Arrowroot and Cassava Mixed Starch Products Identification by Raman Analysis with Chemometrics

Food frauds present a major problem in the foodstuff industry. Arrowroot and cassava may be targeted in adulteration and falsification processes. Raman analysis combined with chemometric techniques was proposed to identify the mixing and adulteration of these foodstuffs in commercial products. 67 cassava and 5 arrowroot samples were prepared in laboratory. 21 cassava and 5 arrowroot commercial samples were purchased in local stores. Raman assays were performed in the range of 400 to 2300 cm−1. Principal component analysis with K-means clustering was used to identify the adulteration of these products. It was possible to observe the separation of three different groups in the data, these groups labelled group 1, 2 and 3 were correspondent to cassava-like samples, mixed samples, and arrowroot-like samples, respectively. Despite the visual analysis related to sensory characteristics and the visual analysis of each Raman spectrum of cassava and arrowroot not being able to differentiate these foodstuffs, the chemometric approaches with the Raman specters data were able to identify which samples were pure arrowroot, pure cassava and which were mixed products. The proposed approach showed to be an effective tool in the investigation of fraud for arrowroot and cassava.

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Discrimination of brazilian red varietal wines according to their sensory descriptors

Ciência e Agrotecnologia ◽

10.1590/s1413-70542011000600018 ◽

2011 ◽

Vol 35 (6) ◽

pp. 1172-1176 ◽

Cited By ~ 4

Author(s):

Alberto Miele ◽

Luiz Antenor Rizzon

Keyword(s):

Principal Component Analysis ◽

Total Variation ◽

Principal Component ◽

Pinot Noir ◽

The Other ◽

Sensory Characteristics ◽

Cabernet Sauvignon ◽

Sensory Panel ◽

Red Grape ◽

Grape Varieties

The purpose of this paper was to establish the sensory characteristics of wines made from old and newly introduced red grape varieties. To attain this objective, 16 Brazilian red varietal wines were evaluated by a sensory panel of enologists who assessed wines according to their aroma and flavor descriptors. A 90 mm unstructured scale was used to quantify the intensity of 26 descriptors, which were analyzed by means of the Principal Component Analysis (PCA). The PCA showed that three important components represented 74.11% of the total variation. PC 1 discriminated Tempranillo, Marselan and Ruby Cabernet wines, with Tempranillo being characterized by its equilibrium, quality, harmony, persistence and body, as well as by, fruity, spicy and oaky characters. The other two varietals were defined by vegetal, oaky and salty characteristics; PC 2 discriminated Pinot Noir, Sangiovese, Cabernet Sauvignon and Arinarnoa, where Pinot Noir was characterized by its floral flavor; PC 3 discriminated only Malbec, which had weak, floral and fruity characteristics. The other varietal wines did not show important discriminating effects.

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Discrimination of different articular cartilage by principal component analysis of Raman spectrum

The Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME ◽

10.1299/jsmebio.2019.32.2f13 ◽

2019 ◽

Vol 2019.32 (0) ◽

pp. 2F13

Author(s):

Kosaku KURATA ◽

Sota MOTONAGA ◽

Hiroshi TAKAMATSU

Keyword(s):

Principal Component Analysis ◽

Raman Spectrum ◽

Articular Cartilage ◽

Principal Component ◽

Component Analysis

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Towards reproducibility of traditional fermented sausages: Texture profile analyses and modelling

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq181224027j ◽

2020 ◽

Vol 26 (1) ◽

pp. 79-87

Author(s):

Marija Jokanovic ◽

Bojana Ikonic ◽

Predrag Ikonic ◽

Vladimir Tomovic ◽

Tatjana Peulic ◽

...

Keyword(s):

Principal Component Analysis ◽

Linear Regression Analysis ◽

Principal Component ◽

Component Analysis ◽

Multiple Linear Regression Analysis ◽

Sensory Characteristics ◽

Small Scale ◽

Regression Equations ◽

Textural Characteristics ◽

Fermented Sausages

The aim of this study was to investigate textural characteristics of three traditional dry fermented sausages (Sremski kulen, Lemeski kulen and Petrovsk? klob?sa) manufactured in different small-scale facilities in northern Serbia, and to correlate them with physicochemical and sensory characteristics. The sample sausages were supplied by different local traditional producers. The textural characteristics were correlated with physicochemical and sensory characteristics using multiple linear regression analysis and principal component analysis. Differences in physicochemical characteristics reflected even more notable differences in texture characteristics. Regarding regression equations, obtained results showed that moisture content was significant for hardness, springiness and cohesiveness. Hardness was also influenced by fat content, while chewiness was influenced by protein content. Principal component analysis separated samples of Petrovsk? klob?sa, as the group with the most reproducible analysed characteristics. Obtained results of statistical analyses should provide knowledge for possible improvements of the traditional production, in a way that these sausages could be produced in different facilities with consistent textural characteristics.

