| dc.description.abstract | This study was to develop a calibration model for a portable near-infrared (NIR)
spectroscopy device to determine the total carbohydrate content in processed foods,
especially for meat-free products. NIR spectra in the 740 - 1070 nm region of 251
food products were analyzed. The results provided new insights into the shortwavelength
NIR spectroscopy. The model between the total carbohydrate content
measured by traditional methods and the spectral data collected from the NIR device
was built with the partial least square (PLS) algorithm combined with the crossvalidation
technique by the SCiO lab software (Consumer Physics, USA). The effects
of number of latent variables (LVs) on the model fitting was also investigated. The
results showed that with LVs = 30 a high correlation coefficient R2 of 0.956 and a low
root mean squared error (RMSE) of 3.88 were obtained, indicating the good fitting of
the model. The correlation coefficient R2 between the predicted and measured values
of 62 testing samples used for model validation was 0.860 with the standard error of
prediction (SEP) of 4.15, re-verifying the high accuracy of the model. The
performance values achieved in this study indicated the possibility of the NIR method
to become a popular one for the fast determination of total carbohydrate content in
foods is fully promising.
Key words: Near-infrared spectroscopy, NIR, SCiO, partial least square, total
carbohydrate content, calibration | en_US |
