Simplified optical fiber RGB system in evaluating intrinsic quality of Sala mango

Abstract. This study presents an alternative approach for the nondestructive assessment of fruit quality parameters with the use of a simplified optical fiber red–green–blue system (OF-RGB). The optical sensor system presented in this work is designed to rapidly measure the firmness, acidity, and soluble solid content of an intact Sala mango on the basis of color properties. The system consists of three light-emitting diodes with peak emission at 635 (red), 525 (green), and 470 nm (blue), as well as a single photodetector capable of sensing visible light. The measurements were conducted using the reflectance technique. The analyses were conducted by comparing the results obtained through the proposed system with those measured using two commercial spectrometers, namely, QE65000 and FieldSpec 3. The developed RGB system showed satisfactory accuracy in the measurement of acidity (R2=0.795) and firmness (R2=0.761), but a relatively lower accuracy in the measurement of soluble solid content (R2=0.593) of intact mangoes. The results obtained through OF-RGB are comparable with those measured by QE65000 and FieldSpec 3. This system is a promising new technology with rapid response, easy operation, and low cost with potential applications in the nondestructive assessment of quality attributes.

[1]  José Blasco,et al.  On-line Fusion of Colour Camera and Spectrophotometer for Sugar Content Prediction of Apples , 1999 .

[2]  Riccardo Leardi,et al.  Multivariate calibration of mango firmness using vis/NIR spectroscopy and acoustic impulse method. , 2009 .

[3]  C. Aprea,et al.  Production Practices and Quality Assessment of Food Crops , 2004, Springer Netherlands.

[4]  A. Gitelson,et al.  Non-Destructive Estimation Pigment Content Ripening Quality and Damage in Apple Fruit with Spectral Reflectance in the Visible Range , 2010 .

[5]  J. Gross Pigments in fruits , 1987 .

[6]  F. Scheer,et al.  Light affects morning salivary cortisol in humans. , 1999, The Journal of clinical endocrinology and metabolism.

[7]  Chun-Chong Fu,et al.  Effects of using light-emitting diodes on the cultivation of Spirulina platensis , 2007 .

[8]  G. Mazza,et al.  Anthocyanins in Fruits, Vegetables, and Grains , 2017 .

[9]  Suwanee Boonmung,et al.  Physical Properties Analysis of Mango using Computer Vision , 2005 .

[10]  Iwona Konopka,et al.  Lipids and carotenoids of wheat grain and flour and attempt of correlating them with digital image analysis of kernel surface and cross-sections , 2004 .

[11]  Adel A. Kader,et al.  Postharvest Quality Maintenance of Fruits and Vegetables in Developing Countries , 1983 .

[12]  Siti Khairunniza-Bejo,et al.  Chokanan Mango Sweetness Determination Using HSB Color Space , 2011, 2011 Third International Conference on Computational Intelligence, Modelling & Simulation.

[13]  D. C. Slaughter,et al.  Nondestructive Maturity Assessment Methods for Mango: A Review of Literature and Identification of Future Research Needs , 2009 .

[14]  A. Gitelson,et al.  Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .

[15]  J. M. Bunn,et al.  Tomato Maturity Evaluation Using Color Image Analysis , 1995 .

[16]  E. Berghofer,et al.  CONSUMER'S COLOR ACCEPTANCE OF STRAWBERRY NECTARS FROM PUREE , 2009 .

[17]  Vincent Leemans,et al.  Defects segmentation on 'Golden Delicious' apples by using colour machine vision , 1998 .

[18]  Alessandro Torricelli,et al.  Selection Models for the Internal Quality of Fruit, based on Time Domain Laser Reflectance Spectroscopy , 2004 .

[19]  Da-Wen Sun,et al.  Recent developments and applications of image features for food quality evaluation and inspection – a review , 2006 .

[20]  Bart Nicolai,et al.  Non-destructive techniques for measuring quality of fruit and vegetables , 2005 .

[21]  D. Slaughter,et al.  Relationship between nondestructive firmness measurements and commercially important ripening fruit stages for peaches, nectarines and plums , 2007 .

[22]  Ingo Truppel,et al.  Spectral Measurements on ‘Elstar’ Apples during Fruit Development on the Tree , 2005 .

[23]  B. Gandul-Rojas,et al.  Measurement of chlorophyllase activity in olive fruit (Olea europaea). , 1994, Journal of biochemistry.

[24]  P. Schaare,et al.  Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis) , 2000 .

[25]  B. Schoefs Chlorophyll and carotenoid analysis in food products. Properties of the pigments and methods of analysis , 2002 .

[26]  Ta Chih Cheng,et al.  Light-emitting diodes--Their potential in biomedical applications , 2010 .

[27]  Ahmad Fairuz Omar,et al.  Turbidimeter Design and Analysis: A Review on Optical Fiber Sensors for the Measurement of Water Turbidity , 2009, Sensors.

[28]  J. Tan,et al.  Determination of animal skeletal maturity by image processing. , 2003, Meat science.

