基于 FT-IR 光谱与波长优化的鲜味物质定量检测

Habib Baraka Omar¹，王益健¹，牛晓虎¹，张胜¹，贾桂锋^1,2,3，朱明^1,2,3，冯耀泽^1,2,3,4,5,6*，Douglas Fernandes Barbin^⁷*

(1. 华中农业大学工学院，武汉430070，中国；

2. 农业农村部长江中下游农业装备重点实验室，武汉430070，中国；

3. 农业农村部水产养殖设施工程重点实验室，武汉430070，中国；

4. 华中农业大学交叉科学研究院，武汉430070，中国；

5. 华中农业大学深圳营养与健康研究院，深圳518000，广东，中国；

6. 中国农业科学院农业基因组研究所深圳，农业农村部基因组分析实验室，广东省岭南现代农业实验室深圳分中心，深圳518120，广东，中国；

7. 巴西坎皮纳斯大学食品工程学院，巴西)

摘要：鲜味是五种基本味觉之一，主要由谷氨酸钠（MSG）和 5′-肌苷酸二钠（IMP）所代表。目前，MSG和IMP的检测方法普遍存在成本高、操作复杂等问题，限制了其在实际中的广泛应用。因此，有必要探索一种新型、经济且高效的鲜味物质表征方法。该研究采用傅里叶变换红外光谱（FT-IR）技术对鲜味物质进行检测，包括谷氨酸钠（MSG）及其与5′-肌苷酸二钠（IMP）的混合体系。

采用均匀光谱间隔法（USS）分别结合连续投影算法（SPA）、竞争性自适应加权算法（CARS）和无信息变量消除法（UVE）进行特征波长筛选，并用于简化偏最小二乘回归（PLSR）和主成分回归（PCR）预测模型。结果表明，对于MSG溶液检测，最优模型为基于17个特征波长的USS-CARS-PCR定量简化模型，其校正集决定系数 R_c² = 0.97，RMSE_c = 0.23 g/L；预测集决定系数 R_p²=0.96，RMSE_p=0.27 g/L。对于MSG与IMP混合体系检测，最优模型为全波长模型，其R_c²=0.97，RMSE_c=0.11 g/L；R_p²=0.98，RMSE_p=0.07 g/L。研究结果表明，FT-IR光谱技术可实现鲜味物质的快速、定量检测，为复杂食品体系中鲜味物质的无损检测提供了理论依据与技术支撑。

关键词：5′-肌苷酸二钠（IMP）；傅里叶变换红外光谱（FT-IR）；谷氨酸钠（MSG）；鲜味；波长筛选

DOI: 10.25165/j.ijabe.20261901.9932

引用信息: Omar H B, Wang Y J, Niu X H, Zhang S, Jia G F, Zhu M, et al. Quantitative detection of umami substances using FT IR spectroscopy and wavelength optimization. Int J Agric & Biol Eng, 2026; 19(1): 263–269.

阅读全文链接：http://ijabe.net/article/doi/10.25165/j.ijabe.20261901.9932

Quantitative detection of umami substances using FT-IR spectroscopy and wavelength optimization

Habib Baraka Omar¹, Yijian Wang¹, Xiaohu Niu¹, Sheng Zhang¹, Guifeng Jia^1,2,3, Ming Zhu^1,2,3, Yaoze Feng^1,2,3,4,5,6*, Douglas Fernandes Barbin^7*

(1.College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 

2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China; 

3. Key Laboratory of Aquaculture Facilities Engineering, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China; 

4. Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China; 

5. Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China; 

6. Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; 

7. School of Food Engineering, University of Campinas – Unicamp, Brazil)

Abstract: Umami is one of the five basic tastes, primarily represented by monosodium glutamate (MSG) and disodium 5′ inosinate (IMP). The current primary methods for detecting MSG and IMP are expensive and complex, limiting their widespread applications. Hence, there is a need to explore novel and more affordable methods to characterize umami taste substances. FT-IR was used to detect umami substances, MSG, and its mixture with IMP. Uniform spectral spacing method (USS) was combined separately with a continuous projection algorithm (SPA), competitive adaptive weighting algorithm (CARS), and uninformed variable elimination method (UVE) to simplify partial least squares regression (PLSR) and principal component regression (PCR) prediction models. The results demonstrated that the optimal model for MSG solution detection was the USS-CARS-PCR quantitative simplified model based on 17 feature wavelengths with R_c² =0.96, and RMSE_p =0.97, RMSE_c =0.23 g/L, R_p² =0.27 g/L. For MSG and IMP mixture detection, the optimal model was the full wavelength model with R_c² =0.97, RMSE_c =0.11 g/L, R_p² =0.98, and RMSE_p =0.07 g/L. These findings indicate the feasibility of using FT-IR spectroscopy for rapid and quantitative detection of umami substances, providing a theoretical basis for detecting complex umami substances in food using FT-IR technology.

Keywords: disodium 5′-inosinate (IMP), FT-IR, monosodium glutamate (MSG), umami, wavelength selection

DOI: 10.25165/j.ijabe.20261901.9932

Citation: Omar H B, Wang Y J, Niu X H, Zhang S, Jia G F, Zhu M, et al. Quantitative detection of umami substances using FT IR spectroscopy and wavelength optimization. Int J Agric & Biol Eng, 2026; 19(1): 263–269.

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