Skip to main content
SpringerLink
Log in

Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest

  • Chapter
  • First Online:
Measurement, Modeling and Automation in Advanced Food Processing

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 161))

Abstract

Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product’s surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cubero S, Lee WS, Aleixos N, Albert F, Blasco J (2016) Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest: a review. Food Bioproc Tech 9:1623–1639

    Google Scholar 

  2. Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J, Blasco J (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioproc Tech 4:487–504

    Article  Google Scholar 

  3. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioproc Tech 5:1121–1142

    Article  Google Scholar 

  4. Hunt RWG, Pointer MR (2011) Measuring colour, 4th edn. Wiley, Chichester

    Book  Google Scholar 

  5. Sharma RM, Singh RR (2000) Harvesting, postharvest, handling and physiology of fruits and vegetables. In: Verma LR, Joshi VK (eds) Postharvest technology of fruit and vegetables. Indus Publishing Co. New Delhi, pp 94–147

    Google Scholar 

  6. Mohammadi V, Kheiralipour K, Ghasemi-Varnamkhasti M (2015) Detecting maturity of persimmon fruit based on image processing technique. Sci Hortic 184:123–128

    Article  Google Scholar 

  7. Taghadomi-Saberi S, Omid M, Emam-Djomeh Z, Faraji-Mahyari KH (2015) Determination of cherry color parameters during ripening by artificial neural network assisted image processing technique. J Agric Sci Technol 17:589–600

    Google Scholar 

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

    Article  Google Scholar 

  9. El-Bendary N, El Hariri E, Hassanien AE, Badr A (2015) Using machine learning techniques for evaluating tomato ripeness. Expert Syst Appl 42:1892–1905

    Article  Google Scholar 

  10. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, Blasco J (2013) Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food Bioproc Tech 6(2):530–541

    Article  Google Scholar 

  11. Guzmán E, Baeten V, Pierna JAF, García-Mesa JA (2015) Determination of the olive maturity index of intact fruits using image analysis. J Food Sci Technol 52:1462–1470

    Article  Google Scholar 

  12. Vidal A, Talens P, Prats-Montalbán JM, Cubero S, Albert F, Blasco J (2013) In-line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food Bioproc Tech 6(12):3412–3419

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Vélez-Rivera N, Blasco J, Chanona-Pérez JJ, Calderón-Domínguez G, Perea-Flores MJ, Arzate-Vázquez I, Cubero S, Farrera-Rebollo R (2014) Computer vision system applied to classification of ‘Manila’ mangoes during ripening process. Food Bioproc Tech 7:1183–1194

    Article  Google Scholar 

  15. Li JB, Huang WQ, Zhao CJ (2015) Machine vision technology for detecting the external defects of fruits: a review. Imaging Sci J 63:241–251

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Al-Rahbi S, Manickavasagan A, Al-Yahyai R, Khriji L, Alahakoon P (2013) Detecting surface cracks on dates using color imaging technique. Food Sci Technol Res 19:795–804

    Article  Google Scholar 

  18. Blasco J, Aleixos N, Gómez J, Moltó E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83:384–393

    Article  Google Scholar 

  19. Blasco J, Aleixos N, Gómez-Sanchis J, Moltó E (2009) Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosyst Eng 103:137–145

    Article  Google Scholar 

  20. Li J, Rao X, Wang F, Wu W, Ying Y (2013) Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biol Technol 82:59–69

    Article  Google Scholar 

  21. Rokunuzzaman M, Jayasuriya HPW (2013) Development of a low cost machine vision system for sorting of tomatoes. Agric Eng Int CIGR J 15:173–180

    Google Scholar 

  22. Xu L, You Z, Wu S, Zhao H, Wu L (2013) Development and experiment on automatic grading equipment for kiwi INMATEH - agricultural. Engineering 41:55–64

    CAS  Google Scholar 

  23. Aleixos N, Blasco J, Navarrón F, Moltó E (2002) Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput Electron Agric 33:121–137

    Article  Google Scholar 

  24. Unay D, Gosselin B, Kleynen O, Leemans V, Destain MF, Debeir O (2011) Automatic grading of bi-colored apples by multispectral machine vision. Comput Electron Agric 75:204–212

    Article  Google Scholar 

  25. Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C (2010) Sensing technologies for precision specialty crop production. Comput Electron Agric 74:2–33

    Article  Google Scholar 

  26. Pourreza A, Lee WS, Ehsani R, Schueller JK, Raveh E (2015) An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Comput Electron Agric 110:221–232

