Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy
With the increasing demand for the multi-dimensional composition detection and rapid traceability of wheat flour, this study presents the application of a combination of laser-induced breakdown spectroscopy (LIBS) and Raman Spectroscopy for traceability of wheat flour from five provinces of China, i...
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| Language: | English |
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Elsevier
2025-07-01
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| Series: | Results in Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211715625004291 |
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| author | Minghui Gu Chao Liu Hansong Huang Xin Zhang Jiguo Li Qingbin Jiao Liang Xu Mingyu Yang Xin Tan |
| author_facet | Minghui Gu Chao Liu Hansong Huang Xin Zhang Jiguo Li Qingbin Jiao Liang Xu Mingyu Yang Xin Tan |
| author_sort | Minghui Gu |
| collection | DOAJ |
| description | With the increasing demand for the multi-dimensional composition detection and rapid traceability of wheat flour, this study presents the application of a combination of laser-induced breakdown spectroscopy (LIBS) and Raman Spectroscopy for traceability of wheat flour from five provinces of China, including Henan, Shanxi, Anhui, Shandong, and Hebei. A 2D Convolutional Neural Network (2D CNN) based on feature selection was proposed for origin classification. First, a hybrid feature selection method, termed ANOVA-Sine Cosine Algorithm (AVSCA), was developed by integrating analysis of variance (ANOVA) with the Sine Cosine Algorithm (SCA) to extract spectral fingerprint information of wheat flour. Next, we established a 2D CNN model by transforming the one-dimensional spectral data into a square matrix, and trained it with LIBS-Raman data under low-level, mid-level and high-level fusion strategies. Then, the model’s metrics were obtained using 10-fold cross-validation optimized by Euclidean distance. Finally, to enhance the practical applicability of the model, model parameter transfer techniques were introduced, with a small amount of wheat flour spectral data from five other provinces used as the training set to fine-tune the established 2D CNN model. The results showed that the proposed method can enhance the ability of origin traceability, and high-level fusion strategies perform the best, achieving an average accuracy of 98%. The transfer model achieved a prediction accuracy of 97% on the remaining data, demonstrating the effectiveness of transfer learning. |
| format | Article |
| id | doaj-art-e469bd0b3dc248ca8adbe8e2f1b3be33 |
| institution | DOAJ |
| issn | 2211-7156 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Chemistry |
| spelling | doaj-art-e469bd0b3dc248ca8adbe8e2f1b3be332025-08-20T03:23:33ZengElsevierResults in Chemistry2211-71562025-07-011610244610.1016/j.rechem.2025.102446Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopyMinghui Gu0Chao Liu1Hansong Huang2Xin Zhang3Jiguo Li4Qingbin Jiao5Liang Xu6Mingyu Yang7Xin Tan8Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of the Chinese Academy of Sciences, Beijing, 100049, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of the Chinese Academy of Sciences, Beijing, 100049, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of the Chinese Academy of Sciences, Beijing, 100049, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of the Chinese Academy of Sciences, Beijing, 100049, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of the Chinese Academy of Sciences, Beijing, 100049, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, ChinaChinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; Corresponding authors.Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; Corresponding authors.With the increasing demand for the multi-dimensional composition detection and rapid traceability of wheat flour, this study presents the application of a combination of laser-induced breakdown spectroscopy (LIBS) and Raman Spectroscopy for traceability of wheat flour from five provinces of China, including Henan, Shanxi, Anhui, Shandong, and Hebei. A 2D Convolutional Neural Network (2D CNN) based on feature selection was proposed for origin classification. First, a hybrid feature selection method, termed ANOVA-Sine Cosine Algorithm (AVSCA), was developed by integrating analysis of variance (ANOVA) with the Sine Cosine Algorithm (SCA) to extract spectral fingerprint information of wheat flour. Next, we established a 2D CNN model by transforming the one-dimensional spectral data into a square matrix, and trained it with LIBS-Raman data under low-level, mid-level and high-level fusion strategies. Then, the model’s metrics were obtained using 10-fold cross-validation optimized by Euclidean distance. Finally, to enhance the practical applicability of the model, model parameter transfer techniques were introduced, with a small amount of wheat flour spectral data from five other provinces used as the training set to fine-tune the established 2D CNN model. The results showed that the proposed method can enhance the ability of origin traceability, and high-level fusion strategies perform the best, achieving an average accuracy of 98%. The transfer model achieved a prediction accuracy of 97% on the remaining data, demonstrating the effectiveness of transfer learning.http://www.sciencedirect.com/science/article/pii/S2211715625004291LIBSRaman spectroscopy2D CNNData fusion strategiesModel parameter transfer |
| spellingShingle | Minghui Gu Chao Liu Hansong Huang Xin Zhang Jiguo Li Qingbin Jiao Liang Xu Mingyu Yang Xin Tan Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy Results in Chemistry LIBS Raman spectroscopy 2D CNN Data fusion strategies Model parameter transfer |
| title | Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy |
| title_full | Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy |
| title_fullStr | Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy |
| title_full_unstemmed | Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy |
| title_short | Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy |
| title_sort | enhancing wheat flour origin traceability by using laser induced breakdown spectroscopy and raman spectroscopy |
| topic | LIBS Raman spectroscopy 2D CNN Data fusion strategies Model parameter transfer |
| url | http://www.sciencedirect.com/science/article/pii/S2211715625004291 |
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