Rapid and comprehensive quality evaluation of Huang-qin from different origins by FT-IR and NIR spectroscopy combined with chemometrics.

INTRODUCTION: Quality evaluation of Huang-qin is significant to ensure its clinical efficacy. OBJECTIVE: This study aims to establish an accurate, rapid and comprehensive Huang-qin quality evaluation method to overcome the time-consuming and laborious shortcomings of traditional herbal medicine quality assessment methods. METHODS: The contents of baicalin, baicalein and scutellarin in Huang-qin from five different origins were analyzed by FT-IR and NIR spectra combined with multivariate data technology. The quality of Huang-qin from different origins was evaluated by TOPSIS and consistency analysis based on the content of three active ingredients. The correlation between ecological factors and the accumulation of active ingredients was explored. RESULTS: Satisfactory prediction results of PLS models were obtained. Relatively, the model based on FT-IR combined with the PLS regression method has higher R2 and smaller RMSE than the NIR combined with the PLS method. TOPSIS and consistency analysis results showed that the quality of Huang-qin from different geographical origins was significantly different. The results showed that the quality of Huang-qin produced in Shanxi Province was the best among the five origins studied. The results also found that the quality of Huang-qin in different growing areas of the same origin was not completely consistent. The correlation study showed that altitude, sunshine duration and rainfall were the main factors that caused the quality difference of medicinal materials in different geographical origins. CONCLUSION: This study provides a reference for the rapid quantitative analysis of the active components of herbal medicine and the quality evaluation of them.

Quality evaluation and traceability of Bletilla striata by fluorescence fingerprint coupled with multiway chemometrics analysis.

BACKGROUND: Traditional methods of evaluating herbs were mainly based on chromatographic techniques. They usually included tedious sample preparation procedures, taking tens of minutes to hours, and consume solvents as well as standards for external calibration. In this paper, the feasibility of employing a fluorescence fingerprint coupled with multi-way chemometrics analysis for quality evaluation and traceability of Bletilla striata were investigated. RESULTS: Relative concentrations of four markers presented in B. striata were determined by using a four-component self-weighted alternating trilinear decomposition (SWATLD) model. These markers could be applied to accurate classification and quality control of B. striata samples from different regions. Furthermore, multiway principal component analysis, multilinear partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA, and SWATLD-PLS-DA models were applied to classify the B. striata samples according to their geographic origins. Consistent results were obtained showing that B. striata samples could be successfully grouped based on their geographical origins and quality. CONCLUSION: Our results revealed that the method developed can be used for quality evaluation and traceability of B. striata. Compared with the chromatographic methods, the method employed in this study was more convenient, simpler, and more sensitive. © 2018 Society of Chemical Industry.

[Discrimination of Honey Varieties Based on Amino Acid Derivative Fluorescence Method Combining with Multilinear Pattern Recognition].

Assessing the authenticity about the botanical and geographical origins of food is an important content for food safety research. Amino acids are the most important nutrients of honey. The types and contents of amino acids are different in various honey samples. Thus they can be used as one of the important parameters to discriminate the honey variety and quality. In this article, amino acids in honey were first derived with formaldehyde and acetyl acetone solution. In the following step, three-dimensional fluorescence spectrums combing with multidimensional pattern recognition methods were used to distinguish the kinds of honey. Five kinds of honey (total 150 honey samples) from different botanicals were studied in this research. Before fluorescence detection, the effect of the amount of derivation reagent, the time of reaction, temperature and pH to the derivation progress of honey samples were first studied. Research showed that the fluorescence intensity of derivatives of honey was the strongest when the amount of derivation reagent was 4.0 mL, the time of reaction being 2 h, pH being 4 and the temperature being 100 ℃. The derivatives of honey were then scanned with three-dimensional fluorescence spectrometry. The collection of fluorescence intensity values occurred within excitation-emission ranges of 300~500 and 380~580 nm. A 150×41×101 cube matrix data sets can be acquired. The three-dimensional fluorescence data sets were decomposed with multilinear pattern recognition methods, such as multilinear principal components analysis (M-PCA), self-weight alternative trilinear decomposition (SWATLD) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods. All of these multilinear pattern recognition methods showed the clustering tendency for five different kinds of honey. Compared with the other two methods, N-PLS-DA got more accurate and reliable classification results because it made full use of all the fluorescence information of the derivative honey samples. Its total recognition rate reached 88%. The result is acceptable for the complexity of the honey samples. It showed this method could be applied to identify the varieties of honey. Compared with the chromatographic analysis method, this method is relatively simpler and more sensitivity. It avoided the chromatographic separation and reduced the consumption of organic solvent. Thus it can be regarded as a kind of relatively green honey classification method. This research will provide a new idea to directly fluorescence analyze for no or weak fluorescence natural substances.

