Detection of Urea Adulteration in Milk Using Near-Infrared Raman Spectroscopy

Ag@CDS SERS substrate coupled with lineshape correction algorithm and BP neural network to detect thiram in beverages

Surface enhanced Raman scattering (SERS) has been proved an effective analytical technique due to its high sensitivity, however, how to identify and extract useful information from raw SERS spectra is still a problem that needs to be resolved. In this work, a composite SERS substrate was prepared by encapsulating Ag nanoparticles within dialdehyde starch (Ag@CDS) to obtain dense "hot spot", and then a novel spectral preprocessing algorithm namely lineshape correction algorithm (LCA) was developed to separate the characteristic peaks of analytes from the original SERS spectra. Based on Ag@CDS and LCA, thiram residues in different beverages were quantitatively detected using back propagation (BP) neural network regression model. It was found that LCA provided an easy-to-use method for improving prediction ability of BP model. The Rp2 of BP model was improved from 0.2384, 0.3647 and 0.5581 to 0.9327, 0.9127 and 0.9251 for the quantitative detection of thiram residue in apple juice, grape juice and milk, respectively, while LCA was used for SERS spectra preprocessing. The optimal model can accurately detect thiram residue with a low limit of detection at 1.0 × 10-7 M, which is far below the maximum residue limit of thiram (2.9 × 10-5 M) regulated by the US Environmental Protection Agency. This study demonstrated that the proposed LCA can be used as a simple and valid spectra-preprocessing method in SERS quantitative detection.

A multicommuted system using bacterial cellulose for urease immobilization and copper (II)-MOF colorimetric sensor for urea spectrophotometric determination in milk

This work describes determining urea in milk samples using a multicommuted approach with a urease enzyme immobilized in bacterial cellulose and solid MOF as a colorimetric reagent. The Cu(2+)-MOF was characterized by FTIR spectroscopy, XRD, and SEM. The urea quantification was based on the urea hydrolysis reaction catalyzed by urease and reacted with Cu(2+)-MOF forming [Cu(NH3)4]2+, monitored at 450 nm. Linear responses were obtained from 1.0 to 50.0 mg dL-1 urea (R = 0.9959, n = 11), detection and quantitation limits of 0.082 mg dL-1 and 0.272 mg dL-1 respectively, analytical frequency of 8 determinations per hour, 0.8 mL sample solution consumption. Potential interfering studies have shown the selectivity of the proposed method. Addition and recovery tests were performed obtaining variation from 90 to 103%. Applying the F-test and t-test, the results showed no significant difference at the 95% confidence level Comparing the proposed and the reference method.

Watermelon Derived Urease Immobilized Gold Nanoparticles-Graphene Oxide Transducer for Direct Detection of Urea in Milk Samples

Urea contamination in milk poses significant health risks, including kidney failure, urinary tract obstruction, fluid loss, shock, and gastrointestinal bleeding. This highlights the need for sensitive, rapid, and reliable methods to detect traces amount of urea in milk. In this study, we designed an electrochemical transducer for urea detection by utilizing purified watermelon urease (Urs), gold nanoparticles (AuNPs), and graphene oxide (GO). The nanomaterials and biosensor probe were characterized using UV-vis spectroscopy, XPS, TEM, XRD, FTIR, AFM, CV, EIS, and DPV. The engineered probe (GCE/AuNPs/GO/Urs) demonstrated a broad linear detection range of 5 to 90 mg/dL and a low limit of detection (LOD) of 0.037 (±0.012) mg/dL (RSD < 3.7%). The biosensor was tested for potential interferents that may be present in adulterated milk and an exceptionally low coefficient of selectivity (ksel <0.1) was obtained. Evaluation of milk samples from a local dairy farm showed good recovery rates from 93.13% to. 98.79% (RSD < 4.28%, n = 3), indicating reliable detection capabilities. Stability tests confirmed the sensor's reproducibility and consistent performance. Additionally, a comparison study of the system was carried out using the purified watermelon urease and the commercially available urease. Herein, the results obtained using the sensor probe was finally validated with the gold standard method.

