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Quantitative prediction of a functional ingredient in apple using Raman spectroscopy and multivariate calibration analysis

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Abstract

We propose a method for predicting the concentration of a functional ingredient, procyanidin, in apple using Raman spectroscopy in combination with multivariate calibration analysis. A regression model was constructed by partial least-squares (PLS) regression using the collected Raman spectra and the procyanidin concentrations measured by high-performance liquid chromatography (HPLC). Four different preprocessing algorithms—baseline correction, noise removal, averaging, and multiplicative scatter correction—were applied to the acquired Raman spectra. HPLC was used to determine the procyanidin concentrations in the edible part of apple specimens. The PLS regression model predicted the procyanidin concentration in apple with a coefficient of determination of 0.74, a root-mean-square error of calibration of 7.09 µg/g, and a root-mean-square error of prediction of 14.89 µg/g. In addition, the spectra of the carotenoid pigments were observed from the factors extracted from the PLS analysis. Consequently, we found that the procyanidin concentration in apple can be predicted using Raman spectroscopy measurements of carotenoid pigments of apple peel. Compared with conventional destructive measurements, Raman spectroscopy with the aid of multivariate analysis shows strong potential for the rapid and nondestructive quantitative analysis of procyanidin in apples.

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Acknowledgements

This research was carried out as a part of the “Hirosaki City Apple Production Technology Advancement Project”. We here express our deepest gratitude for Tsugaru Hirosaki Agricultural Cooperative Guidance Division and Hirosaki City.

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Authors and Affiliations

  1. Hirosaki University Graduate School of Science and Technology, 3 Bunkyo-cho, Hirosaki, Aomori, 036-8561, Japan

    Shinsaku Tsuyama & Yasutaka Hanada

  2. RIKEN Center for Advanced Photonics, 2-1Hirosawa, Wako, Saitama, 351-0198, Japan

    Akinori Taketani, Takeharu Murakami, Michio Sakashita, Saki Miyajima, Takayo Ogawa & Satoshi Wada

  3. Hirosaki University Graduate School of Agriculture and Life Science, 3 Bunkyo-cho, Hirosaki, Aomori, 036-8561, Japan

    Hayato Maeda

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  1. Shinsaku Tsuyama
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  9. Yasutaka Hanada
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Correspondence to Yasutaka Hanada.

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Tsuyama, S., Taketani, A., Murakami, T. et al. Quantitative prediction of a functional ingredient in apple using Raman spectroscopy and multivariate calibration analysis. Appl. Phys. B 127, 92 (2021). https://doi.org/10.1007/s00340-021-07639-0

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