    Article  Google Scholar 

  27. Pourreza A, Lee WS, Etxeberria E, Banerjee A (2015) An evaluation of a vision-based sensor performance in Huanglongbing disease identification. Biosyst Eng 130:13–22

    Article  Google Scholar 

  28. Sun D-W (ed) (2010) Hyperspectral imaging for food quality analysis and control. Academic Press, Elsevier Science, London, England

    Google Scholar 

  29. Gat N (2000) Imaging spectroscopy using tunable filters: a review. Proc SPIE 4056:50–64

    Article  Google Scholar 

  30. Gómez-Sanchis J, Gómez-Chova L, Aleixos N, Camps-Valls G, Montesinos-Herrero C, Moltó E, Blasco J (2008) Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J Food Eng 89:80–86

    Article  Google Scholar 

  31. Gómez-Sanchis J, Martín-Guerrero JD, Soria-Olivas E, Martínez-Sober M, Magdalena-Benedito R, Blasco J (2012) Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Syst Appl 39:780–785

    Article  Google Scholar 

  32. Lorente D, Blasco J, Serrano AJ, Soria-Olivas E, Aleixos N, Gómez-Sanchis J (2013) Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food Bioproc Tech 6:3613–3619

    Article  CAS  Google Scholar 

  33. Brandily ML, Monbet V, Bureau B, Boussard-Plédel C, Loréal O, Adam JL, Sire O (2011) Identification of foodborne pathogens within food matrices by IR spectroscopy. Sens Actuators B 160:202–206

    Article  CAS  Google Scholar 

  34. Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46:99–118

    Article  Google Scholar 

  35. Zhao J, Ouyang Q, Chen Q, Wang J (2010) Detection of bruise on pear by hyperspectral imaging sensor with different classification algorithms. Sens Lett 8:570–576

    Article  Google Scholar 

  36. Bei L, Dennis GI, Miller HM, Spaine TW, Carnahan JW (2004) Acousto-optic tunable filters: fundamentals and applications as applied to chemical analysis techniques. Prog Quantum Electron 28:67–87

    Article  Google Scholar 

  37. Jiménez A, Beltrán G, Aguilera MP, Uceda M (2008) A sensor-software based on artificial neural network for the optimization of olive oil elaboration process. Sens Actuators B 129:985–990

    Article  Google Scholar 

  38. Vila-Francés J, Calpe-Maravilla J, Gómez-Chova L, Amorós-López J (2010) Analysis of acousto-optic tunable filter performance for imaging applications. Opt Eng 49:113203–113209

    Article  Google Scholar 

  39. Wang W, Li C, Tollner EW, Rains GC, Gitaitis RD (2012) A liquid crystal tunable filter based shortwave infrared spectral imaging system: design and integration. Comput Electron Agric 80:126–134

    Article  Google Scholar 

  40. Gowen AA, O'Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18:590–598

    Article  CAS  Google Scholar 

  41. Vila-Francés J, Calpe-Maravilla J, Gómez-Chova L, Amorós-López J (2011) Design of a configurable multispectral imaging system based on an AOTF. IEEE Trans Ultrason Ferroelectr Freq Control 58:259–262

    Article  Google Scholar 

  42. Hecht E (2003) Optics, 4th edn. Addison Wesley, Reading

    Google Scholar 

  43. Geladi PLM (2007) Calibration standards and image calibration. In: Grahn HF, Geladi P (eds) Techniques and applications of hyperspectral image analysis. Wiley, Chichester, pp 203–220

    Chapter  Google Scholar 

  44. Schmilovitch Z, Ignat T, Alchanatis V, Gatker J, Ostrovsky V, Felföldi J (2014) Hyperspectral imaging of intact bell peppers. Biosyst Eng 117:83–93

    Article  Google Scholar 

  45. Rajkumar P, Wang N, ElMasry G, Raghavan GSV, Gariepy Y (2012) Studies on banana fruit quality and maturity stages using hyperspectral imaging. J Food Eng 108:194–200

    Article  Google Scholar 

  46. Munera S, Besada C, Blasco J, Cubero S, Salvador A, Talens P, Aleixos N (2017) Astringency assessment of persimmon by hyperspectral imaging. Postharvest Biol Technol 125:35–4

    Google Scholar 

  47. Gaston E, Frías JM, Cullen PJ, O’Donnell CP, Gowen AA (2010) Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem 58:6226–6233

    Google Scholar 

  48. Yang YC, Sun DW, Pu H, Wang NN, Zhu Z (2015) Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion. Postharvest Biol Technol 103:55–65

