A portable electronic nose as an expert system for aroma-based classification of saffron

Abstract This study focuses on the development and evaluation of a portable electronic nose (e-nose) system for identification of different types of saffron, stigma of Crocus sativus L. (Iridaceae), based on their Volatile Organic Compounds (VOCs). The system utilizes metal oxide semiconductor gas sensors and direct head space sampling. Real-time data acquisition system, microcontroller devices and a laptop computer along with multivariate computational tools were used for development of an expert system. Eleven saffron samples from different regions were prepared for the experiments. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) as unsupervised models and Multilayer Perceptron (MLP) neural networks and Partial Least Squares (PLS) as supervised models were utilized to develop the e-nose discrimination capability. Based on the results, PCA of volatile compounds fingerprints revealed eleven distinct groups corresponding to the eleven different saffron samples. This was further confirmed by HCA which classified the groups into five distinct Quality Classes (QCs) (excellent, very good, good, medium, and poor quality) which were used as the MLP and PLS classification goals. Results of analysis showed that performance of the MLP model for prediction of saffron samples QC was better than the PLS model, with 100% success rate and high correlation coefficients of cross validation (R 2  = 0.989 and relatively low RMSE value of 0.0141). These results show that the developed system is capable of discriminating saffron samples based on their aroma and can be utilized as an aroma quality control system.

[1]  Juan Rodríguez,et al.  Electronic Nose for Quality Control of Colombian Coffee through the Detection of Defects in “Cup Tests” , 2009, Sensors.

[2]  Shyam Narayan Jha,et al.  Nondestructive Evaluation of Food Quality , 2010 .

[3]  Sajad Kiani,et al.  Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review , 2016 .

[4]  B. Bouchikhi,et al.  ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE , 2014 .

[5]  Eduard Llobet,et al.  A portable electronic nose system for the identification of cannabis-based drugs , 2011 .

[6]  Shyam Narayan Jha,et al.  Nondestructive evaluation of food quality : theory and practice , 2010 .

[7]  H. Himeno,et al.  Synthesis of Crocin, Picrocrocin and Safranal by Saffron Stigma-like Structures Proliferated in Vitro , 1987 .

[8]  Matteo Falasconi,et al.  The novel EOS835 electronic nose and data analysis for evaluating coffee ripening , 2005 .

[9]  K. Heidarbeigi,et al.  Detection of Adulteration in Saffron Samples Using Electronic Nose , 2015 .

[10]  Jiewen Zhao,et al.  Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion , 2015 .

[11]  Elizabeth Aparecida Ferraz da Silva Torres,et al.  The application of hierarchical clusters analysis to the study of the composition of foods , 2006 .

[12]  Quansheng Chen,et al.  Recent developments of green analytical techniques in analysis of tea's quality and nutrition , 2015 .

[13]  Bipan Tudu,et al.  Electronic nose for black tea quality evaluation by an incremental RBF network , 2009 .

[14]  Hena Ray,et al.  Characterization of the Volatile Aroma Compounds from the Concrete and Jasmine Flowers Grown in India , 2014 .

[15]  S. G. Agarwal,et al.  Changes in Pigments and Volatiles of Saffron ( Crocus sativusL) During Processing and Storage , 1996 .

[16]  Nabarun Bhattacharyya,et al.  Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks , 2008, IEEE Transactions on Instrumentation and Measurement.

[17]  G. Downey,et al.  Application of principal component and hierarchical cluster analysis to classify fruits and vegetables commonly consumed in Ireland based on in vitro antioxidant activity , 2011 .

[18]  Károly Héberger,et al.  Classification of olive oils using high throughput flow 1H NMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks , 2005 .

[19]  Mahdi Ghasemi-Varnamkhasti,et al.  Electronic and bioelectronic tongues, two promising analytical tools for the quality evaluation of non alcoholic beer , 2011 .

[20]  Bipan Tudu,et al.  Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach , 2014 .

