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Potential of Near-Infrared (NIR) Spectroscopy and Hyperspectral Imaging for Quality and Safety Assessment of Fruits: an Overview

Abstract

Daily consumption of fruits has rendered sophisticated techniques for accurate evaluation of its quality and how it can be initiated in a more rapid way has become a state-of-art for the fruit processors and food safety agencies. With the advent of non-destructive techniques in the past two decades, the quality and safety assessment procedure has become more reliable and non-invasive with reasonable accuracy. They have significantly contributed to the quality and safety inspections of fruits allowing for minimal expense of processing systems. The current review provides an overview of the technical aspects used for evaluating the essential physicochemical components in fruits. The non-destructive techniques described in this study primarily constitute near-infrared (NIR) spectroscopy and hyperspectral imaging in addition to their respective data analysis, classification, and calibration methods. First, the individual mechanism is outlined considering spectral/image acquisition mode followed by the extraction of information from the acquired data. Then, potentials of different chemometrics, image processing, and hyperspectral data reduction and feature extraction methods used in both the techniques have been epitomized for predicting the fruit maturity and other internal components such as firmness, acidity, phenolic content, soluble solids, vitamins C, moisture content, defects, fecal contamination, starch index, sugar content, and dry matter. The literature in this overview portrays the potentials of different chemometric and multivariate image analysis methods used in near-infrared spectroscopy and hyperspectral imaging, respectively, as excellent quality assessment aspects for fruits. However, further improvements are required in handling the voluminous data in industrial applications. The application of both the techniques is limited to a few fruits and further research is required for exploiting their implications.

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References

  1. Amigo JM (2010) Practical issues of hyperspectral imaging analysis of solid dosage forms. Anal Bioanal Chem 398:93–109. https://doi.org/10.1007/s00216-010-3828-z

    CAS  Article  PubMed  Google Scholar 

  2. Amigo JM, Cruz J, Bautista M, Maspoch S, Coello J, Blanco M (2008) Study of pharmaceutical samples by NIR chemical-image and multivariate analysis. TrAC - Trends Anal Chem 27:696–713. https://doi.org/10.1016/j.trac.2008.05.010

    CAS  Article  Google Scholar 

  3. Arana I, Jarén C, Arazuri S (2005) Maturity, variety and origin determination in white grapes (Vitis vinifera L.) using near infrared refl ectance technology. J Near Infrared 357:349–357

    Article  Google Scholar 

  4. Ariana DP, Lu R, Guyer DE (2006) Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Comput Electron Agric 53:60–70. https://doi.org/10.1016/j.compag.2006.04.001

    Article  Google Scholar 

  5. Bag SK, Srivastav PP, Mishra HN (2011) FT-NIR spectroscopy: a rapid method for estimation of moisture content in bael pulp. Br Food J 113:494–504. https://doi.org/10.1108/00070701111123970

    Article  Google Scholar 

  6. 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. https://doi.org/10.1016/j.compag.2012.06.002

    Article  Google Scholar 

  7. Baranowski P, Mazurek W, Wozniak J, Majewska U (2012) Detection of early bruises in apples using hyperspectral data and thermal imaging. J Food Eng 110:345–355. https://doi.org/10.1016/j.jfoodeng.2011.12.038

    Article  Google Scholar 

  8. Barbin D, Elmasry G, Sun DW, Allen P (2012) Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci 90:259–268. https://doi.org/10.1016/j.meatsci.2011.07.011

    Article  PubMed  Google Scholar 

  9. Bhunase M, Patil S (1998) Near infrared spectroscopy for fruit quality analysis. Int J Eng Res Technol 10:1–15

    Google Scholar 

  10. Blakey RJ, Van Rooyen Z (2011) Non-destructive measurement of moisture content using handheld NIR. S Afr Avo Grower Assoc Year Book 34:9–11

  11. Blakey RJ, Bower JP, Bertling I (2009) Influence of water and ABA supply on the ripening pattern of avocado (Persea americana Mill.) fruit and the prediction of water content using near infrared spectroscopy. Postharvest Biol Technol 53:72–76. https://doi.org/10.1016/j.postharvbio.2009.03.004

    CAS  Article  Google Scholar 

  12. Bobelyn E, Serban AS, Nicu M, Lammertyn J, Nicolai BM, Saeys W (2010) Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol Technol 55:133–143. https://doi.org/10.1016/j.postharvbio.2009.09.006

    CAS  Article  Google Scholar 

  13. Burks C, Dowell F, Xie F (2000) Measuring fig quality using near-infrared spectroscopy. J Stored Prod Res 36:289–296. https://doi.org/10.1016/S0022-474X(99)00050-8

    CAS  Article  PubMed  Google Scholar 

  14. Butz P, Hofmann C, Tauscher B (2005) Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis. J Food Sci 70:R131–R141. https://doi.org/10.1111/j.1365-2621.2005.tb08328.x

    CAS  Article  Google Scholar 

  15. Camps C, Christen D (2009) Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT - Food Sci Technol 42:1125–1131. https://doi.org/10.1016/j.lwt.2009.01.015

    CAS  Article  Google Scholar 

  16. Cao F, Wu D, He Y (2010) Soluble solids content and pH prediction and varieties discrimination of grapes based on visible-near infrared spectroscopy. Comput Electron Agric 71 https://doi.org/10.1016/j.compag.2009.05.011

    Article  Google Scholar 

  17. Cayuela JA (2008) Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biol Technol 47:75–80. https://doi.org/10.1016/j.postharvbio.2007.06.005

    CAS  Article  Google Scholar 

  18. Cayuela JA, Weiland C (2010) Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biol Technol 58:113–120. https://doi.org/10.1016/j.postharvbio.2010.06.001

