Rapid Detection of Moisture Content in the Processing of Longjing Tea by Micro-Near-Infrared Spectroscopy and a Portable Colorimeter Based on a Data Fusion Strategy

• Published in: Horticulturae Vol. 8; no. 11; p. 1007
• Main Authors: Zong, Xuyan, Sheng, Xufeng, Li, Li, Zan, Jiezhong, Jiang, Yongwen, Zou, Hanting, Shen, Shuai, Yuan, Haibo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Adaptive sampling; Chemical properties; Correlation coefficient; Correlation coefficients; Data collection; data fusion; Data integration; Infrared spectra; Infrared spectrometers; Infrared spectroscopy; Least squares; Longjing tea; Measurement; micro-NIR spectrometer; Model accuracy; Moisture; Moisture content; Moisture effects; Multisensor fusion; Near infrared radiation; Near infrared spectroscopy; Polyphenols; Portability; Prediction models; Principal components analysis; Quantitative analysis; Sensors; Smartphones; Spectrum analysis; Tea; tea quality; Testing; Water content; China; Japan; Taiwan
• ISSN: 2311-7524; 2311-7524
• DOI: 10.3390/horticulturae8111007

Moisture content (MC) is an important indicator to monitor the quality of Longjing tea during processing; therefore, it becomes more critical to develop digital moisture content detection methods for processing. In this study, based on a micro-near infrared (NIR) spectrometer and portable colorimeter, we used Longjing tea under the full processing process as the research object, and used competitive adaptive reweighted sampling (CARS) and a principal component analysis (PCA) to extract characteristic bands of spectral data as well as the principal component reduction processing of the color difference and glossiness data, respectively, combined with sensor data fusion technology to establish a quantitative prediction model of the partial least squares (PLS) for the moisture content of Longjing tea. The PLS quantitative moisture content prediction model, based on middle-level data fusion, obtained the best prediction accuracy and model robustness, with the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) being 0.9823 and 0.0333, respectively, with a residual predictive deviation (RPD) of 6.5287. The results indicate that a data fusion of a micro NIR spectrometer and portable Colorimeter is feasible to establish a quantitative prediction model of the moisture content in Longjing tea processing, while multi-sensor data fusion can overcome the problem of a low prediction accuracy for the model established by single sensor data. More importantly, data fusion based on low-cost, fast, and portable detection sensors can provide new ideas and methods for real-time online detection in Longjing tea in actual production.