Microplate-based 3D-printed image box for urea determination in milk by digital image colorimetry

In this study, we describe a rapid and high-throughput smartphone-based digital colorimetric method for determining urea in milk. A compact and cost-effective 3D-printed image box microplate-based system was designed to measure multiple samples simultaneously, using minimal sample and reagent volumes. The apparatus was applied for the quantification of urea in milk based on its reaction with p-dimethylaminobenzaldehyde (DMAB). The predictive performance of calibration was evaluated using RGB and different colour models (CMYK, HSV, and CIELAB), with the average blue (B) values of the RGB selected as the analytical signal for urea quantification. Under optimized conditions, a urea concentration linear range from 50 to 400 mg L-1 was observed, with a limit of detection (LOD) of 15 mg L-1. The values found with the smartphone-based DIC procedure are in good agreement with spectrophotometric (spectrophotometer and microplate treader) and reference method (mid-infrared spectroscopy) values. This proposed approach offers an accessible and efficient solution for digital image colorimetry, with potential applications for various target analytes in milk and other fields requiring high-throughput colorimetric analysis.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

The Tandem Nitrate and CO2 Reduction for Urea Electrosynthesis: Role of Surface N-Intermediates in CO2 Capture and Activation

Electrochemical reduction of CO2 and nitrate offers a promising avenue to produce valuable chemicals through the using of greenhouse gas and nitrogen-containing wastewater. However, the generally proposed reaction pathway of concurrent CO2 and nitrate reduction for urea synthesis requires the catalysts to be both efficient in both CO2 and nitrate reduction, thus narrowing the selection range of suitable catalysts. Herein, we demonstrate a distinct mechanism in urea synthesis, a tandem NO3 - and CO2 reduction, in which the surface amino species generated by nitrate reduction play the role to capture free CO2 and subsequent initiate its activation. When using the TiO2 electrocatalyst derived from MIL-125-NH2, it intrinsically exhibits low activity in aqueous CO2 reduction, however, in the presence of both nitrate and CO2, this catalyst achieves an excellent urea yield rate of 43.37 mmol ⋅ g-1 ⋅ h-1 and a Faradaic efficiency of 48.88 % at -0.9 V vs. RHE in a flow cell. Even at a low CO2 level of 15 %, the Faradaic efficiency of urea synthesis remains robust at 42.33 %. The tandem reduction procedure was further confirmed by in situ spectroscopies and theoretical calculations. This research provides new insights into the selection and design of electrocatalysts for urea synthesis.

A Wettability Contrast SERS Droplet Assay for Multiplexed Analyte Detection

Droplet assay platforms have emerged as a significant methodology, providing distinct advantages such as sample compartmentalization, high throughput, and minimal analyte consumption. However, inherent complexities, especially in multiplexed detection, remain a challenge. We demonstrate a novel strategy to fabricate a plasmonic droplet assay platform (PDAP) for multiplexed analyte detection, enabling surface-enhanced Raman spectroscopy (SERS). PDAP efficiently splits a microliter droplet into submicroliter to nanoliter droplets under gravity-driven flow by wettability contrast between two distinct regions. The desired hydrophobicity and adhesive contrast between the silicone oil-grafted nonadhesive hydrophilic zone with gold nanoparticles is attained through (3-aminopropyl) triethoxysilane (APTES) functionalization of gold nanoparticles (AuNPs) using a scotch-tape mask. The wettability contrast surface facilitates the splitting of aqueous droplets with various surface tensions (ranging from 39.08 to 72 mN/m) into ultralow volumes of nanoliters. The developed PDAP was used for the multiplexed detection of Rhodamine 6G (Rh6G) and Crystal Violet (CV) dyes. The limit of detection for 120 nL droplet using PDAP was found to be 134 pM and 10.1 nM for Rh6G and CV, respectively. These results align with those from previously reported platforms, highlighting the comparable sensitivity of the developed PDAP. We have also demonstrated the competence of PDAP by testing adulterant spiked milk and obtained very good sensitivity. Thus, PDAP has the potential to be used for the multiplexed screening of food adulterants.

Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Sequential co-reduction of nitrate and carbon dioxide enables selective urea electrosynthesis

Despite the recent achievements in urea electrosynthesis from co-reduction of nitrogen wastes (such as NO3-) and CO2, the product selectivity remains fairly mediocre due to the competing nature of the two parallel reduction reactions. Here we report a catalyst design that affords high selectivity to urea by sequentially reducing NO3- and CO2 at a dynamic catalytic centre, which not only alleviates the competition issue but also facilitates C-N coupling. We exemplify this strategy on a nitrogen-doped carbon catalyst, where a spontaneous switch between NO3- and CO2 reduction paths is enabled by reversible hydrogenation on the nitrogen functional groups. A high urea yield rate of 596.1 µg mg-1 h-1 with a promising Faradaic efficiency of 62% is obtained. These findings, rationalized by in situ spectroscopic techniques and theoretical calculations, are rooted in the proton-involved dynamic catalyst evolution that mitigates overwhelming reduction of reactants and thereby minimizes the formation of side products.

A simple smartphone-assisted paper-based colorimetric biosensor for the detection of urea adulteration in milk based on an environment-friendly pH-sensitive nanocomposite

Urea is a common milk adulterant that falsely increases its protein content. Excessive consumption of urea is harmful to the kidney, liver, and gastrointestinal system. The conventional methods for urea detection in milk are time-consuming, costly, and require highly skilled operators. So, there is an increasing demand for the development of rapid, convenient, and cost-efficient methods for the detection of urea adulteration in milk. Herein, we report a novel colorimetric paper-based urea biosensor, consisting of a novel environment-friendly nanocomposite of halloysite nanotubes (HNT), that urease enzyme and an anthocyanin-rich extract, as a natural pH indicator are simultaneously immobilized into its internal and external surfaces. The biosensing mechanism of this biosensor is based on anthocyanin color change, which occurs due to urease-mediated hydrolysis of urea and pH increment of the environment. The colorimetric signal of this biosensor is measured through smartphone-assisted analysis of the mean RGB (Red-Green-Blue) intensity of samples and is capable of detecting urea with a detection limit of 0.2 mM, and a linear range from 0.5 to 100 mM. This biosensor has demonstrated promising results for the detection of urea in milk samples, in the presence of other milk adulterants and interferents.

Green structure orienting and reducing agents of wheat peel extract induced abundant surface oxygen vacancies and transformed the nanoflake morphology of NiO into a plate-like shape with enhanced non-enzymatic urea sensing application

Researchers are increasingly focusing on using biomass waste for green synthesis of nanostructured materials since green reducing, capping, stabilizing and orientation agents play a significant role in final application. Wheat peel extract contains a rich source of reducing and structure orienting agents that are not utilized for morphological transformation of NiO nanostructures. Our study focuses on the role of wheat peel extract in morphological transformation during the synthesis of NiO nanostructures as well as in non-enzymatic electrochemical urea sensing. It was observed that the morphological transformation of NiO flakes into nanoplatelets took place in the presence of wheat peel extract during the preparation of NiO nanostructures and that both the lateral size and thickness of the nanostructures were significantly reduced. Wheat peel extract was also found to reduce the optical band gap of NiO. A NiO nanostructure prepared with 5 mL of wheat peel extract (sample 2) was highly efficient for the detection of urea without the use of urease enzyme. It has been demonstrated that the induced modification of NiO nanoplatelets through the use of structure-orienting agents in the wheat peel has enhanced their electrochemical performance. A linear range of 0.1 mM to 13 mM was achieved with a detection limit of 0.003 mM in the proposed urea sensor. The performance of the presented non-enzymatic urea sensor was evaluated in terms of selectivity, stability, reproducibility, and practical application, and the results were highly satisfactory. As a result of the high surface active sites on sample 2, the low charge transfer resistance, as well as the high exposure to the surface active sites of wheat peel extract, sample 2 demonstrated enhanced performance. The wheat peel extract could be used for the green synthesis of a wide range of nanostructured materials, particularly metal/metal oxides for various electrochemical applications.

