A novel approach to temperature-dependent thermal processing authentication for milk by infrared spectroscopy coupled with machine learning

Wang, Yutang; Ren, Haibin; Liang, Wei-Yue; Jin, Xing; Qing, Yuan; Liu, Zhirong; Chen, Dongmei; Zhang, Yinghua

doi:10.1016/j.jfoodeng.2021.110740

Cited by 12 publications

(4 citation statements)

References 43 publications

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“…SVM is a very widely used pattern recognition model proposed based on statistical theory ( Wang et al, 2021b ). In this paper, radial basis function was used as the kernel function of SVM.…”

Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

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Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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“…New samples were then classified in the orthogonal vs. score plot and assigned to the class when under the acceptance area defined for a given alpha value. Wang [65] evaluated four different classification methods (RF, LDA, SVM, and kNN) when dealing with milk authentication by infrared spectroscopy. To evaluate the best algorithm, the means of precision, accuracy, recall (true positive divided by the sum of true positive and false negative), and another parameter F1 (that together evaluate precision and recall) were calculated for each performance evaluation of all classes and for every classifier.…”

Section: Classificationmentioning

confidence: 99%

“…Wang [65] used three pre-processing steps: (1) mean centering, (2) first, and (3) secondorder Savitzky-Golay derivative, selecting at the end the first-order derivative as the better pre-processing method.…”

Section: Pre-processingmentioning

confidence: 99%

“…ReliefF filter works on multiple classes, building a weight vector that indicates for each feature (wavelengths in the NIRS case) how important it is to explain the differences between samples of different classes. Wang [65] instead used just an observation of the first two PCA loadings as the criterion to understand relevant wavelengths, but it was unclear if just the relevant wavelengths in the subsequent classification step were used.…”

Section: Data Reductionmentioning

confidence: 99%

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How Chemometrics Can Fight Milk Adulteration

Grassi

Tarapoulouzi

D’Alessandro

et al. 2022

Foods

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Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

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Detection of β-lactoglobulin under different thermal-processing conditions by immunoassay based on nanobody and monoclonal antibody

Nie

Wang

et al. 2023

Food Chemistry

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A novel approach to temperature-dependent thermal processing authentication for milk by infrared spectroscopy coupled with machine learning

Cited by 12 publications

References 43 publications

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

How Chemometrics Can Fight Milk Adulteration

Detection of β-lactoglobulin under different thermal-processing conditions by immunoassay based on nanobody and monoclonal antibody

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