Intelligent analysis of excitation-emission matrix fluorescence fingerprint to identify and quantify adulteration in camellia oil based on machine learning.

Camellia oil (CAO) is a premium edible vegetable oil with medical value and biological activity, but it is susceptible to adulteration. Therefore, the demand for intelligent analysis to decipher the category and proportion of adulterated oil in CAO was the main driver of this work. Excitation-emission matrix fluorescence (EEMF) spectra of 933 vegetable oil samples were characterized by a chemometric method to obtain chemically meaningful information. Authenticity identification models were constructed using four machine learning methods to realize the discrimination of oil species adulterated in CAO mixtures. Meanwhile, quantitative models were established aiming at the fraud of CAO proportion in blended oil. Results showed that the specially constructed CNN obtained the optimal performance when evaluating unseen real-world samples, with a classification accuracy of 95.8% and 92.2%, and mean-absolute quantitative errors between 2.6 and 6.7%. Therefore, EEMF fingerprints coupled with machine learning are expected to provide intelligent and accurate analysis for authenticity detection of CAO.

Chemometrics-enhanced high-performance liquid chromatography-diode array detection strategy to quantify protoberberine alkaloids in varying Coptidis Rhizoma-related medicines.

Quantitation of protoberberine alkaloids is an essential guarantee for efficacy control and medication safety of Coptidis Rhizoma (CR) related medicines. Traditional univariate chromatography faced challenges with co-elution, unknown interferences, and retention time shift when analyzing isomeric analytes in varying sample matrices. We presented a chemometrics-enhanced high-performance liquid chromatography-diode array detection (HPLC-DAD) strategy for simultaneous quantification of six protoberberine alkaloids and processed multi-channels chromatographic-spectral data with four second-order calibration algorithms. Chromatographic conditions were firstly optimized. Four groups of predicted samples were modeled individually with the designed calibration set. Mathematical resolutions were then obtained, and pseudo-univariate regression gave the quantitative concentration of each analyte. Four models were scored on fit, linearity, recovery, and robustness, where alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) exhibited an optimal and stable performance. Besides, the resolved spectra presented high consistency with the actual spectra (r≥0.9993). Limits of quantification (LOQ) fully met the pharmacopoeia stipulation and were 0.17, 0.60, 0.19, 0.74, 0.15, and 0.38 µg mL-1 for columbamine, epiberberine, jatrorrhizine, coptisine, palmatine, and berberine, respectively. The importance of this strategy is to exploit collinearity resolution and additional selectivity that permit accurate quantitation at poor chromatographic resolutions, avoiding individual pretreatment and HPLC optimizations for different samples. This study provides a universal alternative for routine quality assessment of protoberberine alkaloids in CR-related medicines.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with chemometrics to identify the origin of Chinese medicinal materials.

Geographical origin and authenticity are two core factors to promote the development of traditional Chinese medicine (TCM) herbs perception in terms of quality and price. Therefore, they are important to both sellers and consumers. Herein, we propose an efficient, accurate method for discrimination of genuine and non-authentic producing areas of TCM by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Take Atractylodes macrocephala Koidz (AMK) of compositae as an example, the MALDI-TOF MS spectra data of 120 AMK samples aided by principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA) and random forest (RF) successfully differentiated Zhejiang province, Anhui province and Hunan province AMK according to their geographical location of origin. The correct classification rates of test set were above 93.3%. Furthermore, 5 recollected AMK samples were used to verify the performance of the classification models. The outcome of this study can be a good resource in building a database for AMK. The combined utility of MALDI-TOF MS and chemometrics is expected to be expanded and applied to the origin traceability of other TCMs.

Geographical origin traceability of traditional Chinese medicine Atractylodes macrocephala Koidz. by using multi-way fluorescence fingerprint and chemometric methods.

Atractylodes macrocephala Koidz. (AM) is an important plant of traditional Chinese medicine (TCM), and its status can be comparable with ginseng in China. The efficacy and quality of AM are closely related to the place of origin. Hence, we proposed a simple and fast strategy to classify AM from different geographical origins by using multi-way fluorescence fingerprint combined with chemometric methods. AM samples with different dilution levels have different fluorescence characteristics, resulting from different content of fluorescence components and chemical microenvironment. Therefore, AM samples were diluted 5-fold, 10-fold, and 20-fold with 40% ethanol aqueous solution to obtain excitation-emission matrix data, and multi-way (three-way and four-way) data arrays were constructed. And then, the fluorescence fingerprints of AM samples were characterized by three-way and four-way parallel factor analysis (PARAFAC). In addition, four pattern recognition methods were used to classify AM from different provinces. The results show that the four-way data array can provide more abundant information than three-way data arrays, so it is more conducive to sample classification. According to the results obtained from the analysis of four-way data array, the correct classification rate (CCR) of the cross-validation and prediction set obtained by partial least squares-discrimination analysis (PLS-DA) were 90.5% and 100%, respectively. To sum up, the proposed method can be regarded as a powerful, feasible, convenient, reliable, and universal classification tool for the classification of AM samples from different provinces and can be used as a promising method to realize the geographical origin traceability of other TCMs.

