|
|
|
|
|
|
| A Convolutional Neural Network With Feature-Space Attention for Online Near-Infrared Detection of Tartaric Acid |
| LI Zhi-hao1, 2, XIAO Jin-feng1*, ZHANG Hong-ming2*, LÜ Bo2, 3*, YIN Xiang-hui1, LI Xiao-xing1, 2, ZHAO Ming4, MA Fei5 |
1. College of Electrical Engineering, University of South China, Hengyang 421001, China
2. Institute of Plasma Physics, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China
3. Science Island Branch, Graduate School, University of Science and Technology of China, Hefei 230031, China
4. College of Biological and Food Engineering, Anhui Polytechnic University, Hefei 241000, China
5. College of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
|
|
|
|
|
Abstract Tartaric acid, as an important organic acid, is widely present in wine, fruit juice, carbonated beverages, and certain confectionery products. Its concentration directly influences the balance between sweetness and acidity as well as the stability of flavor. During food production, the tartaric acid concentration may fluctuate due to variations in raw materials and formulation adjustments. Therefore, establishing a method for real-time online monitoring of tartaric acid concentration is crucial for ensuring product quality and production consistency. However, conventional detection methods (e. g., titration, HPLC) suffer from response delays and are unsuitable for real-time monitoring. Considering the multivariate, nonlinear, and dynamic characteristics of industrial processes, more accurate concentration prediction models are required. To address this, we integrate a one-dimensional convolutional neural network (1D-CNN) with a feature-space attention (FSA) mechanism, resulting in a CNN-FSA hybrid model. By conducting near-infrared (NIR) spectroscopy—driven experiments to detect tartaric acid concentration, this study explores the potential of CNN-FSA to improve prediction speed and model robustness, thereby providing an innovative approach for real-time online monitoring of solution-phase chemical processes. Spectral data were first processed using principal component analysis (PCA) combined with Mahalanobis distance to remove outliers, followed by standard normal variate (SNV) transformation to eliminate scattering and baseline drift. Subsequently, the proposed CNN-FSA model and the traditional partial least squares regression (PLSR) model were trained and evaluated. Model performance was comprehensively assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Six rounds of experiments were designed, with each round starting with 500 g of a mixed solution (water, ethanol, glucose, malic acid, and citric acid) as the initial substrate, supplemented with 500 g of a solution (475 g water+25 g tartaric acid). Data from the first four rounds were randomly split into training and test sets at a 7∶3 ratio. In comparison, data from the last two rounds were used as independent test sets to evaluate the model's generalization ability rigorously. On the independent prediction sets, the CNN-FSA model achieved outstanding performance: R2=0.989 6, RMSE=0.000 702, and MAE=0.000 580. In contrast, the PLSR model yielded R2=0.968 8, RMSE=0.001 214, and MAE=0.001 059. Compared with PLSR, CNN-FSA reduced RMSE by 42.17% and MAE by 45.23% on the independent prediction sets. The CNN-FSA model significantly outperforms PLSR in tartaric acid concentration prediction, demonstrating superior generalization and robustness on independent prediction datasets.
|
|
Received: 2025-06-28
Accepted: 2025-09-30
|
|
|
|
Corresponding Authors:
XIAO Jin-feng, ZHANG Hong-ming, LÜ Bo
E-mail: 806609919@qq.com；hmzhang@ipp.ac.cn；blu@ipp.ac.cn
|
|
[1] Li M, Xu Y, Men J, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 251: 119430.
[2] Liang J, Li M, Du Y, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 207: 104179.
[3] LI Lin-hao, XIANG Yu, ZHOU Kui(李琳皓, 向 宇, 周 奎). Speciality Petrochemicals(精细石油化工), 2025, 42(3): 43.
[4] LIANG Ying, GAO Hong-bin, LI Xiao-dan, et al(梁 颖, 高鸿彬, 李晓丹, 等). Guangzhou Chemistry(广州化学), 2020, 45(4): 23.
[5] Porep J U, Kammerer D R, Carle R. Trends in Food Science & Technology, 2015, 46(2): 211.
