Comparison of near-infrared and dielectric spectra for quantitative identification of bovine colostrum adulterated with mature milk

Yang, Ke; An, Changqing; Zhu, Jieliang; Lü, Chang; Zhu, Xinhua

doi:10.3168/jds.2022-21969

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…KS is a widely used partitioning technique in chemometric analysis, and was used here to generate a calibration set [ 56 , 57 , 58 ]. The KS algorithm uses the Euclidean distance technique to select samples that span the entire range of the dataset, facilitating the accuracy of chemometric models.…”

Section: Methodsmentioning

confidence: 99%

SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR

Babatunde,

Collins,

Lukman

et al. 2024

Foods

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Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.

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Section: Methodsmentioning

confidence: 99%

SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR

Babatunde,

Collins,

Lukman

et al. 2024

Foods

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“…It is mainly employed to eliminate the effects of diffuse reflections due to solid particle size, surface scattering, and variations in optical range [ 46 ]. Moreover, OSC is also used to eliminate errors arising from the surface scattering and baseline drift of spectral signals [ 47 ]. OSC is used to remove the information in the spectral matrix that is not related to the components to be measured by orthogonal projection and then carry out multivariate correction calculation.…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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“…In addition, the residual prediction deviation (RPD) was used to assess the accuracy of the model 32 . Details of the equations used to calculate these parameters can be found elsewhere 33 …”

Section: Methodsmentioning

confidence: 99%

Hydration dominates dielectric spectroscopy‐based high accuracy quantification of lactose content in cow's milk

et al. 2023

Self Cite

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BACKGROUND Lactose is a critical factor in the quality of milk and dairy products. Achieving high accuracy and rapid detection of lactose content in cow's milk remains a challenge. Dielectric spectroscopy has emerged as a promising tool for detecting food components. We explored the effect of lactose content on the dielectric spectra of cow's milk and we propose a rapid analytical method for the quantitative determination of lactose content in cow's milk with high accuracy based on dielectric spectra. RESULTS We obtained the dielectric spectra of 316 cow's milk samples in the frequency range 20–4500 MHz and noticed a strong negative correlation between the lactose content and the value of the dielectric loss factor (ε″) below 1500 MHz. Lactose does not affect cow's milk dielectric properties by excluded volume effect, but dominates the effect on the dielectric properties of cow's milk by hydration. The support vector regression model based on the variable importance in projection has the best prediction performance for lactose content. Its root‐mean‐square error of prediction set and residual prediction deviation is 0.29 g kg−1 and 6.968, respectively, and its prediction performance is better than that of the currently reported near‐infrared (NIR) method and other methods. CONCLUSION Despite the weak polarity of lactose molecules, its hydration is a significant factor affecting the dielectric properties of milk. The present study provides a basis for high accuracy and rapid quantitative detection of lactose in cow's milk based on dielectric spectra. © 2023 Society of Chemical Industry.

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Comparison of near-infrared and dielectric spectra for quantitative identification of bovine colostrum adulterated with mature milk

Cited by 10 publications

References 46 publications

SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR

SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Hydration dominates dielectric spectroscopy‐based high accuracy quantification of lactose content in cow's milk

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