Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Apr 6;9(4):441.
doi: 10.3390/foods9040441.

Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

Manuela Mancini  1 , Luca Mazzoni  1 , Francesco Gagliardi  1 , Francesca Balducci  1 , Daniele Duca  1 , Giuseppe Toscano  1 , Bruno Mezzetti  1 , Franco Capocasa  1
Affiliations

Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

Manuela Mancini et al. Foods. .

Abstract

The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing-in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction-the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical-physical properties of the samples, finding remarkable applications in the agro-food market.

Keywords: NIR spectroscopy; PCA; PLS; color; firmness; soluble solids; strawberry; titratable acidity.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Principal Component Analysis (PCA) score plot of the five strawberry genotypes (PC1 vs PC2); (b) PCA score plot of the five strawberry genotypes (PC2 vs PC3). Spectra were pre-treated with first derivative (Savitzky–Golay filter, 21 smoothing points, 2nd polynomial order). PC = principal component.
Figure 2
Figure 2
(a) PCA score plot of the Romina, Sibilla and Cristina cultivars (PC1 vs PC2). (b) PCA score plot of Romina, Sibilla and Cristina cultivars (PC2 vs PC3). Spectra were pre-treated with first derivative (Savitzky-Golay filter, 21 smoothing points, 2nd polynomial order). PC = principal component.
Figure 3
Figure 3
(a) Average spectra of Cristina, Sibilla and Romina cultivars pretreated using first derivative (Savitzky–Golay filter, 21 smoothing points, 2nd polynomial order); (b) Second loading. The dotted lines highlight the most important differences among the three cultivars.
Figure 4
Figure 4
Regression plot of the Partial Least Squares Regression (PLS) model for the prediction of the soluble solids content obtained with Near Infrared (NIR) spectra pretreated with first derivatives (Savitzky–Golay filter, 21 smoothing points, 2nd polynomial order).
Figure 5
Figure 5
First two loadings of Partial Least Squares Regression (PLS) model for the prediction of soluble solid content.
Figure 6
Figure 6
Regression plot of the Partial Least Squares Regression (PLS) model for the prediction of firmness obtained with Near Infrared (NIR) spectra pretreated with first derivatives (Savitzky–Golay filter. 21 smoothing points. 2nd polynomial order).
Figure 7
Figure 7
First two loadings of Partial Least Squares Regression (PLS) model for the prediction of firmness.
See this image and copyright information in PMC

References

    1. Di Vittori L., Mazzoni L., Battino M., Mezzetti B. Pre-harvest factors influencing the quality of berries. Sci. Hortic. 2018;233:310–322. doi: 10.1016/j.scienta.2018.01.058. - DOI
    1. Mezzetti B., Balducci F., Capocasa F., Zhong C.-F., Cappelletti R., Di Vittori L., Mazzoni L., Giampieri F., Battino M. Breeding Strawberry for Higher Phytochemicals Content and Claim It: Is It Possible? Int. J. Fruit Sci. 2016;16:1–13. doi: 10.1080/15538362.2016.1250695. - DOI
    1. Capocasa F., Balducci F., Di Vittori L., Mazzoni L., Stewart D., Williams S., Hargreaves R., Bernardini D., Danesi L., Zhong C.-F., et al. Romina and Cristina: Two New Strawberry Cultivars with High Sensorial and Nutritional Values. Int. J. Fruit Sci. 2016;16:1–13. doi: 10.1080/15538362.2016.1219292. - DOI
    1. Mazzoni L., Di Vittori L., Balducci F., Forbes-Hernández T., Giampieri F., Battino M., Mezzetti B., Capocasa F. Sensorial and nutritional quality of inter and intra—Specific strawberry genotypes selected in resilient conditions. Sci. Hortic. 2020;261:108945. doi: 10.1016/j.scienta.2019.108945. - DOI
    1. Darbellay C., Luisier J.-L., Villettaz J.-C., Azodanlou R. Changes in flavour and texture during the ripening of strawberries. Eur. Food Res. Technol. 2004;218:167–172. doi: 10.1007/s00217-003-0822-0. - DOI

Grants and funding

LinkOut - more resources