Rapid identification of Radix Astragali origin by using fluorescence probe combined with chemometrics.

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

Rapid detection of adulteration of olive oil with soybean oil combined with chemometrics by Fourier transform infrared, visible-near-infrared and excitation-emission matrix fluorescence spectroscopy: A comparative study.

Several spectroscopic techniques have been used to detect olive oil adulteration. To evaluate the performance of these spectral techniques on this issue, this work performed a comparative study on identifying adulterated olive oil with different concentrations of soybean oil based on Fourier-transform infrared (FTIR), visible-near-infrared (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics. Principal component analysis (PCA)/ multi-way-PCA analysis showed the feasibility of the three spectral methods for the identification of olive oil adulteration. The accuracy of FTIR and Vis-NIR based on partial least squares discriminant analysis (PLS-DA) for adulterated olive oil was 100%, while the accuracy of EEMs based on unfold-PLS-DA was only 73%. The accuracy of EEMs combined with back-propagation artificial neural network based on self-weighted alternating trilinear decomposition is 100%. In comparison, FTIR and Vis-NIR are superior for the detection of olive oil adulteration due to the convenience of instrument operation and modeling.

Rapid detection and quality evaluation of Shuang-Huang-Lian injection by ATR-IR and NIR spectroscopy combined with chemometrics.

Quality evaluation and consistency evaluation of drugs are the keys to ensure the therapeutic effect and safety of drugs. In this study, attenuated total refraction infrared (ATR-IR) spectroscopy and near-infrared (NIR) spectroscopy combined with chemometrics were used for rapid detection and quality evaluation of active components of Shuang-Huang-Lian injection (SHLI), a traditional Chinese medicine preparation commonly used in China. Taking the chromatographic detection results as a reference, the partial least squares (PLS) model based on ATR-IR and NIR data was constructed by removing the bands with serious noise interference and low signal frequency band. The results showed that the PLS model achieved satisfactory results for the prediction of the three active components (chlorogenic acid, baicalin and phillyrin) in SHLI, indicating that the two spectral techniques combined with the PLS regression method could be successfully used for rapid quantitative analysis of the three active ingredients in SHLI. Relatively, the PLS model based on the ATR-IR spectrum has higher R2 and smaller RMSE than it based on the NIR spectrum. Furthermore, based on the rapid quantitative analysis of the three active ingredients in SHLI, the quality of 140 SHLI samples from seven manufacturers was evaluated by TOPSIS (technique for order preference by similarity to the ideal solution) analysis, and the consistency of different batches of SHLI products from the same manufacturer was evaluated. The results showed that there were differences in the quality of SHLI produced by different manufacturers, and the quality of different batches of SHLI produced by the same manufacturer was not completely consistent. In conclusion, ATR-IR and NIR spectroscopy combined with chemometrics can be used for accurate and rapid quantitative analysis and quality evaluation of SHLI. This study provides a good idea for the rapid quantitative analysis and quality evaluation of drugs or food based on spectroscopic technology and chemometrics.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics.

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics.

Fraud in the global food and related products supply chain is becoming increasingly common due to the huge profits associated with this type of criminal activity and yet strategies to detect fraudulent adulteration are still far from robust. Herbal medicines such as Radix Astragali suffer adulteration by the addition of less expensive materials with the objective to increase yield and consequently the profit margin. In this paper, diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) was used to detect the presence of Jin Quegen in Radix Astragali. 900 fake samples of Radix Astragali produced by 6 different regions were constructed at the levels of 2%, 5%, 10%, 30% and 50% (w/w). DRIFTS data were analyzed using unsupervised classification method such as principal component analysis (PCA), and supervised classification method such as linear discrimination analysis (LDA), K-nearest neighbor (K-NN), linear discrimination analysis combining K-nearest neighbor (LDA-KNN) and partial least squares discriminant analysis (PLS-DA). The results of PCA showed that it was feasible to detect the adulteration of Radix Astragali by the combination of drift technique and chemometrics. PLS-DA obtained the best classification results in all four supervised methods with mean-centralization as the data preprocessing method, the prediction accuracy of PLS-DA model for the six groups of sample ranged from 95.00% to 98.33%. At the same time, LDA-KNN also achieved good classification results, and its correct prediction rate were also between 86.67% and 100.0%. The prediction results confirmed that the combination of DRIFTS technology and chemometrics can distinguish the amount of adulteration present in Radix Astragali. Additionally, the innovative strategy designed can be used to test the fraud of various forms of herbal medicine in other products.

Comparison and application of fluorescence EEMs and DRIFTS combined with chemometrics for tracing the geographical origin of Radix Astragali.

Selection of the appropriate method for traceability may be of great interest for the characterization of food authenticity and to reveal falsifications. The possibility of tracing the geographical origins of Radix Astragali based on diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) technique and fluorescence fingerprints (EEMs) technique was investigated in this work. DRIFTS technique combined with PCA and PLS-DA and EEMs technique combined with M-PCA and N-PLS-DA were used to determine the geographical origin of Radix Astragali samples, respectively. DRIFTS-PLS-DA provided total recognition rates of 98.4% for all Radix Astragali samples in the training sets and 94.6% in the predicted sets. Compared with the DRIFTS, EEMs combined with chemometrics obtained more accurate recognition results. The total recognition rates (RRs) of the training sets and prediction sets obtained with EEMs-N-PLS-DA were all 100%. The good classification results of fluorescence fingerprints technique should be attributed mainly to two reasons. One reason is that three-dimensional fluorescence spectrum can provide more information than two-dimensional DRIFTS, and the other reason is that fluorescence spectrum has higher sensitivity and selectivity than the DRIFTS. Therefore, fluorescence fingerprint (EEMs) technique combined with chemometrics results more adequate for tracing the food geographical origin. It should be noted that the more the analysis target contains fluorescent substances, the more accurate results are obtained by using the fluorescent fingerprint method. Conversely, if the classification object contains very few fluorescent substances, the classification result may not be as good as the DRIFTS method. Furthermore, due to relatively cumbersome operation of fluorescence method, EEMs fluorescence method is unsuitable for rapid analysis as compared to infrared method.