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Rapid detection of colorants in black tea using mid- and short-wave near infrared spectroscopy

文献类型： 外文期刊

作者： Luo, Zhengfei 1 ; Chai, Yunfeng 2 ; Zhao, Guohua 4 ; Qiao, Dongling 1 ; Ye, Fayin 1 ; Lei, Lin 1 ; Chen, Jia 1 ;

作者机构： 1.Southwest Univ, Coll Food Sci, Chongqing Key Lab Special Food Cobuilt Sichuan & C, Chongqing 400715, Peoples R China

2.Chinese Acad Agr Sci, Tea Res Inst, Key Lab Tea Qual & Safety Control, Minist Agr & Rural Affairs, Hangzhou 310008, Peoples R China

3.West Yunnan Univ, Coll Biotechnol & Engn, Yunnan Key Lab Tea Germplasm Conservat & Utilizat, Lincang 677000, Peoples R China

4.Sichuan Normal Univ, Coll Life Sci, Chengdu 610101, Peoples R China

期刊名称：ANALYTICAL METHODS （ 影响因子：2.6； 五年影响因子：2.8 ）

ISSN： 1759-9660

年卷期： 2025 年 17 卷 28 期

页码：

收录情况： SCI

摘要： This study investigated the feasibility of using mid- and short-wave near-infrared (MS-NIR) spectroscopy for the rapid detection of colorants in black tea. A portable spectrometer was employed to acquire MS-NIR spectra from black tea samples. Support vector machine (SVM) and random forest (RF) models were developed for the discriminative detection of three colorants: tartrazine, sunset yellow, and ponceau 4R. The spectral preprocessing was optimized, and the predictive performance of the models was evaluated using validation data. The results indicated that, owing to the low concentration of colorants in black tea, the MS-NIR-based model was unsuitable for quantitative detection but effective for determining whether colorants were present. Overall, the discriminative capability of the SVM model surpassed that of the RF model. Following spectral preprocessing, the optimal SVM model achieved accuracy, precision, recall, and F1-score values of (97.50%, 96.15%, 100.00%, 0.9804), (95.00%, 96.00%, 96.00%, 0.9600), and (97.50%, 96.15%, 100.00%, 0.9804) for tartrazine, sunset yellow, and ponceau 4R, respectively. These findings demonstrate the feasibility of using MS-NIR for the rapid and discriminative identification of colorants in black tea. In practical applications, discriminative detection can serve as an initial rapid screening tool, followed by more precise quantitative detection methods to determine colorant concentrations.

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