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Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review

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Abstract

Fruit quality inspection and authentication instruments are the essential requirement at the different stages of fruit processing industries from harvesting to market. In recent years, various intelligent analytical methods such as electronic nose, gas chromatography and mass spectroscopy, UV–Vis–NIR spectroscopy, machine vision, hyperspectral imaging and many more have been evolved to access the fruit quality at different stages such as maturity judgement of an on-tree fruit, shelf life measurement of harvested fruit, other quality parameters measurement of various fruit products at processing industries etc. Information extracted from various analytical methods needs to be processed using different data processing approaches and strategies, which plays the major role to bring the intelligence in the analytical instruments. Although, highly promising results have been reported to process data acquired from similar type of sensory panel (gas sensor array in electronic nose) and single sensing technique (impedance measurement) but still there are several challenges to process data acquired from multiple sensing techniques fusion (similar or complementary in nature) to predict better informative results. Recently, there is a growing interest in the direction of multiple sensing techniques fusion to extract better information from fruit samples in a reliable manner and also in less time. This paper presents an extensive review of classical and modern data processing approaches and strategies that have been used for single and multiple non-destructive sensing methods in the area of fruit quality inspection and authentication. Various approaches and strategies for preprocessing, data fusion, feature extraction, model design, multi-modal data processing, training, testing and validation for single and multiple sensing techniques have been briefly explained in the presented review. The presented review also discusses the need, scope, and challenges of data processing methods for multiple sensing techniques fusion. Different commercially available handheld and lab level analytical instruments also have been reviewed based on their intelligence, complexity and quality parameters prediction.

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References

  1. Food loss and waste facts (2015), http://www.fao.org/resources/infographics/infographics-details/en/c/317265/ Accessed 3 Feb 2018

  2. L.S. Kantor et al., Estimating and addressing America’s food losses. Food Rev. 20(1), 2–12 (1997)

    Google Scholar 

  3. D.C. Slaughter et al., Comparison of instrumental and manual inspection of clingstone peaches. Appl. Eng. Agric. 22(6), 883–889 (2006)

    Google Scholar 

  4. S. Khalifa, M.H. Komarizadeh, B. Tousi, Usage of fruit response to both force and forced vibration applied to assess fruit firmness-a review. Aust. J. Crop Sci. 5(5), 516 (2011)

    Google Scholar 

  5. F. Röck, B. Nicolae, W. Udo, Electronic nose: current status and future trends. Chem. Rev. 108(2), 705–725 (2008)

    PubMed  Google Scholar 

  6. N. Kondo et al., Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Comput. Electron. Agric. 29(1–2), 135–147 (2000)

    Google Scholar 

  7. H. Lin, Y. Yibin, Theory and application of near infrared spectroscopy in assessment of fruit quality: a review. Sens. Instrum. Food Qual. Saf. 3(2), 130–141 (2009)

    Google Scholar 

  8. F.J. García-Ramos et al., Non-destructive fruit firmness sensors: a review. Span. J. Agric. Res. 3(1), 61–73 (2005)

    Google Scholar 

  9. B. Diezema Iglesias, M. Ruiz-Altisent, B. Orihuel. Acoustic impulse response for detecting hollow heart in seedless watermelon. In: International Conference: Postharvest Unlimited, vol. 599. 2002

  10. T.C. Pearce (eds.), Handbook of Machine Olfaction: Electronic Nose Technology (Wiley, New York, 2006)

    Google Scholar 

  11. M. Lebrun et al., Discrimination of mango fruit maturity by volatiles using the electronic nose and gas chromatography. Postharvest Biol. Technol. 48(1), 122–131 (2008)

    CAS  Google Scholar 

  12. M. Valente et al., Multivariate calibration of mango firmness using vis/NIR spectroscopy and acoustic impulse method. J. Food Eng. 94(1), 7–13 (2009)

    Google Scholar 

  13. D. Cozzolino et al., Multivariate data analysis applied to spectroscopy: potential application to juice and fruit quality. Food Res. Int. 44(7), 1888–1896 (2011)

    CAS  Google Scholar 

  14. V. Steinmetz, F. Sevila, V. Bellon-Maurel, A methodology for sensor fusion design: application to fruit quality assessment. J. Agric. Eng. Res. 74(1), 21–31 (1999)

