{"id":238643,"date":"2026-07-07T08:00:00","date_gmt":"2026-07-07T06:00:00","guid":{"rendered":"https:\/\/www.alimentibevande.it\/?p=238643"},"modified":"2026-07-06T09:40:19","modified_gmt":"2026-07-06T07:40:19","slug":"coffee-quality-assessment-using-smart-chemical-sensors","status":"publish","type":"post","link":"https:\/\/www.alimentibevande.it\/en\/scientific-studies\/coffee-quality-assessment-using-smart-chemical-sensors\/","title":{"rendered":"Coffee quality: assessment using smart chemical sensors"},"content":{"rendered":"\n<p>Quality control is mandatory in the food industry, and chemical sensors play a crucial role in this field.<\/p>\n\n\n\n<p>Coffee <strong><\/strong>is one of the most consumed and marketed food products in the world, and its quality characteristics are therefore of fundamental importance for the industry in this sector.<\/p>\n\n\n\n<p>The aim of a <strong>recent study<\/strong>, carried out by a group of Italian researchers (<em>Grasso et al.,<\/em> 2025), was to evaluate <strong>the ability of an intelligent electrochemical sensor to distinguish between different beverages <\/strong>prepared with coffee beans having different moisture content (0, 2 and &gt; 4%) and ground to three particle sizes (fine, medium and coarse).<\/p>\n\n\n\n<p>These parameters, in fact, reflect real scenarios in which this product is processed and its quality is affected.<\/p>\n\n\n\n<p>A specific<strong> experimental configuration<\/strong> was designed for the tests, while the data was analysed using <strong>machine learning<\/strong> techniques.<\/p>\n\n\n\n<p>The <strong>results <\/strong>obtained from principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) show the sensor&#8217;s ability to distinguish between samples of different quality, with a correct classification rate of 86.6%. <\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"su-button-center\"><a href=\"https:\/\/publications.mazzmedia.com\/PKwtfNPDZL5Yts5zTHpdufJM6zwNxn\" class=\"su-button su-button-style-default su-button-wide\" style=\"color:#121010;background-color:#fab21d;border-color:#c88f18;border-radius:12px\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color:#121010;padding:0px 30px;font-size:22px;line-height:44px;border-color:#fcca61;border-radius:12px;text-shadow:none\"> <strong><strong>Article from the June issue of Macchine Alimentari<\/strong><\/strong><\/span><\/a><\/div>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"su-button-center\"><a href=\"https:\/\/www.tecnichenuove.com\/riviste\/macchine-alimentari\" class=\"su-button su-button-style-default su-button-wide\" style=\"color:#121010;background-color:#fab21d;border-color:#c88f18;border-radius:12px\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color:#121010;padding:0px 30px;font-size:22px;line-height:44px;border-color:#fcca61;border-radius:12px;text-shadow:none\"> <strong><strong>Click here to subscribe<\/strong><\/strong><\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>A study used a smart electrochemical sensor, demonstrating its ability to distinguish between coffee samples of different qualities, with a correct classification rate of 86.6 per cent<\/p>\n","protected":false},"author":3836,"featured_media":234223,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[20580],"tags":[17187,20899,21896,21586],"tipologia":[],"coauthors":[20682],"class_list":["post-238643","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scientific-studies","tag-caffe","tag-coffee","tag-machine-learning","tag-sensors"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Coffee quality: assessment using smart chemical sensors - 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