Detection of Melamine Adulteration in Milk Powder by Using Optical Spectroscopy Technologies in the Last Decade—a Review

Liang, Wenting; Wei, Yuqiang; Gao, Mengjie; Yan, Xin; Zhu, Xinhua

doi:10.1007/s12161-020-01822-3

Cited by 37 publications

(11 citation statements)

References 58 publications

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“…Bhd., Selangor, Malaysia), and another sample from local store) and gelatine (1 sample from Lafleureshop.com, USA and another 2 samples from local store). For instance, karaya gum and nutrient agar are majoritively made up of carbohydrates [ 31 , 32 ], melamine has different form of non-protein amino nitrogen compounds [ 33 ], and gelatine is made of protein with water binding and emulsifier properties [ 34 ], whereas collagen is a natural protein derived from skin, tendon and bone [ 35 ]. The chemical composition of adulterants is similar but different to the EBN’s properties.…”

Section: Methodsmentioning

confidence: 99%

Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis

Muhammad

Yong

et al. 2022

Foods

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Edible bird’s nests (EBNs) are vulnerable to adulteration due to their huge demand for traditional medicine and high market price. Presently, there are pressing needs to explore field-deployable rapid screening techniques to detect adulteration of EBNs. The objective of this study is to explore the feasibility of using a handheld near-infrared (VIS/SW-NIR) spectroscopic device for the determination of EBN authenticity against the benchmark performance of a benchtop mid-infrared (MIR) spectrometer. Forty-nine authentic EBNs from the different states in Malaysia and 13 different adulterants (five types) were obtained and used to simulate the adulteration of EBNs at 1, 5 and 10% adulteration by mass (a total of 15 adulterated samples). The VIS/SW-NIR and MIR spectra collated were subsequently processed, modelled and classified using multi-class discriminant analysis. The VIS/SW-NIR results showed 100% correct classification for the collagen and nutrient agar classes in authenticity classification, while for the other classes, the lowest correct classification rate was 96.3%. For MIR analysis, only the karaya gum class had 100% correct classification whilst for the other four classes, the lowest rate of correct classification was at 94.4%. In conclusion, the combination of spectroscopic analysis with chemometrics can be a powerful screening tool to detect EBN adulteration.

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Section: Methodsmentioning

confidence: 99%

Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis

Muhammad

Yong

et al. 2022

Foods

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“…49 Spatial inhomogeneity of samples is easily destroyed after homogenization, which is not conducive to accurately locating harmful substances and tracking pollution sources. 50 In addition, textural property is one of the important qualities of food, and homogenization is unfavorable to the acquisition of samples' structural information. www.soci.org C An et al…”

Section: Problems In Rapid Detection Of Non-homogeneous Foodsmentioning

confidence: 99%

Qualitative identification of mature milk adulteration in bovine colostrum using noise‐reduced dielectric spectra and linear model

Yang

Zhu

et al. 2022

J Sci Food Agric

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BACKGROUND:The rapid and accurate identification of colostrum, a strong non-homogeneous food, remains a challenge. In the present study, the dielectric spectra including the dielectric constant (ε 0 ) and loss factor (ε 00 ) of 154 colostrum samples adulterated with 0-50% mature milk were measured from 20 to 4500 MHz. RESULTS:The results showed that the noise-reducing spectral preprocessing, including Savitzky-Golay (S-G), second derivative (SD), and S-G + SD, was significantly better than scattering-eliminating, including standard normal variate (SNV), multiplicative scatter correction (MSC), and SNV + MSC. The combination of S-G and SD was the best. Principal component analysis results demonstrated that dielectric spectroscopy is less susceptible to the inhomogeneity of colostrum and can be used to identify doped colostrum. The identification performance of linear models was better than that of non-linear models. The established linear discriminant analysis model based on full spectra had the best accuracy rates of 99.14% and 97.37% in the calibration and validation sets, respectively. Confirmatory tests on samples from different sources confirmed the satisfactory robustness of the proposed model. CONCLUSION: We found that the main unfavorable effect on the identification based on dielectric spectroscopy was noise interference, rather than scattering effect caused by inhomogeneity of colostrum. The satisfactory results undoubtedly cast light on rapid detection of strongly non-homogeneous foods based on dielectric spectroscopy.

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“…[4][5][6][7] Currently, targeted detection is mainly conducted in the routine control of milk powder. [8][9][10] However, the disadvantage here is that only known adulterants can be investigated, while numerous uncontrolled and unknown adulterants are being ignored. 11,12 Therefore, a nontargeted detection strategy that discriminates the occurrence of adulterants rather than identifying a specific adulterant is urgently needed.…”

Section: Introductionmentioning

confidence: 99%

Nontargeted detection and recognition of adulterants in milk powder using Raman imaging and neural networks

Xia

Huang

Zhang

et al. 2023

Analyst

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Detection of Melamine Adulteration in Milk Powder by Using Optical Spectroscopy Technologies in the Last Decade—a Review

Cited by 37 publications

References 58 publications

Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis

Adulteration Detection of Edible Bird’s Nests Using Rapid Spectroscopic Techniques Coupled with Multi-Class Discriminant Analysis

Qualitative identification of mature milk adulteration in bovine colostrum using noise‐reduced dielectric spectra and linear model

Nontargeted detection and recognition of adulterants in milk powder using Raman imaging and neural networks

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