Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

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Wood discrimination analyses of Pterocarpus tinctorius and endangered Pterocarpus santalinus using DART-FTICR-MS coupled with multivariate statistics

IAWA Journal ◽

10.1163/22941932-40190224 ◽

2019 ◽

Vol 40 (1) ◽

pp. 58-74 ◽

Cited By ~ 7

Author(s):

Maomao Zhang ◽

Guang Jie Zhao ◽

Bo Liu ◽

Tuo He ◽

Juan Guo ◽

...

Keyword(s):

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Partial Least Square ◽

Least Square ◽

Chemical Information ◽

Ion Cyclotron Resonance ◽

Timber Species ◽

Multivariate Statistical ◽

Fticr Ms ◽

Pterocarpus Santalinus

ABSTRACT Pterocarpus santalinus, listed in CITES Appendix II, is an endangered timber species as a result of illegal harvesting due to its high value and commercial demand. The growing demand for P. santalinus and timbers with the morphologically similar Pterocarpus tinctorius has resulted in confusion as well as identification problems. Therefore, it is of vital importance to explore reliable ways to accurately discriminate between P. santalinus and P. tinctorius. In this study, the method of direct analysis in real time and fourier transform ion cyclotron resonance mass spectrometry (DART-FTICR-MS), combined with multivariate statistical analysis, was used to extract chemical information from xylarium wood specimens and to explore the feasibility of distinguishing these two species. Significant differences were observed in their DART-FTICR-MS spectra. Orthogonal partial least square-discriminant analysis (OPLS-DA) showed the highest prediction, with an accuracy of 100%. These findings demonstrate the feasibility of authenticating wood types using DART-FTICR-MS coupled with multivariate statistical analysis.

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The Problem of Multiple Inference in Psychiatric Research

Australian & New Zealand Journal of Psychiatry ◽

10.3109/00048678509158831 ◽

1985 ◽

Vol 19 (3) ◽

pp. 265-274 ◽

Cited By ~ 46

Author(s):

Wayne Hall ◽

Kevin Bird

Keyword(s):

Statistical Analysis ◽

Statistical Inference ◽

Multivariate Statistical Analysis ◽

Research Study ◽

Psychiatric Research ◽

Exploratory Research ◽

Multivariate Statistical ◽

Advantages And Disadvantages ◽

Multivariate Study ◽

Study Designs

This paper deals with the problem of multiple inference in psychiatric research, an issue which arises whenever a researcher has to make more than one statistical inference in a single research study. It frequently arises in psychiatric research because of multivariate study designs, with subjects being measured on more than one dependent variable with the intention of studying differences between groups in mean scores. The disadvantages of the commonly adopted strategy of using multiple univariate tests (e.g. multiple t-tests) are outlined. Two broad strategies — Bonferroni-adjusted univariate tests and multivariate statistical analysis — are introduced. Their advantages and disadvantages are discussed in terms of their usefulness in confirmatory and exploratory research in psychiatry.

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Multivariate Statistical Analysis and Pattern Recognition

Advances in Chemistry - Magnetic Resonance of Carbonaceous Solids ◽

10.1021/ba-1993-0229.ch012 ◽

1992 ◽

pp. 253-267 ◽

Cited By ~ 1

Author(s):

David E. Axelson

Keyword(s):

Pattern Recognition ◽

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Multivariate Statistical

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Multivariate statistical analysis in stabilometry in human upright standing. (Second report). Pattern recognition of a stabilogram.

Nippon Jibiinkoka Gakkai Kaiho ◽

10.3950/jibiinkoka.92.909 ◽

1989 ◽

Vol 92 (6) ◽

pp. 909-922 ◽

Cited By ~ 1

Author(s):

KAZUNORI YAGI

Keyword(s):

Pattern Recognition ◽

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Multivariate Statistical ◽

Upright Standing

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Practical Processing of Spectrum Images by Multivariate Statistical Analysis: Advantages and Disadvantages

Microscopy and Microanalysis ◽

10.1017/s1431927613012130 ◽

2013 ◽

Vol 19 (S2) ◽

pp. 2028-2029 ◽

Cited By ~ 1

Author(s):

M. Watanabe

Keyword(s):

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Multivariate Statistical ◽

Advantages And Disadvantages

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

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Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of CHO cell cultures

10.22541/au.162696287.70196763/v1 ◽

2021 ◽

Author(s):

Hock Chuan Yeo ◽

Seo-Young Park ◽

Tessa Tan ◽

Say Kong Ng ◽

Meiyappan Lakshmanan ◽

...