    Article  Google Scholar 

  19. Cen H, He Y, Huang M (2006) Measurement of soluble solids contents and pH in orange juice using chemometrics and vis-NIRS. J Agric Food Chem 54:7437–7443. https://doi.org/10.1021/jf061689f

    CAS  Article  PubMed  Google Scholar 

  20. Cen H, Lu R, Mendoza FA, Ariana DP (2011) Peach maturity/quality assessment using hyperspectral imaging-based spatially resolved technique. Sens Agric Food Qual Saf III 8027:80270L. https://doi.org/10.1117/12.883573

    Article  Google Scholar 

  21. Che W, Sun L, Zhang Q, Tan W, Ye D, Zhang D, Liu Y (2018) Pixel based bruise region extraction of apple using Vis-NIR hyperspectral imaging. Comput Electron Agric 146:12–21. https://doi.org/10.1016/j.compag.2018.01.013

    Article  Google Scholar 

  22. 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. https://doi.org/10.1016/j.foodchem.2014.09.119

    CAS  Article  PubMed  Google Scholar 

  23. Chia KS, Abdul Rahim H, Abdul Rahim R (2012) Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network. Biosyst Eng 113:158–165. https://doi.org/10.1016/j.biosystemseng.2012.07.003

    Article  Google Scholar 

  24. Choi JH, Chen PA, Lee BHN, Yim SH, Kim MS, Bae YS, Lim DC, Seo HJ (2017) Portable, non-destructive tester integrating VIS/NIR reflectance spectroscopy for the detection of sugar content in Asian pears. Sci Hortic (Amsterdam) 220:147–153. https://doi.org/10.1016/j.scienta.2017.03.050

    CAS  Article  Google Scholar 

  25. Cozzolino D, Cynkar WU, Dambergs RG, Mercurio MD, Smith PA (2008) Measurement of condensed tannins and dry matter in red grape homogenates using near infrared spectroscopy and partial least squares. J Agric Food Chem 56:7631–7636. https://doi.org/10.1021/jf801563z

    CAS  Article  PubMed  Google Scholar 

  26. De Oliveira GA, Bureau S, Renard CMGC et al (2014) Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem 143:223–230. https://doi.org/10.1016/j.foodchem.2013.07.122

    CAS  Article  PubMed  Google Scholar 

  27. Du CJ, Sun DW (2004) Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15:230–249. https://doi.org/10.1016/j.tifs.2003.10.006

    CAS  Article  Google Scholar 

  28. Dvash L, Afik O, Shafir S, Schaffer A, Yeselson Y, Dag A, Landau S (2002) Determination by near-infrared spectroscopy of perseitol used as a marker for the botanical origin of avocado (Persea americana Mill.) honey. J Agric Food Chem 50:5283–5287. https://doi.org/10.1021/jf020329z

    CAS  Article  PubMed  Google Scholar 

  29. ElMasry G, Sun DW (2010) Principles of hyperspectral imaging technology. Hyperspectral Imaging Food Qual Anal Control 3–43. https://doi.org/10.1016/B978-0-12-374753-2.10001-2

    Chapter  Google Scholar 

  30. ElMasry G, Wang N, ElSayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81:98–107. https://doi.org/10.1016/j.jfoodeng.2006.10.016

    CAS  Article  Google Scholar 

  31. ElMasry G, Wang N, Vigneault C (2009) Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks. Postharvest Biol Technol 52:1–8. https://doi.org/10.1016/j.postharvbio.2008.11.008

    Article  Google Scholar 

  32. Eluyode OS, Akomolafe M, MNCS M et al (2013) Comparative study of biological and artificial neural networks. Eur J Appl Eng Sci Res 2:36–46

    Google Scholar 

  33. Fan S, Zhang B, Li J, Liu C, Huang W, Tian X (2016) Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Postharvest Biol Technol 121:51–61. https://doi.org/10.1016/j.postharvbio.2016.07.007

    Article  Google Scholar 

  34. Fan S, Li C, Huang W, Chen L (2017) Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths. Postharvest Biol Technol 134:55–66. https://doi.org/10.1016/j.postharvbio.2017.08.012

    Article  Google Scholar 

  35. Fernandes AM, Oliveira P, Moura JP, Oliveira AA, Falco V, Correia MJ, Melo-Pinto P (2011) Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks. J Food Eng 105:216–226. https://doi.org/10.1016/j.jfoodeng.2011.02.018

    CAS  Article  Google Scholar 

  36. Fernández-Novales J, López MI, Sánchez MT, García-Mesa JA, González-Caballero V (2009) Assessment of quality parameters in grapes during ripening using a miniature fiber-optic near-infrared spectrometer. Int J Food Sci Nutr 60:265–277. https://doi.org/10.1080/09637480903093116

    CAS  Article  PubMed  Google Scholar 

  37. Fletcher JT, Kong SG (2003) Principal component analysis for poultry tumor inspection using hyperspectral fluorescence imaging. Proc Int Jt Conf Neural Netw 1:149–153. https://doi.org/10.1109/IJCNN.2003.1223319

    Article  Google Scholar 

  38. Folch-Fortuny A, Prats-Montalbán JM, Cubero S, Blasco J, Ferrer A (2016) VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemom Intell Lab Syst 156:241–248. https://doi.org/10.1016/j.chemolab.2016.05.005

    CAS  Article  Google Scholar 

  39. Ghosh PK, Jayas DS, Gruwel MLH, White NDG (2007) A magnetic resonance imaging study of wheat drying kinetics. Biosyst Eng 97:189–199. https://doi.org/10.1016/j.biosystemseng.2007.03.002

    Article  Google Scholar 

  40. Giovanelli G, Sinelli N, Beghi R, Guidetti R, Casiraghi E (2014) NIR spectroscopy for the optimization of postharvest apple management. Postharvest Biol Technol 87:13–20. https://doi.org/10.1016/j.postharvbio.2013.07.041

