Identification of cow and buffalo milk based on Beta carotene and vitamin-A concentration using fluorescence spectroscopy

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated the ability of this method to differentiate between diabetic and healthy control urine samples. Fluorescent spectra were captured from urine samples using a Xenon arc lamp emitting light within the 200 to 950 nm wavelength range, with consistent fluorescence emission observed at 450 nm under an excitation wavelength of 370 nm. Healthy control samples were also analyzed within the same spectral range for comparison. To distinguish spectral differences between healthy and infected samples, the random forest (RF) and K-Nearest Neighbors (KNN) machine learning algorithms have been employed. These algorithms automatically recognize spectral patterns associated with diabetes, enabling the prediction of unknown classifications based on established samples. Principal component analysis (PCA) was utilized for dimensionality reduction before feeding the data to RF and KNN for classification. The model's classification performance was evaluated using 10-fold cross-validation, resulting in the proposed RF-based model achieving accuracy of 96 %, specificity of 100 %, sensitivity of 93 %, and precision of 100 %. These results suggest that the proposed method holds promise for a more convenient and potentially more accurate method for diagnosing glycosuria in diabetic patients.

Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm

The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes.

Investigation of the effects of corn flour, Spirulina powder, and buffalo yogurt on the quality characteristics of gluten-free muffins

This study aimed to investigate the properties of buckwheat flour-based gluten-free muffins formulated by using corn flour (CF), Spirulina powder (SP), and buffalo yogurt (BY) levels as the factors in the Box-Behnken experimental design. The color values, total soluble polyphenol content (TPC), radical scavenging activity (RSA), specific volume, moisture content, and baking loss results were evaluated as the response variables in the design. The buckwheat flour in the control sample was substituted with CF and SP. Cow yogurt was used in the control muffin instead of milk and was substituted with BY at different levels in other samples. Among the response variables, TPC and RSA values were selected for the optimization studies. Decreasing the CF and SP levels in the formulations resulted in observing higher TPC and RSA values. Three optimal formulations were obtained by response surface methodology. Both cow and buffalo yogurt did not change the properties of the muffins adversely compared to milk. Increasing the SP content made the muffins firmer and caused a decrease in the L*, a*, b*, and browning index values of the muffins. However, sensory scores of SP-containing muffins were also satisfactory.

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.

Evaluation and quantification of associations between commonly suggested milk biomarkers and the proportion of grassland-based feeds in the diets of dairy cows

This study is a first step approach towards the prediction of the proportion of grassland-based feeds (%GB) in dairy cow diets with the aid of three different groups of milk biomarkers. We aimed to evaluate and quantify the associations between biomarkers commonly suggested in the literature and %GB in individual cows as a hypothesis-generating stage for the prospective establishment of accurate %GB prediction models. Consumers and governments financially encourage sustainable, local milk production making grass-based feeding, in grassland-dominated regions, of major interest. Milk from grassland-fed cows differs from that of other feeding systems by inferential fatty acids (FA), β-carotene content and yellow color; however, these biomarkers have not been evaluated together for their association with %GB. Using approved methods of parametric regression analysis, gas chromatography (GC), mid-infrared spectra (MIR) and color spectroscopy, we aimed to develop a first step towards an easy-to-implement, cost-effective milk-based control to estimate %GB in dairy cow diets. The underlying database was generated with 24 cows each fed one of 24 different diets gradually increasing in grass silage and decreasing in corn silage. Our results indicate that GC-measured α-linolenic acid, total n-3 FA and the n-6:n-3 ratio, MIR-estimated PUFA and milk red-green color index a* are robust milk biomarkers for constructing accurate prediction models to determine %GB. Based on simplified regression analysis, diets containing 75% GB should contain ≥ 0.669 and 0.852 g α-linolenic acid and total n-3 FA per 100 g total FA, respectively, and an n-6:n-3 FA ratio of < 2.02 measured with GC; estimated with MIR, polyunsaturated FA should be ≥ 3.13 g/100 g total FA. β-carotene was not a good predictor for estimating %GB. Unexpectedly, the milk became greener with increasing %GB (negative a* values, ‒6.416 for 75% GB), suggesting the red-green color index, not yellow-blue, as a suitable biomarker.

