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Sudan I

$43

  • Brand : BIOFRON

  • Catalogue Number : BF-S2006

  • Specification : 98%

  • CAS number : 842-07-9

  • Formula : C16H12N2O

  • Molecular Weight : 248.28

  • PUBCHEM ID : 13297

  • Volume : 20mg

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Catalogue Number

BF-S2006

Analysis Method

HPLC,NMR,MS

Specification

98%

Storage

2-8°C

Molecular Weight

248.28

Appearance

Red powder

Botanical Source

synthesis

Structure Type

Others

Category

Standards;Natural Pytochemical;API

SMILES

C1=CC=C(C=C1)N=NC2=C(C=CC3=CC=CC=C32)O

Synonyms

Sudan J/2-Naphthalenol, 1- (phenylazo)-/2-naphthalenol, 1-[(E)-phenylazo]-/1-(2-phenyldiaz-1-enyl)-2-naphthol/Carminaph/1-(Phenyldiazenyl)-2-naphthol/Soudan I/2-Naphthalenol, 1-(2-phenyldiazenyl)-/SCARLET B/1-[(E)-phenyldiazenyl]naphthalen-2-ol/SUDAN 1/1-phenylazo-naphthalen-2-ol/2-Naphthalenol, 1-[(E)-2-phenyldiazenyl]-/C.I. Solvent Yellow 14/benzene-1-azo-2-naphthol/SUDAN/1-[(E)-Phenyldiazenyl]-2-naphthol/2-phenylazo-naphthalen-1-ol/1-benzeneazo-2-naphthol/1-(Phenylazo)-2-naphthol/1-Phenylazo-β-naphthol

IUPAC Name

1-phenyldiazenylnaphthalen-2-ol

Density

1.2±0.1 g/cm3

Solubility

Flash Point

290.2±13.3 °C

Boiling Point

443.7±28.0 °C at 760 mmHg

Melting Point

131-133 °C

InChl

InChl Key

WGK Germany

RID/ADR

HS Code Reference

2927000000

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:842-07-9) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

PMID

31657462

KEYWORDS

1-phenylazo-2-naphthol; CAS no. 842-07-9; CI Solvent Yellow 14; Sudan I; case report; eye; eyewear; glasses

Title

CI Solvent Yellow 14 (Sudan I) identified as the allergen in a plastic part of glasses.

Author

Friis UF1, Dahlin J2, Isaksson M2, Zachariae C3, Johansen JD1.

Publish date

2020 Mar;

PMID

31535956

Abstract

Non-destructive, simple and fast techniques for identifying authentic palm oil and those adulterated with Sudan dyes using portable NIR spectroscopy would be very beneficial to West Africa countries and the world at large. In this study, a portable NIR spectroscopy coupled with multivariate models were developed for detecting palm oil adulteration. A total of 520 samples of palm oil were used comprising; 40 authentic samples together with 480 adulterated samples containing Sudan dyes (I, II, III, IV of 120 samples each). Multiplicative scatter correction (MSC) preprocessing technique plus Principal component analysis (PCA) was used to extract relevant spectral information which gave visible cluster trends for authentic samples and adulterated ones. The performance of Linear discriminant analysis (LDA) and Support vector machine (SVM) were compared, and SVM showed superiority over LDA. The optimised results by cross-validation revealed that MSC-PCA + SVM gave an identification rate above 95% for both calibration and prediction sets. The overall results show that portable NIR spectroscopy together with MSC-PCA + SVM model could be used successfully to identify authentic palm oils from adulterated ones. This would be useful for quality control officers and consumers to manage and control Sudan dyes adulteration in red palm oil.

KEYWORDS

Palm oil; linear discriminant analysis; portable NIR spectroscopy; quality control; support vector machine

Title

Rapid and nondestructive fraud detection of palm oil adulteration with Sudan dyes using portable NIR spectroscopic techniques.

Author

eye E1, Elliott C2, Sam-Amoah LK1, Mingle C3.

Publish date

2019 Nov;

PMID

31180811

Abstract

Spices are added in order to enhance the organoleptic characteristics of food and culinary dishes, making them more attractive for consumers. The use of illicit cheap colourants might be profitable along the food supply chain, posing undue risks to human health. This work evaluates the feasibility of NIR spectroscopy with chemometrics as a rapid, simple, non-destructive and affordable screening tool to determine the presence of Sudan I, II, III, IV and Para-red dyes in paprika. The dataset comprised unadulterated and adulterated samples with the five studied dyes at different concentration levels. Several multivariate classification models were built with Linear Discriminant Analysis (LDA) and different machine learning techniques. Preliminary results show that a classifier based on only six wavenumbers is able to determine the presence of some of these dyes in food samples in levels that may represent risk to human health. Sensitivities and specificities above 90% were obtained in almost all cases. These results show the feasibility of inexpensive and portable devices that can be useful for screening out adulterated stock along the food chain supply.

KEYWORDS

NIR; Paprika adulteration; Sudan dyes; few-variables sensing; machine learning analysis; screening methods

Title

NIR-based Sudan I to IV and Para-Red food adulterants screening.

Author

Trentanni Hansen GJ1, Almonacid J2, Albertengo L1, Rodriguez MS1, Di Anibal C1, Delrieux C3.

Publish date

2019 Aug;


Description :

Sudan I is an organic compound, typically classified as an azo dye.