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supervised pattern recognition in food analysis

JOURNAL OF CHROMATOGRAPHY A 2007年第1-2期1158卷 196-214页

作者： Berrueta, Luis A. Alonso-Salces, Rosa M. Heberger, Karoly Univ Basque Country Fac Ciencia & Tecnol Dept Quim Anal E-48080 Bilbao Spain Hungarian Acad Sci Chem Res Ctr H-1525 Budapest Hungary

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modem analytical instruments. supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed. (c) 2007 Elsevier B.V. All rights reserved.

关键词： supervised pattern recognition food analysis multivariate data analysis chemometrics

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supervised pattern recognition by parallel feature partitioning

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pattern recognition 2004年第3期37卷 463-467页

作者： Valev, V St Louis Univ Parks Coll Engn & Aviat Dept Comp Sci St Louis MO 63103 USA

In the present paper the supervised pattern recognition problem is considered. For solving the problem a mathematical model based on parallel feature partitioning is proposed. The solution is obtained by partitioning the feature space to a minimal number of nonintersecting regions. This is achieved by solving an integer-valued optimization problem, which leads to the construction of minimal covering. Since the classes do not intersect it follows that the solution of the formulated problem exists. Computational complexity of the model and computational procedures are discussed. Geometrical interpretation of the solution is given. (C) 2003 pattern recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： supervised pattern recognition parallel feature partitioning integer-valued optimization

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supervised pattern recognition to discriminate the geographical origin of rice bran oils:: a first study

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MICROCHEMICAL JOURNAL 2003年第3期74卷 239-248页

作者： Marini, F Balestrieri, F Bucci, R Magrì, AL Marini, D Univ Roma La Sapienza Dipartimento Chim I-00185 Rome Italy Univ Perugia Dipartimento Chim Sez Marceol I-06123 Perugia Italy Agenzia Dogane I-00143 Rome Italy

supervised pattern recognition appears to be a useful tool to authenticate foodstuffs according to their geographical or varietal origin, when a set of samples whose classification is known a priori are available. In this work, linear discriminant analysis and artificial neural networks trained by the back-propagation algorithm have been used to discriminate rice bran oils manufactured in three different countries (Italy, Thailand and Switzerland) according to their geographical origin. The variables to be included in the mathematical models have been chosen by means of Fisher F-ratio value among the chemical indices routinely determined on vegetable oils (particularly fatty acids, triglycerides and sterol composition). The prediction ability of all the classifiers was 100% as evaluated by cross-validation. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： artificial neural networks chemometrics rice bran oil supervised pattern recognition

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Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2009年第4期72卷 845-850页

作者： Chen, Quansheng Zhao, Jiewen Lin, Hao Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China

Rapid discrimination of roast green tea according to geographical origin is crucial to quality control. Fourier transform near-infrared (FT-NIR) spectroscopy and supervised pattern recognition was attempted to discriminate Chinese green tea according to geographical origins (i.e. Anhui Province, Henan Province, Jiangsu Province, and Zhejiang Province) in this work. Four supervised pattern recognitions methods were used to construct the discrimination models based on principal component analysis (PCA), respectively. The number of principal components factors (PCs) and model parameters were optimized by cross-validation in the constructing model. The performances of four discrimination models were compared. Experimental results showed that the performance of SVM model is the best among four models. The optimal SVM model was achieved when 4 PCs were used, discrimination rates being all 100% in the training and prediction set. The overall results demonstrated that FT-NIR spectroscopy with supervised pattern recognition could be successfully applied to discriminate green tea according to geographical origins. (C) 2008 Elsevier B.V. All rights reserved.