Characterization of Corn Oil Using Fluorescence Spectroscopy

In these studies, Fluorescence spectroscopy has been utilized for the characterization of pure and commercially available corn oil. The best excitation wavelength of 380 nm has been investigated, where maximum spectral information can be assessed. The emission spectra from pure and commercial corn oil samples disclosed that pure corn oil contained oleic acid, beta-carotenes, chlorophylls, isomers of vitamin E and traces of oxidized products which exhibit fluorescence at 406, 525, 675, 440 and 435/475 nm respectively. Whereas, commercial corn oils lack these valuable ingredients and only contain fats along with their primary and secondary oxidized products that emit a broad emission band centred at 440 nm. The study has also depicted that Fluorescence spectroscopy can even be used to select best quality corn oil among pure corn oil samples with different varieties and seed origins. In addition, the effect of temperature on the composition of pure and commercial corn oil samples have also been investigated by heating them at 100, 120,140, 160, 180 and 200 °C each sample for 30 min. This was done because corn oil is being used for cooking where it is generally heated up to 120 °C and for deep frying up to 180 °C. On heating, in pure corn oil, deterioration of Vitamin-E and beta-carotenes occurred with an increase in the oxidation products, whereas, in commercial oil samples, only the concentration of oxidation products increased. However, it was found that up to 140 °C, pure corn oil can be used safely for cooking purpose where it does not lose much of its valuable ingredients while in commercial corn oils, fat composition does not alter much up to 180 °C and after that oxidized products start to increase rapidly.

Spectroscopic-Based Prediction of Milk Foam Properties for Barista Applications

The important quality parameters of cow’s milk for barista applications are frothability and foam stability. In the past, quality assessment was very time-consuming and could only be carried out after milk treatment had been completed. Since spectroscopy is already established in dairies, it could be advantageous to develop a spectrometer-based measurement method for quality control for barista applications. By integrating online spectroscopy to the processing of UHT (ultra-high temperature processing) milk before filling, it can be checked whether the currently processed product is suitable for barista applications. To test this hypothesis, a feasibility study was conducted. For this purpose, seasonal UHT whole milk samples were measured every 2 months over a period of more than 1 year, resulting in a total of 269 milk samples that were foamed. Samples were frothed using a self-designed laboratory frother. Frothability at the beginning and foam loss after 15 min describe the frothing characteristics of the milk and are predicted from the spectra. Near-infrared, Raman, and fluorescence spectra were recorded from each milk sample. These spectra were preprocessed using 15 different mathematical methods. For each spectrometer, 85% of the resulting spectral dataset was analyzed using partial least squares (PLS) regression and nine different variable selection (VS) algorithms. Using the remaining 15% of the spectral dataset, a prediction error was determined for each model and used to compare the models. Using spectroscopy and PLS modeling, the best results show a prediction error for milk frothability of 3% and foam stability of 2%.

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.

Effect of microencapsulated chavil (Ferulago angulata) extract on physicochemical, microbiological, textural and sensorial properties of UF-feta-type cheese during storage time

Chavil (Ferulago angulata) extract (CE) and microencapsulated chavil extract (MCE) were added to UF- Feta-type Cheese. The aim of this study was to comprising CE and MCE on physicochemical and microbiological properties in cheese. The scanning electron microscope images demonstrate the MCE had elliptical shape. The average size diameter curve of the MCE revealed bimodal distribution with two peaks (1541 and 2222 nm) separately. The hardness value of MCE cheese (212.83 ± 17.63 g) was lower than that of CE (343.67 ± 25.53 g) because of canola oil used in the microencapsulation process. The MCE-cheese showed lower values of acidity (1.67%) in comparison with CE-cheese (1.87%). The viable numbers of Streptococcus thermophilus and Lactococcus lactis were equal among the samples (4.6–4.9 log10 CFU/g respectively). The acid degree value of MCE (2.07 ± 0.21%) and CE (1.83 ± 0.25%) cheese were nearly equal at the end of storage time.

