Raman spectroscopy coupled with chemometric methods for the discrimination of foreign fats and oils in cream and yogurt

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Enhancement of yogurt functionality by adding Mentha piprita phenolic extract and evaluation of its quality during cold storage

New functional food products with health benefits are currently in high demand among health-conscious consumers. The present research aims to improve the functional properties of yogurt by adding peppermint hydroethanolic extract (PHE) at different doses. The impact of PHE (0%, 2%, 4%, and 6%) on yogurt was studied for acidity, pH, organoleptic quality, antioxidant activity, lipid peroxidation, and fatty acid profile. The results revealed that PHE is rich in phenolic compounds, of which rosmarinic acid was the main one (339.88 mg/g lyophilized extract) and has considerable antioxidant potential, which remarkably (p < .01) increased antioxidant capacity in yogurt by over 39.51%, even at a low dose of 2%, giving the product better protection against lipid peroxidation and preserving its physicochemical and sensory quality. At 4%, PHE increased significantly (p < .01) the content of omega-3 fatty acids, notably alpha-linolenic acid, in fortified yogurt compared with the control, and reduced (p < .01) the ratio of omega-6/omega-3, which dropped from 5.21 to 4.11. It looks feasible to prepare a yogurt with health-giving properties by adding Mentha piperita hydroethanolic extract at a concentration of up to 4% as an alternative to synthetic antioxidants, which would also extend its shelf life.

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.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.

Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

Deep-fat frying of food develops lipid oxidation products that deteriorate oil and pose a health risk. This necessitates the development of a rapid and accurate oil quality and safety detection technique. Herein, surface-enhanced Raman spectroscopy (SERS) and sophisticated chemometric techniques were used for rapid and label-free determination of peroxide value (PV) and fatty acid composition of oil in-situ. In the study, plasmon-tuned and biocompatible Ag@Au core-shell nanoparticle-based SERS substrates were used to obtain optimum enhancement despite matrix interference to efficiently detect the oil components. The potent combination of SERS and the Artificial Neural Network (ANN) method could determine the fatty acid profile and PV with upto 99% accuracy. Moreover, the SERS-ANN method could quantify the low level of trans fats, i.e., < 2%, with 97% accuracy. Therefore, the developed algorithm-assisted SERS system enabled the sleek and rapid monitoring and on-site detection of oil oxidation.

Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics

As goat milk has a higher economic value compared to cow milk, the phenomenon of adulterating goat milk with cow milk appears in the market. In this study, the potential of Raman spectroscopy along with chemometrics was investigated for the authentication and quantitation of liquid goat milk adulterated with cow milk. First, the results of principal component analysis (PCA) showed that there were differences between the Raman spectra of cow and goat milk, which made quantitative experiments possible. For quantification, three different brands of cow milk and goat milk were selected randomly and adulterated goat milk with cow milk at the proportion of 5-95%. 342 samples were used for the construction of the partial least squares regression (PLSR) model with 80% for the calibration set and 20% for the prediction set. The PLSR model showed excellent performance in quantifying the level of adulteration, for the prediction set, with a coefficient of determination (R²) of 0.9781, root mean square error (RMSE) of 3.82%, and a ratio of prediction to deviation (RPD) of 6.8. The results demonstrated the potential of Raman spectroscopy as a rapid, low cost and non-destructive analytical tool for detecting adulteration in goat milk.

New research development on trans fatty acids in food: Biological effects, analytical methods, formation mechanism, and mitigating measures

The trans fatty acids (TFAs) in food are mainly generated from the ruminant animals (meat and milk) and processed oil or oil products. Excessive intake of TFAs (>1% of total energy intake) caused more than 500,000 deaths from coronary heart disease and increased heart disease risk by 21% and mortality by 28% around the world annually, which will be eliminated in industrially-produced trans fat from the global food supply by 2023. Herein, we aim to provide a comprehensive overview of the biological effects, analytical methods, formation and mitigation measures of TFAs in food. Especially, the research progress on the rapid, easy-to-use, and newly validated analytical methods, new formation mechanism, kinetics, possible mitigation mechanism, and new or improved mitigation measures are highlighted. We also offer perspectives on the challenges, opportunities, and new directions for future development, which will contribute to the advances in TFAs research.

FTIR-PCA Approach on Raw and Thermally Processed Chicken Lipids Stabilized by Nano-Encapsulation in β-Cyclodextrin

This study evaluated similarities/dissimilarities of raw and processed chicken breast and thigh lipids that were complexed by β-cyclodextrin, using a combined FTIR-PCA technique. Lipid fractions were analyzed as non-complexed and β-cyclodextrin-complexed samples via thermogravimetry, differential scanning calorimetry and ATR-FTIR. The lipid complexation reduced the water content to 7.67-8.33%, in comparison with the β-cyclodextrin hydrate (~14%). The stabilities of the complexes and β-cyclodextrin were almost the same. ATR-FTIR analysis revealed the presence of important bands that corresponded to the C=O groups (1743-1744 cm^-1) in both the non-complexed and nano-encapsulated lipids. Furthermore, the bands that corresponded to the vibrations of double bonds corresponding to the natural/degraded (cis/trans) fatty acids in lipids appeared at 3008-3011 and 938-946 cm^-1, respectively. The main FTIR bands that were involved in the discrimination of raw and processed chicken lipids, and of non-complexed and complexed lipids, were evaluated with PCA. The shifting of specific FTIR band wavenumbers had the highest influence, especially vibrations of the α(1→4) glucosidic bond in β-cyclodextrin for PC₁, and CH_2/3 groups from lipids for PC₂. This first approach on β-cyclodextrin nano-encapsulation of chicken lipids revealed the possibility to stabilize poultry fatty components for further applications in various ingredients for the food industry.

