Skip to main content

Advertisement

Springer Nature Link
Log in

Quality Analysis Prediction and Discriminating Strawberry Maturity with a Hand-held Vis–NIR Spectrometer

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

Predictability of maturity using quality attributes based on Vis–NIR spectra will be beneficial to farmers and consumers alike. Hand-held Vis–NIR spectrometers are a convenient, rapid, non-destructive method that can measure the quality attributes of many fruits and vegetables. The aim of this study is to evaluate the potential of a hand-held Vis–NIR spectrometer to classify the maturity stage and to predict the quality attributes of strawberry such as lightness (L*), chroma colour (C*), hue (H°), total soluble solids (TSS), titratable acidity (TA) and total polyphenol content (TPC). Principal component analysis (PCA) was used to distinguish strawberry at different maturities. Partial least squares regression (PLSR) models of internal quality attributes were developed in the spectral region between 550 and 900 nm for a hand-held NIR instrument. Several pretreatment methods were utilized including standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay algorithm smoothing and second derivative. Different pretreatment methods had effects on the classification performance of the PCA model. In general, SNV gave better results than the other preprocessing techniques. The coefficient of determination (R2) of the PLSR (SNV) model was calculated as 0.92, 0.93, 0.92, 0.96, 0.91 and 0.90 for L*, C*, H°, TSS, TA and TPC, respectively. Given the importance in assessing strawberry quality at different maturity stages, the use of a hand-held spectrometer, which are usable and rapid, should be considered a non-destructive analysis of strawberry quality.

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

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  • Aaby K, Skrede G, Wrolstad RE (2005) Phenolic composition and antioxidant activities in flesh and achenes of strawberries (Fragaria ananassa). J Agric Food Chem 53(10):4032–4040

    Article  CAS  Google Scholar 

  • Agelet LE, Hurburgh CR Jr (2010) A tutorial on near infrared spectroscopy and its calibration. Crit Rev Anal Chem 40(4):246–260

    Article  CAS  Google Scholar 

  • Akhtar, I., & Rab, A. (2015). Effect of fruit ripening stages on strawberry (Fragaria X Ananassa. Duch) fruit quality for fresh consumption. J Agric Res (03681157), 53(3).

  • Amer BM, Azam MM (2019) Using hot water as a pretreatment to extend the shelf life of cucumbers (Cucumis sativus L) under cold storage conditions. J Food Process Eng 42(2):e12958

    Article  Google Scholar 

  • 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 

  • Amuah, C. L., Teye, E., Lamptey, F. P., Nyandey, K., Opoku-Ansah, J., & Adueming, P. O. W. (2019). Feasibility study of the use of handheld NIR spectrometer for simultaneous authentication and quantification of quality parameters in intact pineapple fruits. J Spectroscopy 2019

  • Beghi R, Giovenzana V, Spinardi A, Guidetti R, Bodria L, Oberti R (2013) Derivation of a blueberry ripeness index with a view to a low-cost, handheld optical sensing device for supporting harvest decisions. Trans ASABE 56(4):1551–1559

    Google Scholar 

  • Cao, N. (2013). Calibration optimization and efficiency in near infrared spectroscopy (Doctoral dissertation, Iowa State University).

  • Chandrasekaran I, Panigrahi SS, Ravikanth L, Singh CB (2019) Potential of near-infrared (NIR) spectroscopy and hyperspectral imaging for quality and safety assessment of fruits: an overview. Food Anal Methods 12(11):2438–2458

    Article  Google Scholar 

  • Chen Q, Zhao J, Huang X, Zhang H, Liu M (2006) Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy. Microchem J 83(1):42–47

    Article  CAS  Google Scholar 

  • Cozzolino D, Kwiatkowski MJ, Parker M, Cynkar WU, Dambergs RG, Gishen M, Herderich MJ (2004) Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal Chim Acta 513(1):73–80

    Article  CAS  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 

  • Fan, S. X., Huang, W. Q., Li, J. B., Zhao, C. J., & Zhang, B. H. (2014). Characteristic wavelengths selection of soluble solids content of pear based on NIR spectral and LS-SVM. Guang pu xue yu guang pu fen xi= Guang pu, 34(8), 2089–2093.

  • Giovannini D, Quacquarelli I, Ranieri M, Faedi W (2014) Feasibility study of NIR application to strawberry internal fruit quality traits. In VII International Strawberry Symposium 1049:947–954

    Google Scholar 

  • Gowen AA, Downey G, Esquerre C, O’Donnell CP (2011) Preventing over-fitting in PLS calibration models of near-infrared (NIR) spectroscopy data using regression coefficients. J Chemom 25(7):375–381

    Article  CAS  Google Scholar 

  • Grisanti, E., Totska, M., Huber, S., Krick Calderon, C., Hohmann, M., Lingenfelser, D., & Otto, M. (2018). Dynamic localized snv, peak snv, and partial peak snv: novel standardization methods for preprocessing of spectroscopic data used in predictive modeling. J Spectroscopy, 2018.