A Rapid Method to Measure Serum Retinol Concentrations in Japanese Black Cattle Using Multidimensional Fluorescence

Vitamin A levels in fattening Japanese Black cattle affect meat quality; therefore, it is important to monitor serum retinol concentrations. To simplify and accelerate the evaluation of serum retinol concentrations in cattle, we developed a new predictive method using excitation-emission matrix (EEM) fluorescence spectrophotometry. For analytical comparison, the concentration of serum retinol was also measured using the conventional HPLC method. We examined excitation (Ex) and emission (Em) wavelengths of cattle serum, which were 250-450 and 250-600 nm, respectively. Parallel factor analysis separated four components from EEM data, one of which was related to retinol. Next, a partial least square regression model was created using the obtained EEMs as explanatory variables and accrual measurement values as objective variables. The determination coefficient value (R2), root mean squared error of prediction (RMSEP), and the ratio of performance to deviation (RPD) of the model were determined. A comparison with reference values found that R2, RMSEP, and RPD of the calibration model were 0.95, 6.4 IU/dl, and 4.2, respectively. This implies that EEM can estimate the serum retinol concentration with high accuracy. Additionally, the fluorescent peaks that contributed to the calibration, which were extracted from the regression coefficient and variable importance in projection plots, were Ex/Em = 320/390 and 330/520 nm. Thus, we assume that this method observes not only free retinol, but also retinol-binding protein. In conclusion, multidimensional fluorescence analysis can accurately and quickly determine serum retinol concentrations in fattening cattle.

Discrimination of Milk from Different Animal Species by a Foodomics Approach Based on High-Resolution Mass Spectrometry

An untargeted foodomics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole orbitrap and chemometrics was used to observe subtle differences in the molecule profiles of raw milk from different animal species (cow milk, goat milk, and water buffalo milk), which could prevent the fraud activities in the dairy industry. In data-dependent acquisition (DIA), spectra for all precursor ions facilitated the comprehensive identification of unknown compounds in untargeted foodomics. Chemometrics techniques were used to analyze large amounts of complex data to observe the separation of different sample groups and find the potential markers of sample groups. Finally, five markers were putatively identified by the potential marker identification workflow. The quantification results showed that β-carotene was found only in cow milk; ergocalciferol was found only in water buffalo milk; and the contents of nonanoic acid, decanoic acid, and octanoic acid were higher in goat milk than those in cow milk and water buffalo milk. The quantification of β-carotene enabled the detection of cow milk with a sensitivity threshold of 5% (w/w). This work provided an efficient approach for the discrimination of cow milk, goat milk, and water buffalo milk. Compared with proteomics and genomics, the simpler analytical procedures, lower costs, and higher speed of this work make it of great benefit for routine operations.

Application of Fluorescence Spectroscopy in Wheat Crop: Early Disease Detection and Associated Molecular Changes

The application of fluorescence spectroscopy combined with chemometrics was explored in the current study for the detection of stripe rust in wheat. The healthy and stripe rust leaves were collected from the disease screening nursery. The variations in the blue-green region and chlorophyll fluorescence intensity in leaves provides the basis for the detection of stripe rust infection. With the progress of disease, the variations in the synchronous fluorescence spectroscopy (SFS) spectrum was witnessed. SFS is an excellent tool for the simultaneous measurement of multiple compound samples, in case of plants it generates evidence regarding the occurrence of leaf fluorophore bands thus revealing the biochemical variations going on at different infection stages. Based on the results of the current study, it is inferred that p-coumaric acid has the highest intensity in healthy samples followed by the asymptomatic leaf samples, whereas the band intensity of α-tocopherol, sinapic acid, chlorogenic acid, ferulic acid, tannins, flavonoid, carotenoids and anthocyanins increases in the diseased and the asymptomatic samples accordingly to the rust infection. Principal component analysis (PCA) beautifully differentiated the healthy and the infected leaf samples. It is evident that the asymptomatic samples are grouped with the diseased samples or independently; indicating the start of disease infection, the decision that is hard to make with the visual assessments. The results of the current study suggest that the fluorescence emission and the SFS spectral signatures acquired for stripe rust could be utilized as fingerprints for early disease detection.