    CAS  Article  Google Scholar 

  41. Gishen M, Dambergs RG, Cozzolino D (2005) Grape and wine analysis - enhancing the power of spectroscopy with chemometrics. A review of some applications in the Australian wine industry. Aust J Grape Wine Res 11:296–305. https://doi.org/10.1111/j.1755-0238.2005.tb00029.x

    CAS  Article  Google Scholar 

  42. Golic M, Walsh KB (2006) Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Anal Chim Acta 555:286–291. https://doi.org/10.1016/j.aca.2005.09.014

    CAS  Article  Google Scholar 

  43. Golic M, Walsh K, Lawson P (2003) Short-wavelength near infrared spectra of sucrose, glucose and fructose with respect to sugar concentration and temperature. Appl Spectrosc 57:139–145. https://doi.org/10.1366/000370203321535033

    CAS  Article  PubMed  Google Scholar 

  44. Gómez AH, He Y, Pereira AG (2006) Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. J Food Eng 77:313–319. https://doi.org/10.1016/j.jfoodeng.2005.06.036

    CAS  Article  Google Scholar 

  45. Gómez-Sanchis J, Moltó E, Camps-Valls G, Gómez-Chova L, Aleixos N, Blasco J (2008) Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. J Food Eng 85:191–200. https://doi.org/10.1016/j.jfoodeng.2007.06.036

    Article  Google Scholar 

  46. Gómez-Sanchis J, Martín-Guerrero JD, Soria-Olivas E, Martínez-Sober M, Magdalena-Benedito R, Blasco J (2012) Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Syst Appl 39:780–785. https://doi.org/10.1016/j.eswa.2011.07.073

    Article  Google Scholar 

  47. Gowen AA, O’Donnell CP, Cullen PJ et al (2007) Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18:590–598. https://doi.org/10.1016/j.tifs.2007.06.001

    CAS  Article  Google Scholar 

  48. Gracia A, León L (2011) Non-destructive assessment of olive fruit ripening by portable near infrared spectroscopy. Grasas Aceites 62:268–274. https://doi.org/10.3989/gya.089610

    CAS  Article  Google Scholar 

  49. Guo Z, Huang W, Chen L, Zhao C (2013) Geographical classification of apple based on hyperspectral imaging. Sens Agric Food Qual Saf V 8721:87210J. https://doi.org/10.1117/12.2015559

    Article  Google Scholar 

  50. Guo W, Gu J, Liu D, Shang L (2016) Peach variety identification using near-infrared diffuse reflectance spectroscopy. Comput Electron Agric 123:297–303. https://doi.org/10.1016/j.compag.2016.03.005

    Article  Google Scholar 

  51. Guthrie JA, Walsh KB, Reid DJ, Liebenberg CJ (2005) Assessment of internal quality attributes of mandarin fruit. 1. NIR calibration model development. Aust J Agric Res 56:405–416. https://doi.org/10.1071/AR04257

    Article  Google Scholar 

  52. Guthrie JA, Liebenberg CJ, Walsh KB (2006) NIR model development and robustness in prediction of melon fruit total soluble solids. Aust J Agric Res 57:1–8. https://doi.org/10.1071/AR05123

    Article  Google Scholar 

  53. Haff RP, Saranwong S, Thanapase W, Janhiran A, Kasemsumran S, Kawano S (2013) Automatic image analysis and spot classification for detection of fruit fly infestation in hyperspectral images of mangoes. Postharvest Biol Technol 86:23–38. https://doi.org/10.1016/j.postharvbio.2013.06.003

    Article  Google Scholar 

  54. Hsieh C, Lee Y (2005) Applied visible/near-infrared spectroscopy on detecting the sugar content and hardness of pearl guava. Appl Eng Agric 21:1039–1046

    Article  Google Scholar 

  55. Huang M, Lu R (2010) Optimal wavelength selection for hyperspectral scattering prediction of apple firmness and soluble solids content. Trans ASABE 53:1175–1182

    Article  Google Scholar 

  56. Hussain A, Pu H, Sun DW (2018) Innovative nondestructive imaging techniques for ripening and maturity of fruits – a review of recent applications. Trends Food Sci Technol 72:144–152. https://doi.org/10.1016/j.tifs.2017.12.010

    CAS  Article  Google Scholar 

  57. Jha SN, Narsaiah K, Jaiswal P, et al (2014) Nondestructive prediction of maturity of mango using near infrared spectroscopy. J Food Eng 124:152–157. https://doi.org/10.1016/j.jfoodeng.2013.10.012

    Article  Google Scholar 

  58. Jie D, Xie L, Rao X, Ying Y (2014) Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biol Technol 90:1–6. https://doi.org/10.1016/j.postharvbio.2013.11.009

    CAS  Article  Google Scholar 

  59. Jun-fangl XIA, Xiao-yu LI, Pei-wu LI, Xiao-xia MAQD (2007) Application of wavelet transform in the prediction of navel orange vitamin C content by near-infrared spectroscopy. Agric Sci China 6:1067–1073

    Article  Google Scholar 

  60. Kamruzzaman M, Elmasry G, Sun DW, Allen P (2011) Application of NIR hyperspectral imaging for discrimination of lamb muscles. J Food Eng 104:332–340. https://doi.org/10.1016/j.jfoodeng.2010.12.024

    Article  Google Scholar 

  61. Kawano S, Watanabe H, Iwamoto M (1992) Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode. J Japanese Soc Hortic Sci 61:445–451. https://doi.org/10.2503/jjshs.61.445

    CAS  Article  Google Scholar 

  62. Kawano S, Fujiwara T, Iwamoto M (1993) Nondestructive determination of sugar content in Satsuma mandarin using near infrared (NIR) transmittance. J Japanese Soc Hortic Sci 62:465–470. https://doi.org/10.2503/jjshs.62.465