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.

Optimizing Procedures of Ultrasound-Assisted Extraction of Waste Orange Peels by Response Surface Methodology

The simultaneous effects of three continuous factors: solvent concentration (50−100%), treated times (25−85 min), treated temperatures (25−55 °C), and two categorical factors: type of solvents (methanol or ethanol) and ultrasonic frequency (28 kHz or 40 kHz) on ultrasonic-assisted extraction yield from waste orange peels were evaluated and optimized by response surface methodology. Fourier Transform Infrared (FTIR) spectroscopy with a wavelength of 500 cm−1 to 4000 cm−1 was employed to rapidly identify the orange extracts. The significant polynomial regression models on crude extraction, sediments after evaporation, and precipitation yield were established (p < 0.05). Results revealed that solvent concentration affected crude extraction and precipitation yield linearly (p < 0.01). The optimal and practical ultrasound-assisted extraction conditions for increasing the precipitation yield were using 61.42% methanol with 85 min at 55 °C under 40 kHz ultrasonic frequency. The spectra of extracts showed a similar fingerprint of hesperidin.

Identifying the charge density and dielectric environment of graphene using Raman spectroscopy and deep learning

We built a CNN model to classify graphene Raman spectra. Compared to other deep learning models and machine learning algorithms studied in this work, the CNN model achieves a high accuracy of 99% and is less sensitive to the SNR of Raman spectra.

Simple chromogenic test for detection of adulterated milk with vegetable oil at village milk collection center- A preliminary study

In the present investigation, a novel chromogenic test has been developed to ascertain the presence of vegetable oil in milk. This standardized protocol did not show any false-positive results in the genuine milk samples. Adulteration of milk with vegetable oil @ 1% level could be detected by this chromogenic test protocol. This said protocol is convenient to use in the rural dairy industry especially rural-based milk collection centers; as no costly instrument or trained manpower is required for this said test.

Raman Study on Lipid Droplets in Hepatic Cells Co-Cultured with Fatty Acids

The purpose of the present study was to investigate molecular compositions of lipid droplets changing in live hepatic cells stimulated with major fatty acids in the human body, i.e., palmitic, stearic, oleic, and linoleic acids. HepG2 cells were used as the model hepatic cells. Morphological changes of lipid droplets were observed by optical microscopy and transmission electron microscopy (TEM) during co-cultivation with fatty acids up to 5 days. The compositional changes in the fatty chains included in the lipid droplets were analyzed via Raman spectroscopy and chemometrics. The growth curves of the cells indicated that palmitic, stearic, and linoleic acids induced cell death in HepG2 cells, but oleic acid did not. Microscopic observations suggested that the rates of fat accumulation were high for oleic and linoleic acids, but low for palmitic and stearic acids. Raman analysis indicated that linoleic fatty chains taken into the cells are modified into oleic fatty chains. These results suggest that the signaling pathway of cell death is independent of fat stimulations. Moreover, these results suggest that hepatic cells have a high affinity for linoleic acid, but linoleic acid induces cell death in these cells. This may be one of the causes of inflammation in nonalcoholic fatty liver disease (NAFLD).

Discrimination of milk species using Raman spectroscopy coupled with partial least squares discriminant analysis in raw and pasteurized milk

BACKGROUND

Heat treatment is the most common practice for the microbiological safety of milk; hence, determination of the heat treatment of milk is essential. Also, mislabeling or adulteration of expensive milk samples, like ewe or goat milk, with cow's milk is a growing problem in the dairy market. Thus, the determination of the authenticity of milk samples has crucial importance for both producers and consumers. The aim of this study was to discriminate milk samples using Raman spectroscopy with partial least squares discriminant analysis (PLS-DA), first with regard to whether the milk was heat-treated or not, and second with regard to species (cow, goat, ewe, mixture (adulterated)) in both raw and pasteurized milk.

RESULTS

First, discrimination of milk samples as raw or pasteurized was achieved using PLS-DA. Both in calibration and prediction models, high sensitivity and specificity values were obtained for raw and pasteurized milk samples. Second, the proposed method also discriminated milk samples according to their species (cow, goat, ewe, and mixture) for both raw and pasteurized milk. In both calibration and prediction models, the sensitivity and specificity values were above 0.857 and 0.897 respectively. Also, the accuracy values were above 0.915. The results obtained denote satisfactory accurate classification of the samples.

