{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:04:09Z","timestamp":1760241849310,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Classification is a kernel process in the standardization, grading, and sensory aspects of coffee industries. The chemometric data of fatty acids and crude fat are used to characterize the varieties of coffee. Two category classifiers were used to distinguish the species and roasting degree of coffee beans. However, the fatty acid profiling with normalized data gave a bad discriminant result in the classification study with mixed dimensions in species and roasted degree. The result of the predictive model is in conflict with the context of human cognition, since roasted coffee beans are easily visually distinguished from green coffee beans. By exploring the effects of error analysis and information processing technologies, the lost information was identified as a bias\u2013variance tradeoff derived from the percentile normalization. The roasting degree as extensive information was attenuated by the percentile normalization, but the cultivars as intensive information were enhanced. An informational spiking technique is proposed to patch the dataset and block the information loss. The identified blocking of informational loss could be available for multidimensional classification systems based on the chemometric data.<\/jats:p>","DOI":"10.3390\/sym10100529","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T08:43:36Z","timestamp":1540284216000},"page":"529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advanced Classification of Coffee Beans with Fatty Acids Profiling to Block Information Loss"],"prefix":"10.3390","volume":"10","author":[{"given":"Ying-Che","family":"Hung","sequence":"first","affiliation":[{"name":"Department of Mechatronic Engineering, Huafan University, New Taipei 223, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Chen","sequence":"additional","affiliation":[{"name":"Tea Science Department, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2981-1945","authenticated-orcid":false,"given":"Liang-Y\u00fc","family":"Chen","sequence":"additional","affiliation":[{"name":"Tea Science Department, Zhejiang University, Hangzhou 310058, China"},{"name":"Department of Biotechnology, Mingchuan University, Taoyuan 333, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jfca.2014.04.001","article-title":"Identification markers based on fatty acid composition to differentiate between roasted Arabica and Canephora (Robusta) coffee varieties in mixtures","volume":"35","author":"Romano","year":"2014","journal-title":"J. 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