Raman spectroscopy based characterization of desi ghee obtained from buffalo and cow milk

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.

Thermal Effects on the Quality Parameters of Extra Virgin Olive Oil Using Fluorescence Spectroscopy

Extra virgin olive oil is one of the superlative due to its health benefits. In this work, the Fluorescence spectra of extra virgin olive oil (EVOO) from different olive growing regions of Pakistan and Al-Jouf region from the Kingdom of Saudi Arabia (KSA) were obtained. The emission bands depicted relative intensity variations in all non-heated and heated EVOO samples. Prominent emission bands at 385, 400, 435 and 470 nm represent oxidized products of fatty acids, bands at 520 and 673 nm has been assigned to beta carotene and chlorophyll isomers respectively. All EVOO samples collected from Al-Jouf region, KSA and from Pakistan (Loralai Baluchistan, Barani Agricultural Research Institute, Chakwal and Morgha Biodiversity Park, Rawalpindi) regions showed thermal stability. Other EVOO samples from Chaman Baluchistan and one sample from wild specie (Baluchistan) bought directly from farmers showed denatured spectra even without heating. Chemical characteristics of all EVOO samples changed significantly at 200 °C. Relatively, EVOO samples from Al-Jouf showed more thermal stability which might be due to geographical distribution, environmental effects, genetic background and processing or storage conditions. These results demonstrated fluorescence spectroscopy as a quick, cost-effective and reliable approach to assess the quality and thermal stability of EVOO. These characteristics of fluorescence spectroscopy may lead to the development of portable device for the onsite monitoring of EVOO.

Spectroscopic technologies and data fusion: Applications for the dairy industry

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

Raman spectroscopy based characterization of cow, goat and buffalo fats

In this study, Raman spectroscopy has been utilized to characterize buffalo, cow and goat fat samples by using laser wavelengths at 532 and 785 nm as excitation sources. It has been observed that Raman spectra of cow fats contain beta-carotene at 1006, 1156 and 1520 cm^-1, which are absent in buffalo and goat fats. The Raman bands at 1060, 1080, 1127 and 1440 cm^-1 represent the saturated fatty acids, and their concentration is found relatively higher in buffalo fats than cow and goat. Similarly, the Raman band at 1650 cm^-1 represent conjugated linoleic acid (CLA) which shows its relatively higher concentration in goat fats than cow and buffalo. The Raman band at 1267 cm^-1 represent unsaturated fatty acids, which shows its relatively higher concentration in goat fats than cow and buffalo. The Raman bands at 838, 870 and 1060 cm^-1 depict relatively higher concentration of vitamin D in buffalo fats than cow and goat. Principal component analysis has been applied to highlight the differences among three fat types which based upon the concentration of fatty acids, CLA and vitamin D.

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.

Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks

Raman spectroscopy is widely used in discriminative tasks. It provides a wide-range physio-chemical fingerprint in a rapid and non-invasive way. The Raman spectrometry uses a sensor array to convert photon signals into digital spectroscopic data. This analog-to-digital process can benefit from the compressed sensing (CS) technique. The major benefits include less memory usage, shorter acquisition time, and more cost-efficient sensor. Traditional compressed sensing and reconstruction is a series of mathematical operations performed on the signal. Meanwhile, for discriminative tasks, both the signal and the categorical information are involved. For such scenarios, this paper proposes a method that uses both domain signal and categorical information to optimize CS hyper-parameters, including 1) the sampling ratio or the sensing matrix, 2) the basis matrix for the sparse transform, and 3) the regularization rate or shrinkage factor for L1-norm minimization. A case study of formula milk brand identification proves the proposed method can generate effective compressed sensing while preserving enough discriminative power in the reconstructed signal. Under the optimized hyper-parameters, a 100% classification accuracy is retained by only sampling 20% of the original signal.

Fluorescence Spectroscopy Based Detection of Adulteration in Desi Ghee

Desi ghee, obtained by buffalo and cow milk, is highly expensive because it contains valuable vitamins and conjugated linoleic acid (CLA). Its high demand and cost result in to its adulteration with inferior banaspati ghee. In this study, Fluorescence spectroscopy along with multivariate analysis has been utilised for the detection and quantification of adulteration. Spectroscopic analysis showed that buffalo ghee contains more vitamins and CLA than cow, whereas cow ghee is enriched with beta-carotene. For multivariate analysis, principle component analysis (PCA) and partial least square regression (PLSR) have been applied on the spectral data for the determination of adulteration. PLSR model was authenticated by predicting 23 unknown samples including 3 commercial brands of desi ghee. The root mean square error in prediction (RMSEP) of unknown samples was found to be 1.7 which is a reasonable value for quantitative prediction. Due to non-destructive and requiring no sample pre-treatment, this method can effectively be employed as on line characterization tool for the food safety assurance.

Characterization of desi ghee obtained from different extraction methods using Raman spectroscopy

In this study, the potential of Raman spectroscopy has been utilized to characterize the methods direct cream (DC), milk butter (MB) and milk skin (MS) used for the extraction of desi ghee from buffalo and cow milk. Raman spectra from six types of ghee samples extracted by above methods were acquired using two laser wavelengths of 532 and 785 nm. The Raman spectra of cow ghee revealed that it contains three bands of beta-carotene at 1005, 1156 and 1520 cm^-1 which differentiated it from buffalo ghee. To highlight small spectral differences, statistical analysis through principal component analysis (PCA) has been performed on the Raman spectra of ghee samples to reach subsequent conclusion. Based on the variations in molecular composition of cow ghee samples, it has been found that DC method retain relatively higher concentration of beta-carotene and MB method contain higher concentration of conjugated linoleic acid (CLA) and fatty acids than MS method. Similarly, DC & MS methods were found best for retaining relatively higher concentration of CLA and fatty acids in buffalo ghee as compared to MB method which retains relatively higher concentration of fatty acids.

Changes in milk yield and composition of colostrum and regular milk from four buffalo breeds in China during lactation

BACKGROUND

Chinese local buffalos are mainly used as draft animals because of their low productivity but their crossbreeding with Murrah and Nili-Ravi breeds can produce offspring with a greatly improved milk yield. However, no studies have reported the characteristics of milk produced during lactation by these crossbred buffalo.

RESULTS

This study investigated changes in yield, and the physicochemical composition of milk of Murrah, Nili-Ravi, Murrah×local, Murrah×Nili-Ravi×local buffalos with milk yields of 1022.49 ± 90.26 kg, 1193.02 ± 97.65 kg, 805.46 ± 76.57 kg and 1499.35 ± 105.32 kg respectively over a 210-day period. The protein, fat, ash and total solids contents of milk from the hybrids decreased, but the yield of these nutritional components increased because of the greater improvement in milk yield. As lactation progressed, lactose content increased, but this change was not significant after the 15th day postpartum; the protein content decreased whereas fat content increased significantly during the first three days; ash content changed significantly during the first 24 h postpartum but further changes were not significant. Gel electrophoresis was used to identify the protein profile with no difference found between the four breeds.

CONCLUSION

After crossbreeding, the milk production of triple-crossbred buffalo was higher than Murrah, Nili-Ravi and the local Chinese buffalo, making it a potential resource for the Chinese dairy market. The results of this experiment will provide basic data for making better use of buffalo milk, planning crossbreeding programs, and establishing standards for buffalo milk. © 2019 Society of Chemical Industry.