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Food Science and Biotechnology
→ Food Science and Biotechnology 2021 ; 30(6): 783-791
Prediction of moisture content in steamed and dried purple sweet potato using hyperspectral imaging analysis
Suhyeon Heo1 • Ji-Young Choi1 • Jiyoon Kim1 • Kwang-Deog Moon1,2
1 School of Food Science and Biotechnology, Kyungpook National University, 80 Daehak-ro, Daegu 41566, South Korea 2 Food and Bio-Industry Research Institute, Kyungpook National University, 80 Daehak-ro, Daegu 41566, South Korea
ABSTRACT
Partial least squares regression (PLSR) modeling was performed to predict the moisture content in steamed, dried purple sweet potato based on spectral data obtained from hyperspectral imaging analysis. The PLSR model with a combination of multiplicative scatter correction, Savitzky–Golay, and first derivative exhibited the highest accuracy (RP2 = 0.9754). The wavelengths found that strongly affected the PLSR model were 961.12, 1065.50, 1083.93, 1173.23, and 1233.89 nm. These wavelengths were associated with the O–H second overtone and the second overtone of C–H, C–H2, and C–H3. When PLSR modeling was performed using these selected wavelengths, the prediction accuracy of the PLSR model exhibited high accuracy (RP2 = 0.9521). Therefore, the moisture content could be predicted with high accuracy using only five wavelengths rather than the full spectrum.
KEYWORD
Hyperspectral imaging analysis · Partial least squares regression modeling · Selected wavelengths · Moisture content · Purple sweet potato
Food Science and Biotechnology 2021 ; 30(6): 783-791
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