    Google Scholar 

  15. A.D. Wilson, M. Baietto, Applications and advances in electronic-nose technologies. Sensors 9(7), 5099–5148 (2009)

    CAS  PubMed  Google Scholar 

  16. M. Baietto, A.D. Wilson, Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 15(1), 899–931 (2015)

    CAS  PubMed  Google Scholar 

  17. S. Di Carlo, M. Falasconi. Drift correction methods for gas chemical sensors in artificial olfaction systems: techniques and challenges, in Advances in Chemical Sensors, eds by S. Di Carlo, M. Falasconi (InTech, London, 2012)

    Google Scholar 

  18. H. Liu, Z. Tang, Metal oxide gas sensor drift compensation using a dynamic classifier ensemble based on fitting. Sensors 13(7), 9160–9173 (2013)

    CAS  PubMed  Google Scholar 

  19. R. Gutierrez-Osuna. Signal processing methods for drift compensation. In 2nd NOSE II workshop, Linkoping, 2003

  20. S. Saevels et al., Electronic nose as a non-destructive tool to evaluate the optimal harvest date of apples. Postharvest Biol. Technol. 30(1), 3–14 (2003)

    Google Scholar 

  21. S. Ampuero, J.O. Bosset, The electronic nose applied to dairy products: a review. Sensors Actuators B: Chem 94(1), 1–12 (2003)

    CAS  Google Scholar 

  22. H. Huang, L. Liu, M.O. Ngadi, Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors 14(4), 7248–7276 (2014)

    CAS  PubMed  Google Scholar 

  23. A.H.A. Eissa, A.A.K. Ayman, Understanding color image processing by machine vision for biological materials. In: Structure and Function of Food Engineering, ed. by A.H.A. Eissa (Intech, London, 2012)

    Google Scholar 

  24. S. Cubero et al., Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol. 4(4), 487–504 (2011)

    Google Scholar 

  25. E. Ofek et al., Highlight and reflection independent multiresolution textures from image sequences. IEEE Comput. Gr. Appl. 17(2), 18–29 (1997)

    Google Scholar 

  26. T. Chalidabhongse, P. Yimyam, P. Sirisomboon, 2D/3D vision-based mango’s feature extraction and sorting. In: 9th International Conference on Control, Automation, Robotics and Vision, ICARCV’06, IEEE, 2006

  27. A.A. Gowen et al., Hyperspectral imaging: an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18(12), 590–598 (2007)

    CAS  Google Scholar 

  28. S. Cubero et al., Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food Bioprocess Technol. 9(10), 1623–1639 (2016)

    CAS  Google Scholar 

  29. Z.J. Dolatowski, J. Stadnik, D. Stasiak, Applications of ultrasound in food technology. Acta Sci. Polonorum Technol. Aliment. 6(3), 88–99 (2007)

    Google Scholar 

  30. D. Molina-Delgado et al., Addressing potential sources of variation in several non-destructive techniques for measuring firmness in apples. Biosyst. Eng. 104(1), 33–46 (2009)

    Google Scholar 

  31. R. Cubeddu et al. Measuring fresh fruit and vegetable quality: advanced optical methods. In: Fruit and Vegetable Processing—Improving Quality. ed. by W. Jongen (CRC Press/Woodhead Publishing Limited, Boca Raton, 2002), pp. 150–169

    Google Scholar 

  32. M. Padilla et al., Drift compensation of gas sensor array data by orthogonal signal correction. Chemom. Intell. Lab. Syst. 100(1), 28–35 (2010)

    CAS  Google Scholar 

  33. G. Wei et al. A blind source separation based micro gas sensor array modeling method. In: International Symposium on Neural Networks (Springer, Berlin, Heidelberg, 2004)

    Google Scholar 

  34. M. Blankenburg, J. Krüger, M. Fechteler. Signal separation of gas sensor data for application in counterfeit detection. In: Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, IEEE International. IEEE, 2014

  35. S. Bermejo, J. Solé-Casals. Blind source separation for solid-state chemical sensor arrays. In: Sensor Array and Multichannel Signal Processing Workshop Proceedings, IEEE, 2004

  36. R.J. Barnes, M.S. Dhanoa, S.J. Lister, Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43(5), 772–777 (1989)

    CAS  Google Scholar 

  37. T. Isaksson, T. Næs, The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl. Spectrosc. 42(7), 1273–1284 (1988)

    CAS  Google Scholar 

  38. C.D. Brown, L. Vega-Montoto, P.D. Wentzell, Derivative preprocessing and optimal corrections for baseline drift in multivariate calibration. Appl. Spectrosc. 54(7), 1055–1068 (2000)

