Journal of Food Measurement and Characterization

, Volume 12, Issue 4, pp 2758–2794 | Cite as

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

  • Satyam SrivastavaEmail author
  • Shashikant Sadistap
Review Paper


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.


Data processing Data fusion Algorithmic intelligence Fruit quality Shelf life Maturity 


  1. 1.
    Food loss and waste facts (2015), Accessed 3 Feb 2018
  2. 2.
    L.S. Kantor et al., Estimating and addressing America’s food losses. Food Rev. 20(1), 2–12 (1997)Google Scholar
  3. 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. 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. 5.
    F. Röck, B. Nicolae, W. Udo, Electronic nose: current status and future trends. Chem. Rev. 108(2), 705–725 (2008)PubMedGoogle Scholar
  6. 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. 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. 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. 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. 2002Google Scholar
  10. 10.
    T.C. Pearce (eds.), Handbook of Machine Olfaction: Electronic Nose Technology (Wiley, New York, 2006)Google Scholar
  11. 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)Google Scholar
  12. 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. 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)Google Scholar
  14. 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. 15.
    A.D. Wilson, M. Baietto, Applications and advances in electronic-nose technologies. Sensors 9(7), 5099–5148 (2009)PubMedGoogle Scholar
  16. 16.
    M. Baietto, A.D. Wilson, Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 15(1), 899–931 (2015)PubMedGoogle Scholar
  17. 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. 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)PubMedGoogle Scholar
  19. 19.
    R. Gutierrez-Osuna. Signal processing methods for drift compensation. In 2nd NOSE II workshop, Linkoping, 2003Google Scholar
  20. 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. 21.
    S. Ampuero, J.O. Bosset, The electronic nose applied to dairy products: a review. Sensors Actuators B: Chem 94(1), 1–12 (2003)Google Scholar
  22. 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)PubMedGoogle Scholar
  23. 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. 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. 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. 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, 2006Google Scholar
  27. 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)Google Scholar
  28. 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)Google Scholar
  29. 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. 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. 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. 150–169Google Scholar
  32. 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)Google Scholar
  33. 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. 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, 2014Google Scholar
  35. 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, 2004Google Scholar
  36. 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)Google Scholar
  37. 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)Google Scholar
  38. 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)Google Scholar
  39. 39.
    N.R. Pal, S.K. Pal, A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)Google Scholar
  40. 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. 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)PubMedGoogle Scholar
  42. 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)Google Scholar
  43. 43.
    P.N. Peduzzi, R.J. Hardy, T.R. Holford, A stepwise variable selection procedure for nonlinear regression models. Biometrics 36, 511–516 (1980)PubMedGoogle Scholar
  44. 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. 45.
    B.G. Tabachnick, L.S. Fidell, Experimental designs using ANOVA (Thomson/Brooks/Cole, Grove, 2007)Google Scholar
  46. 46.
    E. Vigneau et al., Clustering of variables to analyze spectral data. J. Chemom. 19(3), 122–128 (2005)Google Scholar
  47. 47.
    Z.M. Hira, D.F. Gillies, A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinform. (2015)CrossRefGoogle Scholar
  48. 48.
    Y. Saeys, I. Inza, P. Larrañaga. A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)Google Scholar
  49. 49.
    I. Guyon (ed.), Feature Extraction: Foundations and Applications, vol. 207 (Springer, New York, 2008)Google Scholar
  50. 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. 51.
    S.M. Holland, Principal Components Analysis (PCA) (Department of Geology, University of Georgia, Athens, GA, 2008), pp. 30602–32501Google Scholar
  52. 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. 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. 54.
    T. Kohonen, The self-organizing map. Neurocomputing 21(1–3), 1–6 (1998)Google Scholar
  55. 55.
    F. Camastra, A. Vinciarelli, Feature Extraction Methods and Manifold Learning Methods (Springer, London, 2008)Google Scholar
  56. 56.
    A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis, vol. 46. (Wiley, New York, 2004)Google Scholar
  57. 57.
    W. Hämäläinen, Descriptive and predictive modelling techniques for educational technology. Licentiate thesis, Department of Computer Science, University of Joensuu, 2006Google Scholar
  58. 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)PubMedPubMedCentralGoogle Scholar
  59. 59.
    H. Stone et al., Sensory evaluation by quantitative descriptive analysis. In: Descriptive Sensory Analysis in Practice (2004), pp. 23–34Google Scholar
  60. 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, 2014Google Scholar
  61. 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)Google Scholar
  62. 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. 63.
    J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)Google Scholar
  64. 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, 2009Google Scholar
  65. 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. 66.
    H. Abdi, Partial least square regression (PLS regression). Encycl. Res. Methods Soc. Sci. 6(4), 792–795 (2003)Google Scholar
  67. 67.
    D.F. Andrews, A robust method for multiple linear regression. Technometrics 16(4), 523–531 (1974)Google Scholar
  68. 68.
    W.F. Massy, Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 60(309), 234–256 (1965)Google Scholar
  69. 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)Google Scholar
  70. 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. 71.
    A. Mizrach, Assessing plum fruit quality attributes with an ultrasonic method. Food Res. Int. 37(6), 627–631 (2004)Google Scholar
  72. 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. 73.
    A. Mizrach, Determination of avocado and mango fruit properties by ultrasonic technique. Ultrasonics 38(1–8), 717–722 (2000)PubMedGoogle Scholar
  74. 74.
    A. Mizrach et al., Determination of avocado maturity by ultrasonic attenuation measurements. Sci. Hortic. 80(3–4), 173–180 (1999)Google Scholar
  75. 75.
    A. Bechar et al., Determination of mealiness in apples using ultrasonic measurements. Biosyst. Eng. 91(3), 329–334 (2005)Google Scholar
  76. 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. 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. 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. 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. 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. 81.
    G. ElMasry et al., Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J. Food Eng. 81(1), 98–107 (2007)Google Scholar
  82. 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. 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, 2012Google Scholar
  84. 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. 85.
    R. Lewis et al., Characterising pressure and bruising in apple fruit. Wear 264(1–2), 37–46 (2008)Google Scholar
  86. 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. 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. 88.
