The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.

Compatibility Rules of Neonatal Parenteral Nutrition Prescriptions Based on Association Rules and Hierarchical Cluster Analysis

[Objectives]To explore the compatibility rules of neonatal parenteral nutrition(PN)prescriptions based on association rules and hierarchical cluster analysis,thereby providing a reference for standardizing neonatal parenteral nutrition supportive therapy.[Methods]The data about neonatal PN formulations prepared by the Pharmacy Intravenous Admixture Services(PIVAS)of the Affiliated Hospital of Chengde Medical University from July 2015 to June 2021 were collected.The general information of the prescriptions and the frequency of drug use were analyzed with Excel 2019;the boxplot of drug dosing was drawn using GraphPad 8.0 software;and SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to perform association rules and hierarchical cluster analysis.[Results]A total of 11488 PN prescriptions were collected from 1421 newborns,involving 18 kinds of drugs,which were divided into 11 types of nutrients.Association rules analysis yielded 84 nutrient substance combinations.The combination of fat emulsion-water-soluble vitamins-fat-soluble vitamins-glucose-amino acids had the highest confidence(99.95%).The hierarchical cluster analysis divided nutrients into 5 types.[Conclusions]The prescriptions of PN for newborns were composed of five types of nutrients:amino acids,fat emulsion,glucose,water-soluble vitamins,and fat-soluble vitamins.According to the lack of electrolytes and trace elements,appropriate drugs can be chosen to meet nutritional demands.This study provides reference basis for reasonable selection of drugs for neonatal PN prescriptions and further standardization of PN supportive therapy in newborns.

Water Quality Assessment in the Bamoun Plateau, Western-Cameroon: Hydrogeochemical Modelling and Multivariate Statistical Analysis Approach

This study focuses on the geochemical and bacteriological investigation of surface and ground water in the Bamoun plateau (Western-Cameroon). During the period from September 2013 to August 2014, 71 samples were collected from two springs, one borehole, four wells and the Nchi stream for analysis of major elements. In order to obtain the characteristics of the various species of bacteria, 7 samples were selected. The analytical method adopted for this study is the conventional hydrochemical technic and multivariate statistical analysis, coupled with the hydrogeochemical modelling. The results revealed that, water from the zone under study are acidic to basic, very weakly to weakly mineralized. Four types of water were identified: 1) CaMg-HCO3;2) CaMg-Cl-SO4;3) NaCl-SO4 and 4) NaK-HCO3. The major elements were all listed in the World Health Organization guidelines for drinking water quality, except for nitrates which was found at a concentration > 50 mg /l NO-3 in the borehole F401. As for the hydrobiological aspect, the entire sample contained all the bacteriological species except for spring S301 and well P401. According to the hydrogeochemical modelling, the Gibbs model and multivariate statistical tests, the quality of surface and ground water of the Foumban locality is influenced by two important factors: 1) the natural factors characterized by the water-rock interaction, evapotranspiration/crystallization, 2) the anthropogenic factors such as: uncontrolled discharges of liquid and solid effluents of all kinds and without any prior treatment within the ground and the strong urbanization accompanied by lack of sanitation and insufficient care.

Study on Rhizome Crops by Fourier Transform Infrared Spectroscopy Combined with Wavelet Analysis

In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieramhical cluster analysis （HCA）. The results showed that the infrared spectra were similar on the whole, but there were differences in peak position, peak shape and peak absorption intensity in the range of 1 800-700 cm-1. The infrared spectra in the range of 1 800-700 cm-1 were selected to perform continuous wavelet transform （CWT） and discrete wavelet transform （DWT）. The 15th-Ievel decomposition coefficients of CWT and the 5=-level detail coefficients of DWT were classified by PCA and HCA. The cumulative contri- bution rates of the first three principal components of CWT and DWT were 93.12% and 89.78%, respectively. The accurate recognition rates of PCA and HCA were all 100%. It is proved that FTIR combined with WT can be used to distinguish different kinds of rhizome crops.

Study on FTIR Spectra of Corn Germs and Endosperms of Three Different Colors Combining with Cluster Analysis

[Objective] This research aimed to study the FTIR spectra of corn germs and endosperms so as to provide a scientific way for identifying corn of different types. [Method] The corn germs and endosperms of three types were studied by using Fourier transform infrared spectroscopy（FTIR） technology, combined with cluster analysis. [Result] The overall characteristics of original FTIR spectra were basically similar within the range of 700-1 800 cm^-1. The FTIR spectra were mainly composed by the absorption peaks of polysaccharides, proteins and lipids. Within the wavelength range of 700-1 800 cm^-1, there were only tiny differences in original FTIR spectra among the corn germs and endosperms of three different types. The spectra were then processed by using first derivative and second derivative. The second derivative spectra were used for hierarchical cluster analysis（HCA）. The results showed that with the wavelength range of 700-1 800 cm^-1, the second derivative spectra of the 52 samples could be better clustered according to the tree types and corn germ and corn endosperm. The clustering correct rate reached 96.1%.[Conclusion] FTIR technology, combined with cluster analysis, can be used to identify different types of corn germs and endosperms, and it is characterized by convenience and rapidness.