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 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 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 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.

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition.

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570nm with excitation in the range of 320-480nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

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.

Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms.

The feasibility of rapid detection of three quality parameters and classification of wines based on visible and near infrared spectroscopy (Vis-NIRs) was investigated. A modified ant colony optimization (ACO) algorithm for wavelength selection in Vis-NIR spectral analysis was proposed to improve the prediction performance of partial least squares regression (PLSR) model. The result proved that feature wavelengths/variables can be selected by the proposed method for building a high performance PLSR model. The root mean square error of total acid, total sugar and alcohol obtained by ACO-PLS were 0.00122 mol/l, 0.0893 g/l and 0.206 (v/v), respectively. Their correlation coefficients obtained by ACO-PLS reach to 0.973, 0.994 and 0.928, respectively. Compared with full-spectral PLS and PLS based on competitive adaptive reweighted sampling (CARS-PLS) method, the application of ACO wavelength selection provided a notably improved regression model. The prediction results were significantly better than the full-spectral PLS model and slightly better than the CARS-PLS method. Meanwhile, a classification study was also constructed based on the ACO-Principal component analysis (ACO-PCA) model showed that Vis-NIR spectra could be used to classify wines according to the geographical origins. Therefore, it can be concluded that the Vis-NIR spectroscopy technique based on ACO wavelength selection has high potential to differentiate the wine origins and predict the quality parameters in a nondestructive way.

Detection of adulteration in acetonitrile using near infrared spectroscopy coupled with pattern recognition techniques.

In this paper, near infrared spectroscopy (NIR) in cooperation with the pattern recognition techniques were used to determine the type of neat acetonitrile and the adulteration in acetonitrile. NIR spectra were collected between 400 nm and 2498 nm. The experimental data were first subjected to analysis of principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Then support vector machine (SVM) were applied to develop classification models and the best parameter combination was selected by grid search. Under the best parameter combination, the classification accuracy rates of three types of neat acetonitrile reached 87.5%, and 100% for the adulteration with different concentration levels. The results showed that NIR spectroscopy combined with SVM could be utilized for determining the potential adulterants including water, ethanol, isopropyl alcohol, acrylonitrile, methanol, and by-products associated with the production of acetonitrile.

Simultaneous and Direct Determination of Vancomycin and Cephalexin in Human Plasma by Using HPLC-DAD Coupled with Second-Order Calibration Algorithms.

A simple, rapid, and sensitive method for the simultaneous determination of vancomycin and cephalexin in human plasma was developed by using HPLC-DAD with second-order calibration algorithms. Instead of a completely chromatographic separation, mathematical separation was performed by using two trilinear decomposition algorithms, that is, PARAFAC-alternative least squares (PARAFAC-ALSs) and self-weight-alternative-trilinear-decomposition- (SWATLD-) coupled high-performance liquid chromatography with DAD detection. The average recoveries attained from PARAFAC-ALS and SWATLD with the factor number of 4 (N = 4) were 101 ± 5% and 102 ± 4% for vancomycin, and 96 ± 3% and 97 ± 3% for cephalexininde in real human samples, respectively. The statistical comparison between PARAFAC-ALS and SWATLD is demonstrated to be similar. The results indicated that the combination of HPLC-DAD detection with second-order calibration algorithms is a powerful tool to quantify the analytes of interest from overlapped chromatographic profiles for complex analysis of drugs in plasma.