    CAS  Google Scholar 

  39. N.R. Pal, S.K. Pal, A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)

    Google Scholar 

  40. A. Chambolle et al., Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans. Image Process 7(3), 319–335 (1998)

    Google Scholar 

  41. E. Borràs et al., Data fusion methodologies for food and beverage authentication and quality assessment–a review. Anal. Chim. Acta 891, 1–14 (2015)

    PubMed  Google Scholar 

  42. S. Wold, N. Kettaneh, K. Tjessem, Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection. J. Chemom. 10(5-6), 463–482 (1996)

    CAS  Google Scholar 

  43. P.N. Peduzzi, R.J. Hardy, T.R. Holford, A stepwise variable selection procedure for nonlinear regression models. Biometrics 36, 511–516 (1980)

    CAS  PubMed  Google Scholar 

  44. D.M. Allen, The relationship between variable selection and data agumentation and a method for prediction. Technometrics 16(1), 125–127 (1974)

    Google Scholar 

  45. B.G. Tabachnick, L.S. Fidell, Experimental designs using ANOVA (Thomson/Brooks/Cole, Grove, 2007)

    Google Scholar 

  46. E. Vigneau et al., Clustering of variables to analyze spectral data. J. Chemom. 19(3), 122–128 (2005)

    CAS  Google Scholar 

  47. Z.M. Hira, D.F. Gillies, A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinform. https://doi.org/10.1155/2015/198363 (2015)

    Article  Google Scholar 

  48. Y. Saeys, I. Inza, P. Larrañaga. A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    CAS  Google Scholar 

  49. I. Guyon (ed.), Feature Extraction: Foundations and Applications, vol. 207 (Springer, New York, 2008)

    Google Scholar 

  50. S. De Backer, A. Naud, P. Scheunders, Non-linear dimensionality reduction techniques for unsupervised feature extraction. Pattern Recognit. Lett. 19(8), 711–720 (1998)

    Google Scholar 

  51. S.M. Holland, Principal Components Analysis (PCA) (Department of Geology, University of Georgia, Athens, GA, 2008), pp. 30602–32501

    Google Scholar 

  52. E. Barshan et al., Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds. Pattern Recognit. 44(7), 1357–1371 (2011)

    Google Scholar 

  53. B. Schölkopf, A. Smola, K.R. Müller, Kernel principal component analysis. In: International Conference on Artificial Neural Networks (Springer, Heidelberg, 1997)

    Google Scholar 

  54. T. Kohonen, The self-organizing map. Neurocomputing 21(1–3), 1–6 (1998)

    Google Scholar 

  55. F. Camastra, A. Vinciarelli, Feature Extraction Methods and Manifold Learning Methods (Springer, London, 2008)

    Google Scholar 

  56. A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis, vol. 46. (Wiley, New York, 2004)

    Google Scholar 

  57. W. Hämäläinen, Descriptive and predictive modelling techniques for educational technology. Licentiate thesis, Department of Computer Science, University of Joensuu, 2006

  58. B. Zhang et al., Determination of fruit maturity and its prediction model based on the pericarp index of absorbance difference (IAD) for peaches. PLoS ONE 12(5), e0177511 (2017)

    PubMed  PubMed Central  Google Scholar 

  59. H. Stone et al., Sensory evaluation by quantitative descriptive analysis. In: Descriptive Sensory Analysis in Practice (2004), pp. 23–34

  60. J. Gill, P.S. Sandhu, T. Singh, A review of automatic fruit classification using soft computing techniques. In: International Conference on Computing Systems in Electronic Engineering, 2014

  61. W. Wu et al., Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data. Anal. Chim. Acta 329(3), 257–265 (1996)

    CAS  Google Scholar 

  62. M. Haenlein, A.M. Kaplan, A beginner’s guide to partial least squares analysis. Underst. Stat. 3(4), 283–297 (2004)

    Google Scholar 

  63. J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Google Scholar 

  64. W.C. Seng, S.H. Mirisaee, A new method for fruits recognition system. In: International Conference on Electrical Engineering and Informatics, ICEEI’09, vol. 1, IEEE, 2009

  65. K.V. Branden, M. Hubert, Robust classification in high dimensions based on the SIMCA method. Chemom. Intell. Lab. Syst. 79(1–2), 10–21 (2005)

    Google Scholar 

  66. H. Abdi, Partial least square regression (PLS regression). Encycl. Res. Methods Soc. Sci. 6(4), 792–795 (2003)

    Google Scholar 

  67. D.F. Andrews, A robust method for multiple linear regression. Technometrics 16(4), 523–531 (1974)

    Google Scholar 

  68. W.F. Massy, Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 60(309), 234–256 (1965)

    Google Scholar 

  69. A. Peirs et al., Uncertainty analysis and modelling of the starch index during apple fruit maturation. Postharvest Biol. Technol. 26(2), 199–207 (2002)