    CAS  Article  Google Scholar 

  63. Khodabakhshian R, Emadi B, Khojastehpour M, Golzarian MR, Sazgarnia A (2017) Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy. Int J Food Prop 20:41–52. https://doi.org/10.1080/10942912.2015.1126725

    CAS  Article  Google Scholar 

  64. Kim MS, Chen YR, Mehl PM (2001) Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Trans ASAE 44:721–729

    Google Scholar 

  65. Kim MS, Lefcourt AM, Chao K et al (2002a) Multispectral detection of fecal contamination on apples based on hyperspectral imagery: part I. Application of visible and near–infrared reflectance imaging. Trans ASAE 45:2027–2037

    Google Scholar 

  66. Kim MS, Lefcourt AM, Chen YR et al (2002b) Multispectral detection of fecal contamination on apples based on hyperspectral imagery: part II. Application of hyperspectral fluorescence imaging. Trans ASAE 45:2039–2047

    Google Scholar 

  67. Lammertyn J, Nicolaï B, Ooms K et al (1998) Non-destructive measurement of acidity, soluble solids, and firmness of Jonagold apples using NIR-spectroscopy. Trans ASAE 41:1089–1094

    Article  Google Scholar 

  68. Lefcout AM, Kim MS, Chen YR, Kang S (2006) Systematic approach for using hyperspectral imaging data to develop multispectral imagining systems: detection of feces on apples. Comput Electron Agric 54:22–35. https://doi.org/10.1016/j.compag.2006.06.002

    Article  Google Scholar 

  69. 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. https://doi.org/10.1016/j.jfoodeng.2012.10.001

    Article  Google Scholar 

  70. Li X, He Y, Fang H (2007) Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy. J Food Eng 81:357–363. https://doi.org/10.1016/j.jfoodeng.2006.10.033

    CAS  Article  Google Scholar 

  71. Li B, Hou B, Zhang D, Zhou Y, Zhao M, Hong R, Huang Y (2016) Pears characteristics (soluble solids content and firmness prediction, varieties) testing methods based on visible-near infrared hyperspectral imaging. Optik (Stuttg) 127:2624–2630. https://doi.org/10.1016/j.ijleo.2015.11.193

    CAS  Article  Google Scholar 

  72. Li B, Cobo-Medina M, Lecourt J, Harrison N, Harrison RJ, Cross JV (2018) Application of hyperspectral imaging for nondestructive measurement of plum quality attributes. Postharvest Biol Technol 141:8–15. https://doi.org/10.1016/j.postharvbio.2018.03.008

    Article  Google Scholar 

  73. Liu Y, Ying Y (2005) Use of FT-NIR spectrometry in non-invasive measurements of internal quality of “Fuji” apples. Postharvest Biol Technol 37:65–71. https://doi.org/10.1016/j.postharvbio.2005.02.013

    CAS  Article  Google Scholar 

  74. Liu M, Zhang L, Guo E (2008a) Hyperspectral laser-induced fluorescence imaging for nondestructive assessing soluble solids content of orange. IFIP Int Fed Inf Process 258:51–59. https://doi.org/10.1007/978-0-387-77251-6_7

    Article  Google Scholar 

  75. Liu Y, Chen X, Ouyang A (2008b) Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry. LWT - Food Sci Technol 41:1720–1725. https://doi.org/10.1016/j.lwt.2007.10.017

    CAS  Article  Google Scholar 

  76. Liu Y, Sun X, Ouyang A (2010a) Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT - Food Sci Technol 43:602–607. https://doi.org/10.1016/j.lwt.2009.10.008

    CAS  Article  Google Scholar 

  77. Liu Y, Sun X, Zhang H, Aiguo O (2010b) Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy. Comput Electron Agric 71:10–14. https://doi.org/10.1016/j.compag.2009.09.005

    Article  Google Scholar 

  78. Liu D, Zeng X-A, Sun D-W (2015) Recent developments and applications of hyperspectral imaging for quality evaluation of agricultural products: a review. Crit Rev Food Sci Nutr 55:1744–1757. https://doi.org/10.1080/10408398.2013.777020

    Article  PubMed  Google Scholar 

  79. Lohumi S, Lee S, Lee H, Cho BK (2015) A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends Food Sci Technol 46:85–98. https://doi.org/10.1016/j.tifs.2015.08.003

    CAS  Article  Google Scholar 

  80. Long RL, Walsh KB (2006) Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy. Aust J Agric Res 57:403–410. https://doi.org/10.1071/AR05285

    Article  Google Scholar 

  81. Lopes MB, Wolff JC (2009) Investigation into classification/sourcing of suspect counterfeit Heptodin™ tablets by near infrared chemical imaging. Anal Chim Acta 633:149–155. https://doi.org/10.1016/j.aca.2008.11.036

    CAS  Article  PubMed  Google Scholar 

  82. Lopes MB, Wolff J (2010) Near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometrics characterization of counterfeit tablets.pdf. Anal Chem 82:1462–1469

    CAS  Article  Google Scholar 

  83. Louw ED, Theron KI (2010) Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. Postharvest Biol Technol 58:176–184. https://doi.org/10.1016/j.postharvbio.2010.07.001

    Article  Google Scholar 

  84. Lu R (2001) Predicting firmness and sugar content of sweet cherries using near–infrared diffuse reflectance spectroscopy. Trans ASAE 44:1265–1271

    CAS  Google Scholar 

  85. Lu R (2003) Detection of bruises on apples using near–infrared hyperspectral imaging. Trans ASAE 46:523–530

    Google Scholar 

  86. Lu R (2004) Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol Technol 31:147–157. https://doi.org/10.1016/j.postharvbio.2003.08.006

    Article  Google Scholar 

  87. Lu R (2007) Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sens & Instrumen Food Qual 1:19–27. https://doi.org/10.1007/s11694-006-9002-9

