{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:58:36Z","timestamp":1787032716592,"version":"build-2736575974"},"reference-count":39,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Universidad Nacional de Frontera","award":["CUI No. 2439545"],"award-info":[{"award-number":["CUI No. 2439545"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\n                    The rising interest in quinoa (\n                    <jats:italic>Chenopodium quinoa<\/jats:italic>\n                    Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose. Consequently, the main objective of this work is to evaluate convolutional neural networks with a learning transfer for foreign bodies identification in quinoa samples. For experimentation, quinoa samples were collected and manually split into 17 classes: quinoa grains and 16 foreign bodies. Then, one thousand images were obtained from each class in RGB space and transformed into four different color spaces (L*a*b*, HSV, YCbCr, and Gray). Three convolutional neural networks (AlexNet, MobileNetv2, and DenseNet-201) were trained using the five color spaces, and the evaluation results were expressed in terms of accuracy and F-score. All the CNN approaches compared showed an F-score ranging from 98% to 99%; both color space and CNN structure were found to have significant effects on the F-score. Also, DenseNet-201 was the most robust architecture and, at the same time, the most time-consuming. These results evidence the capacity of CNN architectures to be used for the discrimination of foreign bodies in quinoa processing facilities.\n                  <\/jats:p>","DOI":"10.7717\/peerj.14808","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T03:26:33Z","timestamp":1675049193000},"page":"e14808","source":"Crossref","is-referenced-by-count":8,"title":["Discrimination of foreign bodies in quinoa\n                    <i>(Chenopodium quinoa<\/i>\n                    Willd.) grains using convolutional neural networks with a transfer learning approach"],"prefix":"10.7717","volume":"11","author":[{"given":"Himer","family":"Avila-George","sequence":"first","affiliation":[{"name":"Departamento de Ciencias Computacionales e Ingenier\u00edas, Universidad de Guadalajara, Ameca,  Jalisco, M\u00e9xico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"De-la-Torre","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias Computacionales e Ingenier\u00edas, Universidad de Guadalajara, Ameca,  Jalisco, M\u00e9xico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"S\u00e1nchez-Garc\u00e9s","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda y Arquitectura, Universidad Peruana Uni\u00f3n, Juliaca, Puno, Per\u00fa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel Jerson","family":"Coaquira Quispe","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda y Arquitectura, Universidad Peruana Uni\u00f3n, Juliaca, Puno, Per\u00fa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Manuel","family":"Prieto","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda y Arquitectura, Universidad Peruana Uni\u00f3n, Juliaca, Puno, Per\u00fa"},{"name":"Escuela de Doctorado en Ciencia, Ingenier\u00eda y Gesti\u00f3n Alimentaria, Universidad Polit\u00e9cnica de Valencia,  Valencia, Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilson","family":"Castro","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda en Industrias Alimentarias, Universidad Nacional de Frontera, Sullana, Piura, Per\u00fa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"10.7717\/peerj.14808\/ref-1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.biosystemseng.2021.11.016","article-title":"NemaNet: a convolutional neural network model for identification of soybean nematodes","volume":"213","author":"Abade","year":"2022","journal-title":"Biosystems 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