Mid-infrared spectrometry of milk for dairy metabolomics: A comparison of two sampling techniques and effect of homogenization

A portable dry film FTIR instrument for industrial food and bioprocess applications

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

Molecular binding mechanism and novel antidiabetic and anti-hypertensive bioactive peptides from fermented camel milk with anti-inflammatory activity in raw macrophages cell lines

The investigation was to determine the effect of camel milk fermented with Limosilactobacillus fermentum KGL4 (MTCC 25515) on ACE-inhibiting, anti-inflammatory, and diabetes-preventing properties and also to release the novel peptides with antidiabetic and anti-hypertensive attributes with molecular interaction studies. Growth conditions were optimised on the basis of total peptide production by inoculating the culture in camel milk at different rates (1.5, 2.0, and 2.5%) along with different incubation periods (12, 24, 36, and 48 h). However, after 48 h of fermentation with a 2.5% rate of inoculum, the highest proteolytic activity was obtained. Reverse phase high-pressure liquid chromatography (RP-HPLC) was used to calculate the % Rpa from permeates of 3 kDa and 10 kDa fractions. Molecular weight distributions of fermented and unfermented camel milk protein fractions were compared using SDS-PAGE. Spots obtained from 2D gel electrophoresis were separated on the basis of pH and molecular weight. Spots obtained from 2D gel were digested with trypsin, and the digested samples were subjected to RP-LC/MS for the generation of peptide sequences. The inhibition of tumour necrosis factor alpha, interleukin-6, and interleukin-1 during fermentation was studied using RAW 264.7 macrophages. In the study, fermented camel milk with KGL4 (CMKGL4) inhibited LPS-induced nitric oxide (NO) production and pro-inflammatory cytokine production (TNF-α, IL-6, and IL-1β) by the murine macrophages. The results showed that the peptide structures (YLEELHRLNK and YLQELYPHSSLKVRPILK) exhibited considerable binding affinity against hPAM and hMGA during molecular interaction studies.

Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands

Milk is one of the most important multicomponent superfoods owing to its rich macronutrient composition. It requires quality control at all the production stages from the farm to the finished products. A localized surface plasmon resonance optical sensor based on a citrate-capped silver nanoparticle (Cit-AgNP)–coated glass substrate was developed. The fabrication of such sensors involved a single-step synthesis of Cit-AgNPs followed by surface modification of glass slides to be coated with the nanoparticles. The scanning electron microscope micrographs demonstrated that the nanoparticles formed monolayer islands on glass slides. The developed surface-enhanced infrared absorption spectroscopy (SEIRA) sensor was coupled to artificial neural networking (ANN) for the qualitative differentiation between cow, camel, goat, buffalo, and infants’ formula powdered milk types. Moreover, it can be used for the quantitative determination of the main milk components such as fat, casein, urea, and lactose in each milk type. The qualitative results showed that the obtained FTIR spectra of cow and buffalo milk have high similarity, whereas camel milk resembled infant formula powdered milk. The most difference in FTIR characteristics was evidenced in the case of goat milk. The developed sensor adds several advantages over the traditional techniques of milk analysis using MilkoScan™ such as less generated waste, elimination of pre-treatment steps, minimal sample volume, low operation time, and on-site analysis.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Application of multivariate data analysis for food quality investigations: An example-based review

These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods.

Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry–Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction.

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow's Lactation

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows’ lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

Use of Fourier Transform Infrared Spectroscopy for Monitoring the Shelf Life and Safety of Yogurts Supplemented With a Lactobacillus plantarum Strain With Probiotic Potential

The current study aimed to explore the performance of a probiotic Lactobacillus strain as an adjunct culture in yogurt production and to assess Fourier transform infrared spectroscopy as a rapid, noninvasive analytical technique to evaluate the quality and the shelf life of yogurt during storage. In this respect, bovine milk (full-fat) was inoculated with the typical yogurt starter culture without (control case) or with the further addition of Lactobacillus plantarum T571 as an adjunct (probiotic case). The milk was also inoculated with a cocktail mixture of three strains of Listeria monocytogenes in two different initial levels of inoculum, and the fermentation process was followed. Accordingly, yogurt samples were stored at 4 and 12°C, and microbiological, physicochemical, molecular, and sensory analyses were performed during storage. Results showed that the lactic acid bacteria exceeded 7 log CFU/g during storage in all samples, where the probiotic samples displayed higher acidity, lower pH, and reduced counts of Lb. monocytogenes in a shorter period than the control ones at both temperatures. Pulsed-field gel electrophoresis verified the presence of the probiotic strain until the end of storage at both temperatures and in adequate amounts, whereas the survival and the distribution of Listeria strains depended on the case. The sensory evaluation showed that the probiotic samples had desirable organoleptic characteristics, similar to the control. Finally, the spectral data collected from the yogurt samples during storage were correlated with microbiological counts and sensory data. Partial least squares and support vector machine regression and classification models were developed to provide quantitative estimations of yogurt microbiological counts and qualitative estimations of their sensory status, respectively, based on Fourier transform infrared fingerprints. The developed models exhibited satisfactory performance, and the acquired results were promising for the rapid estimation of the microbiological counts and sensory status of yogurt.