Determination of bioactive components in the fruits of Cercis chinensis Bunge by HPLC-MS/MS and quality evaluation by principal components and hierarchical cluster analyses

The fruits of leguminous plants Cercis Chinensis Bunge are still overlooked although they have been reported to be antioxidative because of the limited information on the phytochemicals of C.chinensis fruits.A simple,rapid and sensitive HPLC-MS/MS method was developed for the identification and quantitation of the major bioactive components in C.chinensis fruits.Eighteen polyphenols were identified,which are first reported in C.chinensis fruits.Moreover,ten components were simultaneously quantified.The validated quantitative method was proved to be sensitive,reproducible and accurate.Then,it was applied to analyze batches of C.chinensis fruits from different phytomorph and areas.The principal components analysis(PCA)realized visualization and reduction of data set dimension while the hierarchical cluster analysis(HCA)indicated that the content of phenolic acids or all ten components might be used to differentiate C.chinensis fruits of different phytomorph.

Multivariate analysis of fluorescence and source identification of dissolved organic matter in Jiaozhou Bay, China

Hierarchical clustering analysis and principal component analysis （PCA） methods were used to assess the similarities and dissimilarities of the entire Excitation-emission matrix spectroscopy （EEMs） data sets of samples collected from Jiaozhou Bay, China. The results demonstrate that multivariate analysis facilitates the complex data treatment and spectral sorting processes, and also enhances the probability to reveal otherwise hidden information concerning the chemical characteristics of the dissolved organic matter （DOM）. The distribution of different water samples as revealed by multivariate results has been used to track the movement of DOM material in the study area, and the interpretation is supported by the results obtained from the numerical simulation model of substance tracing technique, which show that the substance discharged by Haibo River can be distributed in Jiaozhou Bay.

Derringer desirability and kinetic plot LC-column comparison approach for MS-compatible lipopeptide analysis

Lipopeptides are currently re-emerging as an interesting subgroup in the peptide research field, having historical applications as antibacterial and antifungal agents and new potential applications as antiviral, antitumor, immune-modulating and cell-penetrating compounds. However, due to their specific structure, chromatographic analysis often requires special buffer systems or the use of trifluoroacetic acid, limiting mass spectrometry detection. Therefore, we used a traditional aqueous/acetonitrile based gradient system, containing 0.1% （m/v） formic acid, to separate four pharmaceutically relevant lipopeptides （polymyxin B1, caspofungin, daptomycin and gramicidin A1）, which were selected based upon hierarchical cluster analysis （HCA） and principal component analysis （PCA）.In total, the performance of four different C18 columns, including one UPLC column, were evaluated using two parallel approaches. First, a Derringer desirability function was used, whereby six single and multiple chromatographic response values were rescaled into one overall D-value per column. Using this approach, the YMC Pack Pro C18 column was ranked as the best column for general MS-compatible lipopeptide separation. Secondly, the kinetic plot approach was used to compare the different columns at different flow rate ranges. As the optimal kinetic column performance is obtained at its maximal pressure, the length elongation factor λ（Pmax/Pexp） was used to transform the obtained experimental data （retention times and peak capacities） and construct kinetic performance limit （KPL） curves, allowing a direct visual and unbiased comparison of the selected columns, whereby the YMC Triart C18 UPLC and ACE C18 columns performed as best. Finally, differences in column performance and the （dis）advantages of both approaches are discussed.

Mathematical Tools of Cluster Analysis

The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.

Analysis of genetic diversity in banana cultivars(Musa cvs.) from the South of Oman using AFLP markers and classification by phylogenetic,hierarchical clustering and principal component analyses

Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs.This study employed amplified fragment length polymorphism(AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman.Using 12 primer combinations,a total of 1094 bands were scored,of which 1012 were polymorphic.Eighty-two unique markers were identified,which revealed the distinct separation of the seven cultivars.The results obtained show that AFLP can be used to differentiate the banana cultivars.Further classification by phylogenetic,hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis.Based on the analytical results,a consensus dendrogram of the banana cultivars is presented.

