Argitalpenak (174) JOSÉ SANTIAGO TORRECILLA VELASCO argitalpenak

2023

  1. Application of residual neural networks to detect and quantify milk adulterations

    Journal of Food Composition and Analysis, Vol. 122

  2. Deep quantification of a refined adulterant blended into pure avocado oil

    Food Chemistry, Vol. 404

  3. Residual neural networks to quantify traces of melamine in yogurts through image deconvolution

    Journal of Food Composition and Analysis, Vol. 118

2022

  1. Convolutional capture of the expansion of extra virgin olive oil droplets to quantify adulteration

    Food Chemistry, Vol. 368

  2. Distinct thermal patterns to detect and quantify trace levels of wheat flour mixed into ground chickpeas

    Food Chemistry, Vol. 384

  3. Is my food safe? – AI-based classification of lentil flour samples with trace levels of gluten or nuts

    Food Chemistry, Vol. 386

  4. Single-digit ppm quantification of melamine in powdered milk driven by computer vision

    Food Control, Vol. 131

  5. Standard photographs convolutionally processed to indirectly detect gluten in chickpea flour

    Journal of Food Composition and Analysis, Vol. 110

2021

  1. Deep transfer learning to verify quality and safety of ground coffee

    Food Control, Vol. 122

  2. Learning by playing via survey platforms to comprehend environmental management

    Journal of Higher Education Theory and Practice, Vol. 21, Núm. 8, pp. 237-243

  3. Low requirement imaging enables sensitive and robust rice adulteration quantification via transfer learning

    Food Control, Vol. 127

  4. Profiles of Volatile Biomarkers Detect Tuberculosis from Skin

    Advanced Science, Vol. 8, Núm. 15

  5. Service-learning - Diagnostic technologies presented by Ph.D. students to help socially neglected people during the SARS-CoV-2 pandemic

    International Conference on Higher Education Advances

  6. Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety

    Food Control, Vol. 121

  7. Thinking-Based Learning at Higher Education Levels: Implementation and Outcomes within a Chemical Engineering Class

    Journal of Chemical Education, Vol. 98, Núm. 3, pp. 774-781

2020

  1. Cognitive chaos on spectrofluorometric data to quantitatively unmask adulterations of a PDO vinegar

    Food Control, Vol. 108

  2. Convolutional decoding of thermographic images to locate and quantify honey adulterations

    Talanta, Vol. 209

  3. Deep thermal imaging to compute the adulteration state of extra virgin olive oil

    Computers and Electronics in Agriculture, Vol. 171

  4. Detection of adulterations of extra-virgin olive oil by means of infrared thermography

    Olives and Olive Oil in Health and Disease Prevention (Elsevier), pp. 79-84

  5. Editorial: Artificial Intelligence in Chemistry

    Frontiers in Chemistry