Quality Evaluation of Atractylodis Macrocephalae Rhizoma Based on Combinative Method of HPLC Fingerprint, Quantitative Analysis of Multi-Components and Chemical Pattern Recognition Analysis

A method for the quality evaluation of Atractylodis Macrocephalae Rhizoma (AMR) based on high-performance liquid chromatography (HPLC) fingerprint, HPLC quantification, and chemical pattern recognition analysis was developed and validated. The fingerprint similarity of the 27 batches of AMR samples was 0.887–0.999, which indicates there was very limited variance between the batches. The 27 batches of samples were divided into two categories according to cluster analysis (CA) and principal component analysis (PCA). A total of six differential components of AMR were identified in the partial least-squares discriminant analysis (PLS-DA), among which atractylenolide I, II, III, and atractylone counted 0.003–0.045%, 0.006–0.023%, 0.001–0.058%, and 0.307–1.175%, respectively. The results indicate that the quality evaluation method could be used for quality control and authentication of AMR.

Application of UHPLC Fingerprints Combined with Chemical Pattern Recognition Analysis in the Differentiation of Six Rhodiola Species

Rhodiola, especially Rhodiola crenulate and Rhodiola rosea, is an increasingly widely used traditional medicine or dietary supplement in Asian and western countries. Because of the phytochemical diversity and difference of therapeutic efficacy among Rhodiola species, it is crucial to accurately identify them. In this study, a simple and efficient method of the classification of Rhodiola crenulate, Rhodiola rosea, and their confusable species (Rhodiola serrata, Rhodiola yunnanensis, Rhodiola kirilowii and Rhodiola fastigiate) was established by UHPLC fingerprints combined with chemical pattern recognition analysis. The results showed that similarity analysis and principal component analysis (PCA) could not achieve accurate classification among the six Rhodiola species. Linear discriminant analysis (LDA) combined with stepwise feature selection exhibited effective discrimination. Seven characteristic peaks that are responsible for accurate classification were selected, and their distinguishing ability was successfully verified by partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), respectively. Finally, the components of these seven characteristic peaks were identified as 1-(2-Hydroxy-2-methylbutanoate) β-D-glucopyranose, 4-O-glucosyl-p-coumaric acid, salidroside, epigallocatechin, 1,2,3,4,6-pentagalloyglucose, epigallocatechin gallate, and (+)-isolarisiresinol-4′-O-β-D-glucopyranoside or (+)-isolarisiresinol-4-O-β-D-glucopyranoside, respectively. The results obtained in our study provided useful information for authenticity identification and classification of Rhodiola species.

Application of hyperspectral imaging technology for rapid identification of Ruditapes philippinarum contaminated by heavy metals

Combined with pattern recognition analysis hyperspectral imaging technology can be used to identify heavy metal contamination in Ruditapes philippinarum rapidly and non-destructively, even with only a small number of training samples.

Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis

The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (n = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08–3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (n = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products.

Data Cube Clustering with Improved DBSCAN based on Fuzzy Logic and Genetic Algorithm

Multi-dimensional data, such as data cube, are constructed based on aggregating data in data warehouses and it requires to analyze with high flexibility. Also, clustering, which is an unsupervised pattern recognition analysis, has significant challenges to perform on data cube. In this paper, two new drafts of density-based clustering methods are designed to recognize unsupervised patterns of the data cube. In the first draft, DBSCAN clustering is hybridized by genetic algorithm and called the Improved DBSCAN (IDBSCAN). The motivation of designing the IDBSCAN optimizes the DBSCAN’s parameters by a meta-heuristic algorithm such as GA. The second draft, which is called the Soft Improved DBSCAN (SIDBSCAN), is designed based on fuzzy tuning parameters of the GA in the IDBSCAN. The fuzzy tuning parameters are performed with two fuzzy groups rules of Mamdani (SIDBSCAN-Mamdani) and Sugeno (SIDBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN’s parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the SIDBSCAN-Mamdani and the SIDBSCAN-Sugeno are compared with the IDBSCAN and the DBSCAN. The experimental results, consisted of 20 times running, indicate that the proposed ideas achieved to their targets.