Fast identification of the geographical origin of Gastrodia elata using excitation-emission matrix fluorescence and chemometric methods.

Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.

Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics.

Camellia oil is a high quality oil mainly produced in southern China. It is common that unscrupulous merchants attempt to make huge profits by adulterating camellia oil with other cheaper or lower-quality vegetable oils. Therefore, this paper proposed excitation-emission matrix fluorescence spectroscopy combined with chemometric methods for the rapid identification and quantification of camellia oil adulteration with other cheaper vegetable oils. A five-component parallel factor analysis (PARAFAC) model roughly completed spectral characterization of oil samples, and obtained chemically meaningful information. Four advanced chemometrics methods were used for the classification of camellia oil and other vegetable oils (model 1) and the classification of camellia oil and adulterated camellia oil (models 2 and 3), respectively. Two-directional two-dimensional linear discriminant analysis ((2D)2LDA) was used for chemical data for the first time and showed huge potential. Furthermore, the developed N-PLS regression model used for the prediction of adulteration level in camellia oil showed satisfactory accuracy.

Interference-free spectrofluorometric quantification of aristolochic acid I and aristololactam I in five Chinese herbal medicines using chemical derivatization enhancement and second-order calibration methods.

A rapid interference-free spectrofluorometric method combined with the excitation-emission matrix fluorescence and the second-order calibration methods based on the alternating penalty trilinear decomposition (APTLD) and the self-weighted alternating trilinear decomposition (SWATLD) algorithms, was proposed for the simultaneous determination of nephrotoxic aristolochic acid I (AA-I) and aristololactam I (AL-I) in five Chinese herbal medicines. The method was based on a chemical derivatization that converts the non-fluorescent AA-I to high-fluorescent AL-I, achieving a high sensitive and simultaneous quantification of the analytes. The variables of the derivatization reaction that conducted by using zinc powder in acetose methanol aqueous solution, were studied and optimized for best quantification results of AA-I and AL-I. The satisfactory results of AA-I and AL-I for the spiked recovery assay were achieved with average recoveries in the range of 100.4-103.8% and RMSEPs <0.78ngmL-1, which validate the accuracy and reliability of the proposed method. The contents of AA-I and AL-I in five herbal medicines obtained from the proposed method were also in good accordance with those of the validated LC-MS/MS method. In light of high sensitive fluorescence detection, the limits of detection (LODs) of AA-I and AL-I for the proposed method compare favorably with that of the LC-MS/MS method, with the LODs <0.35 and 0.29ngmL-1, respectively. The proposed strategy based on the APTLD and SWATLD algorithms by virtue of the "second-order advantage", can be considered as an attractive and green alternative for the quantification of AA-I and AL-I in complex herbal medicine matrices without any prior separations and clear-up processes.

Simultaneous determination of umbelliferone and scopoletin in Tibetan medicine Saussurea laniceps and traditional Chinese medicine Radix angelicae pubescentis using excitation-emission matrix fluorescence coupled with second-order calibration method.

A chemometrics-assisted excitation-emission matrix (EEM) fluorescence method is presented for simultaneous determination of umbelliferone and scopoletin in Tibetan medicine Saussurea laniceps (SL) and traditional Chinese medicine Radix angelicae pubescentis (RAP). Using the strategy of combining EEM fluorescence data with second-order calibration method based on the alternating trilinear decomposition (ATLD) algorithm, the simultaneous quantification of umbelliferone and scopoletin in the two different complex systems was achieved successfully, even in the presence of potential interferents. The pretreatment is simple due to the "second-order advantage" and the use of "mathematical separation" instead of awkward "physical or chemical separation". Satisfactory results have been achieved with the limits of detection (LODs) of umbelliferone and scopoletin being 0.06ngmL(-1) and 0.16ngmL(-1), respectively. The average spike recoveries of umbelliferone and scopoletin are 98.8±4.3% and 102.5±3.3%, respectively. Besides, HPLC-DAD method was used to further validate the presented strategy, and t-test indicates that prediction results of the two methods have no significant differences. Satisfactory experimental results imply that our method is fast, low-cost and sensitive when compared with HPLC-DAD method.

