Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

Abstract

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

“…Therefore, in this present study, we utilized three sequential spectral data pre-processing methods: MAS, MN, and SG 1d (MAS + MN + SG 1d). A similar approach was previously used by Zhang et al [39] and Shawky and Selim [40]. Overall, the shape of the spectral curves was quite similar, especially between 250-400 nm, with sharp differences in peak absorbance intensity at 270 and 300 nm.…”

“…It is equivalent to replacing the raw absorbance values by a profile centered on unity: only the relative absorbance values are used to describe the sample, and the information carried by their absolute levels is dropped. Savitzky-Golay first derivative with a second-order polynomial and a window size of 11 points (SG 1d) was used to cancel the baseline drifts and to enhance small spectral differences [39]. Due to similarity in honeybees (entomological), and geographical and botanical origins, especially for Apis dorsata multifloral from Jambi and Apis dorsata monofloral from Jambi, it was expected that the spectral differences within those honey samples were small.…”

“…However, at the same time, as a consequence of derivation, the noises were also enhanced. To avoid this, the spectra were first smoothed using 11 points of moving averaging smoothing pre-processing (MAS) as recommended by previous work [39]. Therefore, in this present study, we utilized three sequential spectral data pre-processing methods: MAS, MN, and SG 1d (MAS + MN + SG 1d).…”

Classification of Lampung robusta Specialty Coffee According to Differences in Cherry Processing Methods Using UV Spectroscopy and Chemometrics

“…However, at the same time, as a consequence of derivation, the noises were also enhanced. To avoid this, the spectra were first smoothed using SGS pre-treatment as recommended by previous work [33,35]. Instead of selecting the best pre-treatments, in order to optimize the effect of spectral pre-treatment, the combination of several spectral pre-treatment was often used.…”

“…Therefore, in this present study we utilized three sequentially spectral pre-treatments: SGS, SNV and SG 1d (SGS + SNV + SG 1d). Our approach was previously used by Shawky and Selim [33] and Zhang et al [35]. Figure 2 shows spectral data of all samples after pre-treatments using combination of SGS, SNV and SG 1d in the interval of 230-350 nm.…”