Green and sustainable synthesis of CaO nanoparticles: Its solicitation as a sensor material and electrochemical detection of urea

Urea is recognized as one of the most frequently used adulterants in milk to enhance artificial protein content, and whiteness. Drinking milk having high urea concentrations which causes innumerable health disputes like ulcers, indigestion, and kidney-related problems. Therefore, herein, a simple and rapid electroanalytical platform was developed to detect the presence of urea in milk using a modified electrode sensor. Calcium oxide nanoparticles (CaO NPs) were green synthesized and used as a catalyst material for developing the sensor. Synthesized materials formation was confirmed by different techniques like FTIR, UV-visible, XRD, SEM-EDX, and Raman spectroscopy. The carbon paste electrode (CPE) was modified using the CaO NPs and used as a working electrode during the analysis followed by cyclic voltammetry and differential pulse voltammetry (DPV) techniques. The fabricated calcium oxide modified carbon paste electrode (CaO/CPE) successfully detected the presence of urea in the lower concentration range (lower limit of detection (LLOD) = 0.032 µM) having a wide linear detection range of 10-150 µM. Adsorption-controlled electrode process was achieved at the scan rate variation parameter. The leading parameters like the selectivity, repeatability, and stability of the CaO/CPE were investigated. The relative standard deviation of sensor was ± 3.8% during the interference and stability study.

Development of a paper strip test for colorimetric detection of urea in raw materials for animal feed

Aiming at developing an easily implementable method for on-site analysis to detect urea adulteration in feed ingredients, a simple and inexpensive paper strip for urea detection via colorimetric assay is herein presented. The paper strip can be simply fabricated by immobilizing urease with bromothymol blue (BTB) as a pH indicator on cellulose fiber. Upon dipping the paper strip into the target sample, the release of ammonia during the reaction between urea in the sample and urease on the paper strip causes a pH change that results in the development of a blue color, thus indicating the presence of urea. A semiquantitative detection method was developed on the basis of the color change on the paper strip, which can be detected by naked eyes and compared with a color chart made by spiking urea at concentrations varying from 0.10% to 1.0% (w/w) in animal protein and fishmeal samples. Moreover, quantitative data were obtained by taking a picture with a smartphone camera and measuring the color intensity using ImageJ software. A comparison between BTB and phenol red as pH indicators revealed that the former afforded better results in terms of resolution. Under optimal conditions, good linear responses of blue intensity were obtained in a concentration range of 0.10%-1.0% (w/w). The recovery was determined to range between 98.1% and 118.3% with a relative standard deviation of <5%. The developed paper strip assay was applied to determine urea in animal protein and fishmeal, finding good agreement with the official AOAC method (No. 967.07). The present paper strip is rapid and requires neither sophisticated devices nor skilled personnel, allowing its use by quality controllers for the routine on-site detection of the urea adulteration of raw materials.

Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system

The aim of this study is to evaluate a previousely developed photoacoustic spectroscopy system with light sources of visible to short-wave near infrared (Vis-SWNIR, 395-940 nm) for detection of adulterations in cow's milk including formalin, urea, hydrogen peroxide, starch, sodium hypochlorite, and detergent powder. The results of principal component analysis (PCA) showed a very good visual differentiation of different adulterations. The artificial neural networks (ANN) showed the highest classification accuracy (97.6 %) in detection of adulteration type and adulteration level (nearly 100 %). It can be generally concluded that the Vis-SWNIR photoacoustic spectroscopy system is a reliable and potent instrument for detecting various types of milk adulterations. Further studies are suggested with including cow's milk of different sources with probable variations in composition to generalize the findings of the present study. With the extension of the light sources to the range of long-wave NIR, the system can be applied as a diagnostic tool for quality evaluation of other liquid foods.