关键词： FT-NIR spectroscopy supervised pattern recognition Green tea Geographical origin Discrimination

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Classification of Chinese Soybean Paste by Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Different supervised pattern recognition

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FOOD ANALYTICAL METHODS 2012年第4期5卷 928-934页

作者： Jiang, Hui Liu, Guohai Xiao, Xiahong Yu, Shuang Mei, Congli Ding, Yuhang Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

Fourier transformation near-infrared (FT-NIR) spectroscopy as an analytical tool combined with supervised pattern recognition was attempted to classify four different brands of Chinese soybean paste in this work. Three supervised pattern recognition methods, which were K-nearest neighbors (KNN), error back-propagation neural network (BP-NN), and support vector machines (SVM), were used to develop the identification models based on principal component analysis. Some parameters of the algorithms and also the number of principal components (PCs) were optimized by cross-validation in developing models. The performances of three identification models were compared. Experimental results showed that the performance of SVM model was superior to KNN and BP-NN models. The optimal SVM model achieved when the 5 PCs were included, and the identification rates both were 100% in the training and validation sets. This work demonstrated that FT-NIR spectroscopy technique coupled with SVM algorithm could be successfully used to discriminate different brands of Chinese soybean paste.

关键词： FT-NIR spectroscopy supervised pattern recognition Classification Soybean paste Brands

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Phase Index-Based Receive Spatial Modulation PHY Security Against supervised pattern recognition

Phase Index-Based Receive Spatial Modulation PHY Security Ag...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Abushattal, Abdelrahman Arslan, Huseyin Istanbul Medipol Univ Dept Elect & Elect Engn Istanbul Turkey Univ S Florida Dept Elect Engn Tampa FL USA

ISBN: (纸本)9781728195056

Receive Spatial Modulation (RSM) uses the index of the receive antenna to convey additional information bits, which reduces the receiver complexity and improves spectral efficiency. Additionally, it provides inherent physical layer security using the Zero-forcing (ZF) precoding technique. However, the channel becomes vulnerable to eavesdropper (Eve) if the channel impulse response is assumed to be time-invariant or stationary through coherent block intervals. In this paper, a physical layer secure scheme is proposed based on phase index RSM to combat eaves-droppers that use k-nearest neighbors (KNN) supervised pattern recognition. The average bit error rate (BER) performance shows the ability of the proposed scheme to provide a suitable level of security while degrading the average BER performance of Eve.

关键词： Multi-input Multi-output k-nearest neighbors (KNN) Zero-forcing precoding supervised pattern recognition receive spatial modulation physical-layer security supervised pattern recognition

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Comparison of several supervised pattern recognition techniques for detecting additive methamidophos in rotenone preparation by near-infrared spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2014年 121卷 678-684页

作者： Tang, Guo Tian, Kuangda Song, Xiangzhong Xiong, Yanmei Min, Shungeng China Agr Univ Coll Sci Beijing 100193 Peoples R China

In this paper, different supervised pattern recognition methods have been applied to detect the manually additive methamidophos in rotenone preparation. The aim of this paper was to examine the performances of different supervised pattern recognition techniques: soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), artificial neutral networks (ANN), and support vector machine (SVM). The results obtained show that SVM is the most effective techniques with 100.0% classification accuracy followed by ANN, PLS-DA and with the accuracy of 97.5% and 93.3% respectively while SIMCA yields the poorest result of 85.8%. We hope that the results obtained in this study will help both further chemometric investigations and investigations in the sphere of applied vibrational spectroscopy of sophisticated multicomponent systems. Furthermore, the use of portable instrument and satisfactory classification also indicated the possibility of detecting illicit-addition at scene by near-infrared (NIR) spectroscopy which makes a great sense in pesticide quality control. (C) 2014 Published by Elsevier B.V.

关键词： supervised pattern recognition Methamidophos Rotenone Near-infrared

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K-CM application for supervised pattern recognition at Mt. Etna: an innovative tool to forecast flank eruptive activity

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BULLETIN OF VOLCANOLOGY 2019年第7期81卷 1页

作者： Brancato, Alfonso Buscema, Paolo Massimo Massini, Giulia Gresta, Stefano Salerno, Giuseppe Della Torre, Francesca Ist Nazl Geofis & Vulcanol Osservatorio Etneo Piazza Roma 2 I-95123 Catania Italy Semeion Res Ctr Sci Commun Via Sersale 117 I-00128 Rome Italy Univ Colorado Dept Math & Stat Sci Denver CO 80202 USA Univ Catania Sez Sci Terra Dipartimento Sci Biol Geol & Ambientali Corso Italia 57 I-95129 Catania Italy