    Article  Google Scholar 

  88. Lu R, Peng Y (2006) Hyperspectral scattering for assessing peach fruit firmness. Biosyst Eng 93:161–171. https://doi.org/10.1016/j.biosystemseng.2005.11.004

    Article  Google Scholar 

  89. Lu R, Guyer DE, Beaudry RM (2000) Determination of firmness and sugar content of apples using near-infrared diffuse reflectance. J Texture Stud 31:615–630. https://doi.org/10.1111/j.1745-4603.2000.tb01024.x

    Article  Google Scholar 

  90. Magwaza LS, Opara UL, Nieuwoudt H, Cronje PJR, Saeys W, Nicolaï B (2012) NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food Bioprocess Technol 5:425–444. https://doi.org/10.1007/s11947-011-0697-1

    CAS  Article  Google Scholar 

  91. Magwaza LS, Opara UL, Terry LA, Landahl S, Cronje PJR, Nieuwoudt HH, Hanssens A, Saeys W, Nicolaï BM (2013) Evaluation of Fourier transform-NIR spectroscopy for integrated external and internal quality assessment of Valencia oranges. J Food Compos Anal 31:144–154. https://doi.org/10.1016/j.jfca.2013.05.007

    CAS  Article  Google Scholar 

  92. Maniwara P, Nakano K, Boonyakiat D, Ohashi S, Hiroi M, Tohyama T (2014) The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality. J Food Eng 143:33–43. https://doi.org/10.1016/j.jfoodeng.2014.06.028

    CAS  Article  Google Scholar 

  93. Marques EJN, De Freitas ST, Pimentel MF, Pasquini C (2016) Rapid and non-destructive determination of quality parameters in the “Tommy Atkins” mango using a novel handheld near infrared spectrometer. Food Chem 197:1207–1214. https://doi.org/10.1016/j.foodchem.2015.11.080

    CAS  Article  PubMed  Google Scholar 

  94. Martinsen P, Schaare P (1998) Measuring soluble solids distribution in kiwifruit using near-infrared imaging spectroscopy. Postharvest Biol Technol 14:271–281. https://doi.org/10.1016/S0925-5214(98)00051-9

    Article  Google Scholar 

  95. McGlone VA, Kawano S (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol Technol 13:131–141. https://doi.org/10.1016/S0925-5214(98)00007-6

    Article  Google Scholar 

  96. McGlone VA, Jordan RB, Seelye R, Martinsen PJ (2002) Comparing density and NIR methods for measurement of kiwifruit dry matter and soluble solids content. Postharvest Biol Technol 26:191–198. https://doi.org/10.1016/S0925-5214(02)00014-5

    Article  Google Scholar 

  97. McGoverin CM, Engelbrecht P, Geladi P, Manley M (2011) Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics. Anal Bioanal Chem 401:2283–2289. https://doi.org/10.1007/s00216-011-5291-x

    CAS  Article  PubMed  Google Scholar 

  98. Mehl PM, Chen YR, Kim MS, Chan DE (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J Food Eng 61:67–81. https://doi.org/10.1016/S0260-8774(03)00188-2

    Article  Google Scholar 

  99. Mendoza F, Lu R, Ariana D, Cen H, Bailey B (2011) Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biol Technol 62:149–160. https://doi.org/10.1016/j.postharvbio.2011.05.009

    Article  Google Scholar 

  100. Menesatti P, Zanella A, D’Andrea S, Costa C, Paglia G, Pallottino F (2009) Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food Bioprocess Technol 2:308–314. https://doi.org/10.1007/s11947-008-0120-8

    CAS  Article  Google Scholar 

  101. Mohapatra D, Mishra S, Singh CB, Jayas DS (2011) Post-harvest processing of banana: opportunities and challenges. Food Bioprocess Technol 4:327–339. https://doi.org/10.1007/s11947-010-0377-6

    Article  Google Scholar 

  102. Moscetti R, Monarca D, Cecchini M, Haff RP, Contini M, Massantini R (2014) Detection of mold-damaged chestnuts by near-infrared spectroscopy. Postharvest Biol Technol 93:83–90. https://doi.org/10.1016/j.postharvbio.2014.02.009

    Article  Google Scholar 

  103. Moscetti R, Haff RP, Stella E, Contini M, Monarca D, Cecchini M, Massantini R (2015) Feasibility of NIR spectroscopy to detect olive fruit infested by Bactrocera oleae. Postharvest Biol Technol 99:58–62. https://doi.org/10.1016/j.postharvbio.2014.07.015

    CAS  Article  Google Scholar 

  104. Munera S, Amigo JM, Blasco J, Cubero S, Talens P, Aleixos N (2017a) Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. J Food Eng 214:29–39. https://doi.org/10.1016/j.jfoodeng.2017.06.031

    Article  Google Scholar 

  105. Munera S, Besada C, Aleixos N, Talens P, Salvador A, Sun DW, Cubero S, Blasco J (2017b) Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT - Food Sci Technol 77:241–248. https://doi.org/10.1016/j.lwt.2016.11.063

    CAS  Article  Google Scholar 

  106. Munera S, Besada C, Blasco J, Cubero S, Salvador A, Talens P, Aleixos N (2017c) Astringency assessment of persimmon by hyperspectral imaging. Postharvest Biol Technol 125:35–41. https://doi.org/10.1016/j.postharvbio.2016.11.006

    CAS  Article  Google Scholar 

  107. Munera S, Amigo JM, Aleixos N, Talens P, Cubero S, Blasco J (2018) Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control 86:1–10. https://doi.org/10.1016/j.foodcont.2017.10.037

    CAS  Article  Google Scholar 

  108. Nagata M, Tallada JG, Kobayashi T et al (2004) Predicting maturity quality parameters of strawberries using hyperspectral imaging. Annu Int Meet 0300