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.

Potentiality of using front face fluorescence spectroscopy for quantitative analysis of cow milk adulteration in buffalo milk

In current study, synchronous front-face fluorescence spectroscopy together with partial least squares regression (PLSR) is used to predict the adulteration of cow and buffalo milk quantitatively. Fresh (unprocessed milk) samples of cow and buffalo were collected from local dairy farms. Fluorescence emission from milk samples mixed in different concentrations, show intensity variations at wavelengths 370-380 nm, 410 nm, 442 nm and 520-560 nm. Among them, the emissions at band position of 442 nm and 525 nm are highly selective between the two species and could help in finding adulteration of cow milk in buffalo milk and vice versa. The emissions at these wavelength positions correspond to fat-soluble vitamin-A as well as β-carotene. PLS regression is used as a statistical prediction model, which is developed by training with the emission spectra of milk samples having known level of adulterations. The developed model predicts the unknown level of adulterations by means of their spectral data. The goodness of the model is determined by the correlation coefficient R-square (r²) value, which in our case is 0.99. Furthermore, the model root mean square error in cross validation (RMSECV) and in prediction (RMSECP) remains 1.16 and 6.24 respectively. This approach can effectively be applied to determine milk adulterations among other species as well as in detecting external agents (fraudulent) added into milk and other dairy products by further studies.

Characterization of Desi Ghee Extracted by Different Methods Using Fluorescence Spectroscopy

In the current study, the effect of ghee extraction methods (direct cream DC, milk butter MB and milk skin MS) on its molecular composition has been investigated using Fluorescence spectroscopy. The excitation wavelength of 300 nm was found the best to produce pronounced spectral signatures of beta-carotene, vitamins and conjugated linoleic acid (CLA) in both cow and buffalo ghee types. Principal component analysis (PCA) has been applied on the spectral data to visualize the classification among ghee samples extracted by three methods. Both cow and buffalo ghee contain spectral signatures of vitamin A, E, K, D and CLA which has been verified through plotting loading vectors. The analysis of loading plots has been suggested that for cow ghee, MS extraction method conserve relatively higher concentration of beta carotene while DC and MB methods are a good choice for preserving relatively more concentrations of vitamins D, E and K. Similarly, for buffalo ghee, MS extraction method appear with higher concentration of CLA, whereas DC extraction method looks to preserve relatively higher concentration of vitamin A while MB method retains relatively low concentration of CLA and vitamins as compared to other two methods.

Marker discovery and associations with β-carotene content in Indian dairy cattle and buffalo breeds