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Tweet Sentiment Analysis Regarding the Brazilian Stock Market

10.5753/brasnam.2019.6550 ◽

2019 ◽

Author(s):

Murilo C. Medeiros ◽

Vinicius R. P. Borges

Keyword(s):

Principal Component Analysis ◽

Stock Market ◽

Sentiment Analysis ◽

Topic Modeling ◽

Visual Analysis ◽

Vector Space Model ◽

Principal Component ◽

Sentiment Classification ◽

Space Model ◽

Analysis Process

This paper describes a methodology for analyzing sentiments and for knowledge discovery in tweets regarding the Brazilian stock market. The proposed methodology starts by preprocessing and characterizing tweets to obtain an associated vector-space model. After that, a dimensionality reduction is em- ployed by using Principal Component Analysis and t-Stochastic Neighbor Embedding. Sentiment analysis of stock market tweets is performed by considering the tasks of sentiment classification, topic modeling and clustering, along with a visual analysis process. Experiments results showed satisfactory performances in single and multi-label sentiment classification scenarios. The visual analysis process also revealed interesting relationships among topics and clusters.

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Determination of the Reactivity Degree of Various Alkaline Solutions: A Chemometric Investigation

Applied Spectroscopy ◽

10.1177/0003702819867956 ◽

2019 ◽

Vol 73 (12) ◽

pp. 1361-1369 ◽

Cited By ~ 1

Author(s):

Marie Arnoult ◽

Colin Dupuy ◽

Maggy Colas ◽

Julie Cornette ◽

Ludovic Duponchel ◽

...

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

Statistical Model ◽

Partial Least Squares Regression ◽

Principal Component ◽

Silicate Solution ◽

Alkaline Solutions ◽

Least Squares Regression ◽

Chemometric Techniques

Knowledge of alkaline silicate solutions is crucial in order to optimize geopolymer properties. Geopolymers are new binders resulting from the activation of an aluminosilicate by an alkaline solution. It is well established that the solution reactivity strongly affects the geopolymerization and therefore the geopolymer working properties. As a consequence, an evaluation of the reactivity degree of alkaline silicate solutions prior synthesis is of the utmost interest. However, the determination of the solution reactivity is currently tedious, and for geopolymer commercialization, it would be necessary to find an easy way to determine it. Therefore, Raman spectroscopy, combined with chemometric techniques, is proposed as a solution to easily determine the alkaline silicate solution reactivity. To conduct this investigation, 65 silicate solutions were characterized by Raman spectroscopy, and reference values of their reactivity degree were determined. Finally, principal component analysis and partial least squares regression were performed to build a statistical model able to predict the alkaline silicate solution reactivity from Raman spectra.

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Discrimination of Four Cinnamomum Species with Physico-Functional Properties and Chemometric Techniques: Application of PCA and MDA Models

Foods ◽

10.3390/foods10112871 ◽

2021 ◽

Vol 10 (11) ◽

pp. 2871

Author(s):

Priya Rana ◽

Shu-Yi Liaw ◽

Meng-Shiou Lee ◽

Shyang-Chwen Sheu

Keyword(s):

Principal Component Analysis ◽

Functional Properties ◽

Principal Component ◽

Training Set ◽

Food Fraud ◽

Score Plot ◽

Food Adulteration ◽

Clear Separation ◽

Prediction Rate ◽

Chemometric Techniques

Discrimination of highly valued and non-hepatotoxic Cinnamomum species (C. verum) from hepatotoxic (C. burmannii, C. loureiroi, and C. cassia) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four Cinnamomum species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different Cinnamomum species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of Cinnamomum species to prevent food fraud.

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AVALIAÇÃO DO PERFIL SENSORIAL DE AMÊNDOAS DE CUPUAÇU E CACAU TORRADAS UTILIZANDO ANÁLISE DESCRITIVA QUANTITATIVA

Boletim do Centro de Pesquisa de Processamento de Alimentos ◽

10.5380/cep.v18i2.1214 ◽

2000 ◽

Vol 18 (2) ◽

Author(s):

MARISE BONIFÁCIO QUEIROZ ◽

NELSON HORÁCIO PEZOA GARCIA

Keyword(s):

