Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species' origin

Rheological and tribological properties of skimmed milk gels: The impact of fish collagen peptides-polysaccharides complexes during acidification

Collagen peptides (CP) are widely utilized in dairy products for stabilization, gelation, and fat replacement. However, the mechanism of CP-polysaccharides complexes on the rheology and lubrication properties of skimmed yogurt remains underexplored. This study investigated effects of 0.5 % fish CP and its complexes with chitosan (CS), β-glucan (βG), and sodium hyaluronate (HA) on the structural, rheological, tribological, and sensory attributes of skimmed milk during acidification. Small-angle X-ray scattering revealed acidification at pH 4.6 caused a smoothing of high-q features, suggesting casein micelles aggregation and a more homogeneous gel structure. Microscopic analyses indicated CP addition, particularly with βG (CP-βG, 8:2 & 6:4), improved gel continuity and reduced syneresis, thereby enhancing creaminess and smoothness. Conversely, the CS addition and elevated HA concentrations disrupted the casein network, increasing syneresis due to polyelectrolyte complex formation and bridging flocculation. These findings suggested specific CP-polysaccharides combinations could potentially improve the quality of skimmed yogurt.

A Systematic Review of Spectroscopic Techniques for Detecting Milk Adulteration

Milk adulteration is a crucial worldwide concern that endangers food safety and public health, as it involves the deliberate tampering with milk by adding foreign substances or removing essential nutrients, often to boost profits or hinder microbial growth. Traditional detection methods frequently lack the sensitivity and speed required to identify adulterants within milk's complex matrix. This systematic review critically examines the application of spectroscopic techniques for detecting milk adulteration, focusing on Nuclear Magnetic Resonance (NMR), Infrared (IR) Spectroscopy, Raman Spectroscopy, Ultraviolet-Visible (UV-Vis) Spectroscopy, Mass Spectrometry, Laser-Based Techniques, Dielectric Spectroscopy, and X-Ray Spectroscopy. Each technique's principles, advantages, limitations, and specific applications in identifying adulterants, such as water, urea, melamine, added sugars, fats, preservatives, and heavy metals are discussed. The review highlights how these methods offer rapid, non-destructive, and sensitive analysis, enhancing the ability to detect adulterants at molecular levels. Despite advancements, challenges persist, including the complexity and natural variability of milk composition, high costs of advanced equipment, need for specialized expertise, and lack of standardized protocols. Future directions emphasize developing portable and cost-effective spectroscopic devices, integrating artificial intelligence and machine learning for advanced data analysis, and fostering international collaboration to establish standardized methodologies and comprehensive spectral databases. By addressing these challenges, spectroscopic techniques can be more widely implemented, ultimately safeguarding public health, ensuring the integrity of dairy products, and maintaining consumer trust in the global food supply chain.

Comparison of the mid-infrared spectra and prediction equations developed from morning and evening milk samples from twice-a-day milked dairy cows

