{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:01:17Z","timestamp":1793408477632,"version":"4.5.0"},"reference-count":68,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["INV\u2010007637"],"award-info":[{"award-number":["INV\u2010007637"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acsess.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Plant Genome"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This study focuses on meeting end\u2010users\u2019 demand for cassava (\n                    <jats:italic>Manihot esculenta<\/jats:italic>\n                    Crantz) varieties with low cyanogenic potential (hydrogen cyanide potential [HCN]) by using near\u2010infrared spectrometry (NIRS). This technology provides a fast, accurate, and reliable way to determine sample constituents with minimal sample preparation. The study aims to evaluate the effectiveness of machine learning (ML) algorithms such as logistic regression (LR), support vector machine (SVM), and partial least squares discriminant analysis (PLS\u2010DA) in distinguishing between low and high HCN accessions. Low HCN accessions averagely scored 1\u20135.9, while high HCN accessions scored 6\u20139 on a 1\u20139 categorical scale. The researchers used 1164 root samples to test different NIRS prediction models and six spectral pretreatments. The wavelengths 961, 1165, 1403\u20131505, 1913\u20131981, and 2491\u00a0nm were influential in discrimination of low and high HCN accessions. Using selected wavelengths, LR achieved 100% classification accuracy and PLS\u2010DA achieved 99% classification accuracy. Using the full spectrum, the best model for discriminating low and high HCN accessions was the PLS\u2010DA combined with standard normal variate with second derivative, which produced an accuracy of 99.6%. The SVM and LR had moderate classification accuracies of 75% and 74%, respectively. This study demonstrates that NIRS coupled with ML algorithms can be used to identify low and high HCN accessions, which can help cassava breeding programs to select for low HCN accessions.\n                  <\/jats:p>","DOI":"10.1002\/tpg2.20403","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T11:43:32Z","timestamp":1699443812000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava"],"prefix":"10.1002","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8162-6640","authenticated-orcid":false,"given":"Michael","family":"Kanaabi","sequence":"first","affiliation":[{"name":"School of Agricultural Sciences Makerere University  Kampala Uganda"},{"name":"National Crops Resources Research Institute (NaCRRI)  Kampala 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