outputs rather than as worker participation in system governance (epistemic); and its operative vocabulary recognises data-production activities without defining corresponding production-worker entitlements (narrative). We identify and examine the Act's implicit rival logic: that regulation of AI labour is intentionally deferred to existing EU labour law and the Platform Work Directive. We examine why this division of regulatory functions can leave gaps for transnational AI production work, illustrated through secondary evidence concerning Sub-Saharan Africa and comparative material on the Arab Gulf. We term this the enforcement paradox: a regulatory architecture with significant sanctioning powers but no independent, general duty governing production-labour conditions. We conclude with targeted proposals for labour-inclusive amendments to the Act's system and model governance frameworks, arguing that a product-risk regime cannot adequately guarantee trustworthy AI while giving insufficient attention to the labour conditions that underpin it.
Abstract: The European Union Artificial Intelligence Act (Regulation (EU) 2024/1689) establishes a comprehensive legal framework for artificial intelligence (AI) governance. Organised around a risk-based classification of AI systems and their deployment contexts, the Act makes workers visible principally as persons affected by AI decisions, not as the distri...
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