Green Synthesis of NiO Nanoflakes Using Bitter Gourd Peel, and Their Electrochemical Urea Sensing Application

To determine urea accurately in clinical samples, food samples, dairy products, and agricultural samples, a new analytical method is required, and non-enzymatic methods are preferred due to their low cost and ease of use. In this study, bitter gourd peel biomass waste is utilized to modify and structurally transform nickel oxide (NiO) nanostructures during the low-temperature aqueous chemical growth method. As a result of the high concentration of phytochemicals, the surface was highly sensitive to urea oxidation under alkaline conditions of 0.1 M NaOH. We investigated the structure and shape of NiO nanostructures using powder X-ray diffraction (XRD) and scanning electron microscopy (SEM). In spite of their flake-like morphology and excellent crystal quality, NiO nanostructures exhibited cubic phases. An investigation of the effects of bitter gourd juice demonstrated that a large volume of juice produced thin flakes measuring 100 to 200 nanometers in diameter. We are able to detect urea concentrations between 1-9 mM with a detection limit of 0.02 mM using our urea sensor. Additionally, the stability, reproducibility, repeatability, and selectivity of the sensor were examined. A variety of real samples, including milk, blood, urine, wheat flour, and curd, were used to test the non-enzymatic urea sensors. These real samples demonstrated the potential of the electrode device for measuring urea in a routine manner. It is noteworthy that bitter gourd contains phytochemicals that are capable of altering surfaces and activating catalytic reactions. In this way, new materials can be developed for a wide range of applications, including biomedicine, energy production, and environmental protection.

A pH-responsive ratiometric fluorescence system based on AIZS QDs and azamonardine for urea detection

Herein, a ratiometric fluorescent nanoprobe was strategically fabricated using pH-sensitive azamonardine (Aza) as a pH indicator and pH-insensitive AIZS QDs as a reference fluorescence signal for urea activity determination and pH sensing. As the pH changed from 9.7 to 11.7, the resorcinol could react with dopamine to form the cyclization product (Aza), producing a fluorescence signal at 455 nm. Meanwhile, the fluorescence intensity of AIZS QDs at 566 nm remained unchanged. Thus, the ratio of the fluorescence intensity (F₄₅₅/F₅₆₆) was able to quantify pH value. Our designed pH-sensing platform showed a linear respond to pH values in the range of 9.7 to 11.7 at intervals of 0.2. In addition, the hydrolysis of urea by urease caused an increase of the system pH value, which can be used to measure the concentration of urea. The developed method for urea determination exhibited a good linear relationship from 0.02 to 20 mM and the limit of detection was 0.0103 mM. Moreover, the practical application was confirmed by urea analysis in real water sample with high feasibility and accuracy, indicating the great application prospects of this sensing platform for urea activity analysis.

Knowledge, Attitudes and Practices of the Lebanese Community toward Food Adulteration

Food adulteration is the purposeful act of decreasing the quality of food goods offered for sale, whether by adding or replacing inferior substances or by the removal of some valuable ingredient. A limited number of studies have explored the knowledge, attitudes and practices (KAPs) concerning food adulteration in Lebanon. The objectives of the present study were to determine the knowledge, attitudes and practices of identifying adulteration in the process of food purchase by Lebanese adult consumers, and to identify factors associated with food adulteration. An online survey (n = 499) was administered among Lebanese adults aged 18 years and above. Results showed that the majority had a low food adulteration knowledge score (73.1%). During shopping, fewer than half of the participants checked the ingredients (42%) and nutrition facts label (33.9%). Regression analyses showed that six predictors were significantly associated with participants’ knowledge scores including gender, age, marital status, education (undergraduate and master degree) and employment status (student). The results of this study show that knowledge and practices of identifying adulteration in the process of food purchase by consumers are lacking among most respondents. Increasing knowledge, awareness and motivation to identify food adulteration products during food shopping will empower consumers to improve buying practices, especially for the public with a lower level of education.