    R. Saggin, J.N. Coupland, Concentration measurement by acoustic reflectance. J. Food Sci. 66(5), 681–685 (2001)Google Scholar
  89. 89.
    K. Peleg, Development of a commercial fruit firmness sorter. J. Agric. Eng. Res. 72(3), 231–238 (1999)Google Scholar
  90. 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. 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. 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. 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. 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. 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. 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. 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. 98.
    X. Liming, Z. Yanchao, Automated strawberry grading system based on image processing. Comput. Electron. Agric. 71, S32–S39 (2010)Google Scholar
  99. 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. 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. 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. 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. 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. 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, 2014Google Scholar
  105. 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. 106.
    Z. Malik et al., Detection and counting of on-tree citrus fruit for crop yield estimation. IJACSA. (2016). CrossRefGoogle Scholar
  107. 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. 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. 109.
    M. Othman et al., Fuzzy ripening mango index using RGB colour sensor model. Res. World 5(2), 1 (2014)Google Scholar
  110. 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. 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)Google Scholar
  112. 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. 113.
    S. Nordiyana et al., Development of electronic nose for fruits ripeness determination. In: 1st International Conference on Sensing Technology, Palmerston North, New Zealand, 2005Google Scholar
  114. 114.
    E.M. Pruteanu et al. Electronic nose for discrimination of Romanian apples. Lucr. Stiintifice (2009). CrossRefGoogle Scholar
  115. 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. 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. 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. 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)Google Scholar
  119. 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)Google Scholar
  120. 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. 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)PubMedGoogle Scholar
  122. 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)Google Scholar
  123. 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)Google Scholar
  124. 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. 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)Google Scholar
  126. 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. 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. 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. 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. 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)PubMedGoogle Scholar
  131. 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. 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)PubMedGoogle Scholar
  133. 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. 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)Google Scholar
  135. 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. 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)PubMedGoogle Scholar
  137. 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)PubMedGoogle Scholar
  138. 138.
    J. Brezmes et al., Fruit ripeness monitoring using an electronic nose. Sensors Actuators B: Chem 69(3), 223–229 (2000)Google Scholar
  139. 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. 140.
    E. Llobet et al., Non-destructive banana ripeness determination using a neural network-based electronic nose. Meas. Sci. Technol. 10(6), 538 (1999)Google Scholar
  141. 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. 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. 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. 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. 145.
    E. Kim et al., Pattern recognition for selective odor detection with gas sensor arrays. Sensors 12(12), 16262–16273 (2012)PubMedGoogle Scholar
  146. 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)Google Scholar
  147. 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)Google Scholar
  148. 148.
    E. Molto et al., An aroma sensor for assessing peach quality. J. Agric. Eng. Res. 72(4), 311–316 (1999)Google Scholar
  149. 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)Google Scholar
  150. 150.
    M. Lebrun et al., The electronic nose: a fast and efficient tool for characterizing dates. Fruits 62(6), 377–382 (2007)Google Scholar
  151. 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)Google Scholar
  152. 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. 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. 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. 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)Google Scholar
  156. 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. 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)Google Scholar
  158. 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)Google Scholar
  159. 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)Google Scholar
  160. 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. 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)Google Scholar
  162. 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. 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)Google Scholar
  164. 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)Google Scholar
  165. 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)Google Scholar
  166. 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)Google Scholar
  167. 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)Google Scholar
  168. 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. 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. 170.
    R. Lu, Imaging spectroscopy for assessing internal quality of apple fruit. In: ASAE Annual Meeting. American Society of Agricultural and Biological Engineers, 2003Google Scholar
  171. 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. 172.
  173. 173.
    Sensight, intelligent sensing solutions, Accessed 08 Feb 2018
  174. 174.
    Air sense analytics, Accessed 08 Feb 2018
  175. 175.
    SCIO by Consumer Physics, Accessed 08 Feb 2018
  176. 176.
    ClariFruit Know Your Fruit, Accessed 08 Feb 2018
  177. 177.
  178. 178.
    Sunforest, Accessed 08 Feb 2018
  179. 179.
    Trturoni, Accessed 08 Feb 2018
  180. 180.
  181. 181.
    Omega, Accessed 08 Feb 2018
  182. 182.
    Food sniffer, Accessed 08 Feb 2018
  183. 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. 184.
    M. Falasconi et al., Electronic nose for microbiological quality control of food products. Int. J. Electrochem. (2012). CrossRefGoogle Scholar
  185. 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. 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)Google Scholar
  187. 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)PubMedGoogle Scholar
  188. 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. 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)Google Scholar
  190. 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)Google Scholar
  191. 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)Google Scholar
  192. 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. 193.
    T.J. Mason, L. Paniwnyk, J.P. Lorimer, The uses of ultrasound in food technology. Ultrason. Sonochem 3(3), S253–S260 (1996)Google Scholar
  194. 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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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Academy of Scientific & Innovative Research (AcSiR)CSIR-CEERIPilaniIndia
  2. 2.CSIR-CEERIPilaniIndia

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