Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms

  • Lei Feng
  • Min ZhangEmail author
  • Benu Adhikari
  • Zhimei Guo


The aim of this study was to assess the applicability of a portable NIR spectroscopy system and chemometric algorithms in intelligently detecting postharvest quality of cherry tomatoes. The postharvest quality of cherry tomatoes was evaluated in terms of firmness, soluble solids content (SSC), and pH, and a portable NIR spectrometer (950–1650 nm) was used to obtain the spectra of cherry tomatoes. Partial least square (PLS), support vector machine (SVM), and extreme learning machine (ELM) were applied to predict the postharvest quality of cherry tomatoes from their spectra. The effects of different preprocessing techniques, including Savitzky–Golay (S-G), multiplicative scattering correction (MSC), and standard normal variate (SNV) on prediction performance were also evaluated. Firmness, SSC and pH values of cherry tomatoes decreased during storage period, based on which the tomato samples could be classified into two distinct clusters. Similarly, cherry tomatoes with different storage time could also be separated by the NIR spectroscopic characteristics. The best prediction accuracy was obtained from ELM algorithms using the raw spectra with Rp2, RMSEP, and RPD values of 0.9666, 0.3141 N, and 5.6118 for firmness; 0.9179, 0.1485%, and 3.6249 for SSC; and 0.8519, 0.0164, and 2.7407 for pH, respectively. Excellent predictions for firmness and SSC (RPD value greater than 3.0), good prediction for pH (RPD value between 2.5 and 3.0) were obtained using ELM model. NIR spectroscopy is capable of intelligently detecting postharvest quality of cherry tomatoes during storage.


Cherry tomato Near infrared spectroscopy Partial least square Support vector machine Extreme learning machine 



This study was funded by the National Key R&D Program of China (No. 2018YFD0700303), Jiangsu Province (China) Key Project in Agriculture (Contract No. BE2015310217), National First-Class Discipline Program of Food Science and Technology (No. JUFSTR20180205), and Jiangsu Province Key Laboratory Project of Advanced Food Manufacturing Equipment and Technology (No. FMZ201803).

Compliance with Ethical Standards

Conflict of Interest

Lei Feng declares that she has no conflict of interest. Min Zhang declares that he has no conflict of interest. Benu Adhikari declares that he has no conflict of interest. Zhimei Guo declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.