  • Guidetti R, Beghi R, Bodria L (2010) Evaluation of grape quality parameters by a simple Vis/NIR system. Trans ASABE 53(2):477–484

    Article  CAS  Google Scholar 

  • 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(4):405–416

    Article  Google Scholar 

  • Kafkas E, Koşar M, Paydaş S, Kafkas S, Başer KHC (2007) Quality characteristics of strawberry genotypes at different maturation stages. Food Chem 100(3):1229–1236

    Article  CAS  Google Scholar 

  • 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(1):41–52

    Article  CAS  Google Scholar 

  • Lakshmi S, Pandey AK, Ravi N, Chauhan OP, Gopalan N, Sharma RK (2017) Non-destructive quality monitoring of fresh fruits and vegetables. Def Life Sci J 2(2):103–110

    Article  Google Scholar 

  • Ma, L., Peng, Y., Pei, Y., Zeng, J., Shen, H., Cao, J., ... & Wu, Z. (2019). Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds. Scientific reports, 9(1), 1-17

  • Magwaza LS, Opara UL, Nieuwoudt H, Cronje PJ, Saeys W, Nicolaï B (2012) NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food Bioprocess Technol 5(2):425–444

    Article  CAS  Google Scholar 

  • Mancini, M., Mazzoni, L., Gagliardi, F., Balducci, F., Duca, D., Toscano, G., ... & Capocasa, F. (2020). Application of the non-destructive NIR technique for the evaluation of strawberry fruits quality parameters. Foods, 9(4), 441

  • Mogollón R, Contreras C, da Silva Neta ML, Marques EJN, Zoffoli JP, de Freitas ST (2020) Non-destructive prediction and detection of internal physiological disorders in’Keitt’mango using a hand-held Vis-NIR spectrometer. Postharvest Biology and Technology 167:111251

    Article  Google Scholar 

  • Nicolaï, B. M., Defraeye, T., De Ketelaere, B., Herremans, E., Hertog, M. L., Saeys, W., ... & Verboven, P. (2014). Nondestructive measurement of fruit and vegetable quality. Annual review of food science and technology, 5, 285-312

  • Nishizawa T, Mori Y, Fukushima S, Natsuga M, Maruyama Y (2009) Non-destructive analysis of soluble sugar components in strawberry fruits using near-infrared spectroscopy. J Japanese Soc Food Sci Technol 56:229–235

    Article  CAS  Google Scholar 

  • Nowicka A, Kucharska AZ, Sokół-Łętowska A, Fecka I (2019) Comparison of polyphenol content and antioxidant capacity of strawberry fruit from 90 cultivars of Fragaria× ananassa Duch. Food Chem 270:32–46

    Article  CAS  Google Scholar 

  • Nunes MCN, Brecht JK, Morais AM, Sargent SA (2006) Physicochemical changes during strawberry development in the field compared with those that occur in harvested fruit during storage. J Sci Food Agric 86(2):180–190

    Article  CAS  Google Scholar 

  • Rahman MM, Moniruzzaman M, Ahmad MR, Sarker BC, Alam MK (2016) Maturity stages affect the postharvest quality and shelf-life of fruits of strawberry genotypes growing in subtropical regions. J Saudi Soc Agric Sci 15(1):28–37

    Google Scholar 

  • Rinnan Å, Van Den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC, Trends Anal Chem 28(10):1201–1222

    Article  CAS  Google Scholar 

  • Panico, A. M., Garufi, F., Nitto, S., Di Mauro, R., Longhitano, R. C., Magrì, G., ... & De Guidi, G. (2009). Antioxidant activity and phenolic content of strawberry genotypes from Fragaria x ananassa. Pharmaceutical Biology, 47(3), 203-208

  • Pissard, A., Fernández Pierna, J. A., Baeten, V., Sinnaeve, G., Lognay, G., Mouteau, A., ... & Lateur, M. (2013). Non‐destructive measurement of vitamin C, total polyphenol and sugar content in apples using near‐infrared spectroscopy. Journal of the Science of Food and Agriculture, 93(2), 238-244

  • Saad AG, Jaiswal P, Jha SN (2014) Non-destructive quality evaluation of intact tomato using VIS-NIR spectroscopy. Int J Adv Res 2(12):632–639

    Google Scholar 

  • Saad AM, Ibrahim A, El-Bialee N (2016a) Internal quality assessment of tomato fruits using image color analysis. Agric Eng Int CIGR J 18(1):339–352

    Google Scholar 

  • Saad A, Jha SN, Jaiswal P, Srivastava N, Helyes L (2016b) Non-destructive quality monitoring of stored tomatoes using VIS-NIR spectroscopy. Eng Agric Environ Food 9(2):158–164

    Article  Google Scholar 

  • Saad AG, Pék Z, Szuvandzsiev P, Gehad DH, Helyes L (2017) Determination of carotenoids in tomato products using Vis/NIR spectroscopy. J Microbiol Biotechnol Food Sci 7(1):27