Anthropogenic disturbance of aquatic biodiversity and water quality of an urban river in Penang, Malaysia

Malaysia's rapid economic and demographic development have placed negative pressure on its water supplies and the quality of the Juru River, which is close to the nation's capital and its major source of water. Healthy aquatic ecosystems are supported by physicochemical properties and biological diversity. This study evaluated the anthropogenic impacts on aquatic biodiversity, especially plankton, fish, and macrobenthos, as well as the water quality of the Juru River in the Penang area. Aquatic biodiversity and river water parameters were collected from ten sampling stations along the Juru River. Seven variables were used to assess the physicochemical environment: pH, temperature, total suspended solids (TSS), salinity, dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand. At each sampling station, the total number of plankton, fish, and macrobenthic taxa were counted and analyzed. The relationships between the physicochemical parameters and aquatic biodiversity were investigated with biotypological analysis, principal component analysis, hierarchical cluster analysis, and linear regression analysis. These analyses showed that the richness and diversity indices were generally influenced by salinity, temperature, TSS, BOD, and pH. The data obtained in this study supported the bioindicator concept. The findings, as they related to scientifically informed conservation, could serve as a model for Juru River management, as well as for river management throughout Malaysia and other tropical Asian countries.

Study on Land Use Regionalization in Henan Province

According to the natural ecology and socio-economic conditions in Henan Province, a land use regionalization index system with 6 factors and 24 factor layers was constructed by combining with the characteristics of land use in Henan Province. Expert scoring method was used to determine the weights of the indicators. Based on the similarities and differences of these factors in the index system at county （city, district） levels, hierarchical clustering method was used to make the quantitative analysis to the land use regionalization in Henan Province. And constrastive analysis and qualitative analysis were made to the regionalization scheme by combining with the acutal conditions in the counties （cities, districts）, and finally, Henan Province was classified into 6 regions.

DNA Sequence Classification Based on the Side Chain Radical Polarity of Amino Acids

The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.

Application of PCA and HCA to the Structure-Activity Relationship Study of Fluoroquinolones

Density functional theory （DFT） was used to calculate molecular descriptors （properties） for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis （PCA） and hierarchical cluster analysis （HCA） were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.

Evaluation preparation technology of Xiaochaihu granules using fingerprint-peak pattern matching

An approach was proposed to evaluate preparation technology by means of fingerprint-peak matching technology of high performance liquid chromatography with diode array detector （HPLC-DAD）. Similarity and hierarchical clustering analysis （HCA） were applied to identify the 15 batches of Xiaochaihu granules from different manufacturers and our laboratory, and peak pattern matching between the composite formulae and Radix Bupleuri Chinensis, which was one of the main ingredients of Xiaochaihu granules, was utilized to evaluate the preparation technology of Xiaochaihu granules via the indexes of the relative deviation of retention time （RT） and UV spectrum feature similarity of their corresponding peaks. Eleven matching peaks were found between Xiaochaihu granules and Radix Bupleuri Chinensis. However, the saikosaponin A and saikosaponin D, which are the important active components in Radix Bupleuri Chinensis, were not found in Xiaochaihu granules from any manufacturers. The peak areas of 11 characteristic peaks of Xiaochaihu granules samples formed a matrix of 11 × 15. The result of HCA showed that Xiaochaihu granules samples were divided into four kinds of category. Xiaochaihu granules samples from the same manufacturer were basically clustered of the same category. The results suggested that the saikosaponin A and saikosaponin D are prone to structural transformation under the condition of decoction and in the presence of the organic acidic components. These active components, existing in raw herb, might transform to a series of non-active secondary saikosaponin due to unfavourable preparation technology. So the conventional decoction-based preparation technology of Xiaochaihu granules might greatly affect its quality and therapeutic effectiveness. This study demonstrates that fingerprint-peak matching technology can not only be used for quality control of this composite formulae, but also provide some guidance for preparation technology of Xiaochaihu granules.