Keyword(s):

Statistical Analysis ◽

Cell Cultures ◽

Cho Cells ◽

Multivariate Statistical Analysis ◽

Chinese Hamster ◽

Partial Least Square ◽

Multivariate Statistical ◽

Flux Balance ◽

A Genome ◽

Commercial Medium

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their growth, productivity, and product quality, practically media reformulation has been one of key focuses with several technical challenges which are due to the myriad of intricate molecular and regulatory mechanisms underlying the media effects on culture behaviours; it is highly required to systematically characterize metabolic bottlenecks of cell cultures in various media conditions. To do so, we combined multivariate statistical analysis with flux balance analysis of a genome-scale metabolic model of CHO cells based on the culture profiles of CHO-DG44 under one commercial medium and two in-house media. At the outset, we used partial least square regression to identify metabolite exchanges that are correlated to specific growth and productivity. By using a commercial medium as reference, we found sub-optimal level of four nutrients and two metabolic wastes that plausibly hinder cell growth and productivity with in-house media. Subsequently, we elucidated that the recycling of lactate and ammonia wastes to be affected by both glutamine and asparagine metabolisms mechanistically, and further modulated by hitherto unsuspected folate and choline supplements. In summary, the current work successfully demonstrated how multivariate statistical analysis can be synergistically combined with in silico analysis of metabolic models to uncover the mechanistic elements underlying the differing performance of various media. Our approach for the systematic identification of promising nutrient targets thus paves the way for cell culture medium reformulation to enhance cellular growth and recombinant protein production.

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Ekstraksi TF-IDF untuk Kansei Word dalam Perancangan Interface E-Kinerja

Journal of Information Technology ◽

10.47292/joint.v3i1.44 ◽

2021 ◽

Vol 3 (1) ◽

pp. 5-16

Author(s):

Tri Sutrisna Bhayukusuma ◽

Ana Hadiana

Keyword(s):

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Local Governments ◽

The State ◽

Partial Least Square ◽

Least Square ◽

Engineering Method ◽

Kansei Engineering ◽

Multivariate Statistical ◽

Design Elements

E-performance web-based software is used to manage and assess the performance of employees in local government agencies. In the process, some of the local governments racing to create and develop E-performance applications. But there are still many E-performance applications that fail because they don't get a good response from their users in this case the State Civil Apparatus. Then it should be carried out supporting studies in the implementation process of making E-performance applications. One method to determine what is needed by the application of information systems in accordance with what is desired by users emotionally is the Kansei Engineering method. Because through Kansei Engineering can be investigated from various points of view that encourage users to use the information system application. In this research, an application program was created using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to select and determine several words from a few sentences in an article that will be used as a kansei word. After the screening and selection finally obtained as many as 20 words used as kansei words. A total of 30 participants were involved in this study, namely the State Civil Apparatus in the Government of Bandung City. Furthermore, the results of the questionnaire were processed using multivariate statistical analysis which includes Correlation Coefficient Analysis (CCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Partial Least Square (PLS). After passing through multivariate statistical analysis, the main factor of emotion concept in the design of the E-performance interface is obtained, the optimal factor. But there are other factors supporting the concept of emotion as an alternative design in designing the E-performance interface, the Smart factor. So as to obtain recommendations for designing the E-performance interface produced through the Kansei Engineering method approach in the form of a proposed matrix in which there are several design elements based on the "Optimal" emotional concept

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Phytochemical analysis of trifoliate orange during fermentation by HPLC–DAD–ESI–MS/MS coupled with multivariate statistical analysis

Acta Chromatographica ◽

10.1556/1326.2020.00818 ◽

2020 ◽

Author(s):

Dan Gao ◽

Chong Woon Cho ◽

Le Ba Vinh ◽

Jin Hyeok Kim ◽

Young Ho Kim ◽

...

Keyword(s):

Quality Control ◽

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Partial Least Square ◽

Array Detector ◽

Phytochemical Analysis ◽

Chemical Compositions ◽

Trifoliate Orange ◽

Multivariate Statistical ◽

Esi Ms

AbstractDuring the process of fermentation, the chemical compositions of trifoliate orange (Poncirus trifoliate (L). Raf) changed greatly. To provide a completely phytochemical profile, high-performance liquid chromatography-diode array detector-hyphenated with tandem mass spectrometry (HPLC–DAD–ESI-MS/MS) has been successfully applied to screen and identify the unknown constituents of trifoliate orange during fermentation, which make it available for the quality control of fermented products. Multivariate statistical analysis was performed to classify the trifoliate oranges based on the status of fermentation. A total of 8 components were identified among the samples. Hierarchical Clustering Analysis (HCA) and Principal Component Analysis (PCA) demonstrated the fermented and unfermented trifoliate oranges were obviously different, an effective and reliable Partial Least Square Discriminate Analysis (PLS-DA) technique was more suitable to provide accurate discrimination of test samples based their different chemical patterns. Furthermore, a permutation validated the reliability of PLS-DA and variable importance plot revealed that the characterized syringing, naringin, and poncirin showed the high ability to distinguish the trifoliate oranges during fermentation. The present investigation could provide detailed information for the quality control and evaluation of trifoliate oranges during the fermentation process.

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