  109. Nanyam Y, Choudhary R, Gupta L, Paliwal J (2012) A decision-fusion strategy for fruit quality inspection using hyperspectral imaging. Biosyst Eng 111:118–125. https://doi.org/10.1016/j.biosystemseng.2011.11.004

    Article  Google Scholar 

  110. Nicolaï BM, Lötze E, Peirs A, Scheerlinck N, Theron KI (2006) Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biol Technol 40:1–6. https://doi.org/10.1016/j.postharvbio.2005.12.006

    Article  Google Scholar 

  111. 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. https://doi.org/10.1016/j.postharvbio.2007.06.024

    Article  Google Scholar 

  112. Noh HK, Lu R (2007) Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biol Technol 43:193–201. https://doi.org/10.1016/j.postharvbio.2006.09.006

    Article  Google Scholar 

  113. Noh HK, Peng Y, Lu R (2007) Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Trans ASABE 50:963–971

    Article  Google Scholar 

  114. Olarewaju OO, Bertling I, Magwaza LS (2016) Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models. Sci Hortic (Amsterdam) 199:229–236. https://doi.org/10.1016/j.scienta.2015.12.047

    CAS  Article  Google Scholar 

  115. Paliwal J, Student G (2002) Quantification of variations in machine-vision- computed morphological features of cereal grains. 2002 ASAE Annu Meet Am Soc Agric Biol Eng 1

  116. Pan L, Zhang Q, Zhang W, Sun Y, Hu P, Tu K (2016) Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chem 192:134–141. https://doi.org/10.1016/j.foodchem.2015.06.106

    CAS  Article  PubMed  Google Scholar 

  117. Park B, Abbott JA, Lee KJ et al (2003) Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of delicious and gala apples. Trans ASAE 46:1721–1731

    Article  Google Scholar 

  118. Páscoa RNMJ (2018) In situ visible and near-infrared spectroscopy applied to vineyards as a tool for precision viticulture. Compr Anal Chem 80:253–279. https://doi.org/10.1016/bs.coac.2018.03.007

    CAS  Article  Google Scholar 

  119. Peirs A, Scheerlinck N, Nicolaï BM (2003) Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biol Technol 30:233–248. https://doi.org/10.1016/S0925-5214(03)00118-2

    Article  Google Scholar 

  120. Peng Y, Lu R (2008) Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biol Technol 48:52–62. https://doi.org/10.1016/j.postharvbio.2007.09.019

    Article  Google Scholar 

  121. Pérez-Marín D, Sánchez MT, Paz P, González-Dugo V, Soriano MA (2011) Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy. LWT - Food Sci Technol 44:1405–1414. https://doi.org/10.1016/j.lwt.2011.01.008

    CAS  Article  Google Scholar 

  122. Peshlov BN, Dowelt FE, Drummond FA, Donahue DW (2009) Comparison of three near infrared spectrophotometers for infestation detection in wild blueberries using multivariate calibration models. J Near Infrared Spectrosc 17:203–212. https://doi.org/10.1255/jnirs.842

    CAS  Article  Google Scholar 

  123. Pissard A, Fernández Pierna JA, Baeten V, Sinnaeve G, Lognay G, Mouteau A, Dupont P, Rondia A, Lateur M (2013) Non-destructive measurement of vitamin C, total polyphenol and sugar content in apples using near-infrared spectroscopy. J Sci Food Agric 93:238–244. https://doi.org/10.1002/jsfa.5779

    CAS  Article  PubMed  Google Scholar 

  124. Polder G, Van Der Heijden GWAM, Young IT (2003) Tomato sorting using independent component analysis on spectral images. Real-Time Imaging 9:253–259. https://doi.org/10.1016/j.rti.2003.09.008

    Article  Google Scholar 

  125. Pu H, Liu D, Wang L, Sun D-W (2016) Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging. Food Anal Methods 9:235–244. https://doi.org/10.1007/s12161-015-0186-7

    Article  Google Scholar 

  126. Qin J, Lu R (2005) Detection of pits in tart cherries by hyperspectral transmission imaging. Trans ASAE 48:1963–1970

    Article  Google Scholar 

  127. Qin J, Burks TF, Ritenour MA, Bonn WG (2009) Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J Food Eng 93:183–191. https://doi.org/10.1016/j.jfoodeng.2009.01.014

    Article  Google Scholar 

  128. Rajkumar P, Wang N, EImasry G, Raghavan GSV, Gariepy Y (2012) Studies on banana fruit quality and maturity stages using hyperspectral imaging. J Food Eng 108:194–200. https://doi.org/10.1016/j.jfoodeng.2011.05.002

    Article  Google Scholar 

  129. Ramalingam G, Neethirajan S (2009) Charecterization of the influence of moisture content on single wheat kernels using machine vision. CSBE/SCGAB 2009 Annu Conf Prince Edward Island

  130. Ravikanth L, Jayas DS, White NDG, Fields PG, Sun DW (2017) Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food Bioprocess Technol 10:1–33. https://doi.org/10.1007/s11947-016-1817-8

    CAS  Article  Google Scholar 

  131. Rungpichayapichet P, Mahayothee B, Nagle M, Khuwijitjaru P, Müller J (2016) Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biol Technol 111:31–40. https://doi.org/10.1016/j.postharvbio.2015.07.006

    Article  Google Scholar 

  132. Saranwong S, Sornsrivichai J, Kawano S (2004) Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biol Technol 31:137–145. https://doi.org/10.1016/j.postharvbio.2003.08.007

    CAS  Article  Google Scholar 

  133. Schmilovitch Z, Mizrach A, Hoffman A et al (2000) Determination of mango physiological indices by near-infrared spectrometry. Postharvest Biol Technol 19:245–252. https://doi.org/10.1016/S0925-5214(00)00102-2

