H. Maamor, F. A. Rashid, N. Z. I. Zakaria, A. Zakaria, L. Kamarudin, M. N. Jaafar, A. Y. Shakaff, Norazian Subari, N. Yusuf, S. Ismail, K. Adnan
{"title":"用多传感技术评价纯蜂蜜和掺假蜂蜜的仿生味道","authors":"H. Maamor, F. A. Rashid, N. Z. I. Zakaria, A. Zakaria, L. Kamarudin, M. N. Jaafar, A. Y. Shakaff, Norazian Subari, N. Yusuf, S. Ismail, K. Adnan","doi":"10.1109/ICED.2014.7015812","DOIUrl":null,"url":null,"abstract":"Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.","PeriodicalId":143806,"journal":{"name":"2014 2nd International Conference on Electronic Design (ICED)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique\",\"authors\":\"H. Maamor, F. A. Rashid, N. Z. I. Zakaria, A. Zakaria, L. Kamarudin, M. N. Jaafar, A. Y. Shakaff, Norazian Subari, N. Yusuf, S. Ismail, K. Adnan\",\"doi\":\"10.1109/ICED.2014.7015812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.\",\"PeriodicalId\":143806,\"journal\":{\"name\":\"2014 2nd International Conference on Electronic Design (ICED)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 2nd International Conference on Electronic Design (ICED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICED.2014.7015812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 2nd International Conference on Electronic Design (ICED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICED.2014.7015812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique
Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.