Mid-infrared spectroscopy is a technology used globally for quantifying the concentration of fat, protein, lactose, and other constituents in the milk samples of both individual animals and bulk tank milk. Differences in the milk components and yield of cows are known to exist between morning and evening milk; nonetheless, differences in the spectra originating from the same cow from morning and evening milkings have never been investigated. Data were obtained from 2,602 dairy cows from 7 research farms in Ireland. A total of 199,288 morning milk spectra with associated evening milk spectra produced by the same cow within 24 h were available. Postediting, spectral data were available on the same 502 wavelengths in the mid-infrared region of the electromagnetic spectrum for all milk samples. Differences between morning and evening milk spectra produced by the same cow in a 24-h period were investigated using (1) the mean and SD of the difference between morning and evening spectra absorbance values, (2) the correlation between the morning and the respective evening wavelength absorbance values, and (3) the L2 distance, all of which were quantified across stages of lactation, years, and farms. The average (SD) difference between the morning and the evening spectrum absorbance values produced by the same cow within 24 h was 0.00097 (0.008), and it was always larger than 0.055 for the wavelengths between 2,920 cm-1 and 2,947 cm-1. The correlation between morning wavelength absorbance values and the respective evening wavelength absorbance values were all strong (i.e., >0.80) in the spectral region of 1,469 cm-1 to 1,473 cm-1; weak correlations of <0.26 existed between morning and evening spectra wavelengths in the region of 1,593 cm-1 to 1,597 cm-1. These trends in correlations generally persisted within different stages of lactation, years, and farms. Results from the L2 distance indicated that early lactation morning and evening spectra were more different from each other than when compared in late lactation; no large differences in the L2 distance across different farms and years were evident. The impact of a prediction equation developed from morning spectral data but applied to evening milk spectral data, and vice versa, was investigated. Nitrogen use efficiency (NUE) was the animal trait explored; the mean NUE (SD) in the validation dataset was 22.16 (4.86). The root mean square error for predictions developed and validated on morning spectra samples was 3.49; this increased to 3.85 for the same validation in the morning spectra when the prediction equation was developed using only evening spectra. Similarly, the root mean square error from predictions developed and validated on evening spectra samples was 3.46, which increased to 3.85 when the prediction equation was developed using only morning spectra. In conclusion, morning and evening milk spectra produced by the same cow within 24 h differ, particularly in some spectral regions; these differences affect the prediction performance of applied prediction equations.

Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy

Salmon fillet was analyzed via hand-held optical devices: fluorescence (@340 nm) and absorption spectroscopy across the visible and near-infrared (NIR) range (400-1900 nm). Spectroscopic measurements were benchmarked with nucleotide assays and potentiometry in an exploratory set of experiments over 11 days, with changes to spectral profiles noted. A second enlarged spectroscopic data set, over a 17 day period, was then acquired, and fillet freshness was classified ±1 day via four machine learning (ML) algorithms: linear discriminant analysis, Gaussian naïve, weighted K-nearest neighbors, and an ensemble bagged tree method. Dual-mode data fusion returned almost perfect accuracies (mean = 99.5 ± 0.51%), while single-mode ML analyses (fluorescence, visible absorbance, and NIR absorbance) returned lower mean accuracies at greater spread (77.1 ± 10.1%). Single-mode fluorescence accuracy was especially poor; however, via principal component analysis, we found that a truncated fluorescence data set of four variables (wavelengths) could predict "fresh" and "spoilt" salmon fillet based on a subtle peak redshift as the fillet aged, albeit marginally short of statistical significance (95% confidence ellipse). Thus, whether by feature selection of one spectral data set, or the combination of multiple data sets through different modes, this study lays the foundation for better determination of fish freshness within the context of rapid spectroscopic analyses.

Impact of Fava Bean (Vicia faba) Processing on Quality Characteristics and Digestibility of a Protein-Rich Snack

The impact of fava bean processing methods (soaking, autoclaving, fermentation) on a legume-based bars' quality, protein characteristics, and digestibility was shown. The antioxidant and the angiotensin-converting enzyme-inhibitory capacity before and after in vitro digestion were investigated to reveal the potential advantages of fava bean usage for snacks. All bars have demonstrated high protein content, varying from 22.1 to 25.1 g/100 g DB. Based on the fermented fava beans of Pleurotus ostreatus, the samples were characterized by a higher concentration of essential amino acids by 8.6% and a reduction of tannins by 18.5% compared with bars based on soaked fava beans. Sensory evaluation improved the color, texture, and overall acceptability of the bars with fermented legumes. Various types of bean processing did not significantly affect the protein digestibility of the bars. The fermentation method positively affected the angiotensin-converting enzyme-inhibitory properties of bars and increased by 16.5% (before digestion) and 15% (after digestion) compared with other samples. After digestion, samples were characterized by a high level of Fe bioaccessibility (100, 83, and 79% for the bars based on soaked, autoclaved, and fermented fava beans, respectively) and increased total phenolic content. These findings highlight the potential health benefits of fava bean usage for snack products.