Infrared spectroscopy combined with random forest to determine tylosin residues in powdered milk

The contamination of milk by antibiotic residues is a worldwide health and food safety problem. There is a need to develop new methods for the rapid determination of antibiotic residues in milk. A method has been developed for determining tylosin residues directly in powdered milk using Fourier Transformed Infrared spectroscopy (FTIR). Tylosin is a broad-spectrum macrolide antibiotic. The spectra obtained were submitted to chemometric analysis to obtain a prediction model for tylosin concentration in powdered milk. Using the Boruta algorithm, the absorption bands related to the milk contamination by the antibiotic were identified. Random forest was shown to be adequate for the prediction of tylosin residues in milk at low concentrations (≤ 100 μg L^-1) and the prediction model generated showed high correlation and determination coefficients (greater than 0.95). The proposed methodology proved to be efficient for the investigation of antibiotic residues in powdered milk.

Application of genetic algorithm and multivariate methods for the detection and measurement of milk-surfactant adulteration by attenuated total reflection and near-infrared spectroscopy

BACKGROUND

The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg^-1 in adulterated samples.

RESULTS

Prominent peaks in the spectral range of 5500-6400 cm^-1 , 1160-1260 cm^-1 and 1049-1080 cm^-1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R² value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test.

CONCLUSION

The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg^-1 in milk. © 2020 Society of Chemical Industry.

FTIR spectroscopy with chemometrics for determination of tylosin residues in milk

BACKGROUND

Contamination of milk by antibiotic residues represents risks to the health of consumers; therefore they should be monitored. The objective of this study was to propose a methodology for the determination of tylosin residues directly in fluid milk based on mid-infrared spectroscopy associated with chemometrics, using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy associated with multilayer perceptron network (MLP) and partial least squares (PLS).

RESULTS

MLP was shown to be adequate for the discrimination of milk samples contaminated with tylosin below or equal to or above the maximum residue limit (MRL), with an accuracy greater than 99%, using FTIR spectra data. PLS was shown to be appropriate for the prediction of the very low concentrations (0-100 μg L^-1 ) of tylosin residues in milk using FTIR spectra data. PLS models with high correlation coefficients (R > 0.99) were generated.

CONCLUSION

FTIR with chemometrics proved to be a non-destructive, efficient and low-cost method for the investigation and quantification of tylosin residues directly in fluid milk. © 2020 Society of Chemical Industry.

Use of near and mid infra-red spectroscopy for analysis of protein, fat, lactose and total solids in raw cow and camel milk

Milk samples (150 cow and 217 camel milk samples) were analyzed for protein, fat, lactose and total solids by near and mid-infrared transmission spectroscopy. Excellent positive correlations between the two methods were obtained for both types of milk (p < 0.001); for protein (r ≥ 0.96), fat (r ≥ 0.99), lactose (r = 0.82) and total solids (r = 0.90). The mean of the relative difference ((MIR values - NIR values)/0.5 (MIR values + NIR values) × 100%) for cow and camel milk were, for protein (+8.2 & +13.4%), fat (-9.3 & +0.9%), lactose (-5.4 &-0.7%) and total solids (-2.2 &-3.4%), respectively. The difference between the two methods may be due to the effects of differences in milk homogeneity, especially with respect to casein micelles and fat globules.

Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows

In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations <0.6 mmol/L with a root mean square error of prediction of <0.143 mmol/L. However, for blood plasma with >1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R² values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping.

Comparison of the Potential Abilities of Three Spectroscopy Methods: Near-Infrared, Mid-Infrared, and Molecular Fluorescence, to Predict Carotenoid, Vitamin and Fatty Acid Contents in Cow Milk

The objective of this work is to compare the ability of three spectroscopy techniques: molecular fluorescence, near-infrared (NIR), and mid-infrared with attenuated total reflectance (MIR-ATR) spectroscopy to predict the concentrations of 8 carotenoids, 6 vitamins and 22 fatty acids (FA) in cow’s milk. A dataset was built through the analysis of 242 frozen milk samples from different experiments. The milk compounds were analysed using reference methods and by NIR, MIR-ATR, and fluorescence to establish different predictive models. NIR spectroscopy allowed for better prediction of cis9-β-carotene, β-cryptoxanthin and the sum of carotenoids than the other techniques, with a coefficient of cross-validation in calibration (R²CV) > 0.60 and a coefficient of determination in validation (R²V) > 0.50. Their standard errors of prediction (SEP) were equal to 0.01, except for the sum of carotenoids (SEP = 0.15). However, MIR-ATR and fluorescence seem usable for the prediction of lutein and all-trans-β-carotene, respectively. These three spectroscopy methods did not allow us to predict (R²CV < 0.30) vitamin contents except, for vitamin A (the best R²CV = 0.65 with NIR and SEP = 0.15) and α-tocopherol (the best R²CV = 0.56 with MIR-ATR and SEP = 0.41), but all R²V were <0.30. NIR spectroscopy yielded the best prediction of the selected milk FA.