Vitamin A is essential for human health, but current intake levels in many developing countries such as India are too low due to malnutrition. According to the World Health Organization, an estimated 250 million preschool children are vitamin A deficient globally. This number excludes pregnant women and nursing mothers, who are particularly vulnerable. Efforts to improve access to vitamin A are key because supplementation can reduce mortality rates in young children in developing countries by around 23%. Three key genes, BCMO1, BCO2, and SCARB1, have been shown to be associated with the amount of β-carotene (BC) in milk. Whole-genome sequencing reads from the coordinates of these 3 genes in 202 non-Indian cattle (141 Bos taurus, 61 Bos indicus) and 35 non-Indian buffalo (Bubalus bubalis) animals from several breeds were collected from data repositories. The number of SNP detected in the coding regions of these 3 genes ranged from 16 to 26 in the 3 species, with 5 overlapping SNP between B. taurus and B. indicus. All these SNP together with 2 SNP in the upstream part of the gene but already present in dbSNP (https://www.ncbi.nlm.nih.gov/projects/SNP/) were used to build a custom Sequenom array. Blood for DNA and milk samples for BC were obtained from 2,291 Indian cows of 5 different breeds (Gir, Holstein cross, Jersey Cross, Tharparkar, and Sahiwal) and 2,242 Indian buffaloes (Jafarabadi, Murrah, Pandharpuri, and Surti breeds). The DNA was extracted and genotyped with the Sequenom array. For each individual breed and the combined breeds, SNP with an association that had a P-value <0.3 in the first round of linear analysis were included in a second step of regression analyses to determine allele substitution effects to increase the content of BC in milk. Additionally, an F-test for all SNP within gene was performed with the objective of determining if overall the gene had a significant effect on the content of BC in milk. The analyses were repeated using a Bayesian approach to compare and validate the previous frequentist results. Multiple significant SNP were found using both methodologies with allele substitution effects ranging from 6.21 (3.13) to 9.10 (5.43) μg of BC per 100 mL of milk. Total gene effects exceeded the mean BC value for all breeds with both analysis approaches. The custom panel designed for genes related to BC production demonstrated applicability in genotyping of cattle and buffalo in India and may be used for cattle or buffalo from other developing countries. Moreover, the recommendation of selection for significant specific alleles of some gene markers provides a route to effectively increase the BC content in milk in the Indian cattle and buffalo populations.

Thermal Effects on Biochemical Signatures of UHT, Pasteurized and Domestically Boiled Buffalo Milk Detected by Synchronous Fluorescence Spectroscopy

Thermal treatment of milk is performed to limit bacterial growth and make it safe for human consumption. To increase the shelf life of milk, either ultrahigh temperature (UHT) or pasteurization techniques are employed that destroy the microorganisms. In this study, the synchronous front face fluorescence spectroscopy was employed for comparative study of raw, UHT treated, pasteurized and conventionally boiled milk at 93 °C (domestic boiling). Principal Component analysis clearly showed distinct clustering of UHT milk due to formation of Maillard reaction products. Fluorescence emission peak at 410 nm showed irreversible change in peak intensity attributed to conformational changes in protein due to UHT treatment while pasteurization and domestic boiling showed reversible changes when milk was cooled down to 10 °C. Furthermore, fluorescence emission peaks at 410 nm previously assigned to vitamin A has also been discussed.

Identification of cow, buffalo, goat and ewe milk species in fermented dairy products using synchronous fluorescence spectroscopy

In the dairy industry, substitution of high priced milk species with low priced ones is a common practice, and determination of milk species is critical. In this study, synchronous fluorescence spectroscopy (SFS) method was developed for identification of milk species in fermented dairy products (yoghurt and cheese). Three partial least square-discriminant analysis models were developed in order to identify pure-mixed samples, milk species and binary mixture type, and partial least square (PLS) model was utilized to quantify the mixing ratio in binary mixtures. PLS models used for yoghurt and cheese samples showed that detection limits of adulteration were below 3.3%. Apart from the buffalo-cow yoghurt and goat-cow cheese, precision of the measurements was found to be below 6.2. It can be said that SFS technique is applicable on yoghurt and cheese samples as it's a less destructive and a less costly method compared to DNA and protein based conventional methods.