Chemometrics-enhanced high performance liquid chromatography-diode array detection strategy for simultaneous determination of eight co-eluted compounds in ten kinds of Chinese teas using second-order calibration method based on alternating trilinear decomposition algorithm.

In this work, an attractive chemometrics-enhanced high performance liquid chromatography-diode array detection (HPLC-DAD) strategy was proposed for simultaneous and fast determination of eight co-eluted compounds including gallic acid, caffeine and six catechins in ten kinds of Chinese teas by using second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm. This new strategy proved to be a useful tool for handling the co-eluted peaks, uncalibrated interferences and baseline drifts existing in the process of chromatographic separation, which benefited from the "second-order advantages", making the determination of gallic acid, caffeine and six catechins in tea infusions within 8 min under a simple mobile phase condition. The average recoveries of the analytes on two selected tea samples ranged from 91.7 to 103.1% with standard deviations (SD) ranged from 1.9 to 11.9%. Figures of merit including sensitivity (SEN), selectivity (SEL), root-mean-square error of prediction (RMSEP) and limit of detection (LOD) have been calculated to validate the accuracy of the proposed method. To further confirm the reliability of the method, a multiple reaction monitoring (MRM) method based on LC-MS/MS was employed for comparison and the obtained results of both methods were consistent with each other. Furthermore, as a universal strategy, this new proposed analytical method was applied for the determination of gallic acid, caffeine and catechins in several other kinds of Chinese teas, including different levels and varieties. Finally, based on the quantitative results, principal component analysis (PCA) was used to conduct a cluster analysis for these Chinese teas. The green tea, Oolong tea and Pu-erh raw tea samples were classified successfully. All results demonstrated that the proposed method is accurate, sensitive, fast, universal and ideal for the rapid, routine analysis and discrimination of gallic acid, caffeine and catechins in Chinese tea samples.

Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data.

Peak detection and background drift correction (BDC) are the key stages in using chemometric methods to analyze chromatographic fingerprints of complex samples. This study developed a novel chemometric strategy for simultaneous automatic chromatographic peak detection and BDC. A robust statistical method was used for intelligent estimation of instrumental noise level coupled with first-order derivative of chromatographic signal to automatically extract chromatographic peaks in the data. A local curve-fitting strategy was then employed for BDC. Simulated and real liquid chromatographic data were designed with various kinds of background drift and degree of overlapped chromatographic peaks to verify the performance of the proposed strategy. The underlying chromatographic peaks can be automatically detected and reasonably integrated by this strategy. Meanwhile, chromatograms with BDC can be precisely obtained. The proposed method was used to analyze a complex gas chromatography dataset that monitored quality changes in plant extracts during storage procedure.

Simultaneous determination of eight flavonoids in propolis using chemometrics-assisted high performance liquid chromatography-diode array detection.

A fast analytical strategy of second-order calibration method based on the alternating trilinear decomposition algorithm (ATLD)-assisted high performance liquid chromatography coupled with a diode array detector (HPLC-DAD) was established for the simultaneous determination of eight flavonoids (rutin, quercetin, luteolin, kaempferol, isorhamnetin, apigenin, galangin and chrysin) in propolis capsules samples. The chromatographic separation was implemented on a Wondasil™ C18 column (250mm×4.6mm, 5µm) within 13min with a binary mobile phase composed of water with 1% formic acid and methanol at a flow rate of 1.0mLmin(-1) after flavonoids were only extracted with methanol by ultrasound extraction for 15min. The baseline problem was overcome by considering background drift as additional compositions or factors as well as the target analytes, and ATLD was employed to handle the overlapping peaks from analytes of interest or from analytes and co-eluting matrix compounds. The linearity was good with the correlation coefficients no less than 0.9947; the limit of detections (LODs) within the range of 3.39-33.05ngmL(-1) were low enough; the accuracy was confirmed by the recoveries ranged from 91.9% to 110.2% and the root-mean-square-error of predictions (RMSEPs) less than 1.1µg/mL. The results indicated that the chromatographic method with the aid of ATLD is efficient, sensitive and cost-effective and can realize the resolution and accurate quantification of flavonoids even in the presence of interferences, thus providing an alternative method for accurate quantification of analytes especially when the complete separation is not easily accomplished. The method was successfully applied to propolis capsules samples and the satisfactory results were obtained.

Simultaneous determination of phenolic antioxidants in edible vegetable oils by HPLC-FLD assisted with second-order calibration based on ATLD algorithm.