Implementation of plant extracts for cheddar-type cheese production in conjunction with FTIR and Raman spectroscopy comparison

Plant extracts have demonstrated the ability to act as coagulants for milk coagulation at an adequate concentration, wide temperatures and pH ranges. This research is focused on the use of different vegetative extracts such as Citrus aurnatium flower extract (CAFE), bromelain, fig latex, and melon extract as economical and beneficial coagulants in the development of plant-based cheddar-type cheese. The cheddar-type cheese samples were subjected to physicochemical analysis in comparison to controlled cheese samples made from acetic acid and rennet. The fat, moisture, protein, and salt contents remained the same over the storage period, but a slight decline was observed in pH. The Ferric reducing antioxidant power (FRAP) increased with the passage of the ripening period. The FTIR and Raman spectra showed exponential changes and qualitative estimates in the binding and vibrational structure of lipids and protein in plant-based cheeses. The higher FTIR and Raman spectra bands were observed in acid, rennet, bromelain, and CAFE due to their firm and strong texture of cheese while lower spectra were observed in cheese made from melon extract due to weak curdling and textural properties. These plant extracts are economical and easily available alternative sources for cheese production with higher protein and nutritional contents.

Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

Utilization of two plant polysaccharides to improve fresh goat milk cheese: Texture, rheological properties, and microstructure characterization

This study aimed to evaluate the effects of added jujube polysaccharide (JP) and Lycium barbarum polysaccharide (LBP) on the texture, rheological properties, and microstructure of goat milk cheese. Seven groups of fresh goat milk cheese were produced with 4 levels (0, 0.2, 0.6, and 1%, wt/wt) of JP and LBP. The goat milk cheese containing 1% JP showed the highest water-holding capacity, hardness, and the strongest rheological properties by creating a denser and more stable casein network structure. In addition, the yield of goat milk cheese was substantially improved as a result of JP incorporation. Cheeses containing LBP expressed lower fat content, higher moisture, and softer texture compared with the control cheese. Fourier-transform infrared spectroscopy and low-field nuclear magnetic resonance analysis demonstrated that the addition of JP improved the stability of the secondary protein structure in cheese and significantly enhanced the binding capacity of the casein matrix to water molecules due to strengthened intermolecular interactions. The current research demonstrated the potential feasibility of modifying the texture of goat milk cheese by JP or LBP, available for developing tunable goat milk cheese to satisfy consumer preferences and production needs.

A simple and fast triplex-PCR for the identification of milk's animal origin in Halloumi cheese and yoghurt

In this research communication we describe a straightforward triplex-PCR protocol able to differentiate the origin of milk from three closely related species (goat, sheep and cow) in Halloumi, a cheese with Protected Designation of Origin (PDO), and yogurts. Halloumi must contain at least 51% sheep or goat milk, therefore, the fraudulent adulteration of this cheese with excess of cow milk must be routinely tested. The assay employs one universal forward primer and three species-specific reverse primers giving rise to 287 bp (cow), 313 bp (goat), and 336 bp (sheep) amplicons, under the same amplification conditions. This protocol, when used to test a small number of Cyprus commercial products, correctly detected mislabeling in Halloumi (2 out of 6 samples were adulterated) and yogurt brands (1 out of 4 was adulterated). The suggested protocol is a reliable tool for identifying the origin of milk in Halloumi cheeses and yogurts and can be used in any laboratory equipped with a thermocycler and an agarose gel electrophoresis apparatus.

