Detection of formaldehyde in raw milk by time domain nuclear magnetic resonance and chemometrics

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.

Characterization of Physicochemical and Sensory Properties of Cheeses Added with Bovine Colostrum

The objective of this study was to develop fresh and matured cheeses with different bovine colostrum levels, aiming to promote the consumption of dairy products with the addition of colostrum. Four different cheese formulations were produced with a mixture of 0:100, 15:85, 20:80, and 25:75, bovine colostrum:milk (v:v), and aged for 0, 10, 20, and 40 days. Milk, colostrum, and fresh and matured cheeses were submitted to physicochemical characterization. Moreover, microbiological quality, yield, texture profile, color, and sensory acceptance of cheese samples were evaluated. Colostrum supplementation favored low acidity, high moisture, a pH range of 5.0-6.2, and water activity of 0.94-99. Sensory attributes and overall evaluation of all cheese formulations achieved an Acceptability Index above 70, indicating good acceptability. Since cheese with colostrum presented the potential to be used as human food, assessing the presence of colostrum bioactive components in those dairy products is a promising goal for further research.

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.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

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.

Development of processed low-sodium Maasdam cheese

ABSTRACT The article assesses the effect of different potassium emulsifying salts concentrations on physicochemical, colorimetric, and texture characteristics of processed cheese manufactured using Maasdam. Except for pH, physicochemical parameters remained unchanged, but the gradual substitution of sodium emulsifying salts with potassium-based salts influenced color and texture. Treatments with at least 50% potassium salts showed a reduction of at least 30% of sodium. The sodium decrease allows the product's classification as processed cheese with low-sodium content (<140mg per 56.7g serving). The data obtained present substantial information that can help the dairy industries develop newly reduced-sodium products.

Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics

Low Field Time-Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry was used to determine moisture, fat, and defatted dry matter contents in “requeijão cremoso” (RC) processed cheese directly in commercial packaged (plastic cups or tubes with approximately 200 g). Forty-five samples of commercial RC types (traditional, light, lactose-free, vegan, and fiber) were analyzed using longitudinal (T₁) and transverse (T₂) relaxation measurements in a wide bore Halbach magnet (0.23 T) with a 100 mm probe. The T₁ and T₂ analyses were performed using CWFP-T₁ (Continuous Wave Free Precession) and CPMG (Carr-Purcell-Meiboom-Gill) single shot pulses. The scores of the principal component analysis (PCA) of CWFP-T₁ and CPMG signals did not show clustering related to the RC types. Optimization by variable selection was carried out with ordered predictors selection (OPS), providing simpler and predictive partial least squares (PLS) calibration models. The best results were obtained with CWFP-T₁ data, with root-mean-square errors of prediction (RMSEP) of 1.38, 4.71, 3.28, and 3.00% for defatted dry mass, fat in the dry and wet matter, and moisture, respectively. Therefore, CWFP-T₁ data modeled with chemometrics can be a fast method to monitor the quality of RC directly in commercial packages.

Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics

This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R²) was 0.9889, and the root means square error (RMSE) was 3.28 × 10^-4, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.

Enzyme decorated dendritic bimetallic nanocomposite biosensor for detection of HCHO

In recent times, bi- and tri-metallic nanocomposites are being extensively studied to improve the catalytic surface and sensitivity of detection. In this study, we designed a formaldehyde dehydrogenase decorated Cys-AuPd-ErGO nanocomposite with fern like AuPd dendrites deposited on reduced graphene oxide (ErGO) on screen printed electrode (SPE) for determination of NADH and successfully demonstrated its application for detection of HCHO. This biosensor exhibited direct electron transfer by lowering the oxidation potential of NADH from +0.63 V to 0.32 V vs Ag/AgCl, avoiding usage of electron mediators. The sensor LOD was 0.3 μM HCHO with excellent sensitivity of 70 μA/μM/cm² and linear detection range between 1 μM and 100 μM during chronoamperometric studies at applied over potential of +0.35 V vs Ag/AgCl. The sensor was tested for its performance in simulated HCHO adulterated samples of fish and milk, and appreciable recoveries (88-104%) at tested concentrations indicated good sensor performance. It was also validated against conventional method of HPLC with highly acceptable correlation coefficient of 0.99, indicating successful fabrication of a simple, "on site" disposable sensor for HCHO detection. The developed biosensor can also find wide application in quantitative measurement of NADH and analytes involved in reactions with the co-enzyme.

Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration

Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

Simultaneous analysis of multiple adulterants in milk using microfluidic paper-based analytical devices

This study reports the simultaneous colorimetric detection of urea, H₂O₂, and pH in milk samples using microfluidic paper-based analytical devices (μPADs) fabricated through a craft cutter printer. Paper-based devices were designed to contain three detection zones interconnected to a sampling zone by microfluidic channels. Colorimetric analysis was performed using images digitalized through an office scanner. The volumes of chromogenic and sample solutions were optimized, and the best colorimetric performance was achieved by adding 0.5 and 10 μL into detection and sampling zones, respectively. Simultaneous assays were then carried out, and the recorded responses revealed a linear behavior in the concentration ranges from 0-30.0 mmol L^-1, 0-10.0 mmol L^-1 and 6.0-9.0 for urea, H₂O₂ and pH, respectively. The limit of detection values obtained for urea and H₂O₂ were 2.4 mmol L^-1 and 0.1 mmol L^-1, respectively. For pH measurements, colorimetric assay allowed the monitoring of solution pH with a resolution of 0.25 units. The use of μPADs to detect target adulterants exhibited suitable reproducibility (RSD ≤ 6.0%), accuracy (91-102%) and no cross-reaction occurrence. When compared to reference techniques, colorimetric assays did not reveal a significant difference at a confidence level of 95%. As a proof-of-concept, the feasibility of the proposed approach was successfully demonstrated through the analysis of potential adulterants in sixteen milk samples, which were tested without any pretreatment requirement. Based on the achievements, μPADs in conjunction with colorimetric measurements emerge as a powerful tool for rapid screening of potential adulterants in milk.

Converting electronic nose into opto-electronic nose by mixing MoS2 quantum dots with organic reagents: Application to recognition of aldehydes and ketones and determination of formaldehyde in milk

A new colorimetric sensor array based on mixing of Molybdenum disulfide quantum dots (MoS₂ QDs) and organic reagents is introduced in this study. MoS₂ QDs shows a specific and higher affinity to oxygen functionalized volatile compounds like aldehydes and ketones. Therefore, this designed sensor array is used for classification of eight different aldehydes and ketones based on Linear Discriminate Analysis (LDA) at first. The classification accuracy of 96% and 83% was obtained for training and prediction phases, respectively. Then the introduced colorimetric sensor array is used for the semi-quantitative and quantitative analysis of formaldehyde in milk samples. Formaldehyde is an adulteration that is added to the milk for increasing the storage time. Cow milk samples were provided directly from dairy farmer and from supermarkets and were spiked by formaldehyde in the concentration range of 1-25 ppm. The response of sensor array to these samples were analyzed by partial least squares regression (PLS-R) method and were calibrated for concentration of formaldehyde. The PLSR results (R² = 0.94 and RMSEC = 2.36) shows that proposed sensor is useable in direct analysis of formaldehyde in milk as a complex matrix.

The recent advance of organic fluorescent probe rapid detection for common substances in beverages

The beverage industry is confronted with tremendous challenges in terms of quality assurance. The allowed contents of common ingredients such as copper ions, hydrogen sulfide, cysteine and caffeine are stipulated by various governing bodies, and the beverage industry must ensure that it meets these requirements. Due to its unique advantages of high sensitivity, low cost and relatively low toxicity over high-performance liquid chromatography, atomic absorption spectrometry and nanomaterials, the use of organic fluorescent probes for the rapid detection of beverage contents has become a hot research topic. This review summarizes the detection of common substances in wine, tea, mineral water, milk and other beverages. Furthermore, the preparation of test paper and simple colour comparison are discussed to display the rapid qualitative capability of designed probes. To improve the current state of beverage safety, future trends and strategies for fast organic fluorescent probe detection in the beverage industry are also discussed.

Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data

Artificial intelligence-based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI-based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.

Fast-forward approach of time-domain NMR relaxometry for solid-state chemistry of chitosan

Chitosans with different average degrees of acetylation and weight molecular weight were analyzed by time-domain NMR relaxometry using the recently proposed pulse sequence named Rhim and Kessemeier - Radiofrequency Optimized Solid-Echo (RK-ROSE) to acquire ¹H NMR signal of solid-state materials. The NMR signal decay was composed of faster (tenths of μs) and longer components, where the mobile-part fraction exhibited an effective relaxation transverse time assigned to methyl hydrogens from N-acetyl-d-glucosamine (GlcNAc) units. The higher intrinsic mobility of methyl groups was confirmed via DIPSHIFT experiments by probing the ¹H-¹³C dipolar interaction. RK-ROSE data were modeled by using Partial Least Square (PLS) multivariate regression, which showed a high coefficient of determination (R² > 0.93) between RK-ROSE signal profile and average degrees of acetylation and crystallinity index, thus indicating that time-domain NMR consists in a promising tool for structural and morphological characterization of chitosan.

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.