    Article  Google Scholar 

  134. Serranti S, Bonifazi G, Luciani V (2017) Non-destructive quality control of kiwi fruits by hyperspectral imaging. Sens Agric Food Qual Saf IX 10217:102170O. https://doi.org/10.1117/12.2255055

    Article  Google Scholar 

  135. Shang L, Guo W, Nelson SO (2015) Apple variety identification based on dielectric spectra and chemometric methods. Food Anal Methods 8:1042–1052. https://doi.org/10.1007/s12161-014-9985-5

    Article  Google Scholar 

  136. Shinya P, Contador L, Predieri S, Rubio P, Infante R (2013) Peach ripening: segregation at harvest and postharvest flesh softening. Postharvest Biol Technol 86:472–478. https://doi.org/10.1016/j.postharvbio.2013.07.038

    Article  Google Scholar 

  137. Siedliska A, Baranowski P, Zubik M, Mazurek W (2017) Detection of pits in fresh and frozen cherries using a hyperspectral system in transmittance mode. J Food Eng 215:61–71. https://doi.org/10.1016/j.jfoodeng.2017.07.028

    Article  Google Scholar 

  138. Siedliska A, Baranowski P, Zubik M, Mazurek W, Sosnowska B (2018) Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Postharvest Biol Technol 139:115–126. https://doi.org/10.1016/j.postharvbio.2018.01.018

    CAS  Article  Google Scholar 

  139. Sinelli N, Spinardi A, Di Egidio V et al (2008) Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. Postharvest Biol Technol 50:31–36. https://doi.org/10.1016/j.postharvbio.2008.03.013

    CAS  Article  Google Scholar 

  140. Singh CB, Jayas DS, Paliwal J, White NDG (2007) Fungal detection in wheat using near-infrared hyperspectral imaging. Trans ASABE 50:2171–2176

    Article  Google Scholar 

  141. Singh CB, Jayas DS, Paliwal J, White NDG (2009) Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. J Stored Prod Res 45:151–158. https://doi.org/10.1016/j.jspr.2008.12.002

    Article  Google Scholar 

  142. Singh CB, Jayas DS, Paliwal J, White NDG (2012) Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging. Int J Food Prop 15:11–24. https://doi.org/10.1080/10942911003687223

    Article  Google Scholar 

  143. Sirisomboon P, Tanaka M, Fujita S, Kojima T (2007) Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy. J Food Eng 78:701–707. https://doi.org/10.1016/j.jfoodeng.2005.11.009

    CAS  Article  Google Scholar 

  144. Slaughter DC (1995) Nondestructive determination of internal quality in peaches and nectarines. Trans ASAE 38:617–623

    Article  Google Scholar 

  145. Slaughter DC, Thompson JF, Tan ES (2003) Nondestructive determination of total and soluble solids in fresh prune using near infrared spectroscopy. Postharvest Biol Technol 28:437–444. https://doi.org/10.1016/S0925-5214(02)00204-1

    Article  Google Scholar 

  146. Subedi PP, Walsh KB (2011) Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy. Postharvest Biol Technol 62:238–245. https://doi.org/10.1016/j.postharvbio.2011.06.014

    CAS  Article  Google Scholar 

  147. Sun X, Liu Y, Li Y, Wu M, Zhu D (2016) Postharvest biology and technology simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy. Postharvest Biol Technol 116:80–87. https://doi.org/10.1016/j.postharvbio.2016.01.009

    CAS  Article  Google Scholar 

  148. Sun M, Zhang D, Liu L, Wang Z (2017) How to predict the sugariness and hardness of melons: a near-infrared hyperspectral imaging method. Food Chem 218:413–421. https://doi.org/10.1016/j.foodchem.2016.09.023

    CAS  Article  PubMed  Google Scholar 

  149. Suphamitmongkol W, Nie G, Liu R, Kasemsumran S, Shi Y (2013) An alternative approach for the classification of orange varieties based on near infrared spectroscopy. Comput Electron Agric 91:87–93. https://doi.org/10.1016/j.compag.2012.11.014

    Article  Google Scholar 

  150. Suzuki Y, Okamoto H, Takahashi M, Kataoka T, Shibata Y (2012) Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging. Grassl Sci 58:1–7. https://doi.org/10.1111/j.1744-697X.2011.00239.x

    Article  Google Scholar 

  151. Tallada JG, Nagata M, Kobayashi T (2006) Non-destructive estimation of firmness of strawberries (Fragaria x ananassa Duch.) using NIR hyperspectral imaging. Environ Control Biol 44:245–255

    Article  Google Scholar 

  152. Tarkosova J, Copikova J (2000) Determination of carbohydrate content in bananas during ripening and storage by near infrared spectroscopy. J Near Infrared Spectrosc 8:21–26. https://doi.org/10.1255/jnirs.260

    CAS  Article  Google Scholar 

  153. 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. https://doi.org/10.1016/j.jspr.2014.09.005

    Article  Google Scholar 

  154. Teerachaichayut S, Ho HT (2017) Non-destructive prediction of total soluble solids, titratable acidity and maturity index of limes by near infrared hyperspectral imaging. Postharvest Biol Technol 133:20–25. https://doi.org/10.1016/j.postharvbio.2017.07.005

    CAS  Article  Google Scholar 

  155. Tian X, Li J, Wang Q, Fan S, Huang W (2018) A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and peel pigments. Food Chem 239:1055–1063. https://doi.org/10.1016/j.foodchem.2017.07.045

    CAS  Article  PubMed  Google Scholar 

  156. Usenik V, Stampar F, Kastelec D (2014) Indicators of plum maturity: when do plums become tasty? Sci Hortic (Amsterdam) 167:127–134. https://doi.org/10.1016/j.scienta.2014.01.002

    Article  Google Scholar 

  157. Vadivambal R, Jayas DS, White NDG (2007) Wheat disinfestation using microwave energy. J Stored Prod Res 43:508–514. https://doi.org/10.1016/j.jspr.2007.01.007

