Sagar Dhakal

Detection and quantification of adulterants in milk powder using a high-throughput Raman chemical imaging technique

ABSTRACT Milk is a vulnerable target for economically motivated adulteration. In this study, a line-scan high-throughput Raman imaging system was used to authenticate milk powder. A 5 W 785 nm line laser (240 mm long and 1 mm wide) was used as a Raman excitation source. The system was used to acquire hyperspectral Raman images in a wave number range of 103–2881 cm–1 from the skimmed milk powder mixed with two nitrogen-rich adulterants (i.e., melamine and urea) at eight concentrations (w/w) from 50 to 10,000 ppm. The powdered samples were put in sample holders with a surface area of 150 ×100 mm and a depth of 2 mm for push-broom image acquisition. Varying fluorescence signals from the milk powder were removed using a correction method based on adaptive iteratively reweighted penalised least squares. Image classifications were conducted using a simple thresholding method applied to single-band fluorescence-corrected images at unique Raman peaks selected for melamine (673 cm–1) and urea (1009 cm–1). Chemical images were generated by combining individual binary images of melamine and urea to visualise identification, spatial distribution and morphological features of the two adulterant particles in the milk powder. Limits of detection for both melamine and urea were estimated in the order of 50 ppm. High correlations were found between pixel concentrations (i.e., percentages of the adulterant pixels in the chemical images) and mass concentrations of melamine and urea, demonstrating the potential of the high-throughput Raman chemical imaging method for the detection and quantification of adulterants in the milk powder. GRAPHICAL ABSTRACT

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders

Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

A Simple Surface-Enhanced Raman Spectroscopic Method for on-Site Screening of Tetracycline Residue in Whole Milk

Therapeutic and subtherapeutic use of veterinary drugs has increased the risk of residue contamination in animal food products. Antibiotics such as tetracycline are used for mastitis treatment of lactating cows. Milk expressed from treated cows before the withdrawal period has elapsed may contain tetracycline residue. This study developed a simple surface-enhanced Raman spectroscopic (SERS) method for on-site screening of tetracycline residue in milk and water. Six batches of silver colloid nanoparticles were prepared for surface enhancement measurement. Milk-tetracycline and water-tetracycline solutions were prepared at seven concentration levels (1000, 500, 100, 10, 1, 0.1, and 0.01 ppm) and spiked with silver colloid nanoparticles. A 785 nm Raman spectroscopic system was used for spectral measurement. Tetracycline vibrational modes were observed at 1285, 1317 and 1632 cm−1 in water-tetracycline solutions and 1322 and 1621 cm−1 (shifted from 1317 and 1632 cm−1, respectively) in milk-tetracycline solutions. Tetracycline residue concentration as low as 0.01 ppm was detected in both the solutions. The peak intensities at 1285 and 1322 cm−1 were used to estimate the tetracycline concentrations in water and milk with correlation coefficients of 0.92 for water and 0.88 for milk. Results indicate that this SERS method is a potential tool that can be used on-site at field production for qualitative and quantitative detection of tetracycline residues.

Detection of color dye contamination in spice powder using 1064 nm Raman chemical imaging system

Spice powders are used as food additives for flavor and color. Economically motivated adulteration of spice powders by color dyes is hazardous to human health. This study explored the potential of a 1064 nm Raman chemical imaging system for identification of azo color contamination in spice powders. Metanil yellow and Sudan-I, both azo compounds, were mixed separately with store-bought turmeric and curry powder at the concentration ranging from 1 % to 10 % (w/w). Each mixture sample was packed in a shallow nickel-plated sample container (25 mm x 25 mm x 1 mm). One Raman chemical image of each sample was acquired across the 25 mm x 25 mm surface area using a 0.25 mm step size. A threshold value was applied to the spectral images of metanil yellow mixtures (at 1147 cm-1) and Sudan-I mixtures (at 1593 cm-1) to obtain binary detection images by converting adulterant pixels into white pixels and spice powder pixels into the black (background) pixels. The detected number of pixels of each contaminant is linearly correlated with sample’s concentration (R2 = 0.99). This study demonstrates the 1064 nm Raman chemical imaging system as a potential tool for food safety and quality evaluation.

Non-targeted and targeted Raman imaging detection of chemical contaminants in food powders

Economically motivated adulteration and fraud to food powders are emerging food safety risks that threaten the health of the general public. In this study, targeted and non-targeted methods were developed to detect adulterants based on macro-scale Raman chemical imaging technique. Detection of potassium bromate (PB) (a flour improver banned in many countries) mixed in wheat flour was used as a case study to demonstrate the developed methods. A line-scan Raman imaging system with a 785 nm line laser was used to acquire hyperspectral image from the flour-PB mixture. Raman data analysis algorithms were developed to fulfill targeted and non-targeted contaminant detection. The targeted detection was performed using a single-band Raman image method. An image classification algorithm was developed based on single-band image at a Raman peak uniquely selected for the PB. On the other hand, a mixture analysis and spectral matching method was used for the non-targeted detection. The adulterant was identified by comparing resolved spectrum with reference spectra stored in a pre-established Raman library of the flour adulterants. For both methods, chemical images were created to show the PB particles mixed in the flour powder.