CONCLUSION

The results suggest that Raman spectroscopy coupled with PLS-DA can be successfully used to discriminate milk samples according to heat treatment (raw/pasteurized) and their species within 20 s per sample. It was seen that Raman spectra provide valuable information to be used especially for discrimination of milk samples according to their origin. © 2020 Society of Chemical Industry.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy

Background

The quality discrimination of dairy products is an important basis on which to achieve quality assurance.

Objective

Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed.

Method

The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined.

Conclusions

We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach.

Highlights

A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.

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.

A rapid method for detection adulteration in goat milk by using vibrational spectroscopy in combination with chemometric methods

Because of the second place of milk adulteration in the food fraud lists, the study focused on the investigation of the cow milk as an adulterant in goat milk based on β-carotene presence in cow milk as s rapid method by Raman and Infrared spectroscopy with chemometric techniques.t Partial least squares regression (PLSR) and the soft independent modelling of class Analogy (SIMCA) models have developed to for the prediction of adulteration ratio and β-carotene content of mixtures on the spectral band at around 1373, 1454, and 956 cm-1 for infrared and 1005, 1154, and 1551 cm-1 for Raman spectroscopy respectively. The correlation coefficient for calibration (R2cal), standard error of calibration, standard error of performance, and correlation coefficient for validation (R2val) have calculated for mid-infrared and Raman techniques. The PLSR models showed excellent fit (R2 value > 96) and could accurately determine β-carotene content and percentage of spiked milk in a short time. SIMCA results showed that 20% intervals of the mixture could be differentiated barely from other mixtures by mid-infrared spectroscopy; however, there could not found significant discrimination by Raman spectroscopy. β-carotene could be considered as a biomarker of determination of adulteration concerning β-carotene content and mixture percentage, and discrimination of spiked mixture for the differentiation of goat and cow milk.

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

Cheese is one of the most widely consumed food products in the world. However, the increasing demand for nutritionally enhanced or functional products by the cheese industry has created new approaches that partially or fully replace milk fat. With this, new methods of adulteration have also been noted, potentially leading to these fully/partially-replaced products being offered as cheese. In this study, Raman spectroscopy was used to determine origins of fats in margarine, corn, and palm oils present in white and ultra-filtered cheese samples. Raman spectra were evaluated with partial least square-discriminant (PLS-DA) and PLS to identify fat/oil origins and adulteration ratios. The coefficients of determination and limits of detection for margarine, and corn and palm oil adulteration were found to be 0.990, 0.993, 0.991 and 3.38%, 3.36% and 3.59%, respectively.

Terahertz spectroscopy technology as an innovative technique for food: Current state-of-the-Art research advances

With the dramatic development of source and detector components, terahertz (THz) spectroscopy technology has recently shown a renaissance in various fields such as medical, material, biosensing and pharmaceutical industry. As a rapid and noninvasive technology, it has been extensively exploited to evaluate food quality and ensure food safety. In this review, the principles and processes of THz spectroscopy are first discussed. The current state-of-the-art applications of THz and imaging technologies focused on foodstuffs are then discussed. The advantages and challenges are also covered. This review offers detailed information for recent efforts dedicated to THz for monitoring the quality and safety of various food commodities and the feasibility of its widespread application. THz technology, as an emerging and unique method, is potentially applied for detecting food processing and maintaining quality and safety.

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335–354 cm⁻¹, 435–454 cm⁻¹, 485–540 cm⁻¹, 820–915 cm⁻¹, 1155–1185 cm⁻¹, 1300–1414 cm⁻¹, and 1415–1520 cm⁻¹ under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control.

At present, practical and rapid identification techniques for dairy products are still scarce.

Authenticity and Concentration Analysis of Extra Virgin Olive Oil Using Spontaneous Raman Spectroscopy and Multivariate Data Analysis

Adulteration of extra virgin olive oil (EVOO) with cheaper edible oils is of considerable concern in the olive oil industry. The potential of Raman spectroscopy combined with multivariate statistics has been investigated for evaluating the authenticity (or purity) and concentration of EVOO irrespective of it being adulterated with one or more adulterants. The adulterated oil samples were prepared by blending different concentrations of EVOO (10–100% v/v) randomly with cheaper edible oils such as corn, soybean and rapeseed oil. As a result, a Raman spectral database of oil samples (n = 214 spectra) was obtained from 11 binary mixtures (EVOO and rapeseed oil), 16 ternary mixtures (EVOO, rapeseed and corn oil) and 44 quaternary mixtures (EVOO, rapeseed, corn and soybean oil). Partial least squares (PLS) calibration models with 10-fold cross validation were constructed for binary, ternary and quaternary oil mixtures to determine the purity of spiked EVOO. The PLS model on the complex dataset (binary + ternary + quaternary) where the spectra obtained with different measurement parameters and sample conditions can able to determine the purity of spiked EVOO inspite of being blended with one or more cheaper oils. As a proof of concept, in this study, we used single batch of commercial oil bottles for estimating the purity of EVOO. The developed method is not only limited to EVOO, but can be applied to clean EVOO obtained from the production site and other types of food.