  1. Amodio ML, Ceglie F, Chaudhry MMA, Piazzolla F, Colelli G (2017) Potential of NIR spectroscopy for predicting internal quality and discriminating among strawberry fruits from different production systems. Postharvest Biol Technol 125:112–121Google Scholar
  2. Arazuri S, Jarén C, Arana JI, Pérez De Ciriza JJ (2007) Influence of mechanical harvest on the physical properties of processing tomato (Lycopersicon esculentum Mill.). J Food Eng 80(1):190–198Google Scholar
  3. Balasundaram S, Gupta D (2016) On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int J Mach Learn Cybern 7(5):707–728Google Scholar
  4. Bian X, Zhang C, Tan X, Dymek M, Guo Y, Lin L, Cheng B, Hu X (2017) A boosting extreme learning machine for near-infrared spectral quantitative analysis of diesel fuel and edible blend oil samples. Anal Methods-UK 9(20):2983–2989Google Scholar
  5. Bobelyn E, Serban A, 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(3):133–143Google Scholar
  6. Brezmes J, Llobet E, Vilanova X, Orts J, Saiz G, Correig X (2001) Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples. Sensors Actuators B Chem 80(1):41–50Google Scholar
  7. Caner C, Aday MS, Demir M (2008) Extending the quality of fresh strawberries by equilibrium modified atmosphere packaging. Eur Food Res Technol 227(6):1575–1583Google Scholar
  8. Caraux G, Pinloche S (2005) PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21(7):1280–1281Google Scholar
  9. Cárdenas-Coronel WG, Carrillo-López A, Vélez De La Rocha R, Labavitch JM, Báez-Sañudo MA, Heredia JB, Zazueta-Morales JJ, Vega-García MO, Sañudo-Barajas JA (2015) Biochemistry and cell wall changes associated with noni (Morinda citrifolia L.) fruit ripening. J Agric Food Chem 64(1):302–309Google Scholar
  10. Cascant MM, Sisouane M, Tahiri S, El Krati M, Cervera ML, Garrigues S, de la Guardia M (2016) Determination of total phenolic compounds in compost by infrared spectroscopy. Talanta 153:360–365Google Scholar
  11. Chorowski J, Wang J, Zurada JM (2014) Review and performance comparison of SVM- and ELM-based classifiers. Neurocomputing 128:507–516Google Scholar
  12. Clément A, Dorais M, Vernon M (2008) Multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by Vis–NIR spectroscopy. J Agric Food Chem 56(5):1538–1544Google Scholar
  13. Dhanoa MS, Lister SJ, Sanderson R, Barnes RJ (1994) The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. J Near Infrared Spectrosc 2(1):43–47Google Scholar
  14. Du W, Olsen CW, Avena-Bustillos RJ, McHugh TH, Levin CE, Mandrell R, Friedman M (2009) Antibacteriale of allspice, garlic, and oregano essential oils in tomato films determined by overlay and vapor-phase methods. J Food Sci 74(7):390–397Google Scholar
  15. Eisenstecken D, Panarese A, Robatscher P, Huck C, Zanella A, Oberhuber M (2015) A near infrared spectroscopy (NIRS) and chemometric approach to improve apple fruit quality management: a case study on the cultivars ‘cripps Pink’ and ‘braeburn’. Molecules 20(8):13603–13619Google Scholar
  16. Escribano S, Biasi WV, Lerud R, Slaughter DC, Mitcham EJ (2017) Non-destructive prediction of soluble solids and dry matter content using NIR spectroscopy and its relationship with sensory quality in sweet cherries. Postharvest Biol Technol 128:112–120Google Scholar
  17. Fadilah N, Mohamad-Saleh J, Abdul Halim Z, Ibrahim H, Syed Ali S (2012) Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch. Sensors-Basel 12(10):14179–14195Google Scholar
  18. Flores K, Sánchez M, Pérez-Marín D, Guerrero J, Garrido-Varo A (2009) Feasibility in NIRS instruments for predicting internal quality in intact tomato. J Food Eng 91(2):311–318Google Scholar
  19. Fu X, Ying Y, Lu H, Xu H, Yu H (2007) FT-NIR diffuse reflectance spectroscopy for kiwifruit firmness detection. Sens & Instrumen Food Qual 1(1):29–35Google Scholar
  20. Gómez AH, Wang J, Hu G, Pereira AG (2008) Monitoring storage shelf life of tomato using electronic nose technique. J Food Eng 85(4):625–631Google Scholar