Characterization of canola oil extracted by different methods using fluorescence spectroscopy

The potential of fluorescence spectroscopy has been utilized for the characterization of three types of canola oil samples: the first type was obtained by dissolving its seeds in hexane solvent, the second by cold press method, and the third from eight commercial brands. Fluorescence spectra from all samples have been acquired by using excitation wavelengths from 280 to 420 nm with step of 10 nm to investigate their valuable ingredients. The emission bands at 375, 525 and 673 nm that represent vitamin E/beta-carotene and chlorophyll, are present only in canola oil samples extracted by chemical and cold press methods and absolutely absent from all commercial brands. The emission band at 440 nm appearing only in the commercial oil brands, is assigned to oxidized products of isomers of vitamin E and fatty acids. In addition, the effect of temperature on the canola oil extracted by cold press method has been investigated which showed that up to 180 ^oC it does not lose much of its natural molecular composition. However, it showed a trend of thermal oxidation with rise of temperature.

Identification differential behavior of Gd@C82(OH)22 upon interaction with serum albumin using spectroscopic analysis

The interaction between Gd@C₈₂(OH)₂₂ and serum albumin (HSA and BSA) were investigated by spectroscopic analysis. From the characteristic feature of fluorescence quenching spectra at different temperatures, the inherent binding information including quenching mechanism, association constants, number of binding site, fraction of initial fluorescence and basic thermodynamic parameters were calculated. The binding of Gd@C₈₂(OH)₂₂ to serum albumin caused strong quenching of protein intrinsic fluorescence and the structural changes of serum albumin. At lower concentrations, Gd@C₈₂(OH)₂₂ was likely to rise fluorescence quenching of serum albumin through individual static quenching process by forming a ground-state complex, while dynamic and static coexisting quenching mechanism occurred in high concentration. Bimolecular quenching (K_q) value is twice the diffusion-controlled quenching constant (2.0 × 10¹⁰ L mol^-1 s^-1); binding sites of BSA were slightly more than those of HAS, and all of them reached to 1; the distance r between donor and acceptor was found to be 3.1494 nm and 3.6479 nm for HSA and BSA, respectively, both of which were fewer than 7 nm. It is confirmed that binding interaction for proteins in the presence of drugs was strong, the binding ratio was 1:1, and non-radiative energy transfer from protein to drug was extremely high probability in lower density. Binding process of Gd@C₈₂(OH)₂₂-HSA was driven mainly through van der Waals forces and hydrogen bonding formation, however more likely to be electrostatic interaction involved in the Gd@C₈₂(OH)₂₂-BSA binding process; Binding sites of Gd@C₈₂(OH)₂₂ to serum albumin were near tryprophan (HSA) and tyrosine residues (BSA), respectively. Moreover, a theoretical model of predicting the binding rate of drug to serum albumin was estimated, further analyzed that the binding rate was dynamically altered in various dose of protein and drug. Overall, these results provide potentially significant information for elucidating the distribution, transportation, the apparent relationship between pharmacologic activity and total plasma drug concentration as well as anti-carcinogenic activity and mechanisms in vivo.

Studying heating effects on desi ghee obtained from buffalo milk using fluorescence spectroscopy

Characterisation and thermal deterioration of desi ghee obtained from buffalo milk is presented for the first time using the potential of Fluorescence spectroscopy. The emission bands in non-heated desi ghee centred at 375 nm is labelled for vitamin D, 390 nm for vitamin K, 440–460 nm for isomers of conjugated linoleic acid (CLA), 490 nm for vitamin A and the region 620–700 nm is assigned to chlorophyll contents. Fluorescence emission spectra from all the heated and non-heated ghee samples were recorded using excitation wavelengths at 280, and 410 nm which were found best for getting maximum spectral signatures. Heating of desi ghee affects its molecular composition, however, the temperature range from 140 to 170°C may be defined safe for cooking /frying where it does not lose much of its molecular composition. Further, the rise in temperature induces prominent spectral variations which confirm the deterioration of valuable vitamins, isomers of CLA and chlorophyll contents. Fluorescence emission peak at 552 nm shows oxidation product and an increase in its intensity with the rise in temperature is observed. In order to classify heated samples at different temperatures, principal component analysis (PCA) has been applied on heated and non-heated ghee samples that further elucidated the temperature effects.