Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms.

Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R2 values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.

[Discrimination of different processing degrees and quantitative study of processing end point of vinegar-processing Cyperi Rhizoma pieces based on electronic sensory technology].

In this study, CM-5 spectrophotometer and Heracles NEO ultra-fast gas-phase electronic nose were used to analyze the changes in color and odor of vinegar-processed Cyperi Rhizoma(VPCR) pieces. Various analysis methods such as DFA and partial least squares discriminant analysis(PLS-DA) were combined to identify different processing degrees and quantify the end point of processing. The results showed that with the increase in vinegar processing, the brightness parameter L~* of VPCR pieces decreased gradua-lly, while the red-green value a~* and yellow-blue value b~* initially increased and reached their maximum at 8 min of processing, followed by a gradual decrease. A discriminant model based on the color parameters L~*, a~*, and b~* was established(with a discrimination accuracy of 98.5%), which effectively differentiated different degrees of VPCR pieces. Using the electronic nose, 26 odor components were identified from VPCR samples at different degrees of vinegar processing. DFA and PLS-DA models were established for different degrees of VPCR pieces. The results showed that the 8-min processed samples were significantly distinct from other samples. Based on variable importance in projection(VIP) value greater than 1, 10 odor components, including 3-methylfuran, 2-methylbuty-raldehyde, 2-methylpropionic acid, furfural, and α-pinene, were selected as odor markers for differentiating the degrees of vinegar processing in VPCR. By combining the changes in color and the characteristic odor components, the optimal processing time for VPCR was determined to be 8 min. This study provided a scientific basis for the standardization of vinegar processing techniques for VPCR and the improvement of its quality standards and also offered new methods and ideas for the rapid identification and quality control of the end point of processing for other traditional Chinese medicine.