Chemometric studies of the effects of milk fat replacement with different proportions of vegetable oils in the formulation of fat-filled milk powders: Implications for quality assurance

Ejeahalaka, Kizito Kene; On, Stephen L.W.

doi:10.1016/j.foodchem.2019.05.120

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…In the end, they concluded that DD-SIMCA was more useful to classify the samples with good accuracy (98.9%). Other two interesting uses of PLS-DA applied to NIR data were conducted by Ejeahalaka et al [62] on cow's milk and by Di Donato et al [63] on donkey's milk. DD-SIMCA is a one-class classification algorithm proposed in 2017 by Zontov [64].…”

Section: Classificationmentioning

confidence: 99%

“…Between the papers explored, some different approaches have been found in NIR pre-processing. Ejeahalaka [62] performed a comparison between two different approaches: first, no pre-processing at all, and second, extended multiplicative signal correction (EMSC) on a selected part of the spectrum. In Galvan [61] some different pre-processing methods were tested before a mean centering for all: (1) raw data, (2) Savitzky-Golay smoothing (third-order polynomial and 21 window points), (3) standard normal variate (SNV), (4) multiplicative scatter correction (MSC), ( 5) first and second derivative with Savitzky-Golay smoothing, (6) SNV plus first and second derivative, and (7) MSC plus first and second derivative.…”

Section: Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Classificationmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

How Chemometrics Can Fight Milk Adulteration

Grassi

Tarapoulouzi

D’Alessandro

et al. 2022

Foods

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“…The method employed for the determination of FA concentrations was based on two major phases, namely onestep methylation and GC analysis as previously described (Ejeahalaka and On, 2019a). In brief, 100 μL of internal standard (C21:0 ester) was added into each of the two Kimax tubes containing 0.15 g of the freeze-dried milk samples.…”

Section: Milk Fatty Acid Determinationmentioning

confidence: 99%

“…Near infrared spectroscopic measurements were carried out on the freeze-dried milk samples using a FOSS NIRSystem (model DS 2500F) scanning spectrometer as previously described (Ejeahalaka and On, 2019a). In brief, 5 g of each of the 220 samples was placed in a 10 mL DS 2500 ring cup (cup type: 2004) and scanned at 0.5 nm intervals in the wavelength range of 850-2,500 nm in triplicates.…”

Section: Spectral Measurementsmentioning

confidence: 99%

Efficacy of near infrared spectroscopy to segregate raw milk from individual cows between herds for product innovation and traceability

Ejeahalaka¹,

Cheng

Kulasiri³

et al. 2020

Qual. Assur. Saf. Crops Foods

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Cows with specialised characteristics and requirements can be aggregated into different herds for targeted nutritional management and to facilitate on-farm segregation of raw milk for the production of high-value niche dairy products, offering improved economic returns. Rapid methods for independent verification of product quality and origin are desirable to support validation and traceability of such products. This study examined the use of near infrared spectroscopy (NIRS) to segregate raw milk from individual cows of multiple breeds from different herds fed on the same or differing feeding regimes, and to correlate and evaluate the efficacy of the predictions for crude protein and the milk fatty acid (FA) phenotypes for each of the herds. Reference values and near infrared spectra were obtained from representative freeze-dried raw milk samples (n = 220) collected from 847 lactating cows of 3 breeds from the Lincoln University dairy farm in New Zealand. The feed sources (i.e. pasture or pasture with lucerne silage) significantly influenced the protein and the FA values, and these differences were reflected in NIRS analyses. The partial least square regression models for crude protein determination showed excellent results, whereas for the most dominant FA, they were not appreciable. Maximum separation was obtained between the herds on the same feeding regime (mean specificity = 95.2%) using the partial least square discriminant analysis, and its overall performance in differentiating the objects was better than that of the soft independent modelling of class analogy. The multiclass analyses conducted in this study offer improvements to current approaches for evaluating and validating raw milk for the manufacture of specific dairy products, and for enhancing product traceability.

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“…A classification of bovine and buffalo anhydrous milk fat fractions and infant formula fats was performed using PCA applied on differential scanning calorimetry (DSC), FTIR and colorimetric data [ 73 ]. In another study, PCA was applied for the evaluation of the quality of milk powders subjected to fat replacement with different proportions of vegetable oils [ 74 ]. Vegetable fat can be easily detected in adulterated fresh cheeses using hierarchical cluster analysis (HCA) on the DSC data [ 63 ].…”

Section: Introductionmentioning

confidence: 99%

FTIR–PCA Approach on Raw and Thermally Processed Chicken Lipids Stabilized by Nano-Encapsulation in β-Cyclodextrin

Hădărugă

Chirilă

Szakal

et al. 2022

Foods

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This study evaluated similarities/dissimilarities of raw and processed chicken breast and thigh lipids that were complexed by β-cyclodextrin, using a combined FTIR–PCA technique. Lipid fractions were analyzed as non-complexed and β-cyclodextrin-complexed samples via thermogravimetry, differential scanning calorimetry and ATR-FTIR. The lipid complexation reduced the water content to 7.67–8.33%, in comparison with the β-cyclodextrin hydrate (~14%). The stabilities of the complexes and β-cyclodextrin were almost the same. ATR-FTIR analysis revealed the presence of important bands that corresponded to the C=O groups (1743–1744 cm−1) in both the non-complexed and nano-encapsulated lipids. Furthermore, the bands that corresponded to the vibrations of double bonds corresponding to the natural/degraded (cis/trans) fatty acids in lipids appeared at 3008–3011 and 938–946 cm−1, respectively. The main FTIR bands that were involved in the discrimination of raw and processed chicken lipids, and of non-complexed and complexed lipids, were evaluated with PCA. The shifting of specific FTIR band wavenumbers had the highest influence, especially vibrations of the α(1→4) glucosidic bond in β-cyclodextrin for PC1, and CH2/3 groups from lipids for PC2. This first approach on β-cyclodextrin nano-encapsulation of chicken lipids revealed the possibility to stabilize poultry fatty components for further applications in various ingredients for the food industry.

show abstract

Chemometric studies of the effects of milk fat replacement with different proportions of vegetable oils in the formulation of fat-filled milk powders: Implications for quality assurance

Cited by 10 publications

References 20 publications

How Chemometrics Can Fight Milk Adulteration

How Chemometrics Can Fight Milk Adulteration

Efficacy of near infrared spectroscopy to segregate raw milk from individual cows between herds for product innovation and traceability

FTIR–PCA Approach on Raw and Thermally Processed Chicken Lipids Stabilized by Nano-Encapsulation in β-Cyclodextrin

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