scholarly journals Correlation of Volatile Compounds and Sensory Attributes of Chinese Traditional Sweet Fermented Flour Pastes Using Hierarchical Cluster Analysis and Partial Least Squares-Discriminant Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Meigui Huang ◽  
Yulin Li ◽  
Ping Zhan ◽  
Ping Liu ◽  
Honglei Tian ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Volatile Compounds ◽  
Partial Least Squares ◽  
Hierarchical Cluster Analysis ◽  
Solid Phase ◽  
Hierarchical Cluster ◽  
Sensory Attributes ◽  
Volatile Components

The aroma compositions, sensory attributes, and their correlations of various traditional Chinese sweet fermented flour pastes (SFFPs) were investigated. SFFPs, including LEEJ, LEEH, and XH6, showed high overall acceptance scores of 8.00, 8.21, and 7.50, respectively. Ninety-six volatile compounds were detected using solid-phase microextraction gas chromatography mass spectrometry. Hierarchical cluster analysis grouped SFFPs into three clusters according to their concentrations and compositions of volatile components. Partial least squares-discriminant analysis showed that volatile compounds, including ethyl phenylacetate, 5-methyl furfural, amyl cinnamal, ethyl myristate, decyl aldehyde, 1-phenylethyl acetate, 1-octen-3-ol, 3-buten-2-ol, butanoic acid, and caproaldehyde, were highly negatively correlated with saltiness, sourness, and bitterness, while they were positively correlated with sweetness, umami, richness, and acceptance. The obvious correlation between flavor profiles and sensory attributes could help online monitoring of SFFPs’ flavor quality during production.

scholarly journals Analysis of the procedures used in the forensic evaluation of illegal substances: a statistical approach to interpreting reports

2019 ◽  
Vol 8 (1) ◽  
pp. 7-23
Author(s):  
Aline Thaís Bruni ◽  
Ricardo Luís Yoshida ◽  
Arthur Serra Lopes Ferreira ◽  
Jesus Antonio Velho ◽  
Bruno Spinosa De Martinis ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hierarchical Cluster Analysis ◽  
Labor Demand ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Final Report

ResumoEste estudo utilizou ferramentas estatísticas para avaliar laudos forenses sobre substâncias ilegais. Avaliamos variáveis quanto às características da análise e abordamos a metodologia empregada pelos peritos. Perguntas baseadas no que é necessário para esclarecer a lei foram formuladas. Analisamos 1008 documentos oficiais de diferentes jurisdições, divididos em 504 conjuntos compostos por um laudo preliminar e um laudo definitivo para cada caso. Os laudos foram examinados por uma equação empírica formulada para fornecer um parâmetro denominado “Report Relevance” (Relevância do Laudo), que teve por finalidade classificar cada documento de acordo com uma pontuação relacionada à quantidade de informação contida. A validação do método foi realizada por análise multivariada de dados: Análise de Componentes Principais (Principal Component Analysis, PCA), Análise de Agrupamentos Hierárquicos (Hierarchical Cluster Analysis, HCA), Soft Independent Modeling of Class Analogy (SIMCA) e Mínimos Quadrados Parciais (Partial Least Squares, PLS). A análise quantitativa mostrou que os documentos foram bem produzidos, com boa qualidade, uma vez que a Relevância do Laudo apresentou valores em torno de 0,74 ± 0,08 para aqueles provenientes da Polícia Estadual. Em comparação, os documentos provenientes da Polícia Federal obtiveram valores em torno de 0,87 ± 0,05. Fatores que podem explicar essas diferenças e as melhores pontuações para os laudos federais incluem maior investimento em tecnologia e treinamento de pessoal, e menor demanda de mão-de-obra e rotina. Para ambas as forças policiais, alguns aspectos poderiam ser melhorados, como imagens das evidências coletadas ou procedimentos analíticos laboratoriais. Finalmente, a metodologia neste estudo pode ser adaptada para ser usada em outros tipos de investigação forense.Palavras-chave: Substâncias Ilícitas, Procedimentos Periciais, Análise MultivariadaAbstractThis study used statistical tools to evaluate forensic reports on illegal substances. We evaluated variables regarding the characteristics of the analysis and we addressed the methodology employed by the experts. Questions based on what is required to clarify the law were formulated. We have parsed 1008 official documents from different jurisdictions, divided into 504 sets comprised of a preliminary and a final report for each case. The reports were examined by an empirical equation formulated to provide a parameter called “Report Relevance”, which intended to classify each report according to a score related to the amount of information it contained.  The validation of the method was performed by multivariate data analysis: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Squares (PLS). Quantitative analysis showed that the expert documents were well produced, with good quality, since the Report Relevance showed values around 0.74 ± 0.08 for the reports from the State Police. By comparison, reports from the Federal Police obtained scores around 0.87 ± 0.05. Factors that might explain these differences and the better scores for the Federal reports include increased investment in technology and training of staff, and a lower labor demand and routine. For both police forces, some aspects could be improved, such as images of the collected evidence or laboratory analytical procedures. Finally, the methodology in this study can be adapted to be used in other kinds of forensic investigation.Keywords: Illegal Substances, Expertise Procedures, Multivariate Analysis.  

