Journal ArticleDOI
Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM).
TLDR
The data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.About:
This article is published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy.The article was published on 2017-04-15. It has received 72 citations till now.read more
Citations
More filters
Journal ArticleDOI
FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng.
TL;DR: The high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng.
Journal ArticleDOI
A deep feature mining method of electronic nose sensor data for identifying beer olfactory information
TL;DR: This study shows that the CNN-SVM model can be used as an effective tool for high precision intelligent identification of beer olfactory information.
Journal ArticleDOI
Information fusion of emerging non-destructive analytical techniques for food quality authentication: A survey
TL;DR: This survey provides clear evidence of the superiority of fusion-based methods for food authentications, and brings some trends of information fusion approaches into attention and discusses the challenges and future perspectives with recommendations.
Journal ArticleDOI
Application of vibrational spectroscopy for classification, authentication and quality analysis of mushroom: A concise review.
Maninder Meenu,Baojun Xu +1 more
TL;DR: This review is focused on various studies related to the application of vibrational spectroscopy for classification, authentication and quality analysis of mushrooms and concluded that vibrational Spectroscopy could be efficiently employed for assessing the quality, authenticity and geographical origin of the mushrooms.
Journal ArticleDOI
Pleurotus Mushrooms Content in Glucans and Ergosterol Assessed by ATR-FTIR Spectroscopy and Multivariate Analysis.
Georgios Bekiaris,Dimitra Tagkouli,Georgios Koutrotsios,Nick Kalogeropoulos,Georgios I. Zervakis +4 more
TL;DR: Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to monitor the infrared absorption spectra of 79 mushroom samples, providing chemical insight into the mushrooms examined, while qualitative and quantitative differences in regions related to proteins, phenolic compounds and polysaccharides were revealed among the species and substrates studied.
References
More filters
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra
TL;DR: In this article, the standard normal variate (SNV) and de-trending (DT) approaches are applied to individual NIR diffuse reflectance spectra to remove the multiplicative interferences of scatter and particle size.
Journal ArticleDOI
A GA-based feature selection and parameters optimizationfor support vector machines
Cheng-Lung Huang,Chieh-Jen Wang +1 more
TL;DR: This research presents a genetic algorithm approach for feature selection and parameters optimization to solve the problem of optimizing parameters and feature subset without degrading the SVM classification accuracy.
Journal ArticleDOI
Quantum Support Vector Machine for Big Data Classification
TL;DR: This work shows that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples, and an exponential speedup is obtained.