Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System

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Abstract

This paper presents a comprehensive analysis of the application of visible–near-infrared (Vis/NIR) spectroscopy for the estimation of various chemical attributes of pear fruit. Specifically, the paper investigates how pH, titratable acidity (TA), soluble solids content (SSC), and Vitamin C change as the pear undergoes different storage durations and temperatures. To obtain the most accurate prediction models, we applied a variety of pre-processing techniques to the acquired spectra. Notably, the combination of Savitzky-Golay (S.G.), Multiplicative Scatter Correction (MSC), and second derivatives (D2) emerged as the most effective method for predicting the fruit’s pH, with an impressive rp = 0.95 and SDR = 4.9. In contrast, combining S.G., MSC, and first derivatives (D1) yielded the most accurate predictions for TA, with a robust rp = 0.98 and SDR = 9.6. The research further delved into understanding how the storage period and temperature can significantly influence the pear fruit’s chemical properties. Our findings established that as the storage duration and temperature rise, the pH of the fruit also escalates, while TA sees a decline. The research further elucidates that prolonged storage periods and elevated temperatures lead to the pear fruit shedding its intrinsic qualities, resulting in a reduction in soluble solids and Vitamin C content. To summarize, this paper underscores the immense potential of Vis/NIR spectroscopy as a non-destructive and expedient tool for monitoring the chemical attributes of pear fruit during storage, especially when subjected to diverse temperature and time conditions. These insights not only add to the existing body of knowledge but also align with earlier research on how storage conditions can affect fruit quality.

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Taghinezhad, E., Rasooli Sharabiani, V., Shahiri, M., Moinfar, A., & Szumny, A. (2023). Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System. Agriculture (Switzerland), 13(10). https://doi.org/10.3390/agriculture13101913

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