{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T22:38:54Z","timestamp":1777329534263,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Innovation Strategy (RIS)","award":["2021RIS-004"],"award-info":[{"award-number":["2021RIS-004"]}]},{"name":"Ministry of Education","award":["2021RIS-004"],"award-info":[{"award-number":["2021RIS-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA\/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.<\/jats:p>","DOI":"10.3390\/s23115020","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T02:30:06Z","timestamp":1684981806000},"page":"5020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy"],"prefix":"10.3390","volume":"23","author":[{"given":"Rahul","family":"Joshi","sequence":"first","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea"}]},{"given":"Lakshmi Priya","family":"GG","sequence":"additional","affiliation":[{"name":"Department of Multimedia, VIT School of Design (V-SIGN), Vellore Institute of Technology (VIT), Vellore 632014, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1829-2502","authenticated-orcid":false,"given":"Mohammad Akbar","family":"Faqeerzada","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea"}]},{"given":"Tanima","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea"}]},{"given":"Moon Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1044-349X","authenticated-orcid":false,"given":"Insuck","family":"Baek","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8397-9853","authenticated-orcid":false,"given":"Byoung-Kwan","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea"},{"name":"Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s12550-018-0337-y","article-title":"Mycotoxins in poultry feed and feed ingredients in Nigeria","volume":"35","author":"Akinmusire","year":"2019","journal-title":"Mycotoxin Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fvets.2022.929904","article-title":"Mycotoxin Contamination of Feeds and Raw Materials in China in Year 2021","volume":"9","author":"Hao","year":"2022","journal-title":"Front. 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