E-nose combined with chemometrics to trace tomato-juice quality

Abstract An e-nose was presented to trace freshness of cherry tomatoes that were squeezed for juice consumption. Four supervised approaches (linear discriminant analysis, quadratic discriminant analysis, support vector machines and back propagation neural network) and one semi-supervised approach (Cluster-then-Label) were applied to classify the juices, and the semi-supervised classifier outperformed the supervised approaches. Meanwhile, quality indices of the tomatoes (storage time, pH, soluble solids content (SSC), Vitamin C (VC) and firmness) were predicted by partial least squares regression (PLSR). Two sizes of training sets (20% and 70% of the whole dataset, respectively) were considered, and R2 > 0.737 for all quality indices in both cases, suggesting it is possible to trace fruit quality through detecting the squeezed juices. However, PLSR models trained by the small dataset were not very good. Thus, our next plan is to explore semi-supervised regression methods for regression cases where only a few experimental data are available.

[1]  W. Ping,et al.  A novel method for diabetes diagnosis based on electronic nose. , 1997 .

[2]  S. Buratti,et al.  Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue , 2004 .

[3]  Roberto Todeschini,et al.  Geographical classification of wine and olive oil by means of classification and influence matrix analysis (CAIMAN). , 2006, Analytica chimica acta.

[4]  Giorgio Sberveglieri,et al.  Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: An emerging diagnostic tool , 2010 .

[5]  M. Pardo,et al.  Random forests and nearest shrunken centroids for the classification of sensor array data , 2008 .

[6]  Kevin L. Goodner,et al.  Comparison of Headspace GC and Electronic Sensor Techniques for Classification of Processed Orange Juices , 2000 .

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  X. Hong,et al.  Authenticating cherry tomato juices—Discussion of different data standardization and fusion approaches based on electronic nose and tongue , 2014 .

[9]  Jun Wang,et al.  Comparison of spectral clustering, K-clustering and hierarchical clustering on e-nose datasets: Application to the recognition of material freshness, adulteration levels and pretreatment approaches for tomato juices , 2014 .

[10]  Peter Chr. Lorenzen,et al.  Development of a method for butter type differentiation by electronic nose technology , 2013 .

[11]  Jun Wang,et al.  Application of Electronic Nose and Statistical Analysis to Predict Quality Indices of Peach , 2009, Food and Bioprocess Technology.

[12]  O. Busto,et al.  Characterization and classification of the aroma of beer samples by means of an MS e-nose and chemometric tools , 2011, Analytical and bioanalytical chemistry.

[13]  Carey L. Williamson,et al.  Offline/realtime traffic classification using semi-supervised learning , 2007, Perform. Evaluation.

[14]  Roman Filipovych,et al.  Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI) , 2011, NeuroImage.

[15]  Cynthia Brandt,et al.  Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management , 2013, J. Biomed. Informatics.

[16]  Carl S. Pederson,et al.  Bulletin: Number 759: The Yield and Quality of Juice Obtained from New York State Tomatoes Graded According to United States Department of Agriculture Standards , 1953 .

[17]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[18]  Zheng Hai,et al.  Discrimination and prediction of multiple beef freshness indexes based on electronic nose , 2012 .

[19]  Gérard Govaert,et al.  A predictive deviance criterion for selecting a generative model in semi-supervised classification , 2013, Comput. Stat. Data Anal..

[20]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[21]  Jun Wang,et al.  Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar , 2013 .

[22]  Lei Xi,et al.  Rough set and ensemble learning based semi-supervised algorithm for text classification , 2011, Expert Syst. Appl..

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

[24]  Eduard Llobet,et al.  Fruit ripeness monitoring using an Electronic Nose , 2000 .

[25]  Hans Reinhard,et al.  Citrus juice classification by SPME-GC-MS and electronic nose measurements , 2008 .

[26]  Eduard Llobet,et al.  A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine , 2011 .

[27]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[28]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[29]  Nong Sang,et al.  Using clustering analysis to improve semi-supervised classification , 2013, Neurocomputing.

[30]  M. Forina,et al.  Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils , 2002 .

[31]  Desire L. Massart,et al.  Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data , 1996 .

[32]  Annia García Pereira,et al.  Monitoring storage shelf life of tomato using electronic nose technique , 2008 .

[33]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .

[34]  Dacheng Tao,et al.  3D human posture segmentation by spectral clustering with surface normal constraint , 2011, Signal Process..

[35]  I. Lundström,et al.  An electronic tongue based on voltammetry , 1997 .