    CAS  Google Scholar 

  70. L. Gaete-Garretón et al., A novel noninvasive ultrasonic method to assess avocado ripening. J. Food Sci. 70(3), E187–E191 (2005)

    Google Scholar 

  71. A. Mizrach, Assessing plum fruit quality attributes with an ultrasonic method. Food Res. Int. 37(6), 627–631 (2004)

    CAS  Google Scholar 

  72. K.B. Kim et al., Determination of apple firmness by nondestructive ultrasonic measurement. Postharvest Biol. Technol. 52(1), 44–48 (2009)

    Google Scholar 

  73. A. Mizrach, Determination of avocado and mango fruit properties by ultrasonic technique. Ultrasonics 38(1–8), 717–722 (2000)

    CAS  PubMed  Google Scholar 

  74. A. Mizrach et al., Determination of avocado maturity by ultrasonic attenuation measurements. Sci. Hortic. 80(3–4), 173–180 (1999)

    Google Scholar 

  75. A. Bechar et al., Determination of mealiness in apples using ultrasonic measurements. Biosyst. Eng. 91(3), 329–334 (2005)

    Google Scholar 

  76. K.B. Kim et al. Evaluation of fruit firmness by ultrasonic measurement. In: Key Engineering Materials. eds by S.S. Lee, D.J. Yoon, J.H. Lee, S. Lee, vol. 270 (Trans Tech Publications, Switzerland, 2004)

    Google Scholar 

  77. B.E. Verlinden, V.De Smedt, B.M. Nicolaı̈, Evaluation of ultrasonic wave propagation to measure chilling injury in tomatoes. Postharvest Biol. Techno. 32(1), 109–113 (2004)

    Google Scholar 

  78. V. Leemans, M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng. 61(1), 83–89 (2004)

    Google Scholar 

  79. A.B. Koc, Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biol. Technol. 45(3), 366–371 (2007)

    Google Scholar 

  80. N. Aleixos et al., Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput. Electron. Agric. 33(2), 121–137 (2002)

    Google Scholar 

  81. G. ElMasry et al., Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J. Food Eng. 81(1), 98–107 (2007)

    CAS  Google Scholar 

  82. A. Mizrach et al., Models of ultrasonic parameters to assess avocado properties and shelf life. J. Agric. Eng. Res. 65(4), 261–267 (1996)

    Google Scholar 

  83. I. Aboudaoud et al., The maturity characterization of orange fruit by using high frequency ultrasonic echo pulse method. In: 1st Conference on IOP Conference Series: Materials Science and Engineering. vol. 42, IOP Publishing, 2012

  84. F. Camarena, J.A. Martinez-Mora, Potential of ultrasound to evaluate turgidity and hydration of the orange peel. J. Food Eng. 75(4), 503–507 (2006)

    Google Scholar 

  85. R. Lewis et al., Characterising pressure and bruising in apple fruit. Wear 264(1–2), 37–46 (2008)

    CAS  Google Scholar 

  86. K.L. Ha et al., A basic study on nondestructive evaluation of potatoes using ultrasound. Jpn. J. Appl. Phys. Part 1 30, 80–82 (1991)

    Google Scholar 

  87. V. Steinmetz et al., Sensors for fruit firmness assessment: comparison and fusion. J. Agric. Eng. Res. 64(1), 15–27 (1996)

    Google Scholar 

  88. R. Saggin, J.N. Coupland, Concentration measurement by acoustic reflectance. J. Food Sci. 66(5), 681–685 (2001)

    CAS  Google Scholar 

  89. K. Peleg, Development of a commercial fruit firmness sorter. J. Agric. Eng. Res. 72(3), 231–238 (1999)

    Google Scholar 

  90. M. Nielsen, H.J. Martens, K. Kaack, Low frequency ultrasonics for texture measurements in carrots (Daucus carota L.) in relation to water loss and storage. Postharvest Biol. Technol 14(3), 297–308 (1998)

    Google Scholar 

  91. U. Flitsanov et al., Measurement of avocado softening at various temperatures using ultrasound. Postharvest Biol. Technol. 20(3), 279–286 (2000)

    Google Scholar 

  92. D. Ariana, D.E. Guyer, B. Shrestha, Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Comput. Electron. Agric. 50(2), 148–161 (2006)

