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As one of the main food ingredients in the world, flour is well-known for its color, aroma and taste. The quality of flour is directly related to people's health, and the use of food additives has attracted much attention. Azoformamide, as a bleaching agent and flour improver, is used for flour curing. It can oxidize the sulfur group (- SH) on flour protein to the disulfide bond (- S-S -), so as to increase flour gluten, improve dough gas retention, increase the elasticity and toughness of baked products, and improve dough operability and conditioning [1]. However, azoformamide is extremely unstable, and it will decompose to form a small amount of semicarbazide [2] in the process of heating. Semicarbazide is a metabolite of furacilin veterinary drugs, which has strong side effects such as carcinogenesis, teratogenesis, mutagenesis [3].
At present, the traditional detection methods for azoformamide in flour are mainly high performance liquid chromatography (HPLC) [4] and high performance liquid chromatography-mass spectrometry (HPLC-MS) [5]. However, the pre-processing is complex, the testing time is long, and it needs skilled operators, which is generally limited to laboratory applications. With the development of spectroscopic technology, some spectroscopic methods have shown good applications in flour additive detection, such as near-infrared spectroscopy [6], terahertz spectroscopy [7], Raman spectroscopy [8], hyperspectral imaging technology, and so on.
Because of the absolute advantage of HPLC in separation and analysis, it can effectively separate the food samples with complex matrix, so it can effectively eliminate the influence of interferences, greatly improve the selectivity and sensitivity of detection, and become one of the most promising detection technologies in the field of food safety. At present, HPLC detection of azoformamide in flour and its products is often reported in the literature. ZHAO Li et al. Established a high-performance liquid chromatography method to determine the content of azoformamide in wheat flour, and verified it by liquid chromatography-mass spectrometry. This method was extracted with N, N-dimethylformamide solution and derived with triphenylphosphine. Separation was performed on a CapcellpakC 18 UG 120 (4.6mm × 250mm, 5 μm) column. The flowability was methanol and aqueous solution for gradient elution. HPLC-diode array detector was used for determination. The detection limit of this method is 1 mg/kg, the limit of quantification is 3 mg/kg, the linear range is 1 to 100 μg/mL, the standard recovery is 99.8% to 106.3%, and the relative standard deviation is 1.12% to 5.09%.
LC-MS combines the advantages of chromatography and mass spectrometry, combining the ability of chromatography to separate complex samples with the high selectivity and sensitivity of mass spectrometry and the ability to provide relative molecular mass and structural information. It is widely used in many fields. Zhou Qiming et al studied the degradation of azoformamide and the pollution level of semicarbazide in flour and its products by LC-MS. It was pointed out that there was a linear relationship between the content of semicarbazide in flour products and the addition of azoformamide. It provides the possibility for the indirect determination of azoformamide by LC-MS. Wu Shuangzheng et al transformed azoformamide into semicarbazide by hygrothermal treatment and added 2-nitrobenzaldehyde to overnight derivatization at 37 ℃. After ethyl acetate extraction, electrospray positive ion scanning and multi-reaction monitoring mode (MRM), external standard method were used for quantitative analysis. By determining the amount of semicarbazide, the content of azoformamide was calculated indirectly, established an ultra high liquid phase-tandem mass spectrometry method to determine azoformamide in flour. The detection limit of this method is 0.21 mg/kg, and the recovery rate is 86.8% ~ 117.2%, RSD <6%.
Hyperspectral imaging technology is a high precision fusion technology of spectral technology and machine vision technology, which has the advantages of both technologies. The collected hyperspectral image contains not only the spectral information of the internal quality of the object to be measured, but also the image information of the external quality of the object to be measured. According to the positions of the light source and the spectroscopic camera, the methods of acquiring hyperspectral images can be divided into three types: diffuse transmission, transmission and diffuse reflection. Among them, diffuse reflection imaging has shown good applications in detecting food contaminants. Wang Xiao-bin et al used near infrared hyperspectral technology to detect azoformamide in flour. Firstly, the hyperspectral images of pure azoformamide, pure flour and flour samples containing different concentrations of azoformamide flour were collected to find the characteristic absorption band of azoformamide, which was different from flour. Secondly, the resolution of absorption peaks in the spectrum was enhanced by second derivative pretreatment, and the threshold of spectral similarity analysis was determined according to pure azoformamide and pure flour samples. Finally, the classification results of ten mixed samples with different concentrations by three spectral similarity analysis methods are analyzed, and the correctness of the classification is verified. The main conclusions are as follows:
(1) by analyzing the average diffuse reflectance spectra of pure azoformamide, four characteristic absorption bands different from those of flour were found: 1574.38, 2038.55, 2166.88 and 2269.91 nm.
(2) the average spectrum of mixed samples containing different concentrations of azoformamide in flour did not show the absorption characteristics of azoformamide, and the corresponding grayscale image of characteristic band could not distinguish between flour and azoformamide pixels. The spectral similarity analysis was used to calculate the single pixel spectrum, so as to realize the classification of azo formamide-flour mixed samples with different concentrations.
(3) three spectral similarity analysis algorithms (SAM, SCA and SCM) were used to classify azoformamide with different concentrations in flour. However, there are some problems in the classification results of azoformamide, such as large quantity difference, overestimation of concentration and zero classification. In the follow-up study, using thinner sample thickness for hyperspectral imaging can achieve better classification results.
(4) the verification results of the spectra corresponding to azoformamide pixels and flour pixels show that the spectral similarity analysis method can correctly classify azoformamide pixels and flour pixels in mixed samples.
Yasui A, Oishi M, Hayafuji C, et al. Shokuhin Eiseigaku Zasshi, 2016, 57(5): 133.
YAO Jing, HUANG Wei-xiong, LI Min, et al. Journal of Food Safety and Quality, 2016, 7(7): 2873.
Wang Y, Chan W. Journal of Agricultural and Food Chemistry, 2016, 64(13): 2802.
XIANG Lu, YI Ting-hui, WANG Ya, et al. Journal of Chongqing Medical University, 2014, 39(4): 533.
Lim H, Pahn K, Kim J, et al. Korean Journal of Food Science and Technology, 2010, 42(4): 377.
Gao S, Sun L, Hui G, et al. Food Analytical Methods, 2016, 9(9): 2642.
FANG Hong-xia, ZHANG Qi, ZHANG Hui-li, et al. Journal of the Chinese Cereals and Oils Association, 2016, 31(1): 107.
Xie Y, Li P, Zhang J, et al. SpectrochimicaActa Part A: Molecular and Biomolecular Spectroscopy, 2013, 114(10): 80.
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