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Zhan GZ, Guo XY, Qiu ZC, Cai LY, Hu Q, Gao Y, Tang SW, Li CY, Zheng YF, Peng GP. Rapid evaluation of the quality of Smilax glabra Roxb. using QADS based on FT-NIR combined with multiple intelligent algorithms. Food Chem 2024; 453:139633. [PMID: 38781896 DOI: 10.1016/j.foodchem.2024.139633] [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: 03/09/2024] [Revised: 04/15/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024]
Abstract
Smilax glabra Roxb. (SGR) is known for its high nutritional and therapeutic value. However, the frequent appearance of counterfeit products causes confusion and inconsistent quality among SGR varieties. Herein, this study collected the proportion of SGR adulteration and used high-performance liquid chromatography (HPLC) to measure the astilbin content of SGR. Then Fourier-transform near-infrared (FT-NIR) technology, combined with multivariate intelligent algorithms, was used to establish partial least squares regression quantitative models for detecting SGR adulteration and measuring astilbin content, respectively. The method conducted a quantitative analysis of dual indicators through single-spectrum data acquisition (QADS) to comprehensively evaluate the authenticity and superiority of SGR. The coefficients of determination (R2) for both the calibration and prediction sets exceeded 0.96, which successfully leverages FT-NIR combined with multivariate intelligent algorithms to considerably enhance the accuracy and reliability of quantitative models. Overall, this research holds substantial value in the comprehensive quality evaluation in functional health foods.
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Affiliation(s)
- Geng-Zhi Zhan
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Xin-Yue Guo
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Zi-Chao Qiu
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Lu-Yao Cai
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Qian Hu
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Ye Gao
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Shu-Wan Tang
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Cun-Yu Li
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China; National Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Nanjing 211100, China; Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yun-Feng Zheng
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China; National Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Nanjing 211100, China; Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Guo-Ping Peng
- Department of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China; National Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Nanjing 211100, China; Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China.
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de Araújo Gomes A, Azcarate SM, Diniz PHGD, de Sousa Fernandes DD, Veras G. Variable selection in the chemometric treatment of food data: A tutorial review. Food Chem 2022; 370:131072. [PMID: 34537434 DOI: 10.1016/j.foodchem.2021.131072] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/15/2021] [Accepted: 09/03/2021] [Indexed: 12/13/2022]
Abstract
Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.
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Affiliation(s)
- Adriano de Araújo Gomes
- Universidade Federal do Rio Grande do Sul, Instituto de Química, 90650-001 Porto Alegre, RS, Brazil
| | - Silvana M Azcarate
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa, Instituto de Ciencias de la Tierra y Ambientales de La Pampa (INCITAP), Av. Uruguay 151, 630 0 Santa Rosa, La Pampa, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnicas (CONICET), Godoy Cruz 2290 CABA (C1425FQB), Argentina
| | | | | | - Germano Veras
- Laboratório de Química Analítica e Quimiometria, Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, 58429-500 Campina Grande, PB, Brazil
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