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Mapping the Pungency of Green Pepper Using Hyperspectral Imaging

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Abstract

The pungency level of green peppers is dependent on the amounts of capsaicin and dihydrocapsaicin they contain. This study was conducted to develop a non-destructive method for the prediction and mapping of the capsaicin and dihydrocapsaicin contents in green pepper. Hyperspectral images of 200 total green peppers of three varieties were acquired in the wavelength range of 1000–1600 nm, from which the mean spectra of each pepper variety were extracted. The reference capsaicin and dihydrocapsaicin contents of the samples were measured by high-performance liquid chromatography. Quantitative calibration models were built using partial least squares (PLS) regression with different spectral preprocessing techniques; the best performance was found by normalizing the preprocessed spectra with correlation coefficients (rpred) of 0.86 and 0.59, which showed the standard errors of prediction (SEPs) of 0.09 and 0.03 mg/g for capsaicin and dihydrocapsaicin, respectively. Seventeen and 16 optimal wavebands were selected using the successive projections algorithm; rpred of 0.88 and 0.68 and SEPs of 0.084 and 0.027 mg/g were obtained for capsaicin and dihydrocapsaicin, respectively, from the newly developed PLS calibration models using these optimal wavebands. The successive projections algorithm (SPA)-PLS model was used to map the capsaicin and dihydrocapsaicin contents of the green peppers. These maps provided detailed information on the pungency levels of the tested green peppers. The results of this study indicated that hyperspectral imaging is useful for the rapid and non-destructive evaluation of the pungency of green peppers.

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Funding

This work was funded by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries(IPET) through Agriculture, Food and Rural Affairs Research Center Support Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA), Republic of Korea (No. 717001071WT111).

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Authors and Affiliations

  1. Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, 34134, Republic of Korea

    Anisur Rahman & Byoung-Kwan Cho

  2. Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh

    Anisur Rahman

  3. Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD, 20705, USA

    Hoonsoo Lee & Moon S. Kim

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  1. Anisur Rahman
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  2. Hoonsoo Lee
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Correspondence to Byoung-Kwan Cho.

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Anisur Rahman declares that he has no conflict of interest. Hoonsoo Lee declares that he has no conflict of interest. Moon S. Kim declares that he has no conflict of interest. Byoung-Kwan Cho declares that he has no conflict of interest.

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Rahman, A., Lee, H., Kim, M.S. et al. Mapping the Pungency of Green Pepper Using Hyperspectral Imaging. Food Anal. Methods 11, 3042–3052 (2018). https://doi.org/10.1007/s12161-018-1275-1

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