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Sun X, Zhang H, Liu C, Zhang S, Yan S, Zhao K, Hu Y. Characterizing the concentration of ethanol-water solutions by oblique-incidence reflectivity difference combined with deep learning algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 325:125069. [PMID: 39241400 DOI: 10.1016/j.saa.2024.125069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Revised: 08/08/2024] [Accepted: 08/26/2024] [Indexed: 09/09/2024]
Abstract
The detection of ethanol-water solution concentration plays an important role in industries, medical care, food and other aspects, which has attracted much attention. In this paper, a 632.8 nm laser combined with the oblique-incidence reflectivity difference (OIRD) method was used to obtain a signal linearly related to the solution concentration and containing the information of the dielectric constant of the solution. Combined with a variety of deep learning algorithms, ethanol-water solutions with a volume concentration of 0-95 % are detected. Among them, the prediction accuracy of the MLP, CNN, LSTM, CNN + BiLSTM + Attention models were 93.65 %, 96.54 %, 97.12 %, 99.23 %, respectively. The experimental results indicate that the OIRD method can achieve rapid, non-destructive, accurate and reliable detection of ethanol-water solutions.
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Affiliation(s)
- Xiaorong Sun
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Haoyue Zhang
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Shanzhe Zhang
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Sining Yan
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Kun Zhao
- College of New Energy and Materials, China University of Petroleum, Beijing 102249, China.
| | - Yiran Hu
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
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Scharinger M, Kuntz M, Scharinger A, Teipel J, Kuballa T, Walch SG, Lachenmeier DW. Rapid Approach to Determine Propionic and Sorbic Acid Contents in Bread and Bakery Products Using 1H NMR Spectroscopy. Foods 2021; 10:526. [PMID: 33802459 PMCID: PMC7998730 DOI: 10.3390/foods10030526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/25/2021] [Accepted: 02/28/2021] [Indexed: 11/16/2022] Open
Abstract
The food additive sorbic acid is considered as an effective preservative for certain cereal products, and propionic acid is commonly added in bakery wares, e.g., bread and fine bakery wares. The aim of this study was to develop and validate a new nuclear magnetic resonance spectroscopy (1H NMR) method for the routine screening and quantification of sorbic and propionic acids in bread and several bakery products for quality control purposes. Results showed that none of the screened samples contained higher concentrations than regulatory maximum limits. However, for some samples, labelling of preservatives was lacking or they were used in food categories, for which the use is not approved. It can be concluded that the developed NMR method can be used for the routine screening of bakery products.
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Affiliation(s)
- Marwa Scharinger
- Unit of Research of Plant Ecology, Faculty of Sciences, Campus Academia, University of Tunis El-Manar II, Tunis 2092, Tunisia;
| | - Marcel Kuntz
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, D-76187 Karlsruhe, Germany; (M.K.); (A.S.); (J.T.); (T.K.); (S.G.W.)
| | - Andreas Scharinger
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, D-76187 Karlsruhe, Germany; (M.K.); (A.S.); (J.T.); (T.K.); (S.G.W.)
| | - Jan Teipel
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, D-76187 Karlsruhe, Germany; (M.K.); (A.S.); (J.T.); (T.K.); (S.G.W.)
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, D-76187 Karlsruhe, Germany; (M.K.); (A.S.); (J.T.); (T.K.); (S.G.W.)
| | - Stephan G. Walch
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, D-76187 Karlsruhe, Germany; (M.K.); (A.S.); (J.T.); (T.K.); (S.G.W.)
| | - Dirk W. Lachenmeier
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, D-76187 Karlsruhe, Germany; (M.K.); (A.S.); (J.T.); (T.K.); (S.G.W.)
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