    Google Scholar 

  93. B.S. Bennedsen, D.L. Peterson, Performance of a system for apple surface defect identification in near-infrared images. Biosyst. Eng. 90(4), 419–431 (2005)

    Google Scholar 

  94. J. Blasco, N. Aleixos, E. Moltó, Machine vision system for automatic quality grading of fruit. Biosyst. Eng. 85(4), 415–423 (2003)

    Google Scholar 

  95. J. Blasco, N. Aleixos, E. Molto, Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. J. Food Eng. 81(3), 535–543 (2007)

    Google Scholar 

  96. G. ElMasry, N. Wang, C. Vigneault, Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biol. Technol. 52(1), 1–8 (2009)

    Google Scholar 

  97. J. Blasco et al., Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J. Food Eng. 90(1), 27–34 (2009)

    Google Scholar 

  98. X. Liming, Z. Yanchao, Automated strawberry grading system based on image processing. Comput. Electron. Agric. 71, S32–S39 (2010)

    Google Scholar 

  99. L. Lleó et al., Multispectral images of peach related to firmness and maturity at harvest. J. Food Eng. 93(2), 229–235 (2009)

    Google Scholar 

  100. F. Lpez-Garca et al., Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Comput. Electron. Agric. 71(2), 189–197 (2010)

    Google Scholar 

  101. S. Cubero et al., Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices. Biosyst. Eng. 167, 63–74 (2018)

    Google Scholar 

  102. J. Blasco et al., Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosyst. Eng. 103(2), 137–145 (2009)

    Google Scholar 

  103. D.S. Prabha, J. Satheesh, Kumar, Assessment of banana fruit maturity by image processing technique. J. Food Sci. Technol. 52(3), 1316–1327 (2015)

    Google Scholar 

  104. O.K.M. Yahaya et al., Non-destructive quality evaluation of fruit by color based on RGB LEDs system. In: 2nd International Conference on Electronic Design (ICED), IEEE, 2014

  105. S.K. Bejo, S. Kamaruddin, Determination of Chokanan mango sweetness (‘Mangifera indica’) using non-destructive image processing technique. Austr. J. Crop Sci. 8(4), 475 (2014)

    Google Scholar 

  106. Z. Malik et al., Detection and counting of on-tree citrus fruit for crop yield estimation. IJACSA. (2016). https://doi.org/10.14569/IJACSA.2016.070569

    Article  Google Scholar 

  107. H.K. Noh, R. Lu, Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biol. Technol. 43(2), 193–201 (2007)

    Google Scholar 

  108. S. Elsayed et al., Passive reflectance sensing and digital image analysis for assessing quality parameters of mango fruits. Sci. Hortic. 212, 136–147 (2016)

    Google Scholar 

  109. M. Othman et al., Fuzzy ripening mango index using RGB colour sensor model. Res. World 5(2), 1 (2014)

    Google Scholar 

  110. K. Mollazade et al., Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging. Comput. Electron. Agric. 98, 34–45 (2013)

    Google Scholar 

  111. J. Brezmes et al., Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples. Sensors Actuators B: Chem. 80(1), 41–50 (2001)

    CAS  Google Scholar 

  112. A. Sanaeifar et al., Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM). Czech J. Food Sci. 32, 538–548 (2014)

    Google Scholar 

  113. S. Nordiyana et al., Development of electronic nose for fruits ripeness determination. In: 1st International Conference on Sensing Technology, Palmerston North, New Zealand, 2005

  114. E.M. Pruteanu et al. Electronic nose for discrimination of Romanian apples. Lucr. Stiintifice (2009). https://doi.org/10.3390/s150100899

    Article  Google Scholar 

  115. C. Di Natale et al., Electronic nose based investigation of the sensorial properties of peaches and nectarines. Sensors Actuators B: Chem. 77(1–2), 561–566 (2001)

    Google Scholar 

  116. A.H. Gómez et al., Electronic nose technique potential monitoring mandarin maturity. Sensors Actuators B: Chem 113(1), 347–353 (2006)

    Google Scholar 

  117. J. Brezmes et al., Evaluation of an electronic nose to assess fruit ripeness. IEEE Sens. J. 5(1), 97–108 (2005)

    Google Scholar 

  118. S. Benedetti et al., Electronic nose as a non-destructive tool to characterise peach cultivars and to monitor their ripening stage during shelf-life. Postharvest Biol. Technol. 47(2), 181–188 (2008)

    CAS  Google Scholar 

  119. J.A. Ragazzo-Sanchez et al., Off-flavours detection in alcoholic beverages by electronic nose coupled to GC. Sensors Actuators B: Chem 140(1), 29–34 (2009)

