Effect of homogenizer performance on accuracy and repeatability of mid-infrared predicted values for major milk components

Marzo, Larissa Di; Barbano, D.M.

doi:10.3168/jds.2016-11618

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Many efforts have been made to explore rapid detection methods without homogenization of samples, but the results are not satisfactory 22,46,48 . A high‐pressure homogenizer is often embedded in dairy analyzer, making instrument expensive in cost and large in size 49 …”

Section: Discussionmentioning

confidence: 99%

“…22,46,48 A high-pressure homogenizer is often embedded in dairy analyzer, making instrument expensive in cost and large in size. 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.…”

Section: Problems In Rapid Detection Of Non-homogeneous Foodsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Because of the mathematical cross-talk between molecule absorptions inherent in IR absorptiometry applied to complex fluids/ emulsions, the quality of homogenization also affects the precision of protein measurements. It has been shown that focussing and standardizing homogenization improves the quality of fat and protein measurements [14,23,24].…”

Section: Discussionmentioning

confidence: 99%

Milk analysis using milk analyzers in a standardized setting (MAMAS) study: A multicentre quality initiative

et al. 2020

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Background: Human milk analyzers are increasingly used to rapidly measure the macronutrient content in breast milk for individual target fortification, to reduce the risk of postnatal growth restriction. However, many milk analyzers are used without calibration, validation or quality assurance. Aims: To investigate measurement quality between different human milk analyzers, to test whether accuracy and precision of devices can be improved by establishing individual calibration curves, and to assess long-term stability of measurements, following good clinical laboratory practice (GCLP). Methods: Sets of identical breast milk samples were sent to 13 participating centres in North America and Europe, for a total of 15 devices. The study included 3 sets of samples: A) initial assessment of the device's performance consisting of 10 calibration samples with random replicates; B) long term stability and quality control consisting of 2 batches of samples to be measured every time before the device is used, over 6 months; C) ring trial consisting of 2 samples to be measured monthly. The devices tested were Unity SpectraStar (n ¼ 5) and MIRIS Human Milk Analyzer (n ¼ 10). Results: There are significant variations in accuracy and precision between different milk analyzers' fat, protein and lactose measurements. However, the accuracy of measurements can be improved by establishing individual correction algorithms. Repeated measurements are more robust when coming from a larger batch volume. Long term stability also varies between devices. Conclusion:The variations in measurements between devices are clinically significant and would impact both daily dietary prescriptions, and the outcomes of clinical studies assessing the effect of targeted adjustment of nutrient intake in preterm babies. This study shows that it is crucial to follow GCLP when using milk analyzers to ensure proper measurement of macronutrients, similar to what is required of other medical devices.

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“…True protein was calculated as TN − NPN × 6.38. Fat particle size was determined with a Mastersizer 2000 (Malvern) as previously described (Di Marzo and Barbano, 2016).…”

Section: Total Solids Fat Protein and Particle Sizementioning

confidence: 99%

Children's perceptions of fluid milk with varying levels of milkfat

Keefer

Sipple

Carter

et al. 2022

Journal of Dairy Science

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Schools participating in federal meal programs are limited to serving skim or low-fat (≤1%) flavored and unflavored milk. Few studies have directly addressed child perceptions and preferences for milk containing different amounts of milkfat. The objective of this study was to determine whether children can differentiate between flavored and unflavored fluid milk containing varying levels of milkfat and whether preferences for certain levels of milkfat exist. Flavored and unflavored milks containing 4 different percentages of milkfat (≤0.5, 1, 2, and 3.25%) were high-temperature, shorttime processed, filled into half-gallon light-shielded milk jugs, and stored at 4°C in the dark. Milks were evaluated by children (ages 8-13 yr) following 7 d at 4°C. Acceptance testing and tetrad difference testing were conducted on flavored and unflavored milks with and without visual cues to determine if differences were driven by visual or flavor or mouthfeel cues. Child acceptance testing (n = 138 unflavored; n = 123 flavored) was conducted to evaluate liking and perception of selected attributes. Tetrad testing (n = 127 unflavored; n = 129 flavored) was conducted to determine if children could differentiate between different fat levels even in the absence of a difference in acceptance. The experiment was replicated twice. When visual cues were present, children had higher overall liking for 1% and 2% milks than skim for unflavored milk and higher liking for chocolate milks containing at least 1% milk fat than for skim. Differences in liking were driven by appearance, viscosity, and flavor. In the absence of visual cues, no differences were observed in liking or flavor or mouthfeel attributes for unflavored milk but higher liking for at least 1% milk fat in chocolate milk compared with skim was consistent with the presence of visual cues. From tetrad testing, children could visually tell a difference between all unflavored pairs except 2% versus whole milk and could not detect consistent differences between milkfat pairs in the absence of visual cues. For chocolate milk, children could tell a difference between all milk fat pairs with visual cues and could tell a difference between skim versus 2% and skim versus whole milk without visual cues. These results demonstrate that in the absence of package-related flavors, schoolage children like unflavored skim milk as well as milk with higher fat content in the absence of visual cues. In contrast, appearance as well as flavor and mouthfeel attributes play a role in children's liking as well as their ability to discriminate between chocolate milks containing different amounts of fat, with chocolate milk containing at least 1% fat preferred. The sensory quality of school lunch milk is vital to child preference, and processing efforts are needed to maximize school milk sensory quality.

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Effect of homogenizer performance on accuracy and repeatability of mid-infrared predicted values for major milk components

Cited by 7 publications

References 12 publications

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

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

Milk analysis using milk analyzers in a standardized setting (MAMAS) study: A multicentre quality initiative

Children's perceptions of fluid milk with varying levels of milkfat

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