    Article  Google Scholar 

  • Sánchez MT, De la Haba MJ, Benítez-López M, Fernández-Novales J, Garrido-Varo A, Pérez-Marín D (2012) Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. J Food Eng 110(1):102–108

    Article  Google Scholar 

  • Schulz H, Engelhardt UH, Wegent A, Drews HH, Lapczynski S (1999) Application of near-infrared reflectance spectroscopy to the simultaneous prediction of alkaloids and phenolic substances in green tea leaves. J Agric Food Chem 47(12):5064–5067

    Article  CAS  Google Scholar 

  • Shao Y, He Y (2008) Nondestructive measurement of acidity of strawberry using Vis/NIR spectroscopy. Int J Food Prop 11(1):102–111

    Article  CAS  Google Scholar 

  • Shen F, Zhang B, Cao C, Jiang X (2018) On-line discrimination of storage shelf-life and prediction of post-harvest quality for strawberry fruit by visible and near infrared spectroscopy. J Food Process Eng 41(7):e12866

    Article  Google Scholar 

  • Sirisomboon P, Tanaka M, Kojima T, Williams P (2012) Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’by near infrared spectroscopy. J Food Eng 112(3):218–226

    Article  Google Scholar 

  • Tijskens, L. M. M., Zerbini, P. E., Schouten, R. E., Vanoli, M., Jacob, S., Grassi, M., ... & Torricelli, A. (2007). Assessing harvest maturity in nectarines. Postharvest Biology and Technology, 45(2), 204-213

  • Varmuza K, Filzmoser P (2016) Introduction to multivariate statistical analysis in chemometrics. CRC Press

    Book  Google Scholar 

  • Wang, D., Wei, W., Lai, Y., Yang, X., Li, S., Jia, L., & Wu, D. (2019). Comparing the potential of near-and mid-infrared spectroscopy in determining the freshness of strawberry powder from freshly available and stored strawberry. J Anal Methods Chem 2019.

  • Weng S, Yu S, Dong R, Pan F, Liang D (2020) Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging. Int J Food Prop 23(1):269–281

    Article  CAS  Google Scholar 

  • Wei, K., Ma, C., Sun, K., Liu, Q., Zhao, N., Sun, Y., ... & Pan, L. (2020). Relationship between optical properties and soluble sugar contents of apple flesh during storage. Postharvest Biology and Technology, 159, 111021.

  • Włodarska K, Szulc J, Khmelinskii I, Sikorska E (2019) Non-destructive determination of strawberry fruit and juice quality parameters using ultraviolet, visible, and near-infrared spectroscopy. J Sci Food Agric 99(13):5953–5961

    Article  Google Scholar 

  • Yu, F., Qiu, F., & Meza, J. (2016). Design and statistical analysis of mass-spectrometry-based quantitative proteomics data. In Proteomic Profiling and Analytical Chemistry (211–237). Elsevier

  • Zhang C, Guo CT, Liu F, Kong WW, He Y, Lou BG (2016) Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J Food Eng 179:11–18

    Article  Google Scholar 

  • Zhao N, Wu ZS, Zhang Q, Shi XY, Ma Q, Qiao YJ (2015) Optimization of parameter selection for partial least squares model development. Sci Rep 5(1):1–10

    Google Scholar 

Download references

Acknowledgements

We are grateful to the Agricultural Engineering Research Institute (AEnRI), for providing the laboratory devices and chemicals. We thank Prof. Bernie Engel, Senior Associate Dean and Director of Agricultural Research and Graduate Education, Purdue University, for improving the use of English in the manuscript.

Author information

Authors and Affiliations

  1. Agricultural Research Center (ARC), Agricultural Engineering Research Institute (AEnRI), 12311 Dokki, Giza, Egypt

    AbdelGawad Saad

  2. Department of Agricultural Systems Engineering, College of Agriculture & Food Sciences, King Faisal University, Al-Ahsa, P.O. Box 42031982, Saudi Arabia

    Mostafa M. Azam & Baher M. A. Amer

  3. Agricultural Engineering Department, Faculty of Agriculture, Menofia University, Shibin El-Kom, Egypt

    Mostafa M. Azam

  4. Agricultural Engineering Department, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt

    Baher M. A. Amer

Authors
  1. AbdelGawad Saad
    View author publications

    Search author on:PubMed Google Scholar

  2. Mostafa M. Azam
    View author publications

    Search author on:PubMed Google Scholar

  3. Baher M. A. Amer
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to AbdelGawad Saad.

Ethics declarations

Informed Consent

Informed consent not applicable.

Conflict of Interest

Author AbdelGawad Saad declares that he has no conflict of interest. Author Mostafa M. Azam declares that he has no conflict of interest. Author Baher M. A. Amer declares that he 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.

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

Saad, A., Azam, M.M. & Amer, B.M.A. Quality Analysis Prediction and Discriminating Strawberry Maturity with a Hand-held Vis–NIR Spectrometer. Food Anal. Methods 15, 689–699 (2022). https://doi.org/10.1007/s12161-021-02166-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-021-02166-2

Keywords

Profiles

  1. Baher M. A. Amer View author profile