scholarly journals Ultraviolet Spectroscopy Used to Fingerprint Five Wild-Grown Edible Mushrooms (Boletaceae) Collected from Yunnan, China

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Li ◽  
Ji Zhang ◽  
Tao Li ◽  
Tianwei Yang ◽  
Yuanzhong Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Hierarchical Cluster Analysis ◽  
Visual Discrimination ◽  
Uv Spectroscopy ◽  
Hierarchical Cluster ◽  
Uv Spectra ◽  
Edible Mushrooms ◽  
Ultraviolet Spectroscopy

Nowadays, wild-grown edible mushrooms which are natural, nutritious, and healthy get more and more popular by large consumers. In this paper, UV spectra of different Boletaceae mushrooms with the aid of partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were shown to be a practical and rapid method for discrimination purpose. The specimens of Boletus edulis, Boletus ferrugineus, Boletus tomentipes, Leccinum rugosiceps, and Xerocomus sp. were described based on the UV spectra. From the results, all the specimens were characterized by strong absorption at the wavelengths of 274 and 284 nm and showed the shoulder at 296 nm. However, changes could be seen in the peak heights at the same wavelength for different samples. After analyzing by chemometrics, visual discrimination among samples was presented and the relationships among them were also obtained. This study showed that UV spectroscopy combined with chemometrics methods could be used successfully as a simple and effective approach for characterization of these five wild-grown edible mushrooms at species and genus levels. Meanwhile, this rapid and simple methodology could also provide reference for the discrimination of edible mushrooms.

Spatial–Temporal Neighborhood Patterns in Four Legacy Cities

2020 ◽  
pp. 107808741989782
Author(s):  
Kelly L. Kinahan
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Hierarchical Cluster Analysis ◽  
Temporal Trends ◽  
Hierarchical Cluster ◽  
Neighborhood Scale ◽  
Legacy Cities ◽  
Over 40 Years ◽  
Neighborhood Typology

Legacy cities are characterized by long-term, declining trends in both population and economic characteristics, but how these events translate to the neighborhood scale is less well understood. This research investigates the evolution of neighborhood types in four legacy cities—Baltimore, Cleveland, Philadelphia, and St. Louis—from 1970 to 2010. Working from a multidimensional framework of variables across five census decades, hierarchical cluster analysis and discriminant analysis are used to develop a neighborhood typology, identify temporal trends, and describe the pathways of transition. Results reveal four crosscutting neighborhood types: Black Distressed; Lower Middle; Multifamily, Educated, Turnover; and Upper Middle, which persist across space and time. Most neighborhoods (61%) remained classified within the same type over 40 years, and transitions were concentrated in two decades (1970–1980 and 2000–2010). The results offer a new descriptive dimension for understanding the unevenness present across legacy cities and suggest that existing policy approaches continually reproduce the same neighborhood outcomes.

scholarly journals Novel anti-tuberculosis drug designs by data mining for similarity in substituent substitution and structure modification

2011 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Ronald Lee Bartzatt
Keyword(s):  
Data Mining ◽  
Molecular Weight ◽  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Surface Area ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Log P ◽  
Polar Surface Area ◽  
Pharmaceutical Properties

<em>Mycobacterium tuberculosis</em> (TB) is among the most common of infectious diseases that cause death, and as many as one-third of the world’s population may be infected. This work presents 17 novel hydrazide agents formed by focused in silico data mining utilizing search parameters restricted to substituent replacement only. Substituent substitution has been highly successful in design of novel antibacterial and antiviral drugs. This diverse set of hydrazide constructs possess molecular properties indicating favorable bioavailability with excellent intestinal absorption for oral administration. All agents have zero violations of the Rule of 5, indicating favorable druglikeness. Important pharmaceutical properties including polar surface area, Log P, and formula weight were determined and compared to that of the parent structure of isoniazid by hierarchical cluster analysis and discriminant analysis. The average Log P with range is -0.258 and -2.165 to 1.373, respectively. The average polar surface area (PSA) with range is 75.19 A2 and 55.121 A2 to 94.036 A2, respectively. The diverse range of PSA and Log P, with other descriptors, portend a versatile group of hydrazide drugs having substantial potential to expand the application and effectiveness for clinical treatment of multi-organ infected TB patients. Analysis of similarity indicated that all 17 agents are significantly similar to isoniazid, however discriminant analysis and hierarchical cluster analysis are able to differentiate isoniazid based upon molecular properties. Molecular weight and number of atoms were highly correlated by Pearson r (r &gt; 0.9000), with Log P moderately correlated (r &gt; 0.5500) to number of atoms, molecular weight, and volume. Seventeen hydrazide compounds (success rate of approximately 38%) having diverse pharmaceutical properties resulted from substituent data mining with potential for clinical application.