Identification and determination of the major constituents in traditional Chinese medicine Longdan Xiegan Pill by HPLC-DAD-ESI-MS

A novel and sensitive HPLC-UV method has been developed for the simultaneous determination of twelve major compounds in Longdan Xiegan Pill.The chemical profile of the twelve compounds,including geniposidic acid（1）,geniposide（2）,gentiopicroside（3）,liquiritin（4）,crocin（5）,baicalin（6）,wogonoside（7）,baicalein（8）,glycyrrhizic acid（9）,wogonin（10）,oroxylin A（11）and aristolochic acid A（12）,was acquired using high-performance liquid chromatography-diode array detector coupled with an electrospray tandem mass spectrometer（HPLC-DAD-ESI-MS）.The analysis was performed on a Dikma Platisil ODS C18 column（250 mm×4.6 mm,5 μm）with a gradient solvent system of acetonitrile-0.1% aqueous formic acid.The validation was carried out and the linearities（r〉0.9996）,repeatability（RSD〈1.8%）,intra-and inter-day precision（RSD〈1.3%）,and recoveries（ranging from 96.6% to 103.4%）were acceptable.The limits of detection（LOD）of these compounds ranged from 0.29 to 4.17 ng.Aristolochic acid A,which is the toxic ingredient,was not detected in all the batches of Longdan Xiegan Pill.Furthermore,hierarchical cluster analysis was used to evaluate the variation of the herbal prescription.The proposed method is simple,effective and suitable for the quality control of this traditional Chinese medicine（TCM）.

Quantification of six bioactive compounds in Zhenqi Fuzheng preparation by high-performance liquid chromatography coupled with diode array detector and evaporative light scattering detector

A simple and accurate high-performance liquid chromatography（HPLC）coupled with diode array detector（DAD）and evaporative light scattering detector（ELSD）was established for the determination of six bioactive compounds in Zhenqi Fuzheng preparation（ZFP）.The monitoring wavelengths were 254,275 and 328 nm.Under the optimum conditions,good separation was achieved,and the assay was fully validated in respect of precision,repeatability and accuracy.The proposed method was successfully applied to quantify the six ingredients in 31 batches of ZFP samples and evaluate the variation by hierarchical cluster analysis（HCA）,which demonstrated significant variations on the content of these compounds in the samples from different manufacturers with different preparation procedures.The developed HPLC method can be used as a valid analytical method to evaluate the intrinsic quality of this preparation.

Stream water quality and its influencing factor in lower order streams in upriver sections of Ashihe River

For understanding the reasons that caused the degradation of water quality in lower order streams, systematic sampling was conducted at different spatial locations aiong the low order streams （1 st-5th） of Ashihe River continuum in Maoershan Experimental Forest of Northeast Forestry University, Sharlgzhi City, Heilongjiang Province, China. The indexes of stream water quality, i.e., the pH, dissolved oxygen（DO）, turbidity, temperature, PO43-P, NO3-N and NH4^＋-N concentrations, total dissolved inorganic nitrogen （TDIN-N, including concentrations of NO3^＋-N and NH4^＋-N）, and total phosphorus （inorganic and organic phosphorous, TP） were measured and analyzed. The stream order, related environmental settings and land-use type were recorded for each sampling location. The indexes of stream water quality at different locations with different stream orders and land use types were compared by ANOVA analysis. The indexes of stream water quality at different sampling locations were analyzed by Hierarchical cluster analysis. Result showed that water quality had significant difference in different stream orders and land use types; some locations with different stream features （stream order and land use type） were grouped into same clusters, indicating that random disturbances produced the variations in water quality, which made the spatial variances of stream water quality inconsistent with the general rules.

Simultaneous quantification of 18 bioactive constituents in Ziziphus jujuba fruits by HPLC coupled with a chemometric method

The fruit of Ziziphus jujuba Mill., known as Hongzao(or Hong-Zao) in Chinese and cultivated in China for more than 4 000 years, has shown to have hepatoprotective property. In previous study, we have isolated and identified 27 known compounds from Z. jujuba fruits, which demonstrated anti-tumor activity. In this study, a high-performance liquid chromatography-diode-array detection-mass spectrometry(HPLC-DAD-MS) method was successfully applied to the simultaneous characterization and quantitation of 18 constituents in 28 Z. jujuba samples, comprised of 12 cultivars from different regions in China, by comparing their HPLC retention times, MS spectra, UV spectra, and NMR data with those of reference compounds. The quantitative method was validated with excellent linearity(R^(2 )> 0.999 1), preferable intra-and inter-day precisions(RSD < 2.78%), and good recoveries(94.96%–102.65%). The content variation of 18 compounds was analyzed by a chemometric method(hierarchical cluster analysis). In addition, these constituents showed protection against carbon tetrachloride(CCl_(4)) intoxicated Hep G2 cell lines by decreasing lactic dehydrogenase(LDH) levels. Results in this study illustrated that the content of all 18 compounds examined has significant difference and variation among cultivars and extracts. The proposed method can serve as a prerequisite for quality control of bioactive compounds in Z. jujuba products.

Assessment of temporal and spatial variations in surface water quality using multivariate statistical techniques: A case study of Nenjiang River basin, China

Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.