Rapid detection of adulteration of milks from different species using Fourier Transform Infrared Spectroscopy (FTIR)

Recent advances in detection techniques and chemometric methods for identifying adulterants in milk and dairy products

Milk adulteration poses a significant threat to food safety, driven by increasing global demand for milk and dairy products, particularly in developing countries. Supply limitations, compounded by challenges like pandemics and livestock diseases such as mastitis, often lead to unethical practices that compromise product quality and consumer health. Adulterants, including preservatives, non-milk fats, thickening agents, nitrogen-based compounds, and surfactants, are intentionally added to mimic natural properties or enhance the solid-not-fat (SNF) content of milk. Advanced detection techniques, such as spectroscopy, chromatography, biosensors (protein and DNA-based), and electromigration methods like SDS-PAGE, have emerged as critical tools for identifying these adulterants. Innovative approaches, including electrical sensors like electronic noses and tongues, further enhance detection accuracy. This study highlights recent advancements in instrumental techniques alongside the pivotal role of chemometric methods in analyzing complex datasets, improving precision, and ensuring reliable detection, offering insights into current progress, challenges, and opportunities in safeguarding milk integrity.

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.

Challenges in Using the Official Italian Method to Detect Bovine Whey Proteins in Protected Designation of Origin Buffalo Mozzarella: A Proteomic Approach to Face Observed Limits

This study critically examines the limitations of the official Italian methodology used for detecting bovine adulteration milk in Protected Designation of Origin (PDO) Mozzarella di Bufala Campana (MdBC). This method focuses on the whey fraction of cheese samples, which comprises about 1% of total MdBC proteins, and is based on a high-performance liquid chromatography (HPLC) quantification of the bovine β-lactoglobulin A (β-Lg A) as a marker. Here, we have demonstrated that this official methodology suffers from measurement inconsistencies due to its reliance on raw bovine whey standards, which fail to account for β-Lg genetic polymorphisms in real MdBC samples and protein thermal modifications during cheesemaking. To overcome these limitations, we propose a dual proteomics-based approach using matrix-assisted laser desorption ionization (MALDI-TOF) mass spectrometry (MS) and nano-HPLC-electrospray (ESI)-tandem mass spectrometry (MS/MS) analysis of MdBC extracted whey. MALDI-TOF-MS focused on identifying proteotypic peptides specific to bovine and buffalo β-Lg and α-lactalbumin (α-La), enabling high specificity for distinguishing the two animal species at adulteration levels as low as 1%. Complementing this, nano-HPLC-ESI-MS/MS provided a comprehensive profile by identifying over 100 bovine-specific peptide markers from β-Lg, α-La, albumin, lactoferrin, and osteopontin. Both methods ensured precise detection and quantification of bovine milk adulteration in complex matrices like pasta filata cheeses, achieving high sensitivity even at minimal adulteration levels. Accordingly, the proposed dual proteomics-based approach overcomes challenges associated with whey protein polymorphism, heat treatment, and processing variability, and complements casein-based methodologies already validated under European standards. This integrated framework of analyses focused on whey and casein fraction enhances the reliability of adulteration detection and safeguards the authenticity of PDO buffalo mozzarella, upholding its unique quality and integrity.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

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.

Comprehensive species- and processing-specific peptide profiling of pasteurized, extended shelf-life and ultra-high temperature milk from cow, goat, sheep, buffalo, and mare

The present study aimed to identify endogenous milk peptides for species differentiation independent of heat exposure. Thus, comprehensive milk peptide profiles from five species and three types of heat treatments were analyzed by micro-flow liquid chromatography ion mobility quadrupole time-of-flight mass spectrometry (microLC-IM-QTOF) with subsequent database search leading to ≥ 3000 identified peptides. In the milks, 1154 peptides were unique for cow, 712 for sheep, 466 for goat, 197 for buffalo, and 69 for mare. Most peptides were detected in extended-shelf life (ESL) milk (2010), followed by ultra-high temperature (UHT) processed (1474) and pasteurized milk (1459 peptides), with 693 peptides present in all milk types. A blind test set of 64 samples confirmed eight species-specific, but heat-independent marker peptides in milk from cow, seven from goat, six from sheep, nine from buffalo, and three from mare. The generated peptide profiles can also be used to identify species- and heat-specific markers.