A novel strategy that combines the chemometrics method with high performance liquid chromatography with fluorescence detector (HPLC-FLD) was developed for the simultaneous determination of seven phenolic antioxidants in six kinds of oil samples. After a simple dilution step, oil samples can be directly injected into the detecting system and the data were measured in a short time with a chromatographic system operating in the gradient elution mode. Since the chromatographic and spectral peaks among interesting analytes and interferences were heavily overlapped, second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm which fully exploiting the second-order advantage was adopted. Successful resolution was obtained in the presence of different matrix interferences in different oil samples, and the developed approach allows the quantification of the antioxidants at levels found in edible vegetable oils, without the necessity of applying either preconcentration or extraction steps, moreover, a column washing is also not required. Meanwhile, the effectiveness and reproducibility of the proposed method were also validated by some statistical parameters like root mean squared error of prediction (RMSEP), limits of detection (LOD) and relative standard deviation (RSD). Then the proposed method was compared with several commonly selected methods in sample preparation, elution time and LOD.

Fast analysis of synthetic antioxidants in edible vegetable oil using trilinear component modeling of liquid chromatography-diode array detection data.

A new chromatographic methodology is presented for fast quantitative analysis of ten synthetic phenolic antioxidants in five kinds of oil samples: propyl gallate (PG), 2,4,5-trihydroxybutyrophenone (THBP), tert-butylhydroquinone (TBHQ), nordihydroguaiaretic acid (NDGA), ethoxyquin (EQ), 3-tert-butyl-4- hydroxyanisole (BHA), octyl gallate (OG), 2,6-di-tert-butyl-4-hydroxymethyphenol (Ionox-100), dodecyl gallate (DG), 3,5-di-tert-butyl-4-hydroxytoluene (BHT). The second-order calibration, with second-order advantage, based on the alternating penalty trilinear decomposition (APTLD) algorithm has shown to be an excellent tool for modeling the three-way data, where overlapping peaks, uncalibrated inteferences, and baseline drift existed, making the fast determination and resolution of the phenolic antioxidants in oils possible. Such extraction procedure in which the antioxidants of interest would be seperated is unnecessary and the ten antioxidants can be eluted within 6 mins. For the validation of the method, linearity, root-mean-square error of prediction (RMSEP) and limit of detection (LOD) have been performed. The average recovery of antioxidants ranges from 94.9 to 106.1% and the ten analytes can be adequately determined with limits of detection of 0.18-5.72 µgm l(-1).

Chemometric resolution of coeluting peaks of eleven antihypertensives from multiple classes in high performance liquid chromatography: a comprehensive research in human serum, health product and Chinese patent medicine samples.

A novel chemometric-assisted high performance liquid chromatography method coupled with diode array detector (HPLC-DAD) was presented for the simultaneous determination of eleven antihypertensives from multiple classes in most concerned matrix systems. With the aid of second-order calibration which enables specific information of analytes to be well extracted, the heavily overlapping profiles between analytes and the coeluting interferences can be successfully separated and thus accurately quantified. A great advantage of the novel strategy lies in the fact that the analysis could be carried out with the same isocratic mobile phase (methanol/KH(2)PO(4): 58:42, v/v, pH 2.60) in a short time regardless of the changes of matrices, such as human serum, health product and Chinese patent medicine. Both qualitative and quantitative results indicate that the hybrid strategy that using HPLC-DAD coupled with second-order chemometric method would be a high performance approach for the purpose of simultaneously quantifying multiple classes of antihypertensives in complex systems. Additionally, the analytical strategy can potentially benefit drug monitoring in both therapeutic research and pharmaceutical quality control. Moreover, the accuracy and reliability of the proposed methodology has been evaluated using several statistical parameters such as root mean squared error of prediction (RMSEP), figures of merit (FOM) and reproducibility of inter-day analysis.

Second-order calibration applied to quantification of two active components of Schisandra chinensis in complex matrix.

The effectiveness of traditional Chinese medicine (TCM) against various diseases urges more low cost, speed and sensitive analytical methods for investigating the phamacology of TCM and providing a theoretical basis for clinical use. The potential of second-order calibration method was validated for the quantification of two effective ingredients of Schisandra chinensis in human plasma using spectrofluorimetry. The results obtained in the present study demonstrate the advantages of this strategy for multi-target determination in complex matrices. Although the spectra of the analytes are similar and a large number of interferences also exist, second-order calibration method could predict the accurate concentrations together with reasonable resolution of spectral profiles for analytes of interest owing to its 'second-order advantage'. Moreover, the method presented in this work allows one to simply experimental procedure as well as reduces the use of harmful chemical solvents.