  21. Hong X, Wang J, Qi G (2015) E-nose combined with chemometrics to trace tomato-juice quality. J Food Eng 149:38–43Google Scholar
  22. Huang G, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163Google Scholar
  23. Huang G, Huang G, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48Google Scholar
  24. Huang Y, Lu R, Chen K (2018a) Prediction of firmness parameters of tomatoes by portable visible and near-infrared spectroscopy. J Food Eng 222:185–198Google Scholar
  25. Huang Y, Lu R, Chen K (2018b) Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy. J Food Eng 236:19–28Google Scholar
  26. Jiang H, Liu G, Meia C, Chen Q (2013) Qualitative and quantitative analysis in solid-state fermentation of protein feed by FT-NIR spectroscopy integrated with multivariate data analysis. Anal Methods-UK 5(7):1872–1880Google Scholar
  27. Jiang J, Cen H, Zhang C, Lyu X, Weng H, Xu H, He Y (2018) Nondestructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis. Postharvest Biol Technol 146:147–154Google Scholar
  28. Kang Q, Ru Q, Liu Y, Xu L, Liu J, Wang Y, Zhang Y, Li H, Zhang Q, Wu Q (2016) On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms. Spectrochim Acta A Mol Biomol Spectrosc 152:431–437Google Scholar
  29. Kavdir I, Lu R, Ariana D, Ngouajio M (2007) Visible and near-infrared spectroscopy for nondestructive quality assessment of pickling cucumbers. Postharvest Biol Technol 44(2):165–174Google Scholar
  30. Kong W, Liu F, Zhang C, Zhang J, Feng H (2016) Non-destructive determination of malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging. Sci Rep 6:35393Google Scholar
  31. Li M, Pullanagari RR, Pranamornkith T, Yule IJ, East AR (2017) Quantitative prediction of post storage ‘Hayward’ kiwifruit attributes using at harvest vis-NIR spectroscopy. J Food Eng 202:46–55Google Scholar
  32. Liu D, Li Q, Li W, Yang B, Guo W (2017) Discriminating forchlorfenuron-treated kiwifruits using a portable spectrometer and Vis/NIR diffuse transmittance spectroscopy technology. Anal Methods-UK 9(28):4207–4214Google Scholar
  33. 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 197:1207–1214Google Scholar
  34. Menezes CM, Da Costa AB, Renner RR, Bastos LF, Ferrao MF, Dresslere VL (2014) Direct determination of tannins in Acacia mearnsii bark using near-infrared spectroscopy. Anal Methods-UK 6(20):8299–8305Google Scholar
  35. Moghimi A, Aghkhani MH, Sazgamia A, Abbaspour-Fard MH (2011) Improvement of NIR transmission mode for internal quality assessment of fruit using different orientations. J Food Process Eng 34(5):1759–1774Google Scholar
  36. 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(2):99–118Google Scholar
  37. Peng J, Ji W, Ma Z, Li S, Chen S, Zhou L, Shi Z (2016) Predicting total dissolved salts and soluble ion concentrations in agricultural soils using portable visible near-infrared and mid-infrared spectrometers. Biosyst Eng 152:94–103Google Scholar
  38. Pérez-Marín D, Sánchez M, Paz P, Soriano M, Guerrero J, Garrido-Varo A (2009) Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Postharvest Biol Technol 52(2):180–188Google Scholar
  39. Pérez-Marín D, Sánchez M, Paz P, González-Dugo V, Soriano M (2011) Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy. LWT Food Sci Technol 44(6):1405–1414Google Scholar
  40. Shao Y, He Y, Gómez AH, Pereir AG, Qiu Z, Zhang Y (2007) Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’ (Lycopersicum esculentum) quality characteristics. J Food Eng 81(4):672–678Google Scholar
  41. Sinelli N, Casiraghi E, Barzaghi S, Brambilla A, Giovanelli G (2011) Near infrared (NIR) spectroscopy as a tool for monitoring blueberry osmo-air dehydration process. Food Res Int 44(5):1427–1433Google Scholar
  42. Sirisomboon P, Tanaka M, Kojima T (2012) Evaluation of tomato textural mechanical properties. J Food Eng 111(4):618–624Google Scholar
  43. Sisouane M, Cascant MM, Tahiri S, Garrigues S, EL Krati M, Boutchich GELK, Cervera ML, de la Guardia M (2017) Prediction of organic carbon and total nitrogen contents in organic wastes and their composts by infrared spectroscopy and partial least square regression. Talanta 167:352–358Google Scholar