[6] Pedersen T, Rantanen J, Naelapää K, et al. Journal of Pharmaceutical and Biomedical Analysis, 2020, 181: 113059.
[7] Toscano G, Rinnan Å, Pizzi A, et al. Energy Fuels, 2017, 31(3): 2814.
[8] PENG Bin-qian, SHEN Fu-miao(彭彬倩, 沈福苗). China Food Safety Magazine(食品安全导刊), 2025, (5): 181.
[9] CAO Hui-ling, SHU He-lin, SHAO Jian-hui, et al(曹慧玲, 舒河霖, 邵建辉, 等). China Fruits(中国果树), 2021, (4): 8.
[10] JIA Yu-rong, YOU Kun(贾玉荣, 尤 昆). Tianjin Chemical Industry(天津化工), 2022, 36(2): 96.
[11] TAN Ai-ling, WANG Xiao-si, CHU Zhen-yuan, et al(谈爱玲, 王晓斯, 楚振原, 等). Food and Fermentation Industries(食品与发酵工业), 2020, 46(23): 213.
[12] CHEN Jing, WEN Ya-jun, ZHANG Yan-guo, et al(陈 晶, 温雅君, 张延国, 等). China Vegetables(中国蔬菜), 2024, (11): 106.
[13] ZHU Yu-kang, LU Chang-hua, ZHANG Yu-jun, et al(朱御康, 鲁昌华, 张玉钧, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2024, 44(9): 2607.
[14] ZHANG Yan-chen(张延琛). Agricultural Products Processing(农产品加工), 2025, (1): 57.
[15] Yan C. iScience, 2025, 28(7): 112759.
[16] LI Bo, ZHU Li, YAO Qing-yu, et al(李 博, 朱 莉, 姚庆宇, 等). Modern Food Science & Technology(现代食品科技), 2025, https://doi.org/10.13982/j.mfst.1673-9078.2025.8.0821.
[17] QI Dong-ming, YANG Qiang, LIU Qiang(戚栋铭, 杨 强, 刘 强). Journal of Beijing Institute of Petrochemical Technology(北京石油化工学院学报), 2024, 32(2): 5.
[18] Wang D, Zhao F, Wang R, et al. Frontiers in Plant Science, 2023, 14: 1138693.
[19] Beck T, Gatterning B, Delgado A. Heliyon, 2023, 9(11): e22039.
[20] LI Qiang, CHEN Bei, ZHANG Fang(李 强, 陈 蓓, 张 芳). Chinese Journal of Analytical Chemistry(分析化学), 2025, 53(3): 451.
[21] DENG Di, LI Hao, MOU Hong-bo(邓 迪, 李 昊, 牟洪波). Technology Innovation and Application(科技创新与应用), 2025, 15(14): 76.
[22] ZHOU Li-yuan, ZHAO Qi-jun, GAO Ding-guo(周丽媛, 赵启军, 高定国). World Science and Technology-Modernization of Traditional Chinese Medicine(世界科学技术-中医药现代化), 2022, 24(12): 4825.