    Article  CAS  Google Scholar 

  49. Hua MH, Dong QL, Liu BL, Opara UL, Chen L (2015) Estimating blueberry mechanical properties based on random frog selected hyperspectral data. Postharvest Biol Technol 106:1–10

    Article  Google Scholar 

  50. Leiva-Valenzuela GA, Lu R, Aguilera JM (2013) Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J Food Eng 115:91–98

    Article  Google Scholar 

  51. Leiva-Valenzuela GA, Lu R, Aguilera JM (2014) Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Innov Food Sci Emerg Technol 24:2–13

    Google Scholar 

  52. Liu C, Liu W, Chen W, Yang J, Zheng L (2015) Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit. Food Chem 173:482–488

    Article  CAS  Google Scholar 

  53. Nogales-Bueno J, Hernández-Hierro JM, Rodríguez-Pulido FJ, Heredia FJ (2014) Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: a preliminary approach. Food Chem 152:586–591

    Article  CAS  Google Scholar 

  54. Nogales-Bueno J, Rodríguez-Pulido FJ, Heredia FJ, Hernández-Hierro JM (2015) Comparative study on the use of anthocyanin profile, color image analysis and near-infrared hyperspectral imaging as tools to discriminate between four autochthonous red grape cultivars from La Rioja (Spain). Talanta 131:412–416

    Article  CAS  Google Scholar 

  55. Chen S, Zhang F, Ning J, Liu X, Zhang Z, Yang S (2015) Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging. Food Chem 172:788–793

    Article  CAS  Google Scholar 

  56. Baiano A, Terracone C, Peri G, Romaniello R (2012) Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Comput Electron Agric 87:142–151

    Article  Google Scholar 

  57. Lü Q, Tang M, Cai J, Zhao J, Vittayapadung S (2011) Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits. Czech J Food Sci 29:595–602

    Google Scholar 

  58. Baranowski P, Mazurek W, Pastuszka-Wozniak J (2013) Supervised classification of bruised apples with respect to the time. Postharvest Biol Technol 86:249–258

    Google Scholar 

  59. Vélez-Rivera N, Gómez-Sanchis J, Chanona-Pérez J, Carrasco JJ, Millán-Giraldo M, Lorente D, Cubero S, Blasco J (2014) Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst Eng 122:91–98

    Article  Google Scholar 

  60. Lee WH, Kim MS, Lee H, Delwiche SR, Bae H, Kim DY, Cho BK (2014) Hyperspectral near-infrared imaging for the detection of physical damages of pear. J Food Eng 130:1–7

    Article  Google Scholar 

  61. Cho BK, Kim MS, Baek IS, Kim DY, Lee WH, Kim J, Bae H, Kim YS (2013) Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery. Postharvest Biol Technol 76:40–49

    Article  Google Scholar 

  62. Yu K, Zhao Y, Li X, Shao Y, Zhu F, He Y (2014) Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing. Comput Electron Agric 103:1–10

    Article  Google Scholar 

  63. Haffa RP, Saranwongb S, Thanapase W, Janhiran A, Kasemsumran S, Kawano S (2013) Automatic image analysis and spot classification for detection of fruitfly infestation in hyperspectral images of mangoes. Postharvest Biol Technol 86:23–28

    Article  Google Scholar 

  64. Wang J, Nakano K, Ohashi S, Kubota Y, Takizawa K, Sasaki Y (2011) Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging. Biosyst Eng 108:345–351

    Article  Google Scholar 

  65. Gómez-Sanchis J, Blasco J, Soria-Olivas E, Lorente D, Escandell-Montero P, Martínez-Martínez JM, Martínez-Sober M, Aleixos N (2013) Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biol Technol 82:76–86

    Article  Google Scholar 

  66. Gómez-Sanchis J, Lorente D, Soria-Olivas E, Aleixos N, Cubero S, Blasco J (2014) Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food Bioproc Tech 7:1047–1056

    Article  Google Scholar 

  67. Simko I, Jimenez-Berni JA, Furbank RT (2015) Detection of decay in fresh-cut lettuce using hyperspectral imaging. Postharvest Biol Technol 106:44–52

    Article  CAS  Google Scholar 

  68. Qin J, Burks TF, Ritenour MA, Gordon Bonn W (2009) Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J Food Eng 93:183–191

    Article  Google Scholar 

  69. Zhao X, Burks TF, Qin J, Ritenour MA (2010) Effect of fruit harvest time on citrus canker detection using hyperspectral reflectance imaging. Sens Instrum Food Qual Saf 4:126–135

