Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis

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.

Detection of carbon nanotubes in bovine raw milk through Fourier transform Raman spectroscopy

The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

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.

Component identification for the SERS spectra of microplastics mixture with convolutional neural network

With the increasing interest in microplastics (MPs) pollutants, relevant detection technologies are also developing. In MPs analysis, vibrational spectroscopy represented by surface-enhanced Raman spectroscopy (SERS) is widely used because they can provide unique fingerprint characteristics of chemical components. However, it is still a challenge to separate various chemical components from the SERS spectra of MPs mixture. In this study, it is innovatively proposed to combine the convolutional neural networks (CNN) model to simultaneously identify and analyze each component in the SERS spectra of six common MPs mixture. Different from the traditional method, which requires a series of spectral preprocessing such as baseline correction, smoothing and filtering, the average identification accuracy of MP components is as high as 99.54 % after the unpreprocessed spectral data is trained by CNN, which is better than other classical algorithms such as support vector machine (SVM), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), Random Forest (RF), and K Near Neighbor (KNN), with or without spectral preprocessing. The high accuracy shows that CNN can be used to quickly identify MPs mixture with unpreprocessed SERS spectra data.

Portable near-infrared (NIR) spectrometer and chemometrics for rapid identification of butter cheese adulteration

Artisanal cheeses are highly valued around the world for their distinct sensory characteristics, thus being prone to adulteration by substituting authentic material for cheaper products, such as vegetable oil. In this work, we developed a method based on a portable NIR spectrometer as a non-destructive and low-cost alternative to identify adulteration in butter cheese. Dataset consisted of authentic and intentionally adulterated cheeses in the laboratory and commercial cheeses, which were identified as authentic and adulterated with vegetable oil after analysis of the fatty acid profile. PLS-DA classification models identified adulterated samples with an accuracy of 94.44%. PLS prediction models showed excellent performance (RPD > 3.0) to predict the adulterant level. These results demonstrate that NIR spectra can be used to identify the replacement of authentic fat by soybean oil in butter cheese and that the developed models can be used to identify adulteration in external samples with good performance.

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 an optical immunoassay based on peroxidase-mimicking Prussian blue nanoparticles and a label-free electrochemical immunosensor for accurate and sensitive quantification of milk species adulteration

In contrast to reported enzyme-based immunoassays, an enzyme-free immunoassay (optical and electrochemical) is presented here for the first time that can be used as point-of-need detection bioplatforms of bovine IgG as goat milk adulterant. In the first format, Prussian blue nanoparticles (PBNPs) were used as antibody catalytic labels in a competitive colorimetric microplate immunoassay. Absorbance measurement was performed photometrically at 450 nm. After in-depth optimization, excellent sensitivity was achieved (0.01% cow/goat volume ratio), which is 100 times lower than the limit allowed by the European legislation (EL) (1% v/v), thanks to the high catalytic activity of PBNPs compared with natural peroxidase. Moreover, the antibody-PBNPs bioconjugates showed excellent stability over 4 weeks (> 94% of the initial response) confirming the successful anchoring of the antibodies to the surface of the PBNPs. On the other hand, a label-free voltammetric immunoassay for the detection of bovine IgG was developed. The sensing principle was based on the hindrance of charge transfer between ferri-ferrocyanide redox couple and the screen-printed gold electrodes modified with bovine IgG antibody. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to characterize the step-by-step modification of the electrode surface. Under optimal conditions, this single-step electrochemical analysis achieved a high sensitivity of 0.1% (cow/goat) when monitoring the ferrocyanide oxidation at + 0.092 V (vs. Ag/AgCl) using differential pulse voltammetry (DPV). The selectivity of the developed immunoassays was evaluated for different species of milk of similar composition, and both immunoassays exhibited a selective response only to bovine IgG. Unlike conventional immunoassays, the developed enzyme-free immunoassays have many attractive features for the detection of milk adulteration, whether they are used in quality control laboratories for routine milk analysis (optical immunoassay) or at on-site checkpoints (electrochemical immunoassay) using wireless electrochemical detectors. The sensors provide high sensitivity (≤ 0.1%), excellent precision (RSD < 6%), low cost (no enzyme is required) and ease of operation, including handling of milk samples.

Near-infrared techniques for fraud detection in dairy products: A review

The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (R_Pre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.