[29]  Ahmad Fairuz Omar,et al.  Specialized optical fiber sensor for nondestructive intrinsic quality measurement of Averrhoa Carambola , 2013 .

[30]  S. Limsiroratana,et al.  On image analysis for harvesting tropical fruits , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[31]  Margarita Ruiz-Altisent,et al.  Fruit and Vegetables Harvesting Systems , 2004 .

[32]  D. Mery,et al.  Color measurement in L ¿ a ¿ b ¿ units from RGB digital images , 2006 .

[33]  B. Akbudak Effects of harvest time on the quality attributes of processed and non-processed tomato varieties , 2010 .

[34]  Da-Wen Sun,et al.  Inspecting pizza topping percentage and distribution by a computer vision method , 2000 .

[35]  Da-Wen Sun,et al.  Pizza sauce spread classification using colour vision and support vector machines , 2005 .

[37]  Graeme D. Coles,et al.  Estimating potato crisp colour variability using image analysis and a quick visual method , 1993, Potato Research.

[38]  Haisheng Gao,et al.  A Review of Non-destructive Detection for Fruit Quality , 2009, CCTA.

[39]  M. Li,et al.  Optical chlorophyll sensing system for banana ripening , 1997 .

[40]  Dolores Pérez-Marín,et al.  First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery , 2010 .

[41]  V. A. McGlone,et al.  Prediction of storage disorders of kiwifruit (Actinidia chinensis) based on visible-NIR spectral characteristics at harvest , 2004 .

[42]  P. Butz,et al.  Recent Developments in Noninvasive Techniques for Fresh Fruit and Vegetable Internal Quality Analysis , 2006 .

[43]  Tom C. Pearson,et al.  Machine vision system for automated detection of stained pistachio nuts , 1995, Other Conferences.

[44]  Yibin Ying,et al.  Comparison of diffuse reflectance and transmission mode of visible-near infrared spectroscopy for detecting brown heart of pear , 2007 .

[45]  C. Camps,et al.  Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy , 2009 .

[46]  P. Zerbini,et al.  Nondestructive assessment of fruit biological age in Brazilian mangoes by time-resolved reflectance spectroscopy in the 540-900 nm spectral range , 2013 .

[47]  J. Tan,et al.  Beef Marbling and Color Score Determination by Image Processing , 1996 .

[48]  C. T. Morrow,et al.  Machine Vision for Color Inspection of Potatoes and Apples , 1995 .

[49]  A. Solovchenko,et al.  Relationships between internal ethylene and optical reflectance in ripening 'Antonovka' apples grown under sunlit and shaded conditions , 2011 .

[50]  Naichia Yeh,et al.  High-brightness LEDs—Energy efficient lighting sources and their potential in indoor plant cultivation , 2009 .

[51]  Tatjana Unuk,et al.  Spectrophotometrically determined pigment contents of intact apple fruits and their relations with quality: a review. , 2013 .

[52]  M. Z. Abdullah,et al.  Automated inspection system for colour and shape grading of starfruit (Averrhoa carambola L.) using machine vision sensor , 2005 .

[53]  Michael Knee,et al.  Methods of measuring green colour and chlorophyll content of apple fruit , 2007 .

[54]  S. Boonmung,et al.  A Preliminary Study on Classification of Mango Maturity by Compression Test , 2008 .

[55]  D. L. Betemps,et al.  Pear quality characteristics by Vis / NIR spectroscopy. , 2012, Anais da Academia Brasileira de Ciencias.

[56]  Mauro Barni,et al.  Colour-based detection of defects on chicken meat , 1997, Image Vis. Comput..

[57]  Ran Du,et al.  Determination of soluble solids and firmness of apples by Vis/NIR transmittance. , 2009 .

[58]  Gerhard Jahns,et al.  Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading , 2001 .

[59]  D. Barrett,et al.  Changes in pH, acids, sugars and other quality parameters during extended vine holding of ripe processing tomatoes. , 2011, Journal of the science of food and agriculture.

[60]  Jong Kyu Kim,et al.  Solid-State Light Sources Getting Smart , 2005, Science.

[61]  Sophie Jost-Boissard,et al.  Perceived lighting quality of LED sources for the presentation of fruit and vegetables , 2009 .

[62]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[63]  Mahmod Othman,et al.  Mango Grading By Using Fuzzy Image Analysis , 2012 .

[64]  Lijuan Xie,et al.  Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics. , 2009 .

[65]  A. Pentland,et al.  Effects of Continuous‐Wave (670‐nm) Red Light on Wound Healing , 2008, Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.].

[66]  R. B. Dodd,et al.  Nondestructive detection of split-pit peaches. , 1992 .

[67]  A. Syaifudin,et al.  Image processing and analysis techniques for estimating weight of Chokanan mangoes , 2007 .

[68]  Chaoxin Zheng,et al.  Correlating colour to moisture content of large cooked beef joints by computer vision , 2006 .