    Article  Google Scholar 

  70. Qin J, Burks TF, Zhao X, Niphadkar N, Ritenour MA (2011) Multispectral detection of citrus canker using hyperspectral band selection. Trans ASABE 54:2331–2341

    Article  Google Scholar 

  71. Qin J, Burks TF, Zhao X, Niphadkar N, Ritenour MA (2012) Development of a two-band spectral imaging system for real-time citrus canker detection. J Food Eng 108:87–93

    Article  Google Scholar 

  72. Wang W, Li C, Tollner EW, Gitaitis RD, Rains GC (2012) Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia) infected onions. J Food Eng 109:36–48

    Google Scholar 

  73. Everard CD, Kim MS, Lee H (2014) A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves. J Food Eng 143:139–145

    Article  CAS  Google Scholar 

  74. Yang CC, Kim MS, Millner P, Chao K, Cho B-K, Mo C, Lee H, Chan DE (2014) Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths. Postharvest Biol Technol 93:1–8

    Google Scholar 

  75. Yang CC, Kim MS, Kang S, Cho BK, Chao K, Lefcourt AM, Chan DE (2012) Red to far-red multispectral fluorescence image fusion for detection of fecal contamination on apples. J Food Eng 108:312–319

    Google Scholar 

  76. Teena MA, Manickavasagan A, Ravikanth L, Jayas DS (2014) Near infrared (NIR) hyperspectral imaging to classify fungal infected date fruits. J Stored Prod Res 59:306–313

    Article  Google Scholar 

  77. Al-Mallahi A, Kataoka T, Okamoto H, Shibata Y (2010) Detection of potato tubers using an ultraviolet imaging-based machine vision system. Biosyst Eng 105:257–265

    Article  Google Scholar 

  78. ElMasry G, Cubero S, Moltó E, Blasco J (2012) In-line sorting of irregular potatoes by using automated computer-based machine vision system. J Food Eng 112:60–68

    Article  Google Scholar 

  79. Kohno Y, Kondo N, Iida M, Kurita M, Shiigi T, Ogawa Y, Kaichi T, Okamoto S (2011) Development of a mobile grading machine for citrus fruit. Eng Agric Environ Food 4:7–11

    Article  Google Scholar 

  80. Cubero S, Aleixos N, Albert A, Torregrosa A, Ortiz C, García-Navarrete O, Blasco J (2014) Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precis Agric 15:80–94

    Article  Google Scholar 

  81. Leemans V, Destain M-F (2004) A real-time grading method of apples based on features extracted from defects. J Food Eng 6:83–89

    Article  Google Scholar 

  82. Bennedsen BS, Peterson DL, Tabb A (2005) Identifying defects in images of rotating apples. Comput Electron Agric 48:92–102

    Article  Google Scholar 

  83. Xiao-bo Z, Jie-wen Z, Yanxiao L, Holmes M (2010) In-line detection of apple defects using three color cameras system. Comput Electron Agric 70:129–134

    Article  Google Scholar 

  84. Reese D, Lefcourt AM, Kim MS, Lo YM (2010) Using parabolic mirrors for complete imaging of apple surfaces. Bioresour Technol 100:4499–4506

    Article  Google Scholar 

  85. Blasco J, Aleixos N, Cubero S, Gómez-Sanchis J, Moltó E (2009) Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Comput Electron Agric 66:1–8

    Article  Google Scholar 

  86. Blasco J, Cubero S, Gómez-Sanchis J, Mira P, Moltó E (2009) Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J Food Eng 90:27–34

    Article  Google Scholar 

Download references

Acknowledgement

This work has been partially funded by INIA through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds.

Author information

Authors and Affiliations

  1. IVIA, Centro de Agroingeniería, Cra. Moncada-Náquera km 5, 46113, Moncada, Spain

    José Blasco, Sandra Munera, Sergio Cubero & Enrique Molto

  2. Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Nuria Aleixos

Authors
  1. José Blasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandra Munera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nuria Aleixos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergio Cubero
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Enrique Molto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enrique Molto .

Editor information

Editors and Affiliations

  1. Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Baden-Württemberg, Germany

    Bernd Hitzmann

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Blasco, J., Munera, S., Aleixos, N., Cubero, S., Molto, E. (2017). Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest. In: Hitzmann, B. (eds) Measurement, Modeling and Automation in Advanced Food Processing. Advances in Biochemical Engineering/Biotechnology, vol 161. Springer, Cham. https://doi.org/10.1007/10_2016_51

Download citation

Publish with us

Policies and ethics

Search

Navigation