Peng Yuhan, Xia Jiaxu, Li Qingxiang, Bi Yiming, Li Shitou, Wang Hui
Technology Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou 310012, China.
Key Laboratory of Refrigeration and Cryogenic Technology of Zhejiang Province, Zhejiang University, Hangzhou 310027, China.
ACS Omega. 2024 Nov 26;9(49):48196-48204. doi: 10.1021/acsomega.4c05472. eCollection 2024 Dec 10.
A methodology for predicting proximate and ultimate analysis data was developed by using near-infrared spectroscopy (NIR) combined with chemometric methods. The quantitative model has high accuracy, as evidenced by low root-mean-square-error of prediction (RMSEP) values (e.g., 0.41% for volatile matter and 0.29% for carbon). The model was further applied to tobaccos with distinct aroma profiles, and the predicted ultimate and proximate data lead to aroma classification with 86.6% accuracy. This methodology can be expanded to the aroma discrimination of imported tobaccos from Brazil, the United States, Canada, and Zimbabwe, demonstrating its broad reliability. Compared with traditional analyses, this NIR-based approach offers a fast and accurate method for large-scale tobacco evaluation, highlighting its potential for enhancing tobacco quality characterization through a quantifiable, digital, and high-throughput process.
通过结合近红外光谱（NIR）和化学计量学方法，开发了一种预测烟草工业分析和元素分析数据的方法。该定量模型具有很高的准确性，预测均方根误差（RMSEP）值较低（例如，挥发物为0.41%，碳为0.29%）即可证明。该模型进一步应用于具有不同香气特征的烟草，预测的元素分析和工业分析数据实现了86.6%准确率的香气分类。该方法可扩展到对来自巴西、美国、加拿大和津巴布韦的进口烟草进行香气鉴别，证明了其广泛的可靠性。与传统分析相比，这种基于近红外光谱的方法为大规模烟草评估提供了一种快速准确的方法，突出了其通过可量化、数字化和高通量过程提升烟草质量特征描述的潜力。