  44. Talari ACS, Martinez MAG, Movasaghi Z, Rehman S, Rehman IU (2016) Advances in Fourier transform infrared (FTIR) spectroscopy of biological tissues. Appl Spectrosc Rev 52(5):456–506Google Scholar
  45. Tamburini E, Vincenzi F, Costa S, Mantovi P, Pedrini P, Castaldelli G (2017) Effects of moisture and particle size on quantitative determination of total organic carbon (TOC) in soils using near-infrared spectroscopy. Sensors (Basel) 17(10):1–15Google Scholar
  46. Teixeira GHA, Durigan JF, Ferraudo AS, Alves RE, O Hare TJ (2012) Multivariate analysis of fresh-cut carambola slices stored under different temperatures. Postharvest Biol Technol 63(1):91–97Google Scholar
  47. Torres I, Pérez-Marín D, Haba MDL, Sánchez M (2015) Fast and accurate quality assessment of Raf tomatoes using NIRS technology. Postharvest Biol Technol 107:9–15Google Scholar
  48. Wang J, Wang J, Chen Z, Han D (2017) Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear (Pyrus communis L.) using portable vis-NIR spectroscopy. Postharvest Biol Technol 129:143–151Google Scholar
  49. Wanitchang J, Terdwongworakul A, Wanitchang P, Noypitak S (2010) Maturity sorting index of dragon fruit: Hylocereus polyrhizus. J Food Eng 100(3):409–416Google Scholar
  50. Wei Y, Xu M, Wu H, Tu S, Pan L, Tu K (2016) Defense response of cherry tomato at different maturity stages to combined treatment of hot air and Cryptococcus laurentii. Postharvest Biol Technol 117:177–186Google Scholar
  51. Wu Z, Xu E, Long J, Wang F, Xu X, Jin Z, Jiao A (2015) Rapid measurement of antioxidant activity and γ-aminobutyric acid content of Chinese rice wine by Fourier-transform near infrared spectroscopy. Food Anal Method 8(10):2541–2553Google Scholar
  52. Wu Z, Xu E, Long J, Pan X, Xu X, Jin Z, Jiao A (2016) Comparison between ATR-IR, Raman, concatenated ATR-IR and Raman spectroscopy for the determination of total antioxidant capacity and total phenolic content of Chinese rice wine. Food Chem 194:671–679Google Scholar
  53. Xiao G, Sha L, Yan L, Yu J, Zhao H, Zhou Q (2012) Effect of paperboard packaging inner coated with preservation agents on fresh-keeping of honey peach. Trans Chin Soc Agric Eng 28(6):274–277Google Scholar
  54. Xiaobo Z, Jiewen Z, Holmes M, Hanpin M, Jiyong S, Xiaopin Y, Yanxiao L (2010) Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves. Chemom Intell Lab Syst 104(2):265–270Google Scholar
  55. Xia-ping F, Jian-ping L, Ying Z, Yi-bin Y, Li-Juan X, Xiao-ying N, Zhan-ke Y, Hai-yan Y (2009) Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy. J Zhejiang Univ Sci B 10(2):120–125Google Scholar
  56. Xu L, Zhou Y, Tang L, Wu H, Jiang J, Shen G, Yu R (2008) Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration. Anal Chim Acta 616(2):138–143Google Scholar
  57. Yahui L, Xiaobo Z, Tingting S, Jiyong S, Jiewen Z, Holmes M (2017) Determination of geographical origin and anthocyanin content of black goji berry (Lycium ruthenicum Murr.) using near-infrared spectroscopy and chemometrics. Food Anal Method 10(4):1034–1044Google Scholar
  58. Yun J, Fan X, Li X, Jin TZ, Jia X, Mattheis JP (2015) Natural surface coating to inactivate Salmonella enterica serovar Typhimurium and maintain quality of cherry tomatoes. Int J Food Microbiol 193:59–67Google Scholar
  59. Zhang H, Wang J, Ye S (2008) Predictions of acidity, soluble solids and firmness of pear using electronic nose technique. J Food Eng 86(3):370–378Google Scholar
  60. Zhang G, Li P, Zhang W, Zhao J (2017) Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy. Anal Bioanal Chem 409(14):3515–3525Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Food Science and TechnologyJiangnan UniversityWuxiChina
  2. 2.Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and TechnologyJiangnan UniversityWuxiChina
  3. 3.School of Food Science and TechnologyJiangnan UniversityWuxiChina
  4. 4.School of Science-RMIT UniversityMelbourneAustralia
  5. 5.Wuxi Haihe Equipment Co.WuxiChina

Personalised recommendations