Identifying adulteration of raw bovine milk with urea through electrochemical impedance spectroscopy coupled with chemometric techniques

This study aimed to evaluate the applicability of electrochemical impedance spectroscopy to identify raw bovine milk adulteration with urea. Three batches of raw milk adulterated with urea were studied. Hierarchical clustering indicated that the samples could be split in three groups corresponding to low adulteration (less than 7 wt%), medium adulteration (between 8 and 16 wt%) and high adulteration (over than 16 wt%). A linear discriminant analysis was performed resulting in 90% of accuracy in classifying between groups. Besides, a partial least squares model containing three directions provided good accuracy in quantitatively predicting the urea mass fraction added to raw bovine milk. Finally, calculations using an approximated electric circuit model suggested the formation of urea aggregates that hinder charge transportation within the milk thus diminishing the solution conductivity. Results indicate that electrochemical impedance spectroscopy can be a useful, low cost and rapid tool to identify milk adulteration with urea.

OUP accepted manuscript

A reversed-phase isocratic elution high-performance liquid chromatography method coupled with fluorescence detection has been developed to determine urea concentration via online postcolumn derivatization. Swimming pool water samples were filtered through 0.20 μm syringe filters. When the temperature of reaction coil was 40°C, urea was derivatized well with xanthydrol methanol solution (0.1 g/L) containing 0.50% hydrochloric acid with a flow rate of 0.20 mL/min. Successful separation was achieved by using Shim-pack VP-ODS C18 (250 mm × 4.6 mm, 5 μm) column, with a mobile phase containing phosphoric acid solution (0.01 mol/L) at a flow rate of 0.80 mL/min. Retention time and external standard method were used for qualitative and quantitative urea analysis, respectively. Under the established conditions, the limit of detection, linear range, correlation coefficient, recovery and relative standard deviation was 0.09 mg/L, 1.0-100.0 mg/L, 0.9998, 87.0-105.3% and 0.95-4.8%, respectively. Ammonia, thiourea and trichloroisocyanuric acid did not interfere with urea analysis. The method showed satisfactory results with high precision, accuracy, recovery, as well as sensitivity, for the determination of urea in swimming pool water.

Urea detection in milk by urease-assisted pH-sensitive carbon dots

Excessive urea in milk will lead to serious health problems. To detect whether the urea concentration in milk exceeds the standard and ensure the quality of milk, it is necessary to develop detection technology for urea in milk. But it is difficult to detect urea in milk conveniently and accurately by traditional methods. To measure the concentration of urea in milk, stable green light carbon dots (CDs) were synthesized by a one-step method as a fluorescent probe. Then, 3, 5-diaminobenzoic acid was used as the precursor for CD synthesis. Experimental results showed that CDs can generate strong fluorescence when excited by light (350-450 nm). The fluorescence peak wavelength is 490 nm, and the optimum excitation wavelength is 390 nm. The fluorescence intensity of CDs has a significant change with variations of pH (pH of 6-9), and the higher the pH, the lower the fluorescence intensity. Additionally, urea can be hydrolyzed by urease to produce ammonia and carbon dioxide. Ammonia is ionized in water to produce OH^-, which increases the pH of the solution. After adding standard urea to milk, urease and CDs are added. The fluorescence intensity of CDs in the mixed solution decreases as the concentration of standard added urea increases. Thus the concentration of urea in milk can be calculated. The experimental results show that the CD method for detecting urea in milk has advantages of high sensitivity and wide measurement range. The linear interval is 25-500 mg/L, R² is 0.998, and the limit of detection is 6.27 mg/L. The concentration of urea in the milk used in the experiment is 265.46 mg/L. CDs are easy to fabricate, and the advantages of the method are simple operation, no pretreatment, safety, and low cost. A new method for the detection of urea in milk was established, to the best of our knowledge, and this method can aid in food quality control.

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.

