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Zhou C, Liu L, Chen J, Fu Q, Chen Z, Wang J, Sun X, Ai L, Xu X, Wang J. Rapid authentication of characteristic milk powders by recombinase polymerase amplification assays. Food Chem 2024; 443:138540. [PMID: 38277935 DOI: 10.1016/j.foodchem.2024.138540] [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: 10/16/2023] [Revised: 12/30/2023] [Accepted: 01/21/2024] [Indexed: 01/28/2024]
Abstract
The authentication of dairy species has great significance for food safety. This study focused on a more rapid method for identifying major dairy species, and specific recombinase polymerase amplification (RPA)-based assays for cattle, goat, sheep, camel and donkey were developed. Through the developed RPA-based assays, goats and sheep could be simultaneously identified and bovine families could be differentiated. The performances of the RPA assays were validated using 37 milk powder samples, of which 16.2% (6/37) were suspected of being adulterated and 24.3% (9/37) were potentially at risk of being wrongly identified as adulteration. The effectiveness of the developed assays for crude DNA detection was also validated by a rapid nucleic acid extraction kit, and results showed that the presence of large amounts of protein and fat did not affect the qualitative results. Therefore, these assays could combine with the rapid nucleic acids extraction methods for being used in field detection.
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Affiliation(s)
- Cang Zhou
- School of Public Health, Hebei Medical University, Shijiazhuang 050017, China; Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Libing Liu
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Jia Chen
- College of Chemical Technology, Shijiazhuang University, Shijiazhuang 050035, China
| | - Qi Fu
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Zhimin Chen
- Shijiazhuang Food and Drug Inspection Center, Shijiazhuang 050020, China
| | - Jinfeng Wang
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Xiaoxia Sun
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Lianfeng Ai
- Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China
| | - Xiangdong Xu
- School of Public Health, Hebei Medical University, Shijiazhuang 050017, China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China.
| | - Jianchang Wang
- School of Public Health, Hebei Medical University, Shijiazhuang 050017, China; Food Microbiology and Animal Quarantine Laboratory, Technology Center of Shijiazhuang Customs, Shijiazhuang 050051, China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China.
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Ai N, Liu R, Chi X, Song Z, Shao Y, Xi Y, Zhao T, Sun B, Xiao J, Deng J. Rapid discrimination of the identity of infant formula by triple-channel models. Food Chem 2023; 423:136302. [PMID: 37167671 DOI: 10.1016/j.foodchem.2023.136302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/11/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023]
Abstract
Infant formula is related to children's life and health. However, the existing identification methods for infant formula are time-consuming, costly and prone to environmental pollution. Therefore, a simple, efficient and less polluting identification method for infant formula is urgently needed. The aim of this study was to distinguish between goat and cow infant formula using HS-SPME-GC-MS and E-nose combined with triple-channel models. The results indicated that the main difference of them attributed to thirteen volatile compounds and three sensor variables. Based on this, the linear discriminant and partial least squares discriminant analyses were conducted, and a multilayer perceptron neural network model was constructed to identify the commercial samples. There was a high percentage of correct classifications (>90%) in samples. Together, our work demonstrated that the volatile compounds of infant formula combined with chemometric analysis were effective and rapid for detecting two infant formulas.
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Affiliation(s)
- Nasi Ai
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Ruirui Liu
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Xuelu Chi
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Zheng Song
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Yiwei Shao
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Yanmei Xi
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Tong Zhao
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Baoguo Sun
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Higher Institution Engineering Research Center of Food Additives and Ingredients, Beijing Technology & Business University, Beijing 100048, China
| | - Jianbo Xiao
- Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo - Ourense Campus, E-32004 Ourense, Spain.
| | - Jianjun Deng
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Xiao F, Gu M, Zhang Y, Xian Y, Zheng Y, Zhang Y, Sun J, Ding C, Zhang G, Wang D. Detection of Soybean-Derived Components in Dairy Products Using Proofreading Enzyme-Mediated Probe Cleavage Coupled with Ladder-Shape Melting Temperature Isothermal Amplification (Proofman-LMTIA). Molecules 2023; 28:molecules28041685. [PMID: 36838673 PMCID: PMC9964665 DOI: 10.3390/molecules28041685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/28/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Food adulteration is a serious problem all over the world. Establishing an accurate, sensitive and fast detection method is an important part of identifying food adulteration. Herein, a sequence-specific ladder-shape melting temperature isothermal amplification (LMTIA) assay was reported to detect soybean-derived components using proofreading enzyme-mediated probe cleavage (named Proofman), which could realize real-time and visual detection without uncapping. The results showed that, under the optimal temperature of 57 °C, the established Proofman-LMTIA method for the detection of soybean-derived components in dairy products was sensitive to 1 pg/μL, with strong specificity, and could distinguish soybean genes from those of beef, mutton, sunflower, corn, walnut, etc. The established Proofman-LMTIA detection method was applied to the detection of actual samples of cow milk and goat milk. The results showed that the method was accurate, stable and reliable, and the detection results were not affected by a complex matrix without false positives or false negatives. It was proved that the method could be used for the detection and identification of soybean-derived components in actual dairy products samples.
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Affiliation(s)
- Fugang Xiao
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
- Correspondence: (F.X.); (D.W.); Tel.: +86-374-2968805 (F.X.); +86-374-2968907 (D.W.)
| | - Menglin Gu
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
- College of Grain and Food, Henan University of Technology, Zhengzhou 450001, China
| | - Yaoxuan Zhang
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
- College of Grain and Food, Henan University of Technology, Zhengzhou 450001, China
| | - Yaodong Xian
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
| | - Yaotian Zheng
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
| | - Yongqing Zhang
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
| | - Juntao Sun
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
| | - Changhe Ding
- College of Grain and Food, Henan University of Technology, Zhengzhou 450001, China
| | - Guozhi Zhang
- College of Grain and Food, Henan University of Technology, Zhengzhou 450001, China
| | - Deguo Wang
- Henan Key Laboratory of Biomarker Based Rapid-Detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, China
- Correspondence: (F.X.); (D.W.); Tel.: +86-374-2968805 (F.X.); +86-374-2968907 (D.W.)
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