[21]  P. Winterhalter,et al.  SAFFRON—RENEWED INTEREST IN AN ANCIENT SPICE , 2000 .

[22]  M. Recio,et al.  An Update Review of Saffron and its Active Constituents , 1996 .

[23]  Zulfiqur Ali,et al.  Data analysis for electronic nose systems , 2006 .

[24]  Khalil Arshak,et al.  A review of gas sensors employed in electronic nose applications , 2004 .

[25]  J. Iborra,et al.  A non-destructive method to determine the safranal content of saffron (Crocus sativus L.) by supercritical carbon dioxide extraction combined with high-performance liquid chromatography and gas chromatography. , 2000, Journal of biochemical and biophysical methods.

[26]  Mahdi Ghasemi-Varnamkhasti,et al.  Discriminatory Power Assessment of the Sensor Array of an Electronic Nose System for the Detection of Non Alcoholic Beer Aging , 2018 .

[27]  Amaya Zalacain,et al.  Analysis of saffron volatile fraction by TD–GC–MS and e-nose , 2006 .

[28]  Jun Wang,et al.  Qualitative and quantitative analysis on aroma characteristics of ginseng at different ages using E-nose and GC-MS combined with chemometrics. , 2015, Journal of pharmaceutical and biomedical analysis.

[29]  Nabarun Bhattacharyya,et al.  Electronic Nose and Electronic Tongue , 2010 .

[30]  G. Sberveglieri,et al.  The novel EOS/sup 835/ electronic nose sniffs out the Italian espresso coffee quality , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[31]  Wei Peng,et al.  A Novel Method for the Discrimination of Semen Arecae and Its Processed Products by Using Computer Vision, Electronic Nose, and Electronic Tongue , 2015, Evidence-based complementary and alternative medicine : eCAM.

[32]  M. Manfait,et al.  FT-IR, FT-Raman spectroscopic study of carotenoids from saffron (Crocus sativus L.) and some derivatives , 1998 .

[33]  Charles L. Cantrell,et al.  Quantification of saffron (Crocus sativus L.) metabolites crocins, picrocrocin and safranal for quality determination of the spice grown under different environmental Moroccan conditions , 2009 .

[34]  R. Bauer,et al.  Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose , 2014, Evidence-based complementary and alternative medicine : eCAM.

[35]  Matteo Pardo,et al.  Coffee analysis with an electronic nose , 2002, IEEE Trans. Instrum. Meas..

[36]  Sajad Kiani,et al.  Fusion of artificial senses as a robust approach to food quality assessment , 2016 .

[37]  Masaki Kanamori,et al.  Identification of coumarin-enriched Japanese green teas and their particular flavor using electronic nose , 2009 .

[38]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[39]  Ingemar Lundström,et al.  Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose , 2000 .

[40]  Mahdi Ghasemi-Varnamkhasti,et al.  Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools , 2012, Expert Syst. Appl..

[41]  Peter Schieberle,et al.  Identification of the key aroma compounds in cocoa powder based on molecular sensory correlations. , 2006, Journal of agricultural and food chemistry.

[42]  Nuria Aleixos,et al.  Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

[43]  Rafael Masot,et al.  Design of an electronic system and its application to electronic tongues using variable amplitude pulse voltammetry and impedance spectroscopy , 2012 .

[44]  A. Alkarkhi,et al.  Cluster analysis of antioxidant compounds in dates (Phoenix dactylifera): Effect of long-term cold storage , 2009 .

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

[46]  J. Iborra,et al.  A quantitative high-performance liquid chromatographic method to analyse commercial saffron (Crocus sativus L.) products. , 1999 .

[47]  Shilong Yang,et al.  A novel method for rapid discrimination of bulbus of Fritillaria by using electronic nose and electronic tongue technology , 2015 .

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

[49]  Hong Liu,et al.  Studies on the chemical and flavor qualities of white pepper (Piper nigrum L.) derived from five new genotypes , 2013, European Food Research and Technology.