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Wang S, Altaner C, Feng L, Liu P, Song Z, Li L, Gui A, Wang X, Ning J, Zheng P. A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions. Food Res Int 2025; 205:115870. [PMID: 40032446 DOI: 10.1016/j.foodres.2025.115870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Revised: 01/16/2025] [Accepted: 01/29/2025] [Indexed: 03/05/2025]
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.
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Affiliation(s)
- Shengpeng Wang
- Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064 China; Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Wuhan 430064 China
| | - Clemens Altaner
- School of Forestry, University of Canterbury, Christchurch 8140 New Zealand
| | - Lin Feng
- Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064 China; Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Wuhan 430064 China
| | - Panpan Liu
- Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064 China; Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Wuhan 430064 China
| | - Zhiyu Song
- Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014 China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036 China
| | - Anhui Gui
- Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064 China; Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Wuhan 430064 China
| | - Xueping Wang
- Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064 China; Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Wuhan 430064 China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036 China.
| | - Pengcheng Zheng
- Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064 China; Key Laboratory of Tea Resources Comprehensive Utilization, Ministry of Agriculture and Rural Affairs, Wuhan 430064 China.
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Alaoui Mansouri M, Kharbach M, Bouklouze A. Current Applications of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in Pharmaceutical Analysis: Review. J Pharm Sci 2024; 113:856-865. [PMID: 38072117 DOI: 10.1016/j.xphs.2023.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/22/2023]
Abstract
The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed.
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Affiliation(s)
- Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, FI-90014 Oulu, Finland; University of Liege (ULiege), CIRM, Vibra-Santé HUB, Laboratory of Pharmaceutical Analytical Chemistry, CHU, B36, B-4000, Liege, Belgium.
| | - Mourad Kharbach
- Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland.
| | - Abdelaziz Bouklouze
- Bio-Pharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat, Morocco
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