首页|New Machine Learning Research Has Been Reported by Researchers at Nanjing Univer sity of Finance and Economics (Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy)
New Machine Learning Research Has Been Reported by Researchers at Nanjing Univer sity of Finance and Economics (Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news reporting from Jiangsu, People's Republic of China, by NewsRx journalists, research stated, "Discriminant analysis of sim ilar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination te chniques." The news journalists obtained a quote from the research from Nanjing University of Finance and Economics: "Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herei n, this work takes the discrimination of three brands of dairy products as an ex ample to investigate the Raman spectral feature based on the support vector mach ines (SVM), extreme learning machines (ELM) and convolutional neural network (CN N) algorithms. The results show that there are certain differences in the optima l spectral feature interval corresponding to different machine learning algorith ms. Selecting the appropriate spectral feature interval can maintain high recogn ition accuracy and improve the computational efficiency of the algorithm. For ex ample, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 200 s. Th e ELM algorithm also has a recognition accuracy of 100% in the 890 -980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes less than 0.3 s. Th e CNN algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1050-1180 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 80 s."
Nanjing University of Finance and Econom icsJiangsuPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging Te chnologiesFood QualityMachine Learning
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