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Non-destructive detection of the quality attributes of fruits by visible-near infrared spectroscopy

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Abstract

How to rapidly and nondestructively detect fruit quality has always been a hot topic in fruit agriculture. In this paper, the quality detection of cream strawberry (CS) and rabbit-eye blueberry (RB) was studied by using visible-near infrared spectroscopy. The preprocessing methods such as de-trending, moving average smoothing, standard normal variable and baseline correction are used to reduce spectral data errors. The competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and the combination of the two algorithms (CARS+SPA) were used to reduce the data dimension. A partial least squares regression was used to establish the prediction model. The results showed that the characteristic wavelength extracted by CARS+SPA algorithm was the most suitable for predicting the contents of soluble solids content (SSC), total acid (TA), and vitamin C (VC) in CS and RB, and the determination coefficients were 0.96, 0.91, 0.91 and 0.93, 0.91, respectively. The relative percent deviation values of the prediction set were 4.47, 3.28, 3.69 and 3.74, 3.57, respectively. The correlation coefficients between the predicted value and the measured value of SSC, TA and VC content were 0.97, 0.92, 0.97 and 0.96, 0.95, respectively, which indicates that the established prediction model is very stable and reliable. This study can provide a theoretical basis for effectively solving the problem of rapid quality detection of multi-variety fruits and the development of multi-variety fruits quality detector.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China Program (No.11164004, 61835003), the Guizhou Provincial Photonic Science and Technology Innovation team (Qianke Joint talents team [2015] 4017) and the First-class Physics Promotion Programme (2019) of Guizhou University.

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Authors and Affiliations

  1. School of Physics, Guizhou University, Guiyang, 550025, China

    Li Li, De-Yuan Hu, Tian-Yu Tang & Yan-Lin Tang

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  1. Li Li
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  2. De-Yuan Hu
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  3. Tian-Yu Tang
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  4. Yan-Lin Tang
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Contributions

LL: conceptualization, methodology, investigation, writing-original draft. D-YH: conceptualization, investigation. T-YT: methodology, investigation. Y-LT: conceptualization, investigation, funding acquisition.

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Correspondence to Yan-Lin Tang.

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Li, L., Hu, DY., Tang, TY. et al. Non-destructive detection of the quality attributes of fruits by visible-near infrared spectroscopy. Food Measure 17, 1526–1534 (2023). https://doi.org/10.1007/s11694-022-01724-4

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  • DOI: https://doi.org/10.1007/s11694-022-01724-4

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