We investigated the relationship between the temporal monitoring series routinely recorded at Mt. Etna and the flank eruptions that occurred between January 2001 and April 2005 by the K-contractive map (K-CM) method pattern classifier with supervised learning. The reference dataset includes 28 variables and 1580 records collected over 52months for a total of 301 eruptive days. A two-step analysis was performed. In the first step analysis, we used the 28 parameters of each day to recognize anomalies heralding a flank eruption. K-CM estimated a sensitivity higher than 95% and a specificity close to 100%. In the second step analysis, we considered each record comprising the 28 variables for 6days as an input (for a total of 180 inputs) and the outcomes of the seventh day as an output to predict eruption or rest. In this case, K-CM showed sensitivity and specificity close to 98% and 100%, respectively. Results highlight the reliability of the K-CM method to build up a prediction algorithm able to alert the volcano experts a day before the occurrence of a potential flank eruption. The robustness of the two analyses was investigated by the behavior of the receiver operating characteristic curve. The relative area under the curve showed values close to 1, thus providing a valid measure of the performance of the classifier. Finally, a complete overview of the performance levels of the method used was explored analyzing the retrieved Molchan error diagram, in both cases, trajectories very close to the theoretical minimum.

关键词： Mt.Etna volcano Flank eruption Monitoring data complex system Neural networks supervised pattern recognition

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Authentication and discrimination of tissue origin of bovine gelatin using combined supervised pattern recognition strategies

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MICROCHEMICAL JOURNAL 2023年第1期187卷

作者： Forooghi, Elaheh Zade, Somaye Vali Sahebi, Hamed Abdollahi, Hamid Sadeghi, Naficeh Jannat, Behrooz Minist Hlth & Med Educ Food & Drug Adm Halal Res Ctr IRI Tehran Iran Inst Adv Studies Basic Sci Dept Chem POBo 45195 1159 Zanjan Iran Univ Tehran Med Sci Sch Pharm Dept Food & Drug Control Tehran Iran 24 Daneshkian St Valiasr Ave Tehran *** Iran

Gelatin is a protein-based product derived from bovine and porcine bone and skin. Gelatin is increasingly used in food and non-food products, but there are errors in labeling concerning the origin of commercial gelatin raw materials. These mislabeling may negatively impact religious or safety communities. In gelatin studies, species identification is common, but determining the origin of the tissues, bone and skin, is also crucial. The current study demonstrated the use of Raman spectroscopy as a rapid and economical method coupled with chemometrics to differentiate tissue origins of bovine gelatin. The proposed technique could also detect adulteration of bone origin gelatin with skin gelatin with 100% sensitivity and specificity. Accordingly, Raman spectroscopy, along with DD- SIMCA and PLS-DA methods, provides a reliable technique that can easily be applied to detecting the tissue source of bovine gelatin. Also, two- step classification method with goal of finding a suitable authentication of target classes have been applied. DD-SIMCA as class modeling for authentication and PLS-DA for discrimination of groups have been combined in this strategy.

关键词： Chemometrics Authentication Discrimination supervised pattern recognition Raman spectroscopy Bovine bone gelatin Bovine skin gelatin

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supervised fuzzy pattern recognition

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FUZZY SETS AND SYSTEMS 1998年第1-3期100卷 257-265页

作者： Shukhat, B Informat Builders Inc New York NY 10001 USA

This paper is devoted to the problem of supervised fuzzy pattern recognition. The cases with non-fuzzy and fuzzy labels are considered. Based on the properties of linearly separable fuzzy classes, some algorithms are proposed for building matching functions of these classes. All algorithms are computer oriented and can be implemented for the automatic recognition of fuzzy patterns. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： fuzzy sets supervised pattern recognition fuzzy partition images labels

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