A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions

Abstract

With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an emerging technology, near infrared spectroscopy (NIRS) offers numerous advantages, such as preserving sample integrity, generating objective results, and enabling rapid, straightforward assessments. These features make it an ideal choice for real-time tea quality testing. This paper systematically reviews the principles of NIRS, spectral preprocessing methods, statistical modeling techniques, and commonly used machine learning approaches. Furthermore, it provides an in-depth discussion of the research progress of NIRS in areas such as fresh tea leaf quality evaluation, rapid detection of tea-specific components, tea quality assessment and species identification, geographic traceability, development of NIRS equipment, and standardization. Future research directions in the tea field are also proposed. This review serves as a valuable resource for researchers aiming to understand the application and development of NIRS technology in the tea field. It offers insights to facilitate real-time tea quality monitoring and ultimately achieve intelligent quality control.