    CAS  Google Scholar 

  120. M. Ruiz-Altisent, L. Lleó, F. Riquelme, Instrumental quality assessment of peaches: fusion of optical and mechanical parameters. J. Food Eng. 74(4), 490–499 (2006)

    Google Scholar 

  121. K.M. Nunes et al., Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy. Food Chem. 205, 14–22 (2016)

    CAS  PubMed  Google Scholar 

  122. C. Li, P. Heinemann, R. Sherry, Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sensors Actuators B. Chem. 125(1), 301–310 (2007)

    CAS  Google Scholar 

  123. J.A. Ragazzo-Sanchez et al., Identification of different alcoholic beverages by electronic nose coupled to GC. Sensors Actuators B. Chem. 134(1), 43–48 (2008)

    CAS  Google Scholar 

  124. F.S.A. Sa’ad et al., Bio-inspired sensor fusion for quality assessment of harumanis mangoes. Proc. Chem. 6, 165–174 (2012)

    Google Scholar 

  125. L. Pan et al., Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography–mass spectrometry. Food Res. Int. 62, 162–168 (2014)

    CAS  Google Scholar 

  126. C. Di Natale et al., Outer product analysis of electronic nose and visible spectra: application to the measurement of peach fruit characteristics. Anal. Chim. Acta 459(1), 107–117 (2002)

    Google Scholar 

  127. K.K. Vursavus et al., Classification of the firmness of peaches by sensor fusion. Int. J. Agric. Biol. Eng. 8(6), 104 (2015)

    Google Scholar 

  128. A. Baltazar, J.I. Aranda, G. González-Aguilar, Bayesian classification of ripening stages of tomato fruit using acoustic impact and colorimeter sensor data. Comput. Electron. Agric. 60(2), 113–121 (2008)

    Google Scholar 

  129. A. Herrero-Langreo et al., Combination of optical and non-destructive mechanical techniques for the measurement of maturity in peach. J. Food Eng. 108(1), 150–157 (2012)

    Google Scholar 

  130. L. Huang et al., Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem. 145, 228–236 (2014)

    CAS  PubMed  Google Scholar 

  131. V. Steinmetz et al., On-line fusion of colour camera and spectrophotometer for sugar content prediction of apples. J. Agric. Eng. Res. 73(2), 207–216 (1999)

    Google Scholar 

  132. D. Liu et al., Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. Food Chem. 160, 330–337 (2014)

    CAS  PubMed  Google Scholar 

  133. S. Roussel et al., Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry. J. Food Eng. 60(4), 407–419 (2003)

    Google Scholar 

  134. F. Mendoza, R. Lu, H. Cen, Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biol. Technol. 73, 89–98 (2012)

    CAS  Google Scholar 

  135. C. Ortíz et al., PH—postharvest technology: non-destructive identification of woolly peaches using impact response and near-infrared spectroscopy. J. Agric. Eng. Res. 78(3), 281–289 (2001)

    Google Scholar 

  136. S. Qiu, J. Wang, L. Gao, Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM. J. Agric. Food Chem. 62(27), 6426–6434 (2014)

    CAS  PubMed  Google Scholar 

  137. H. Young et al., Characterization of Royal Gala apple aroma using electronic nose technology potential maturity indicator. J. Agric. Food Chem. 47(12), 5173–5177 (1999)

    CAS  PubMed  Google Scholar 

  138. J. Brezmes et al., Fruit ripeness monitoring using an electronic nose. Sensors Actuators B: Chem 69(3), 223–229 (2000)

    CAS  Google Scholar 

  139. H. Guohua et al., Fuji apple storage time predictive method using electronic nose. Food Anal. Methods 6(1), 82–88 (2013)

    Google Scholar 

  140. E. Llobet et al., Non-destructive banana ripeness determination using a neural network-based electronic nose. Meas. Sci. Technol. 10(6), 538 (1999)

    CAS  Google Scholar 

  141. L.P. Pathange et al., Non-destructive evaluation of apple maturity using an electronic nose system. J. Food Eng. 77(4), 1018–1023 (2006)

    Google Scholar 

  142. H. Zhang, J. Wang, S. Ye, Predictions of acidity, soluble solids and firmness of pear using electronic nose technique. J. Food Eng. 86(3), 370–378 (2008)

    Google Scholar 

  143. C. Di Natale et al., The evaluation of quality of post-harvest oranges and apples by means of an electronic nose. Sensors Actuators B: Chem. 78(1–3), 26–31 (2001)

