Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk

Yao, Zhiqiu; Nie, Pei; Zhang, Xinxin; Chen, Chao; An, Zhigao; Wei, Ke; Zhao, Junwei; Lv, Haimiao; Niu, Kaifeng; Yang, Ying; Zou, Wenna; Yang, Liguo

doi:10.3390/foods12061199

Cited by 1 publication

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“…The devised approach was subjected to validation following the guidelines outlined in the International Conference on Harmonization (ICH) Q2 (R1). The Limit of Detection (LOD) was determined through analysis of six pure camel milk samples, with the standard deviation of the matrix being calculated for this purpose [27].…”

Section: Quality Control For the Methodsmentioning

confidence: 99%

Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models

Yao,

Zhang,

Nie

et al. 2023

Foods

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Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10–90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.

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Section: Quality Control For the Methodsmentioning

confidence: 99%