The Combination of Vibrational Spectroscopy and Chemometrics for Analysis of Milk Products Adulteration

Milk products obtained from cow, goat, buffalo, sheep, and camel as well as fermented forms such as cheese, yogurt, kefir, and butter are in a category of the most nutritious foods due to their high contents of high protein contributing to total daily energy intake. For certain reasons, high price milk products may be adulterated with low-quality ones or with foreign substances such as melamine and formalin which are added into them; therefore, a comprehensive review on analytical methods capable of detecting milk adulteration is needed. The objective of this narrative review is to highlight the use of vibrational spectroscopies (near infrared, mid infrared, and Raman) combined with multivariate analysis for authentication of milk products. Articles, conference reports, and abstracts from several databases including Scopus, PubMed, Web of Science, and Google Scholar were used in this review. By selecting the correct conditions (spectral treatment, normal versus derivative spectra at wavenumbers region, and chemometrics techniques), vibrational spectroscopy is a rapid and powerful analytical technique for detection of milk adulteration. This review can give comprehensive information for selecting vibrational spectroscopic methods combined with chemometrics techniques for screening the adulteration practice of milk products.

Fluorescence photobleaching of urine for improved signal to noise ratio of the Raman signal - An exploratory study

Urine analysis is an important clinical test routinely performed in pathology labs for disease diagnosis and prognosis. In recent years, near-infrared Raman spectroscopy has drawn considerable attention for urine analysis as it can provide rapid, reliable, and reagent-free analysis of urine samples. However, one important practical problem encountered in such Raman measurements is the orders of magnitude stronger spectral background preventing one to utilize the full dynamic range of the detector which is required for the measurement of Raman signal with good signal-to-noise ratio (SNR). We report here the results of an exploratory study carried out on human urine samples to show that the photobleaching, which is a major disadvantage during the fluorescence measurement, could be utilized for suppressing the measured background to improve the SNR of the Raman peaks. It was found that once the photobleaching reached its plateau, there were improvements by ~67% and ~47% in the SNR and the signal to background ratio (SBR), respectively, of the Raman signals as compared to the spectra measured at the start of acquisition. Further, the reduced background also allowed us to utilize the full dynamic range of the detector at increased integration time without saturating the detector indicating the possibility of obtaining an improved detection limit.

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Influence of interfering co-appearing container peaks on the accuracy of direct quantitative Raman measurement of a sample in a plastic container

The axially perpendicular offset (APO) scheme was previously demonstrated as a versatile scheme able to minimize or eliminate the glass background in the direct and non-sampling Raman measurement of an ethanol sample housed in a glass bottle. Alternatively, when directly analyzing a sample housed in a plastic container, another typical container yielding strong Raman peaks itself, the Raman peaks of both the container and the housed sample are unavoidably present together in a collected spectrum. Therefore, a crucial issue to investigate under this situation is how the magnitude of the co-appearing container peaks influences the accuracy for quantitative analysis of the housed sample. For the evaluation, a non-sampling Raman spectroscopic measurement of the urea concentration in a urea gel housed in a circular polypropylene (PP) container was attempted by employing two axially perpendicular offset (APO) schemes with detection windows of different sizes (25.4 and 10.0 mm, referred to as the wide-window APO (WW-APO) and narrow-window APO (NW-APO), respectively), and transmission and back-scattering schemes incorporating a 25.4 mm detection window. The intensity ratios between the container and urea peaks in the collected spectra were different depending on the adopted measurement scheme. The intensity ratio was greatest (smallest container peak) in the NW-APO measurement due to the narrowed detection window, making the generated container Raman photons at the side-wall less detectable to the bottom-positioned detector. A spectral acquisition scheme allowing the maximal suppression of the container peaks, while still maintaining the sample features, was a key requirement to secure an accurate measurement of the sample concentration. In addition, a Monte Carlo simulation was used to visualize the distributions of the container and urea photons inside the sample-housed container.

Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling

A two-step chemometric procedure was developed on the attenuated total reflection-Fourier transform infrared data of human breastmilk to detect adulteration by water or cow milk. The samples, collected from a Milk Bank, were analyzed before and after adulteration with whole, skimmed, semi-skimmed cow milk and water. A preliminary clustering via principal component analysis distinguished three classes: pure milk, milk adulterated with water, and milk adulterated with cow milk. A first partial least square-discriminant analysis (PLS-DA) classification model was built and then applied on new samples to identify the specific adulterants. The external validation on this model reached 100% of the correct identification of pure milk and 90% of the type of adulterants. In the following step, four PLS calibration models were built to quantify the amount of the adulterant detected in the classification analysis. The prediction performance of these models on new samples showed satisfactory parameters with root mean square error of prediction and percentage relative error lower than 1.38% and 3.31%, respectively.

Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis

The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein concentrate (WPC) powder. Soft independent modeling class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) models were built based on two spectral regions (400-1800 cm-1 and 500-1100 cm-1) to classify different types of adulterated samples. The most effective was the SIMCA model in 500-1100 cm-1 with an accuracy of 96.9% and an error rate of 5%. Partial least squares regression (PLSR) models for each adulterant were developed using two different Raman spectral ranges (400-1800 cm-1 and selected specific region) and data pretreatment methods. The determination coefficients (R2) of all models were higher than 0.96. PLSR models based on typical Raman regions (500-1100 cm-1 for creatine and taurine, the combination of range 800-1000 cm-1 and 1300-1500 cm-1 for glutamine) were superior to models in the full spectrum. The lowest root mean squared error of prediction (RMSEP) was 0.21%, 0.33%, 0.42% for creatine, taurine and glutamine, and the corresponding limit of detection (LOD) values for them were 0.53%, 0.71% and 1.13%, respectively. This proves that Raman spectroscopy with the help of multivariate approaches is a powerful method to detect adulterants in WPC.

Applications of Raman spectroscopic techniques for quality and safety evaluation of milk: A review of recent developments

Milk is a complete nutrient source for humans. The quality and safety of milk are critical for both producers and consumers, thereby the dairy industry requires rapid and nondestructive methods to ensure milk quality and safety. However, conventional methods are time-consuming and laborious, and require complicated preparation procedures. Therefore, the exploration of new milk analytical methods is essential. This current review introduces the principles of Raman spectroscopy and presents recent advances since 2012 of Raman spectroscopic techniques mainly involving surface-enhanced Raman spectroscopy (SERS), fourier-transform (FT) Raman spectroscopy, near-infrared (NIR) Raman spectroscopy, and micro-Raman spectroscopy for milk analysis including milk compositions, microorganisms and antibiotic residues in milk, as well as milk adulterants. Additionally, some challenges and future outlooks are proposed. The current review shows that Raman spectroscopic techniques have the promising potential for providing rapid and nondestructive detection of milk parameters. However, the application of Raman spectroscopy on milk analysis is not common yet since some limitations of Raman spectroscopy need to be overcome before making it a routine tool for the dairy industry.

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

The adulteration of milk fat in dairy products with cheaper non-milk based fats or oils is frequently encountered in the dairy industry. In this study, Raman spectroscopy with chemometric was used for the discrimination of foreign fats and oils in milk cream and yogurt. Firstly, binary mixtures of cream and oils (corn and sunflower oil), and vegetable fat blends which are potentially or currently used by the dairy industry were prepared. All fat or oil samples and their binary mixtures were examined by using Raman spectroscopy. Then, fat content of skim milk was adjusted to 3% (w/w) by the milk fat, external oils or fats, and binary mixtures, and was used in yogurt production. The lipid fraction of yogurt was extracted and characterized by Raman spectroscopy. The spectral data were then pre-processed and principal component analysis (PCA) was performed. Raman spectral data showed successful discrimination for about the source of the fats or oils. Temperature effect was also studied at six different temperatures (25, 30, 40, 50, 60 and 70 °C) in order to obtain the best spectral information. Raman spectra collected at higher temperatures were more intense. Obtained results showed that the performance of Raman spectroscopy with PCA was very promising and can be expected to provide a simple and quick way for the discrimination of foreign fats and oils in both milk cream and yogurt. Fermentation and yogurt processing affected clustering of fat samples by PCA, probably depending on some lipolysis or production of new products that can affect the Raman scattering. However, those changes did not affect differentiation of samples by Raman spectroscopy.