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

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 = 5) and MIRIS Human Milk Analyzer (n = 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.

Mass spectrometry-based metabolomics of volatiles as a new tool for understanding aroma and flavour chemistry in processed food products

BACKGROUND

When foods are processed or cooked, many chemical reactions occur involving a wide range of metabolites including sugars, amino acids and lipids. These chemical processes often lead to the formation of volatile aroma compounds that can make food tastier or may introduce off-flavours. Metabolomics tools are only now being used to study the formation of these flavour compounds in order to understand better the beneficial and less beneficial aspects of food processing.

AIM OF REVIEW

To provide a critical overview of the diverse MS-based studies carried out in recent years in food metabolomics and to review some biochemical properties and flavour characteristics of the different groups of aroma-related metabolites. A description of volatiles from processed foods, and their relevant chemical and sensorial characteristics is provided. In addition, this review also summarizes the formation of the flavour compounds from their precursors, and the interconnections between Maillard reactions and the amino acid, lipid, and carbohydrate degradation pathways.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review provides new insights into processed ingredients and describes how metabolomics will help to enable us to produce, preserve, design and distribute higher-quality foods for health promotion and better flavour.

High-throughput quantitation of bovine milk proteins and discrimination of commercial milk types by external cavity-quantum cascade laser spectroscopy and chemometrics

Mid-infrared (IR) external cavity-quantum cascade laser (EC-QCL) spectroscopy combined with partial least square modeling (PLS) enables quantitation of bovine milk proteins and discrimination of commercial milk types.

Comparative appraisal of ghee and common vegetable oils for spectral characteristics in FT-MIR reflectance spectroscopy

FT-MIR spectra of ghee (anhydrous milk fat) and common vegetable oils were acquired using HATR in 4000-650 cm^-1 region. The differences in absorbance by carbon-hydrogen (C-H) stretch in fatty acid chain at 3.48 μm and absorbance by carbonyl (C-O) stretch of ester linkage at 5.7 μm in ghee and that in vegetable oils were studied. The clear differences in the spectra of ghee and that of the vegetable oils were noticed in fingerprint region, which can be very well utilized to develop FT-MIR spectroscopy as a promising tool to detect presence of common vegetable oils mixed in the ghee as an adulterant.

Changes in the Milk Metabolome of the Giant Panda (Ailuropoda melanoleuca) with Time after Birth--Three Phases in Early Lactation and Progressive Individual Differences

Ursids (bears) in general, and giant pandas in particular, are highly altricial at birth. The components of bear milks and their changes with time may be uniquely adapted to nourish relatively immature neonates, protect them from pathogens, and support the maturation of neonatal digestive physiology. Serial milk samples collected from three giant pandas in early lactation were subjected to untargeted metabolite profiling and multivariate analysis. Changes in milk metabolites with time after birth were analysed by Principal Component Analysis, Hierarchical Cluster Analysis and further supported by Orthogonal Partial Least Square-Discriminant Analysis, revealing three phases of milk maturation: days 1–6 (Phase 1), days 7–20 (Phase 2), and beyond day 20 (Phase 3). While the compositions of Phase 1 milks were essentially indistinguishable among individuals, divergences emerged during the second week of lactation. OPLS regression analysis positioned against the growth rate of one cub tentatively inferred a correlation with changes in the abundance of a trisaccharide, isoglobotriose, previously observed to be a major oligosaccharide in ursid milks. Three artificial milk formulae used to feed giant panda cubs were also analysed, and were found to differ markedly in component content from natural panda milk. These findings have implications for the dependence of the ontogeny of all species of bears, and potentially other members of the Carnivora and beyond, on the complexity and sequential changes in maternal provision of micrometabolites in the immediate period after birth.

Application of hand-held and portable infrared spectrometers in bovine milk analysis

A simple and fast method for the detection and quantification of milk adulteration was developed using portable and hand-held infrared (IR) spectrometers. Milk samples were purchased from local supermarkets (Columbus, OH, USA) and spiked with tap water, whey, hydrogen peroxide, synthetic urine, urea, and synthetic milk in different concentrations. Spectral data were collected using mid-infrared (MIR) and near-infrared (NIR) spectrometers. Soft independent modeling of class analogy (SIMCA) classification models exhibited tight and well-separated clusters allowing the discrimination of control from adulterated milk samples. Partial least-squares regression (PLSR) was used to estimate adulteration levels, and results showed high coefficients of determination (R(2)) and low standard errors of prediction (SEP). Classification and quantification models indicated that the tested MIR systems were superior to NIR systems in monitoring milk adulteration. This method can be potentially used as an alternative to traditional methods due to their simplicity, sensitivity, low energy cost, and portability.

Metabolomics in food science

Metabolomics, the newest member of the omics techniques, has become an important tool in agriculture, pharmacy, and environmental sciences. Advances in compound extraction, separation, detection, identification, and data analysis have allowed metabolomics applications in food sciences including food processing, quality, and safety. This chapter discusses recent advances and applications of metabolomics in food science.