Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.

[1]  Amadou Dicko,et al.  Aging fingerprint characterization of beer using electronic nose , 2011 .

[2]  M H Asareh,et al.  THE EFFECTS OF DIFFERENT DRYING METHODS ON ESSENTIAL OIL CONTENT AND COMPOSITION OF THREE GENOTYPES OF ROSA DAMASCENA MILL , 2008 .

[3]  Abdul Hamid Adom,et al.  Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor , 2012, Sensors.

[4]  Ibrahim A. Maghrabi,et al.  Antibacterial and Antifungal Activity of Rosa damascena MILL. Essential Oil, Different Extracts of Rose Petals , 2014 .

[5]  Yonghong Yan,et al.  Quality control of Lonicera japonica stored for different months by electronic nose. , 2014, Journal of pharmaceutical and biomedical analysis.

[6]  Rosa Baby,et al.  Quality control of medicinal plants with an electronic nose , 2005 .

[7]  S. Buratti,et al.  Discrimination and characterisation of three cultivars of Perilla frutescens by means of sensory descriptors and electronic nose and tongue analysis , 2010 .

[8]  R. Adams,et al.  Identification of Essential Oil Components By Gas Chromatography/Mass Spectrometry , 2007 .

[9]  E. Llobet,et al.  Discrimination between different samples of olive oil using variable selection techniques and modified fuzzy artmap neural networks , 2005, IEEE Sensors Journal.

[10]  Qi Ye,et al.  Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine , 2012 .

[11]  Ricardo Gutierrez-Osuna,et al.  Pattern analysis for machine olfaction: a review , 2002 .

[12]  Abdolali Shojaeiyan,et al.  EVALUATION OF PHYTOCHEMICAL PROFILING OF DAMASK ROSE (ROSA DAMASCENA MILL.) AT VARIOUS POST-HARVEST INCUBATION CONDITIONS AND DETERMINATION OF THE BEST HYDRO-DISTILLATION TIME , 2015 .

[13]  Evor L. Hines,et al.  Detection of vapours and odours from a multisensor array using pattern-recognition techniques Part 2. Artificial neural networks , 1992 .

[14]  H. T. Nagle,et al.  Handbook of Machine Olfaction , 2002 .

[15]  Eduard Llobet,et al.  Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose , 2006, Sensors (Basel, Switzerland).

[16]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[17]  Eduard Llobet,et al.  Fuzzy ARTMAP based electronic nose data analysis , 1999 .

[18]  Jin Luo,et al.  Pattern recognition for sensor array signals using Fuzzy ARTMAP , 2009 .

[19]  Robert Fullér,et al.  Neural Fuzzy Systems , 1995 .

[20]  J. Gardner.  Detection of vapours and odours from a multisensor array using pattern recognition Part 1. Principal component and cluster analysis , 1991 .

[21]  Lutgarde M. C. Buydens,et al.  ADAPTIVE RESONANCE THEORY-BASED NEURAL NETWORKS - THE ART OF REAL-TIME PATTERN-RECOGNITION IN CHEMICAL PROCESS MONITORING , 1995 .

[22]  Osman Sagdic,et al.  Note: Antioxidant and Antibacterial Activities of Rosa Damascena Flower Extracts , 2004 .

[23]  J. Gardner,et al.  An electronic nose system for monitoring the quality of potable water , 2000 .

[24]  Probir Kumar Pal,et al.  Impact of level and timing of pruning on flower yield and secondary metabolites profile of Rosa damascena under western Himalayan region , 2014 .

[25]  Hyuntae Kim,et al.  Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design , 2012 .

[26]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[27]  E. Llobet,et al.  Evaluation of an electronic nose to assess fruit ripeness , 2005, IEEE Sensors Journal.

[28]  Ramaswamy Palaniappan,et al.  Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance , 2009, Appl. Soft Comput..

[29]  E. Llobet,et al.  Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat , 2008, Sensors.

[30]  Eduard Llobet,et al.  Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage , 2007 .

[31]  Jun Wang,et al.  Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice , 2015 .

[32]  Sylwia Mildner-Szkudlarz,et al.  DETECTION OF OLIVE OIL ADULTERATION WITH RAPESEED AND SUNFLOWER OILS USING MOS ELECTRONIC NOSE AND SMPE‐MS , 2010 .

[33]  Ammar Zakaria,et al.  A Hybrid Sensing Approach for Pure and Adulterated Honey Classification , 2012, Sensors.

[34]  Anderson Rocha,et al.  Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[35]  M. Peris,et al.  A 21st century technique for food control: electronic noses. , 2009, Analytica chimica acta.

[36]  Robert Verpoorte,et al.  Cultivation of medicinal and aromatic plants for specialty industrial materials , 2011 .

[37]  Kun-song Chen,et al.  Scent profiling of Cymbidium ensifolium by electronic nose , 2011 .

[38]  Guohua Hui,et al.  Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose. , 2015, Food chemistry.

[39]  Paramvir Singh Ahuja,et al.  Morphological and molecular analyses of Rosa damascena × R. bourboniana interspecific hybrids. , 2009 .

[40]  Pradeep Kurup,et al.  Decision tree approach for classification and dimensionality reduction of electronic nose data , 2011 .

[41]  Simona Benedetti,et al.  Electronic nose as a non-destructive tool to characterise peach cultivars and to monitor their ripening stage during shelf-life , 2008 .

[42]  Bo Zhang,et al.  Fragrance discrimination of Chinese Cymbidium species and cultivars using an electronic nose , 2014 .

[43]  Hadi Parastar,et al.  Development of a method for analysis of Iranian damask rose oil: combination of gas chromatography-mass spectrometry with Chemometric techniques. , 2008, Analytica chimica acta.

[44]  J. Brezmes,et al.  Fast detection of rancidity in potato crisps using e-noses based on mass spectrometry or gas sensors , 2005 .

[45]  Mahdi Ghasemi-Varnamkhasti,et al.  Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology) , 2009, Sensors.

[46]  Mahdi Ghasemi-Varnamkhasti,et al.  From simple classification methods to machine learning for the binary discrimination of beers using electronic nose data , 2014 .

[47]  Mahdi Ghasemi-Varnamkhasti,et al.  Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements , 2010 .

[48]  Mohd Ali,et al.  Volatile oil composition of Rosa damascena Mill. (Rosaceae) , 2014 .