Raman spectroscopy method for subsurface detection of food powders through plastic layers

Proper chemical analyses of materials in sealed containers are important for quality control purpose. Although it is feasible to detect chemicals at top surface layer, it is relatively challenging to detect objects beneath obscuring surface. This study used spatially offset Raman spectroscopy (SORS) method to detect urea, ibuprofen and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785 nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. With increasing offset distance, the fraction of information from the deeper subsurface material increased compared to that from the top surface material. The series of measurements was analyzed to differentiate and identify the top surface and subsurface materials. Containing mixed contributions from the powder and capsule, the SORS of each sample was decomposed using self modeling mixture analysis (SMA) to obtain pure component spectra of each component and corresponding components were identified using spectral information divergence values. Results show that SORS technique together with SMA method has a potential for non-invasive detection of chemicals at deep subsurface layer.

Detection of metanil yellow contamination in turmeric using FT-Raman and FT-IR spectroscopy

Turmeric is well known for its medicinal value and is often used in Asian cuisine. Economically motivated contamination of turmeric by chemicals such as metanil yellow has been repeatedly reported. Although traditional technologies can detect such contaminants in food, high operational costs and operational complexities have limited their use to the laboratory. This study used Fourier Transform Raman Spectroscopy (FT-Raman) and Fourier Transform - Infrared Spectroscopy (FT-IR) to identify metanil yellow contamination in turmeric powder. Mixtures of metanil yellow in turmeric were prepared at concentrations of 30%, 25%, 20%, 15%, 10%, 5%, 1% and 0.01% (w/w). The FT-Raman and FT-IR spectral signal of pure turmeric powder, pure metanil yellow powder and the 8 sample mixtures were obtained and analyzed independently to identify metanil yellow contamination in turmeric. The results show that FT-Raman spectroscopy and FT-IR spectroscopy can detect metanil yellow mixed with turmeric at concentrations as low as 1% and 5%, respectively, and may be useful for non-destructive detection of adulterated turmeric powder.

Raman spectroscopy-based detection of chemical contaminants in food powders

Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.

Depth of penetration of a 785nm wavelength laser in food powders

Raman spectroscopy is a useful, rapid, and non-destructive method for both qualitative and quantitative evaluation of chemical composition. However it is important to measure the depth of penetration of the laser light to ensure that chemical particles at the very bottom of a sample volume is detected by Raman system. The aim of this study was to investigate the penetration depth of a 785nm laser (maximum power output 400mw) into three different food powders, namely dry milk powder, corn starch, and wheat flour. The food powders were layered in 5 depths between 1 and 5 mm overtop a Petri dish packed with melamine. Melamine was used as the subsurface reference material for measurement because melamine exhibits known and identifiable Raman spectral peaks. Analysis of the sample spectra for characteristics of melamine and characteristics of milk, starch and flour allowed determination of the effective penetration depth of the laser light in the samples. Three laser intensities (100, 200 and 300mw) were used to study the effect of laser intensity to depth of penetration. It was observed that 785nm laser source was able to easily penetrate through every point in all three food samples types at 1mm depth. However, the number of points that the laser could penetrate decreased with increasing depth of the food powder. ANOVA test was carried out to study the significant effect of laser intensity to depth of penetration. It was observed that laser intensity significantly influences the depth of penetration. The outcome of this study will be used in our next phase of study to detect different chemical contaminants in food powders and develop quantitative analysis models for detection of chemical contaminants.

Raman-spectroscopy-based chemical contaminant detection in milk powder

Addition of edible and inedible chemical contaminants in food powders for purposes of economic benefit has become a recurring trend. In recent years, severe health issues have been reported due to consumption of food powders contaminated with chemical substances. This study examines the effect of spatial resolution used during spectral collection to select the optimal spatial resolution for detecting melamine in milk powder. Sample depth of 2mm, laser intensity of 200mw, and exposure time of 0.1s were previously determined as optimal experimental parameters for Raman imaging. Spatial resolution of 0.25mm was determined as the optimal resolution for acquiring spectral signal of melamine particles from a milk-melamine mixture sample. Using the optimal resolution of 0.25mm, sample depth of 2mm and laser intensity of 200mw obtained from previous study, spectral signal from 5 different concentration of milk-melamine mixture (1%, 0.5%, 0.1%, 0.05%, and 0.025%) were acquired to study the relationship between number of detected melamine pixels and corresponding sample concentration. The result shows that melamine concentration has a linear relation with detected number of melamine pixels with correlation coefficient of 0.99. It can be concluded that the quantitative analysis of powder mixture is dependent on many factors including physical characteristics of mixture, experimental parameters, and sample depth. The results obtained in this study are promising. We plan to apply the result obtained from this study to develop quantitative detection model for rapid screening of melamine in milk powder. This methodology can also be used for detection of other chemical contaminants in milk powders.