    Article  Google Scholar 

  158. Vargas AM, Kim MS, Tao Y et al (2004) Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery. J Food Sci 70:e471–e476

    Article  Google Scholar 

  159. Vesali F, Gharibkhani M, Komarizadeh MH (2011) An approach to estimate moisture content of apple with image processing method. Aust J Crop Sci 5:111–115

    Google Scholar 

  160. Wang W, Paliwal J (2007) Near-infrared spectroscopy and imaging in food quality and safety. Sens & Instrumen Food Qual 1:193–207. https://doi.org/10.1007/s11694-007-9022-0

    Article  Google Scholar 

  161. Wang J, Nakano K, Ohashi S, Takizawa K, He JG (2010) Comparison of different modes of visible and near-infrared spectroscopy for detecting internal insect infestation in jujubes. J Food Eng 101:78–84. https://doi.org/10.1016/j.jfoodeng.2010.06.011

    Article  Google Scholar 

  162. Wang J, Nakano K, Ohashi S (2011) Nondestructive evaluation of jujube quality by visible and near-infrared spectroscopy. LWT - Food Sci Technol 44:1119–1125. https://doi.org/10.1016/j.lwt.2010.11.012

    CAS  Article  Google Scholar 

  163. Wang A, Hu D, Xie L (2014) Comparison of detection modes in terms of the necessity of visible region (VIS) and influence of the peel on soluble solids content (SSC) determination of navel orange using VIS-SWNIR spectroscopy. J Food Eng 126:126–132. https://doi.org/10.1016/j.jfoodeng.2013.11.011

    CAS  Article  Google Scholar 

  164. Wang H, Peng J, Xie C, Bao Y, He Y (2015) Fruit quality evaluation using spectroscopy technology: a review. Sensors 15:11889–11927. https://doi.org/10.3390/s150511889

    Article  PubMed  Google Scholar 

  165. Wang N-N, Sun D-W, Yang Y-C, Pu H, Zhu Z (2016) Recent advances in the application of hyperspectral imaging for evaluating fruit quality. Food Anal Methods 9:178–191. https://doi.org/10.1007/s12161-015-0153-3

    Article  Google Scholar 

  166. Wei X, Liu F, Qiu Z, Shao Y, He Y (2014) Ripeness classification of astringent persimmon using hyperspectral imaging technique. Food Bioprocess Technol 7:1371–1380. https://doi.org/10.1007/s11947-013-1164-y

    Article  Google Scholar 

  167. Wu L, He J, Liu G, Wang S, He X (2016) Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging. Postharvest Biol Technol 112:134–142. https://doi.org/10.1016/j.postharvbio.2015.09.003

    CAS  Article  Google Scholar 

  168. Xie A, Sun DW, Zhu Z, Pu H (2016) Nondestructive measurements of freezing parameters of frozen porcine meat by NIR hyperspectral imaging. Food Bioprocess Technol 9:1444–1454. https://doi.org/10.1007/s11947-016-1766-2

    CAS  Article  Google Scholar 

  169. Xing J, Guyer D (2008) Comparison of transmittance and reflectance to detect insect infestation in Montmorency tart cherry. Comput Electron Agric 64:194–201. https://doi.org/10.1016/j.compag.2008.04.012

    Article  Google Scholar 

  170. Xing J, Bravo C, Jancsók PT, Ramon H, de Baerdemaeker J (2005) Detecting bruises on “Golden Delicious” apples using hyperspectral imaging with multiple wavebands. Biosyst Eng 90:27–36. https://doi.org/10.1016/j.biosystemseng.2004.08.002

    Article  Google Scholar 

  171. Xing J, Symons S, Hatcher D, Shahin M (2011) Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian Western red spring (CWRS) wheat kernels. Biosyst Eng 108:303–310. https://doi.org/10.1016/j.biosystemseng.2011.01.002

    Article  Google Scholar 

  172. Xu H, Qi B, Sun T, Fu X, Ying Y (2012) Variable selection in visible and near-infrared spectra: application to on-line determination of sugar content in pears. J Food Eng 109:142–147. https://doi.org/10.1016/j.jfoodeng.2011.09.022

    CAS  Article  Google Scholar 

  173. Ying YB, Liu YD, Wang JP et al (2005) Fourier transform near-infrared determination of total soluble solids and available acid in intact peaches. Trans ASAE 48:229–234

    Article  Google Scholar 

  174. Zhang S, Zhang H, Zhao Y, Guo W, Zhao H (2013) A simple identification model for subtle bruises on the fresh jujube based on NIR spectroscopy. Math Comput Model 58:545–550. https://doi.org/10.1016/j.mcm.2011.10.067

    Article  Google Scholar 

  175. Zhang C, Guo C, Liu F et al (2016) Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J Food Eng 179:11–18. https://doi.org/10.1016/j.jfoodeng.2016.01.002

    Article  Google Scholar 

  176. Zhu Q, Huang M, Zhao X, Wang S (2013) Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples. Food Anal Methods 6:334–342. https://doi.org/10.1007/s12161-012-9442-2

    Article  Google Scholar 

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Indurani Chandrasekaran declares that she has no conflict of interest. Shubham Subrot Panigrahi declares that he has no conflict of interest. Lankapalli Ravikanth declares that he has no conflict of interest. Chandra B Singh declares that he has no conflict of interest.

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Chandrasekaran, I., Panigrahi, S.S., Ravikanth, L. et al. Potential of Near-Infrared (NIR) Spectroscopy and Hyperspectral Imaging for Quality and Safety Assessment of Fruits: an Overview. Food Anal. Methods 12, 2438–2458 (2019). https://doi.org/10.1007/s12161-019-01609-1

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Keywords

  • Near-infrared spectroscopy
  • Hyperspectral imaging
  • Chemometric
  • Fruit quality