We analyzed the major quality characteristics of red pepper powders from various
regions and predicted these characteristics nondestructively using shortwave
infrared hyperspectral imaging (HSI) technology. We conducted partial least
squares regression analysis on 70% (n=71) of the acquired
hyperspectral data of the red pepper powders to examine the major quality
characteristics. Rc2 values of >0.8 were obtained
for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The
developed quality prediction model was validated using the remaining 30%
(n=35) of the hyperspectral data; the highest accuracy was achieved for
the ASTA color value (Rp2=0.8488), and similar
validity levels were achieved for the capsaicinoid and moisture contents. To
increase the accuracy of the quality prediction model, we conducted spectrum
preprocessing using SNV, MSC, SG-1, and SG-2, and the model’s accuracy
was verified. The results indicated that the accuracy of the model was most
significantly improved by the MSC method, and the prediction accuracy for the
ASTA color value was the highest for all the spectrum preprocessing methods. Our
findings suggest that the quality characteristics of red pepper powders, even
powders that do not conform to specific variables such as particle size and
moisture content, can be predicted via HSI.