{"title":"利用电子鼻和多元算法更快地预测迷迭香中主要非挥发性化合物的含量","authors":"","doi":"10.1016/j.foodcont.2024.110886","DOIUrl":null,"url":null,"abstract":"<div><p>To establish a rapid and simple method for predicting the content of key non-volatile compounds in rosemary, compounds from rosemary were analyzed using an electronic nose (E-nose) with 18 sensors (S1-S18), headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and liquid chromatography-mass spectrometry (LC-MS). The data were analyzed by cluster analysis, principal component analysis (PCA). A total of 161 volatile compounds were detected using GC-MS, including 40 alcohols, 2 aromatic hydrocarbons, 5 phenolic compounds, 2 furan compounds, 1 sulfur compound, 6 ethers, 6 aldehydes, 2 acids, 5 terpene, 19 ketones, 16 esters, and 57 other compounds. The content of caffeic acid, nepetin, luteolin, apigenin, diosmetin, rosmarinic acid, carnosic acid, and rosmanol in rosemary samples was determined using LC-MS. The odor profile of rosemary was analyzed using the E-nose. The PCA indicated using the E-nose for discriminating the quality of rosemary was feasible. Meanwhile, the partial least squares (PLS) and artificial neural networks (ANN) model for predicting the content of key non-volatile compounds in rosemary was established using E-nose. In comparison with the PLS model, the constructed ANN model possessed greater predictive capability. Predicting the content of non-odors from rosemary using odor detector was feasible. This provides a basis for the rapid detection method for rosemary using an E-nose.</p></div>","PeriodicalId":319,"journal":{"name":"Food Control","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Faster predicting the content of key non-volatile compound in rosemary using electronic nose with multivariate algorithms\",\"authors\":\"\",\"doi\":\"10.1016/j.foodcont.2024.110886\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To establish a rapid and simple method for predicting the content of key non-volatile compounds in rosemary, compounds from rosemary were analyzed using an electronic nose (E-nose) with 18 sensors (S1-S18), headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and liquid chromatography-mass spectrometry (LC-MS). The data were analyzed by cluster analysis, principal component analysis (PCA). A total of 161 volatile compounds were detected using GC-MS, including 40 alcohols, 2 aromatic hydrocarbons, 5 phenolic compounds, 2 furan compounds, 1 sulfur compound, 6 ethers, 6 aldehydes, 2 acids, 5 terpene, 19 ketones, 16 esters, and 57 other compounds. The content of caffeic acid, nepetin, luteolin, apigenin, diosmetin, rosmarinic acid, carnosic acid, and rosmanol in rosemary samples was determined using LC-MS. The odor profile of rosemary was analyzed using the E-nose. The PCA indicated using the E-nose for discriminating the quality of rosemary was feasible. Meanwhile, the partial least squares (PLS) and artificial neural networks (ANN) model for predicting the content of key non-volatile compounds in rosemary was established using E-nose. In comparison with the PLS model, the constructed ANN model possessed greater predictive capability. Predicting the content of non-odors from rosemary using odor detector was feasible. This provides a basis for the rapid detection method for rosemary using an E-nose.</p></div>\",\"PeriodicalId\":319,\"journal\":{\"name\":\"Food Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Control\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0956713524006030\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Control","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0956713524006030","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Faster predicting the content of key non-volatile compound in rosemary using electronic nose with multivariate algorithms
To establish a rapid and simple method for predicting the content of key non-volatile compounds in rosemary, compounds from rosemary were analyzed using an electronic nose (E-nose) with 18 sensors (S1-S18), headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and liquid chromatography-mass spectrometry (LC-MS). The data were analyzed by cluster analysis, principal component analysis (PCA). A total of 161 volatile compounds were detected using GC-MS, including 40 alcohols, 2 aromatic hydrocarbons, 5 phenolic compounds, 2 furan compounds, 1 sulfur compound, 6 ethers, 6 aldehydes, 2 acids, 5 terpene, 19 ketones, 16 esters, and 57 other compounds. The content of caffeic acid, nepetin, luteolin, apigenin, diosmetin, rosmarinic acid, carnosic acid, and rosmanol in rosemary samples was determined using LC-MS. The odor profile of rosemary was analyzed using the E-nose. The PCA indicated using the E-nose for discriminating the quality of rosemary was feasible. Meanwhile, the partial least squares (PLS) and artificial neural networks (ANN) model for predicting the content of key non-volatile compounds in rosemary was established using E-nose. In comparison with the PLS model, the constructed ANN model possessed greater predictive capability. Predicting the content of non-odors from rosemary using odor detector was feasible. This provides a basis for the rapid detection method for rosemary using an E-nose.
期刊介绍:
Food Control is an international journal that provides essential information for those involved in food safety and process control.
Food Control covers the below areas that relate to food process control or to food safety of human foods:
• Microbial food safety and antimicrobial systems
• Mycotoxins
• Hazard analysis, HACCP and food safety objectives
• Risk assessment, including microbial and chemical hazards
• Quality assurance
• Good manufacturing practices
• Food process systems design and control
• Food Packaging technology and materials in contact with foods
• Rapid methods of analysis and detection, including sensor technology
• Codes of practice, legislation and international harmonization
• Consumer issues
• Education, training and research needs.
The scope of Food Control is comprehensive and includes original research papers, authoritative reviews, short communications, comment articles that report on new developments in food control, and position papers.