1
|
Ma H, Zhao Y, He W, Wang J, Hu Q, Chen K, Yang L, Ma Y. Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124273. [PMID: 38615417 DOI: 10.1016/j.saa.2024.124273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 03/25/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.
Collapse
Affiliation(s)
- Hongliang Ma
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China.
| | - Yu Zhao
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Wenxiu He
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Jiwen Wang
- National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Qianqian Hu
- National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Kehan Chen
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China
| | - Lianlin Yang
- National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| | - Yonglin Ma
- Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China; National and Local Joint Engineering Research Center for Ultrafine Granular Powder of Herbal Medicine, Zhongshan Zhongzhi Pharmaceutical Group Co., Ltd., Zhongshan 528437, China
| |
Collapse
|
2
|
Rapid determination of protein, starch and moisture contents in wheat flour by near-infrared hyperspectral imaging. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
3
|
Sun Y, Zhang H, Liu G, He J, Cheng L, Li Y, Pu F, Wang H. Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02275-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
4
|
Spatio-Temporal Variation Characteristics of Aboveground Biomass in the Headwater of the Yellow River Based on Machine Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13173404] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Accurate estimation of the aboveground biomass (AGB) of grassland is a key link in understanding the regional carbon cycle. We used 501 aboveground measurements, 29 environmental variables, and machine learning algorithms to construct and verify a custom model of grassland biomass in the Headwater of the Yellow River (HYR) and selected the random forest model to analyze the temporal and spatial distribution characteristics and dynamic trends of the biomass in the HYR from 2001 to 2020. The research results show that: (1) the random forest model is superior to the other three models (R2val = 0.56, RMSEval = 51.3 g/m2); (2) the aboveground biomass in the HYR decreases spatially from southeast to northwest, and the annual average value and total values are 176.8 g/m2 and 20.73 Tg, respectively; (3) 69.51% of the area has shown an increasing trend and 30.14% of the area showed a downward trend, mainly concentrated in the southeast of Hongyuan County, the northeast of Aba County, and the north of Qumalai County. The research results can provide accurate spatial data and scientific basis for the protection of grassland resources in the HYR.
Collapse
|