    Google Scholar 

  144. E.G. Breijo et al., Odour sampling system with modifiable parameters applied to fruit classification. J. Food Eng. 116(2), 277–285 (2013)

    Google Scholar 

  145. E. Kim et al., Pattern recognition for selective odor detection with gas sensor arrays. Sensors 12(12), 16262–16273 (2012)

    CAS  PubMed  Google Scholar 

  146. T. Nilsson, K.E. Gustavsson, Postharvest physiology of ‘Aroma’apples in relation to position on the tree. Postharvest Biol. Technol 43(1), 36–46 (2007)

    CAS  Google Scholar 

  147. M. Su et al., Pulp volatiles measured by an electronic nose are related to harvest season, TSS concentration and TSS/TA ratio among 39 peaches and nectarines. Sci. Hortic. 150, 146–153 (2013)

    CAS  Google Scholar 

  148. E. Molto et al., An aroma sensor for assessing peach quality. J. Agric. Eng. Res. 72(4), 311–316 (1999)

    Google Scholar 

  149. H.F. Hawari et al., Highly selective molecular imprinted polymer (MIP) based sensor array using interdigitated electrode (IDE) platform for detection of mango ripeness. Sensors Actuators B: Chem. 187, 434–444 (2013)

    CAS  Google Scholar 

  150. M. Lebrun et al., The electronic nose: a fast and efficient tool for characterizing dates. Fruits 62(6), 377–382 (2007)

    CAS  Google Scholar 

  151. Y.C. Yang et al., Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion. Postharvest Biol. Technol. 103, 55–65 (2015)

    CAS  Google Scholar 

  152. P.N. Schaare, D.G. Fraser, Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biol. Technol. 20(2), 175–184 (2000)

    Google Scholar 

  153. R. Lu, D.E. Guyer, R.M. Beaudry, Determination of firmness and sugar content of apples using near-infrared diffuse reflectance. J. Texture Stud. 31(6), 615–630 (2000)

    Google Scholar 

  154. Z. Schmilovitch et al., Determination of mango physiological indices by near-infrared spectrometry. Postharvest Biol. Technol. 19(3), 245–252 (2000)

    Google Scholar 

  155. N. Sinelli et al., Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. Postharvest Biol. Technol. 50(1), 31–36 (2008)

    CAS  Google Scholar 

  156. J. Lammertyn et al., Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biol. Technol. 18(2), 121–132 (2000)

    Google Scholar 

  157. C. Camps, D. Christen, Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT-Food Sci. Technol. 42(6), 1125–1131 (2009)

    CAS  Google Scholar 

  158. A.H. Gomez, Y. He, A.G. Pereira, Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. J. Food Eng. 77(2), 313–319 (2006)

    CAS  Google Scholar 

  159. S. Saranwong, J. Sornsrivichai, S. Kawano, Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biol. Technol. 31(2), 137–145 (2004)

    CAS  Google Scholar 

  160. A. Peirs et al., Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biol. Technol. 21(2), 189–199 (2001)

    Google Scholar 

  161. S. Bureau et al., Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chem 113(4), 1323–1328 (2009)

    CAS  Google Scholar 

  162. J. Xia et al., Visible-near infrared spectrum-based classification of apple chilling injury on cloud computing platform. Comput. Electron. Agric. 145, 27–34 (2018)

    Google Scholar 

  163. M. Zude et al., Non-destructive analysis of anthocyanins in cherries by means of Lambert–Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis. J. Food Eng. 103(1), 68–75 (2011)

    CAS  Google Scholar 

  164. M. Silvestri et al., A mid level data fusion strategy for the varietal classification of lambrusco PDO wines. Chemom. Intell. Lab. Syst. 137, 181–189 (2014)

    CAS  Google Scholar 

  165. A.G. Mignani et al., Optical measurements and pattern-recognition techniques for identifying the characteristics of beer and distinguishing Belgian beers. Sensors Actuators B: Chem. 179, 140–149 (2013)

    CAS  Google Scholar 

  166. I. Arana, C. Jarén, S. Arazuri, Maturity, variety and origin determination in white grapes (Vitis vinifera L.) using near infrared reflectance technology. J. Near Infrared Spectrosc. 13(6), 349–357 (2005)

    CAS  Google Scholar 

  167. R. Beghi et al., Apples nutraceutic properties evaluation through a visible and near-infrared portable system. Food Bioprocess Technol. 6(9), 2547–2554 (2013)

    CAS  Google Scholar 

  168. V. Cortés et al., A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy. Postharvest Biol. Technol. 118, 148–158 (2016)