Principal Component Analysis ◽

Descriptive Analysis ◽

Principal Component ◽

The Other ◽

Sensory Characteristics ◽

Sensory Profile ◽

Discriminative Ability ◽

Tukey Test ◽

Similarities And Differences ◽

Sensory Descriptive Analysis

Utilizou-se a análise descritiva quantitativa (ADQ) para desenvolver terminologia descritiva e perfil sensorial de amêndoas de cupuaçu e cacau torradas. Quatro amostras de cupuaçu torradas em forno elétrico rotativo a 150 ºC por 38, 40, 42 e 44 minutos e uma amostra de cacau torrada por 38 minutos foram avaliadas sensorialmente e comparadas entre si. Foram gerados 12 termos que melhor descreveram as similaridades e diferenças entre as amostras, sendo apontados 8 com maior predominância segundo a análise de componente principal (ACP). Equipe de 9 julgadores foi selecionada com base no poder de discriminação (F amostra significativo para p≤0,50), repetibilidade (F repetição não significativo para p>0,05) e concordância de cada um com a equipe. A intensidade de cada descritor ou atributo foi fixada em escala não estruturada de 10 cm, ancorada por extremos que variavam de fraco para forte, baixa para alta, etc. Os dados obtidos foram avaliados estatisticamente (ANOVA, Teste de Tukey e ACP). Observou-se que o cacau torrado apresentou características sensoriais diferentes do cupuaçu, sendo bem mais ácido e amargo, com coloração mais escura e acentuado sabor de chocolate. Já o cupuaçu apresentou sabor de chocolate mais suave e mais doce que o cacau. Abstract Descriptive terminology and sensory profile of cupuasu and cocoa roasted bean were developed by sensory descriptive analysis (QDA). Four cupuasu samples roasted in an electrical rotating oven at 150 ºC and time of 38, 40, 42 and 44 minutes and one cocoa sample roasted for 38 minutes were sensory evaluated and compared. Twelve terms which best described similarities and differences among the samples were generated, being noted eight terms with greater predominance, according to the Principal Component Analysis (PCA). Nine panelists were selected on the basis of their discriminative ability (F sample significant for p≤0.50), reproducibility of judgments (F reproducibility significant for >0.05) and panel agreement. The intensity of each descriptor or attribute were fixed in an 10 cm unstructured scale, anchored in the ends on the terms weak and strong, low and high, etc. Data were analyzed by ANOVA, Tukey test and PCA, presenting results in tables and charts. It was observed that roasted cocoa presented different sensory characteristics, being sourer, bitter, darker and with accentuated chocolate taste than cupuasu. In the other hand the cupuasu presented smoother chocolate taste and sweeter than cocoa.

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Differences in heritability of craniofacial skeletal and dental characteristics between hypo- and hyper-divergent patterns using Falconer's method and principal component analysis

The Angle Orthodontist ◽

10.2319/062518-475.1 ◽

2018 ◽

Vol 89 (2) ◽

pp. 242-251

Author(s):

Do-Keun Kim ◽

Joohon Sung ◽

Yun-Mi Song ◽

Eung-Min Kim ◽

Young Ho Kim ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Cranial Base ◽

Mandibular Incisor ◽

High Heritability ◽

Same Gender ◽

Group 2 ◽

Incisor Inclination ◽

Group 1

ABSTRACT Objectives: To investigate the difference in heritability of craniofacial skeletal and dental characteristics between hypodivergent and hyperdivergent patterns. Materials and Methods: 53 Korean adult monozygotic (MZ) and dizygotic (DZ) twins and their siblings were divided into a hypodivergent group (Group 1, SN-MP < 35°, 17 MZ pairs; 11 DZ and sibling [DS] pairs of the same gender) and hyper-divergent group (Group 2, SN-MP > 35°, 16 MZ pairs; 9 DS pairs of the same gender). A total of 56 cephalometric variables were measured using lateral cephalographs. Craniofacial structures were divided into anteroposterior, vertical, dental, mandible, and cranial base characteristics. Falconer's method was used to calculate heritability (h2 > 0.8, high). After principal component analysis (PCA), the mean h2 value of each component was calculated. Results: Group 1 exhibited high heritability values in shape and position of the mandible, vertical angular/ratio variables, cranial base shape, and maxillary incisor inclination. Group 2 showed high heritability values in anteroposterior position of the maxilla, intermaxillary relationship, vertical angular variables, cranial base length, and mandibular incisor inclination. Occlusal plane inclination showed high heritability in both groups. Although vertical structure presented a high overall mean h2 value in Group 1, there were no structures that exhibited a high overall mean h2 value in Group 2. PCA derived 10 components with 91.2% and 92.7% of cumulative explanation in Groups 1 and 2, respectively. Conclusions: It is necessary to estimate or predict growth according to vertical pattern for providing differential diagnosis and orthodontic/orthopedic treatment planning.

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