Development of a method based on ATR-FTIR spectroscopy to detect maple syrup adulterated with added syrups

BACKGROUND

Food adulteration is a global concern, whether it takes place intentionally or incidentally. In Canada, maple syrup is susceptible to being adulterated with cheaper syrups such as corn, beet, cane syrups, and many more due to its high price and economic importance.

RESULTS

In this study, the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was investigated to detect maple syrups adulterated with 15 different sugar syrups at different concentration levels. The spectra were collected in the range of 4000-650 cm-1 in the absorbance unit. These spectra were used to build six libraries and three models. A method that is capable of performing a qualitative library search using a similarity search, which is based on the first derivative correlation search algorithm, was developed. This method was further evaluated and proved to be able to capture adulterated and reject non-adulterated maple syrups, belonging to the color grades golden and amber maple syrups, with an accuracy of 93.9% and 92.3%, respectively. However, for the maple syrup belonging to the dark color grade, this method demonstrated low specificity of 33.3%, and for this reason it was only able to adequately detect adulterated samples from the non-adulterated ones with an accuracy of 81.4%.

CONCLUSION

This simple and rapid method has strong potential for implementation in different stages of the maple syrup supply chain for early adulteration detection, particularly for golden and amber samples. Further evaluation and improvements are required for the dark color grade. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

How Chemometrics Can Fight Milk Adulteration

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.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Mid-Infrared (MIR) Spectroscopy for the quantitative detection of cow’s milk in buffalo milk

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R²cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R²val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow’s milk at no additional cost.

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.

Milk as a Complex Multiphase Polydisperse System: Approaches for the Quantitative and Qualitative Analysis

Milk is a product that requires quality control at all stages of production: from the dairy farm, processing at the dairy plant to finished products. Milk is a complex multiphase polydisperse system, whose components not only determine the quality and price of raw milk, but also reflect the physiological state of the herd. Today’s production volumes and rates require simple, fast, cost-effective, and accurate analytical methods, and most manufacturers want to move away from methods that use reagents that increase analysis time and move to rapid analysis methods. The review presents methods for the rapid determination of the main components of milk, examines their advantages and disadvantages. Optical spectroscopy is a fast, non-destructive, precise, and reliable tool for determination of the main constituents and common adulterants in milk. While mid-infrared spectroscopy is a well-established off-line laboratory technique for the routine quality control of milk, near-infrared technologies provide relatively low-cost and robust solutions suitable for on-site and in-line applications on milking farms and dairy production facilities. Other techniques, discussed in this review, including Raman spectroscopy, atomic spectroscopy, molecular fluorescence spectroscopy, are also used for milk analysis but much less extensively. Acoustic methods are also suitable for non-destructive on-line analysis of milk. Acoustic characterization can provide information on fat content, particle size distribution of fat and proteins, changes in the biophysical properties of milk over time, the content of specific proteins and pollutants. The basic principles of ultrasonic techniques, including transmission, pulse-echo, interferometer, and microbalance approaches, are briefly described and milk parameters measured with their help, including frequency ranges and measurement accuracy, are given.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

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

Food adulteration is an issue of major concern, as numerous foodstuffs and beverages do not follow their labeling. Our research interest is in the field of authenticity of dairy products and particularly cheese. Adulteration of dairy products is a well-known phenomenon, and there are numerous published studies specifically on the authenticity of cheese. In fact, substitution of a portion of fat and/or proteins, adulteration with milk of other species' origin, and mislabeling of ingredients are some of the main issues that the science of dairy products' authenticity is regularly facing. Discrimination of dairy products can be determined through several chemical or microbiological methods as presented in the literature. In addition, chemometric analysis is an important tool for interpretation of a huge load of measurements. The aim of this study is to discriminate between various milk samples, which is the primary ingredient of dairy products. Milk samples with different trademarks were analyzed. That data was combined with Halloumi cheese samples for chemometric discrimination of species' origin. The innovative point of this study is the fact that it is the first time that a research study related to dairy products includes Halloumi cheese which is a traditional Cypriot cheese, not well-studied until now. The first step of the methodology was the freeze-drying via lyophilization of the samples. Fourier transformed infrared spectroscopy (FTIR) was chosen for their chemical characterization. Moreover, interpretation of the measurements was carried out by chemometric analysis using SIMCA software. For this study, FTIR data combined with chemometrics have given a very good discrimination of the samples according to their species' origin. Chemometric methods such as PCA and OPLS-DA have been used with great success. In the future, this model will be studied regarding geographical origin of the samples.