scholarly journals Comparative Study of the Petal Structure and Fragrance Components of the Nymphaea hybrid, a Precious Water Lily

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 408
Author(s):  
Qi Zhou ◽  
Man Shi ◽  
Huihui Zhang ◽  
Zunling Zhu
Keyword(s):  
Volatile Compounds ◽  
Solid Phase ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Volatile Components ◽  
Ecological Value ◽  
Water Lily ◽  
Transmission Electron ◽  
Yellow Type ◽  
Different Strains

Nymphaea hybrid, a precious water lily, is a widely-cultivated aquatic flower with high ornamental, economic, medicinal, and ecological value; it blooms recurrently and emits a strong fragrance. In the present study, in order to understand the volatile components of N. hybrid and its relationship with petals structure characteristics, the morphologies and anatomical structures of the flower petals of N. hybrid were investigated, and volatile compounds emitted from the petals were identified. Scanning and transmission electron microscopy were used to describe petal structures, and the volatile constituents were collected using headspace solid-phase microextraction (HS-SPME) fibers and analyzed using gas chromatography coupled with mass spectrometry (GC-MS). The results indicated that the density and degree of protrusion and the number of plastids and osmiophilic matrix granules in the petals play key roles in emitting the fragrance. There were distinct differences in the components and relative contents of volatile compounds among the different strains of N. hybrid. In total, 29, 34, 39, and 43 volatile compounds were detected in the cut flower petals of the blue-purple type (Nh-1), pink type (Nh-2), yellow type (Nh-3) and white type (Nh-4) of N. hybrid at the flowering stage, with total relative contents of 96.78%, 97.64%, 98.56%, and 96.15%, respectively. Analyses of these volatile components indicated that alkenes, alcohols, and alkanes were the three major types of volatile components in the flower petals of N. hybrid. The predominant volatile compounds were benzyl alcohol, pentadecane, trans-α-bergamotene, (E)-β-farnesene, and (6E,9E)-6,9-heptadecadiene, and some of these volatile compounds were terpenes, which varied among the different strains. Moreover, on the basis of hierarchical cluster analysis (HCA) and principal component analysis (PCA), the N. hybrid samples were divided into four groups: alcohols were the most important volatile compounds for Nh-4 samples; esters and aldehydes were the predominant volatiles in Nh-3 samples; and ketones and alkenes were important for Nh-2 samples. These compounds contribute to the unique flavors and aromas of the four strains of N. hybrid.

scholarly journals Pengelompokan 33 Aksesi Kakao Berdasarkan Karakter Morfologi Komponen Buah

2017 ◽  
Vol 4 (2) ◽  
pp. 67
Author(s):  
Edi Wardiana ◽  
Juniaty Towaha ◽  
Syafaruddin Syafaruddin
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Hierarchical Cluster Analysis ◽  
Morphological Character ◽  
Hierarchical Cluster ◽  
Dry Weight ◽  
Morphological Characters ◽  
Fresh Weight ◽  
Experimental Station

<em>Identification and grouping of diverse cacao accessions based on morphological character of pod components is important in breeding activities to generate superior varieties. This study aimed to grouping 33 cocoa accessions based on morphological characters of pod components, conducted at Pakuwon Experimental Station, Sukabumi, West Java, at an altitude of 450 asl with Latosol soil and B type climate (Schmidt and Fergusson), from January until December 2015. Observation was taken on 33 cacao accessions of Kaliwining (KW), planted in 2012 with 3 m x 3 m spacing under 26 years old Genjah Salak coconut trees as shading. Ten mature pods were randomly harvested from 10 cocoa plants of each accession at respective two seasons (February and December 2015). The variables observed were 7 characters of pod components i.e.: (1) fresh weight of pod, (2) number of beans, (3) fresh weight of beans, (4) dry weight of beans, (5) fresh weight of pod husk, (6) number of pod furrow, and (7) fresh weight of pulp. Data were analyzed by factor analysis followed by hierarchical cluster analysis in Ward's method and discriminant analysis. The result showed that 19 accessions were classified as high in bean and pod husk component characters, 9 accessions were classified as high in pulp characters, and 5 accessions i.e. KW 162, KW 528, KW 570, KW 571, and KW 720 were classified as high in all characters of pod components, and potential as parents in generating superior cacao varieties.</em>

scholarly journals Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2307 ◽  
Author(s):  
Xuexiao Cao ◽  
Lili Sun ◽  
Di Li ◽  
Guangjiao You ◽  
Meng Wang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Quality Evaluation ◽  
Herbal Medicines ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Maximum Thickness

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.

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