Animal Species Authentication in Dairy Products

Milk is one of the most important nutritious foods, widely consumed worldwide, either in its natural form or via dairy products. Currently, several economic, health and ethical issues emphasize the need for a more frequent and rigorous quality control of dairy products and the importance of detecting adulterations in these products. For this reason, several conventional and advanced techniques have been proposed, aiming at detecting and quantifying eventual adulterations, preferentially in a rapid, cost-effective, easy to implement, sensitive and specific way. They have relied mostly on electrophoretic, chromatographic and immunoenzymatic techniques. More recently, mass spectrometry, spectroscopic methods (near infrared (NIR), mid infrared (MIR), nuclear magnetic resonance (NMR) and front face fluorescence coupled to chemometrics), DNA analysis (real-time PCR, high-resolution melting analysis, next generation sequencing and droplet digital PCR) and biosensors have been advanced as innovative tools for dairy product authentication. Milk substitution from high-valued species with lower-cost bovine milk is one of the most frequent adulteration practices. Therefore, this review intends to describe the most relevant developments regarding the current and advanced analytical methodologies applied to species authentication of milk and dairy products.

Authenticity assessment of dairy products by capillary electrophoresis

Milk and derivatives are a very important part in the diet of the world population. Products from goat, buffalo, and sheep species have a greater economic value than the cow ones, therefore, authenticity frauds by improperly adding cow's milk occur frequently: dairy products are among the seven more attractive foods for adulteration. Milk from each of the above-cited animal species has its own definite profile of whey proteins (variants of α-lactalbumin and β-lactoglobulin) and its definite profile of caseins (variants of α_S1 -, α_S2 -, β-, and κ-casein). Such proteins can be usefully exploited as markers of authenticity by using capillary electrophoresis which is the technique of choice for the analysis of proteins. Due to the multiple adjustable parameters that are unknown to other analytical techniques, capillary electrophoresis is able to detect frauds in milk mixtures and cheese with little use of solvents, fast analysis time, and ease of operation. This makes it attractive and competitive for routine checks that are very important to fight the adulteration market. Advantages and limitations are discussed.

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Table olives, the number one consumed fermented food in Europe, are widely consumed as they contain many valuable ingredients for health. It is also a food which may be the subject of adulteration, as many different olive varieties with different geographical origin, exist all over the word. In the present study, the image analysis of stones of six main Greek protected designation of origin (PDO) table olive varieties was performed for the control of their authentication and discrimination, with cv. Prasines Chalkidikis, cv. Kalamata Olive, cv. Konservolia Stylidas, cv. Konservolia Amfissis, cv. Throuba Thassos and cv. Throuba Chios being the studied olive varieties. Orthogonal partial least square discriminant analysis (OPLS-DA) was used for discrimination and classification of the six Greek table olive varieties. With a 98.33% of varietal discrimination, the OPLS-DA model proved to be an efficient tool to authentify table olive varieties from their morphological characteristics.

Chemometric Discrimination of the Geographical Origin of Three Greek Cultivars of Olive Oils by Stable Isotope Ratio Analysis

Α stable isotope ratio mass spectrometer was used for stable isotope ratio (i.e., δ¹³C, δ¹⁸O, and δ²H) measurements, achieving geographical discrimination using orthogonal projections to latent structures discriminant analysis. A total of 100 Greek monovarietal olive oil samples from three different olive cultivars (cv. Koroneiki, cv. Lianolia Kerkyras, and cv. Maurolia), derived from Central Greece and Peloponnese, were collected during the 2019-2020 harvest year aiming to investigate the effect of botanical and geographical origin on their discrimination through isotopic data. The selection of these samples was made from traditionally olive-growing areas in which no significant research has been done so far. Samples were discriminated mainly by olive cultivar and, partially, by geographical origin, which is congruent with other authors. Based on this model, correct recognition of 93.75% in the training samples and correct prediction of 100% in the test set were achieved. The overall correct classification of the model was 91%. The predictability based on the externally validated method of discrimination was good (Q² (cum) = 0.681) and illustrated that δ¹⁸O and δ²H were the most important isotope markers for the discrimination of olive oil samples. The authenticity of olive oil based on the examined olive varieties can be determined using this technique.