Skip to main content
SpringerLink
Log in

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

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • 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 125:112–121

    Article  CAS  Google Scholar 

  • 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):190–198

    Article  Google Scholar 

  • 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):707–728

    Article  CAS  Google Scholar 

  • 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):2983–2989

    Article  CAS  Google Scholar 

  • 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):133–143

    Article  CAS  Google Scholar 

  • 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):41–50

    Article  CAS  Google Scholar 

  • Caner C, Aday MS, Demir M (2008) Extending the quality of fresh strawberries by equilibrium modified atmosphere packaging. Eur Food Res Technol 227(6):1575–1583

    Article  CAS  Google Scholar 

  • Caraux G, Pinloche S (2005) PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21(7):1280–1281

    Article  CAS  PubMed  Google Scholar 

  • 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):302–309

    Article  CAS  PubMed  Google Scholar 

  • 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 153:360–365

    Article  CAS  PubMed  Google Scholar 

  • Chorowski J, Wang J, Zurada JM (2014) Review and performance comparison of SVM- and ELM-based classifiers. Neurocomputing 128:507–516

    Article  Google Scholar 

  • 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):1538–1544

    Article  CAS  PubMed  Google Scholar 

  • 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):43–47

    Article  CAS  Google Scholar 

  • 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):390–397

    Article  CAS  Google Scholar 

  • 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):13603–13619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 128:112–120

    Article  CAS  Google Scholar 

  • 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):14179–14195

    Article  PubMed  PubMed Central  Google Scholar 

  • 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):311–318

    Article  CAS  Google Scholar 

  • 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):29–35

    Article  Google Scholar 

  • Gómez AH, Wang J, Hu G, Pereira AG (2008) Monitoring storage shelf life of tomato using electronic nose technique. J Food Eng 85(4):625–631

    Article  Google Scholar 

  • Hong X, Wang J, Qi G (2015) E-nose combined with chemometrics to trace tomato-juice quality. J Food Eng 149:38–43

    Article  CAS  Google Scholar 

  • Huang G, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163

    Article  Google Scholar 

  • Huang G, Huang G, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Article  PubMed  Google Scholar 

  • Huang Y, Lu R, Chen K (2018a) Prediction of firmness parameters of tomatoes by portable visible and near-infrared spectroscopy. J Food Eng 222:185–198

    Article  Google Scholar 

  • 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 236:19–28

    Article  CAS  Google Scholar 

  • 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):1872–1880

    Article  CAS  Google Scholar 

  • 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 146:147–154

    Article  CAS  Google Scholar 

  • 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 152:431–437

    Article  CAS  PubMed  Google Scholar 

  • 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):165–174

    Article  CAS  Google Scholar 

  • 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 6:35393

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 202:46–55

    Article  CAS  Google Scholar 

  • 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):4207–4214

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • 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):8299–8305

    Article  CAS  Google Scholar 

  • 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):1759–1774

    Article  Google Scholar 

  • 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):99–118

    Article  Google Scholar 

  • 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 152:94–103

    Article  Google Scholar 

  • 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):180–188

    Article  Google Scholar 

  • 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):1405–1414

    Article  CAS  Google Scholar 

  • 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):672–678

    Article  CAS  Google Scholar 

  • 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):1427–1433

    Article  CAS  Google Scholar 

  • Sirisomboon P, Tanaka M, Kojima T (2012) Evaluation of tomato textural mechanical properties. J Food Eng 111(4):618–624

    Article  Google Scholar 

  • 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 167:352–358

    Article  CAS  PubMed  Google Scholar 

  • 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):456–506

    Article  CAS  Google Scholar 

  • 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):1–15

    Article  CAS  Google Scholar 

  • 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):91–97

    Article  CAS  Google Scholar 

  • Torres I, Pérez-Marín D, Haba MDL, Sánchez M (2015) Fast and accurate quality assessment of Raf tomatoes using NIRS technology. Postharvest Biol Technol 107:9–15

    Article  CAS  Google Scholar 

  • 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 129:143–151

    Article  Google Scholar 

  • Wanitchang J, Terdwongworakul A, Wanitchang P, Noypitak S (2010) Maturity sorting index of dragon fruit: Hylocereus polyrhizus. J Food Eng 100(3):409–416

    Article  Google Scholar 

  • 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 117:177–186

    Article  Google Scholar 

  • 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):2541–2553

    Article  Google Scholar 

  • 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 194:671–679

    Article  CAS  PubMed  Google Scholar 

  • 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):274–277

    CAS  Google Scholar 

  • 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):265–270

    Article  CAS  Google Scholar 

  • 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):120–125

    Article  CAS  Google Scholar 

  • 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):138–143

    Article  CAS  PubMed  Google Scholar 

  • 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):1034–1044

    Article  Google Scholar 

  • 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 193:59–67

    Article  CAS  PubMed  Google Scholar 

  • 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):370–378

    Article  Google Scholar 

  • Zhang G, Li P, Zhang W, Zhao J (2017) Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy. Anal Bioanal Chem 409(14):3515–3525

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

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).

Author information

Authors and Affiliations

  1. State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Lei Feng & Min Zhang

  2. Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, Wuxi, China

    Min Zhang

  3. School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Min Zhang

  4. School of Science-RMIT University, Melbourne, VIC, 3083, Australia

    Benu Adhikari

  5. Wuxi Haihe Equipment Co., Wuxi, China

    Zhimei Guo

Authors
  1. Lei Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benu Adhikari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhimei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Min Zhang.

Ethics declarations

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.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, L., Zhang, M., Adhikari, B. et al. Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms. Food Anal. Methods 12, 914–925 (2019). https://doi.org/10.1007/s12161-018-01429-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-018-01429-9

Keywords

Search

Navigation