[Bioremediation of river water quality by consecutively adjustable submerged vegetation net].

A series of consecutively adjustable submerged vegetation nets were constructed in a polluted shallow river with a length of about 200 m and nearby the water resource protection area of Taihu Lake in East China, forming an aquatic vegetation consisted of submerged plant species Cabomba caroliniana, Vallisneria natans, Elodea nuttallii, Hydrilla verticillata, and Potamogeton crispus. The water quality indices including total nitrogen (TN), ammonium nitrogen (NH4(+)-N), nitrite nitrogen (NO2(-)-N), nitrate nitrogen (NO3(-)-N), total phosphorus (TP), and phosphate (PO4(3-)-P) were monitored, and the bioremediation effect of the vegetation nets was evaluated. After setting up the vegetation nets, the Secchi depth (SD) of the river changed from 0.5 m to 1.7-1.8 m, and the TN and TP concentrations 15 and 20 days after the nets constructed decreased by 35.6% and 66.3%, and 29.4% and 63.2%, respectively. After five months, the concentrations of NH4(+)-N, NO2(-)-N, NO3(-)-N, TN, TP, and PO4(3-)-P decreased by 92.4%, 76.8%, 72.7%, 73.9%, 90.5%, and 92.0%, respectively. This study showed that consecutively adjustable submerged vegetation net could be a potential approach for treating polluted river waters, particularly for the bioremediation of polluted small landscape shallow water bodies.

Simultaneous determination of psoralen and isopsoralen in plasma and Chinese medicine Xian Ling Gu Bao capsule by using HPLC-DAD coupled with alternating trilinear decomposition algorithm.

A method using high performance liquid chromatography with photodiode-array detection (HPLC-DAD) coupled with alternating trilinear decomposition (ATLD) algorithm was proposed for simultaneous determination of psoralen and isopsoralen in plasma and Chinese medicine "Xian Ling Gu Bao" capsule (XLGBC). In this paper, the application of ATLD algorithm into traditional chromatographic method can handle this problem that the chromatographic and spectral peaks are heavily overlapped among the analytes and even between the analytes and interferences from the background matrices. A simple improvement of chromatographic condition like mobile phase is not enough to realize effective separation for the two isomeric compounds, especially in the presence of interferences. However, the ATLD algorithm utilized "mathematical separation" instead of partial "physical or chemical separation" to directly determine the spectral profiles of the analytes of interests in complex system. The satisfactory quantification results have been gained with simple mobile phase. In the analysis of real Chinese medicine samples, the accuracy of the concentrations which were obtained by ATLD was also validated by HPLC-MS method.

Construction of an efficacious model for a nondestructive identification of traditional Chinese medicines Liuwei Dihuang pills from different manufacturers using near-infrared spectroscopy and moving window partial least-squares discriminant analysis.

This paper reports on the construction of an efficacious model for a non-invasive identification of traditional Chinese medicines, Liuwei Dihuang pills from different manufacturers, on the basis of near-infrared spectra (NIRS) coupled with moving window partial least-squares discriminant analysis (MWPLSDA). Considering the continuity of near-infrared spectral measurements, MWPLSDA is used to identify continuous and highly classification-related information intervals, a simple, yet effective classification model that can be developed for identifying accurate 150 Liuwei Dihuang pills from five different manufacturers. Meanwhile, the method is compared with some traditional pattern-recognition methods including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLSDA). The obtained results show that the method not only can reduce the operation time, but also significantly improves the classification accuracy. Hence, the nondestructive method can be expected to be promising for more practical applications on quality control and the discrimination of traditional Chinese medicine.

Trilinear decomposition method applied to removal of three-dimensional background drift in comprehensive two-dimensional separation data.

A novel technique for removal of three-dimensional background drift in comprehensive two-dimensional (2D) liquid chromatography coupled with diode array detection (LCxLC-DAD) data is proposed. The basic idea is to perform trilinear decomposition on the instrumental response data, which is based on the alternating trilinear decomposition (ATLD) algorithm. In model construction, the background drift is modeled as one component or factor as well as the analytes of interest, hence, the drift is explicitly included into the calibration. The method involves performing trilinear decomposition on the raw data, then extracting the background component and subtracting this background data from the raw data, leaving the analytes' signal on a flat baseline. Simultaneous evaluation of three-dimensional background drift and true signals may improve the quality of the data. This method is applied to the determination and removal of three-dimensional background drifts in simulated multidimensional data as well as experimental comprehensive two-dimensional liquid chromatographic data. It is shown that this technique yield a good removal of background drift, without the need to perform a blank chromatographic run, and required no prior knowledge about the sample composition.