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. 2022 Dec 17;11(24):4086.
doi: 10.3390/foods11244086.

Effect of Moisture Content Difference on the Analysis of Quality Attributes of Red Pepper (Capsicum annuum L.) Powder Using a Hyperspectral System

Ji-Young Choi  1 , Jeong-Seok Cho  1 , Kee Jai Park  2 , Jeong Hee Choi  1 , Jeong Ho Lim  1
Affiliations

Effect of Moisture Content Difference on the Analysis of Quality Attributes of Red Pepper (Capsicum annuum L.) Powder Using a Hyperspectral System

Ji-Young Choi et al. Foods. .

Abstract

The variety of characteristics of red pepper makes it difficult to analyze at the production field through hyperspectral imaging. The importance of pretreatment to adjust the moisture content (MC) in the process of predicting the quality attributes of red pepper powder through hyperspectral imaging was investigated. Hyperspectral images of four types of red pepper powder with different pungency levels and MC were acquired in the visible near-infrared (VIS-NIR) and short-wave infrared (SWIR) regions. Principal component analysis revealed that the powders were grouped according to their pungency level, color value, and MC (VIS-NIR, Principal Component 1 = 95%; SWIR, Principal Component 1 = 91%). The loading plot indicated that 580-610, 675-760, 870-975, 1020-1130, and 1430-1520 nm are the key wavelengths affected by the presence of O-H and C-H bonds present in red pigments, capsaicinoids, and water molecules. The R2 of the partial least squares model for predicting capsaicinoid and free sugar in samples with a data MC difference of 0-2% was 0.9 or higher, and a difference of more than 2% in MC had a negative effect on prediction accuracy. The color value prediction accuracy was barely affected by the difference in MC. It was demonstrated that adjusting the MC is essential for capsaicinoid and free sugar analysis of red pepper.

Keywords: hyperspectral imaging; moisture adjustment; multivariate analysis; red pepper powder.

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Conflict of interest statement

The authors declare no potential conflict of interest.

Figures

Figure 1
Figure 1
Weight change of red pepper powder according to drying time and calculated moisture content.
Figure 2
Figure 2
Mean spectra of red pepper powders in the Vis-NIR (A) and SWIR (B) wavelength ranges according to pungency levels and moisture contents.
Figure 2
Figure 2
Mean spectra of red pepper powders in the Vis-NIR (A) and SWIR (B) wavelength ranges according to pungency levels and moisture contents.
Figure 3
Figure 3
PCA score plot of hyperspectral spectra in the VIS−NIR (A) and SWIR band (B).
Figure 4
Figure 4
Loading plot of PC1 and PC2 derived from PCA of hyperspectral spectra in VIS–NIR (A) and SWIR band (B).
Figure 4
Figure 4
Loading plot of PC1 and PC2 derived from PCA of hyperspectral spectra in VIS–NIR (A) and SWIR band (B).
Figure 5
Figure 5
Prediction accuracy of capsaicinoid (A), free sugar (B) and ASTA color (C) of red pepper powders using VIS-NIR wavelength range in accordance with moisture content. RMSEC, root mean square error of calibration; RMSECV, root mean square error of cross-validation.
Figure 5
Figure 5
Prediction accuracy of capsaicinoid (A), free sugar (B) and ASTA color (C) of red pepper powders using VIS-NIR wavelength range in accordance with moisture content. RMSEC, root mean square error of calibration; RMSECV, root mean square error of cross-validation.
Figure 6
Figure 6
Prediction accuracy of capsaicinoid (A), free sugar (B) and ASTA color (C) of red pepper powders using SWIR wavelength range in accordance with moisture content. RMSEC, root mean square error of calibration; RMSECV, root mean square error of cross-validation.
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References

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