FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration
Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were iden...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Food Chemistry: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157524006862 |
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| author | Ying Chen Si Li Jia Jia Chuanduo Sun Enzhong Cui Yunyan Xu Fangchao Shi Anfu Tang |
| author_facet | Ying Chen Si Li Jia Jia Chuanduo Sun Enzhong Cui Yunyan Xu Fangchao Shi Anfu Tang |
| author_sort | Ying Chen |
| collection | DOAJ |
| description | Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR. |
| format | Article |
| id | doaj-art-d5c2700bfc774ce8b4ac98ce9f4de7ff |
| institution | Kabale University |
| issn | 2590-1575 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry: X |
| spelling | doaj-art-d5c2700bfc774ce8b4ac98ce9f4de7ff2024-12-13T11:01:14ZengElsevierFood Chemistry: X2590-15752024-12-0124101798FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentrationYing Chen0Si Li1Jia Jia2Chuanduo Sun3Enzhong Cui4Yunyan Xu5Fangchao Shi6Anfu Tang7Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR ChinaDepartment of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR ChinaDepartment of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR ChinaCentral Medical Branch of PLA General Hospital, PR ChinaDepartment of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR ChinaDepartment of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR ChinaDepartment of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China; Corresponding authors.Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China; Corresponding authors.Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR.http://www.sciencedirect.com/science/article/pii/S2590157524006862Pericarpium citri reticulataeFood adulterationFT-NIRMachine learningQuantitative analysis |
| spellingShingle | Ying Chen Si Li Jia Jia Chuanduo Sun Enzhong Cui Yunyan Xu Fangchao Shi Anfu Tang FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration Food Chemistry: X Pericarpium citri reticulatae Food adulteration FT-NIR Machine learning Quantitative analysis |
| title | FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration |
| title_full | FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration |
| title_fullStr | FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration |
| title_full_unstemmed | FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration |
| title_short | FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration |
| title_sort | ft nir combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae chenpi and predict the adulteration concentration |
| topic | Pericarpium citri reticulatae Food adulteration FT-NIR Machine learning Quantitative analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2590157524006862 |
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