Determination of Total Saccharide Content in Auricularia auricula Based on Near-Infrared Spectroscopy

• Published in: Journal of food quality 2022, Vol.2022, p.1-11
• Main Authors: Yang, Gen, Li, Shanlei, Ji, Shixian, Wang, Yunjie, Wang, Jinmei, Ji, Liqiang, Li, Changqin
• Format: Article
• Language: English
• Subjects: Analysis; Calibration; Food quality; Glucose; Infrared spectroscopy; Mean square errors; Methods; Spectrum analysis
• ISSN: 0146-9428; 1745-4557
• DOI: 10.1155/2022/8858235

In this paper, the content of total saccharide in Auricularia auricula from different regions was determined. Then, near-infrared (NIR) technology was used to collect the spectral information of the samples. The sample data were divided into calibration set and validation set. The best quantitative model of the total saccharide content of A. auricula was established by selecting the parameters such as spectral range, pretreatment method, and partial least square method (PLS) main factor number of the calibration set data. The validation set data were used to verify the reliability of this model. In this model, the original spectrum was used to preprocess by standard normal variate (SNV) + second derivative (SD) to eliminate the scattering effect caused by uneven particle distribution and the influence of noise on spectral data. The spectrum range was 4000–10000 cm−1, and the final choice of PLS main factor number was 11. Under this condition, the calibration set Rc2 of the model was 0.9092, the root mean square error of calibration (RMSEC) was 1.405, the root mean square error of prediction (RMSEP) was 1.507, and the residual predictive deviation (RPD) was 3.32. The validation samples were used to test the model, and the result showed that Rv2 = 0.9048 of the validation set. The result proved that the predicted value of the validation samples had a good linear relationship with the measured value. According to the T-test of the two sets of data in the validation set, the difference between the predicted value and the chemical value was not significant (P ≥ 0.05). The results were in line with the expected objectives. The established NIR quantitative model can be used to predict the total saccharide content of the black fungus sample to be tested.