Data fusion of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging to predict quality parameters of yellow flesh “Jintao” kiwifruit

Cevoli, Chiara; Iaccheri, Eleonora; Fabbri, Angelo; Ragni, Luigi

doi:10.1016/j.biosystemseng.2023.12.011

Cited by 14 publications

(1 citation statement)

References 27 publications

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“…The Vis/NIR spectroscopy technique has been utilized to evaluate the qualitative attributes of various fruits, such as apples, citrus fruits, and kiwifruits [12]. Non-destructive techniques like NIR, along with hyperspectral imaging, have been extensively employed to assess the qualitative characteristics of kiwifruit, such as firmness, pH, soluble solid content (SSC), and dry matter (DM) [2,[13][14][15][16][17][18][19][20][21][22]. The integration of Vis/NIR into machine learning (ML) algorithms enhances the effectiveness of learning, estimating, predicting, and classifying crucial quality parameters [23].…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Tziotzios,

Pantazi,

Paraskevas

et al. 2024

Horticulturae

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The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits.

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