Bio-inspired Sensor Fusion for Quality Assessment of Harumanis Mangoes

Abstract In recent years, there have been a number of reported studies on the use of non-destructive technique to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose (e-nose), acoustics, CCD, IR sensor or by other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using data fusion of the electronic nose (e-nose) and acoustic sensor and combine with CCD and IR sensor. A Fourier-based shape separation method was developed from CCD camera images to grade mango by its shape and able to correctly classify 100%. Colour intensity from infrared image was used to distinguish and classify the level of maturity and ripeness of the fruits. The finding shows 92% correct classification of maturity levels by using infrared vision Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved.

[1]  Elizabeth A. Baldwin,et al.  Discrimination of mango fruit maturity by volatiles using the electronic nose and gas chromatography , 2008 .

[2]  Ashok P. Giri,et al.  Cultivar relationships in mango based on fruit volatile profiles , 2009 .

[3]  S. A. Spence,et al.  The relationship between management practices and estimated weight for age of Friesian heifers in north-eastern New South Wales , 2000 .

[4]  A. S. Fathinul-Syahir,et al.  Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system , 2006 .

[5]  Abdul Hamid Adom,et al.  Classification of Agarwood Oil Using an Electronic Nose , 2010, Sensors.

[6]  Martin T. Hagan,et al.  Neural network design , 1995 .

[7]  Ahmad Khalilian,et al.  Detecting stink bugs/damage in cotton utilizing a portable electronic nose , 2010 .

[8]  Marie-Noelle Ducamp-Collin,et al.  DEVELOPMENT OF ELECTRONIC NOSE MEASUREMENTS FOR MANGO ( MANGIFERA INDICA ) HOMOGENATE AND WHOLE FRUIT , 2004 .

[9]  J. Delwiche.  The impact of perceptual interactions on perceived flavor , 2004 .

[10]  Chad Perry,et al.  Marketing fresh fruit to Japanese consumers: exploring issues for Australian exporters , 1999 .

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

[12]  Zhiyong Wang,et al.  Shape based leaf image retrieval , 2003 .

[13]  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 .

[14]  Charles T. Morrow,et al.  An automated inspection station for machine-vision grading of potatoes , 2005, Machine Vision and Applications.

[15]  C. T. Morrow,et al.  Machine Vision Inspection of ‘Golden Delicious’ Apples , 1995 .

[16]  Ron B. H. Wills,et al.  Ethylene levels associated with fruit and vegetables during marketing , 2000 .

[17]  Ron B. H. Wills,et al.  Ripening of climacteric fruits initiated at low ethylene levels , 2001 .

[18]  C. T. Morrow,et al.  Fourier-based Separation Technique for Shape Grading of Potatoes Using Machine Vision , 1995 .

[19]  Kerry B. Walsh,et al.  Prediction of mango eating quality at harvest using short-wave near infrared spectrometry , 2007 .

[20]  Ralph Roskies,et al.  Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.

[21]  Reinaldo Campos-Vargas,et al.  The aroma development during storage of Castlebrite apricots as evaluated by gas chromatography, electronic nose, and sensory analysis , 2009 .

[22]  D. F. Morrison,et al.  Multivariate Statistical Methods , 1968 .

[23]  Margarita Ruiz-Altisent,et al.  Addressing potential sources of variation in several non-destructive techniques for measuring firmness in apples , 2009 .

[24]  Zora Singh,et al.  Aroma volatiles production during fruit ripening of ‘Kensington Pride’ mango , 2003 .

[25]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[26]  Z M Ali,et al.  beta-Galactosidase and its significance in ripening mango fruit. , 1995, Phytochemistry.

[27]  Pascal Boilot,et al.  Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions , 2003 .

[28]  V. Steinmetz,et al.  Sensors for fruit firmness assessment : Comparison and fusion , 1996 .

[29]  I. Lundstrom,et al.  A Calibration Technique For An Electronic Nose , 1995, Proceedings of the International Solid-State Sensors and Actuators Conference - TRANSDUCERS '95.

[30]  Faridah Hassan,et al.  Examining market accessibility of Malaysia's Harumanis mango in Japan: challenges and potentials , 2010 .

[31]  Yubin Lan,et al.  Multisensor data fusion for high quality data analysis and processing in measurement and instrumentation , 2007 .

[32]  R. Paolesse,et al.  Outer product analysis of electronic nose and visible spectra: application to the measurement of peach fruit characteristics , 2002 .

[33]  Sumio Kawano,et al.  Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy , 2004 .

[34]  Abdul Hamid Adom,et al.  Intelligent electronic nose system for basal stem rot disease detection , 2009 .