[23] GAO Yu-min, ZHANG Yu-ru, DING Xiao-li, et al(高钰敏, 张雨茹, 丁小莉, 等). Journal of Tea Communication(茶叶通讯), 2025, 52(2): 239. |
| [1] |
WU Bin1, 2*, LIU Fu-bei3, WU Xiao-hong3. Spectral Classification Using Fuzzy Hyperbolic Cosine Discriminant Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(12): 3381-3386. |
| [2] |
YIN Wei-jian1, WEN Yu-kuan1, DONG Gui-mei1, YANG Ren-jie1, LI Liu-an2, YU Xiao-xue2, YU Ya-ping1*. Qualitative and Quantitative Analyses of Egg Yolks Adulterated With
Sudan Red Ⅰ Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(12): 3415-3421. |
| [3] |
WU Tian1, 3*, WANG Ling-zhi1, 3, QIU Zhong-hua2, LI Ming-dian1, WU Chen1, 3, WU Bin-fan1, 3, GU Tong1, 3. Research on the Near-Infrared Spectroscopy Detection Method of Water Absorption Rate of Silicone Rubber Based on CPO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(12): 3514-3523. |
| [4] |
ZHANG Ying1, 2, ZHANG Chi1, 2, SHI Jian-bo1, 2, LIU Si-si3*. Advances in the Application of Machine Learning to the Spectrum
Detection of Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(11): 3001-3010. |
| [5] |
ZHANG Sheng-jie1, ZHAI Chen2*, XING Wei-hai2, FENG Xiao-hui2, YANG Ying-kang2, YANG You-you2, SHI Chao3, YANG Zhou2, WANG Wen-xiu1*. Development of Predictive Models for Fatty Acids Content in Multiple Pork Muscle Sites Using Spectroscopic Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(11): 3122-3129. |
| [6] |
LI Han, SUN Wei-zhe, CHEN Xi-yuan, ZHANG Guan-jun, LI Yuan*. Comparison Study of Four Transfer Learning Architectures for Degree of Polymerization Assessment of Insulating Paper Across Different
Instruments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(11): 3145-3152. |
| [7] |
SUN Wei-zhe, LI Han, CHEN Xi-yuan, ZHANG Guan-jun, LI Yuan*. Study on Spectral On-Site Rapid Identification Method of Cable Insulation Material Models[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(11): 3153-3159. |
| [8] |
ZHANG Shuo1, 2, 3, LI Yu1, JIANG Tong1*, HUANG Jian-han1, HUANG Yin-wei1, 4, TIAN Jing-chun3, SHAN Mao-yu1, LI Pei-yao1. SWIR Spectral Characteristics and Mineral Phase Transformations: Multi-Indicator Synergistic Response for Granite Weathering Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(11): 3235-3245. |
| [9] |
ZHANG Xu1, XIE Zhuo-jun1, QIN Zi-quan1, ZHAO Rui-jie1, LIU Wen-zheng1, BAI Xue-bing1, XIONG Xiao-lin2*, LIU Xu1. Research Progress of Miniaturized Vis/NIR Spectrometers in Leaf and Fruit Nondestructive Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2720-2729. |
| [10] |
SUN Xu-dong1, LONG Tao1, WANG Jia-hua2, FENG Shao-ran3, ZENG Ti-wei4, XIE Dong-fu1, FU Wei1. Near-Infrared Spectroscopy Prediction of the Optimal Harvest Date for Autumn Moon Pear: Considering the Correction of Ambient Light Changes and Inter-Instrument Differences[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2774-2782. |
| [11] |
ZHAO Xiao-yan1*, ZHENG Shao-wen1, WU Xian-hao1, SUN Zhi-yan2, 3, TAO Rui2, 3, ZHANG Tian-yao1, YUAN Yuan1, LIU Xing4, ZHOU Da-biao2, 3, ZHANG Zhao-hui1, YANG Pei2, 3*. Prediction of EGFR Amplification Status of Glioma Based on Terahertz Spectral Data With Convolutional Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2856-2862. |
| [12] |
ZHANG Xin-zhi1, SAIMAITI Patiguli1, ZHANG Lu-wen1, YANG Ya-fei2*, BIAN Xi-hui1, 3*. Research Progress on Geographical Origin Discrimination of Traditional Chinese Medicines Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2410-2416. |
| [13] |
ZHANG Xue-li1, 2, YANG Hao1, 2, LI Chen-fei1, 2, SUN Yi-le1, 2, LIU Zong-lin1, 2, ZHENG De-cong1, 2, SONG Hai-yan1, 2*. Origin Discrimination and Soluble Protein Content Prediction of Dried Daylily Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2491-2495. |
| [14] |
BU Zi-chuan, LIU Ji-hong*, REN Kai-li, LIU Chi, ZHANG Jia-geng, YAN Xue-wen. Raman Spectral Sample Data Enhancement Method Based on Voigt
Function[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2502-2510. |
| [15] |
ZHU Li-wei, DU Qian-xi, TANG Guo-hong, LI Hong-you, ZHANG Xiao-na, CHEN Qing-fu, SHI Tao-xiong*. Near-Infrared Modeling for Total Flavonoid and Protein Contents in
Buckwheat Leaves Based on CARS Feature Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2585-2589. |
|
|
|
|
