Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy

Orche, Aimen El; Mamad, Amine; Elhamdaoui, Omar; Cheikh, Amine; Karbane, Miloud El; Bouatia, Mustapha

doi:10.1155/2021/5845422

Cited by 16 publications

(5 citation statements)

References 41 publications

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“…Principal component analysis is part of the group of multidimensional descriptive methods known as factorial methods (descriptive, unsupervised)(El Orche, Mamad, et al, 2021). PCA searches for the spatial directions in which individuals are most dispersed, assuming that these directions are the most interesting.…”

Section: Principal Component Analysis(pca)mentioning

confidence: 99%

Evaluation of FTIR and multivariate data analysis for the control of medical devices

2022

pnr

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Verification of sterilization number of medical devices is one of the main objectives to control its quality and ensure the protection of patients. The objective of this study is to investigate the capability of an automatic tool based on the combination of mid-infrared spectroscopy with a mathematical and statistical algorithm to estimate the number of sterilization cycles of medical pipes. Data analysis is performed by three main methods of pattern recognition: principal component analysis (PCA), support vector learning regression (SVR), and partial least squares regression (PLSR). The result obtained shows high performance capabilities for the prediction of sterilization cycle number. These technologies could be successfully applied in hospitals to ensure the quality of medical devices.

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Section: Principal Component Analysis(pca)mentioning

confidence: 99%

Evaluation of FTIR and multivariate data analysis for the control of medical devices

2022

pnr

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“…By utilizing a smaller number of principal components that explain the majority of the variance in the data with respect to the target variable, PCR is more effective in mitigating overfitting than linear regression on all original features, particularly for high-dimensional data such as spectra [ 22 ]. In recent studies, artificial neural networks (ANNs) have recently been investigated in FT-MIR spectroscopic analysis [ 23 , 24 ]. Random forests (RF) employ an evaluation of the relevance of variables to selectively choose informative variables, thereby facilitating the construction of models that are both parsimonious and robust, and ultimately enhancing the predictive power [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Nie

Zhang

et al. 2023

Foods

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Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.

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“…As computational power and machine learning methods have advanced, multivariate models are increasingly employed for calibrating component concentrations in milk. Recent studies have explored the use of Support Vector machines (SVM) and Artificial Neural Networks (ANN) in FT-MIR spectroscopic analysis [18,19]. Ana et al employed a convolutional neural networkbased image method to identify water adulteration in milk, achieving an accuracy of 93% [20].…”

Section: Introductionmentioning

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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Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy

Cited by 16 publications

References 41 publications

Evaluation of FTIR and multivariate data analysis for the control of medical devices

Evaluation of FTIR and multivariate data analysis for the control of medical devices

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

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

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