    Google Scholar 

  169. T. Ignat et al., Nonlinear methods for estimation of maturity stage, total chlorophyll, and carotenoid content in intact bell peppers. Biosyst. Eng. 114(4), 414–425 (2013)

    Google Scholar 

  170. R. Lu, Imaging spectroscopy for assessing internal quality of apple fruit. In: ASAE Annual Meeting. American Society of Agricultural and Biological Engineers, 2003

  171. G.Y. Kim et al., Defect and ripeness inspection of citrus using NIR transmission spectrum. In: Key Engineering Materials, vol. 270, eds. by S.S. Lee, D.J. Yoon, J.H. Lee, S. Lee (Trans Tech Publications, Switzerland, 2004)

    Google Scholar 

  172. Alpha MOS, http://saba.kntu.ac.ir/eecd/ecourses/inst%2086/Projects/Electronic%20Nose/final%20atashzar/New%20Folder/Alpha%20M.O.S..htm. Accessed 08 Feb 2018

  173. Sensight, intelligent sensing solutions, http://www.sensigent.com/products/cyranose.html. Accessed 08 Feb 2018

  174. Air sense analytics, https://airsense.com/en/products/portable-electronic-nose. Accessed 08 Feb 2018

  175. SCIO by Consumer Physics, https://www.consumerphysics.com/scio-for-consumers/. Accessed 08 Feb 2018

  176. ClariFruit Know Your Fruit, https://www.clarifruit.com/. Accessed 08 Feb 2018

  177. Felix instruments, https://felixinstruments.com/food-science instruments/portable-nir-analyzers/f-750-produce-quality-meter/. Accessed 08 Feb 2018

  178. Sunforest, http://sunforest.en.ec21.com/. Accessed 08 Feb 2018

  179. Trturoni, http://www.trturoni.com/en/content/8-da-meter. Accessed 08 Feb 2018

  180. Unitech, http://www.postharvest.biz/en/company/unitecspa/_id:29711,seccion:productcatalog,producto:10531/. Accessed 08 Feb 2018

  181. Omega, http://in.omega.com/pptst/HFH80.html. Accessed 08 Feb 2018

  182. Food sniffer, http://www.myfoodsniffer.com/. Accessed 08 Feb 2018

  183. Y.Y. Pu, Y.Z. Feng, D.W. Sun, Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: a review. Compr. Rev. Food Sci. Food Saf. 14(2), 176–188 (2015)

    Google Scholar 

  184. M. Falasconi et al., Electronic nose for microbiological quality control of food products. Int. J. Electrochem. (2012). https://doi.org/10.1155/2012/715763

    Article  Google Scholar 

  185. T. Brosnan, D.W. Sun, Improving quality inspection of food products by computer vision: a review. J. Food Eng. 61(1), 3–16 (2004)

    Google Scholar 

  186. C.J. Du, D.W. Sun, Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Technol. 15(5), 230–249 (2004)

    CAS  Google Scholar 

  187. K.K. Patel et al., Machine vision system: a tool for quality inspection of food and agricultural products. J. Food Sci. Technol. 49(2), 123–141 (2012)

    PubMed  Google Scholar 

  188. C.J. Du, D.W. Sun, Learning techniques used in computer vision for food quality evaluation: a review. J. Food Eng 72(1), 39–55 (2006)

    Google Scholar 

  189. H. Cen, Y. He, Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Technol. 18(2), 72–83 (2007)

    CAS  Google Scholar 

  190. S.N. JHA, T. Matsuoka, Non-destructive techniques for quality evaluation of intact fruits and vegetables. Food Sci. Technol. Res. 6(4), 248–251 (2000)

    CAS  Google Scholar 

  191. H. Huang et al., Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J. Food Eng. 87(3), 303–313 (2008)

    CAS  Google Scholar 

  192. B.M. Nicolai et al., Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46(2), 99–118 (2007)

    Google Scholar 

  193. T.J. Mason, L. Paniwnyk, J.P. Lorimer, The uses of ultrasound in food technology. Ultrason. Sonochem 3(3), S253–S260 (1996)

    CAS  Google Scholar 

  194. A. Mizrach, Ultrasonic technology for quality evaluation of fresh fruit and vegetables in pre-and postharvest processes. Postharvest Biol. Technol 48(3), 315–330 (2008)

    Google Scholar 

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Srivastava, S., Sadistap, S. Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review. Food Measure 12, 2758–2794 (2018). https://doi.org/10.1007/s11694-018-9893-2

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