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Spectroscopic techniques for food quality analysis are limited to surface inspections and are highly affected by the superficial layers (skin or packaging material) of the food samples. The ability of spatially offset Raman spectroscopy (SORS) to obtain chemical information from below the surface of a sample makes it a promising candidate for the non-destructive analysis of the quality of packaged food. In the present study, we developed a line-scan SORS technique for obtaining the Raman spectra of packaged-food samples. This technique was used to quantify butter adulteration with margarine through two different types of packaging. Further, the significant commercial potential of the developed technique was demonstrated by its being able to discriminate between ten commercial varieties of butter and margarine whilst still in their original, unopened packaging. The results revealed that, while conventional backscattering Raman spectroscopy cannot penetrate the packaging, thus preventing its application to the quality analysis of packaged food, SORS analysis yielded excellent qualitative and quantitative analyses of butter samples. The partial least-square regression analysis predictive values for the SORS data exhibit correlation coefficient values of 0.95 and 0.92, associated with the prediction error 3.2 % and 3.9 % for cover-1 & 2, respectively. The developed system utilizes a laser line (ca. 14-cm wide) that enables the simultaneous collection of a large number of spectra from a sample. Thus, by averaging the spectra collected for a given sample, the signal-to-noise ratio of the final spectrum can be enhanced, which will then have a significant effect on the multivariate data analysis methods used for qualitative and/or qualitative analyses. This recently presented line-scan SORS technique could be applied to the development of high-throughput and real-time analysis techniques for determining the quality and authenticity various packaged agricultural products. [ABSTRACT FROM AUTHOR]

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products. [ABSTRACT FROM AUTHOR]

Featured Application: This study used a 1064 nm dispersive point-scan Raman system for simultaneous detection of Sudan-I and metanil yellow in curry powder. The 1064 nm dispersive Raman system is a potential tool to detect multiple chemical contaminants in a complex food matrix for food authentication. Curry powder is extensively used in Southeast Asian dishes. It has been subject to adulteration by azo dyes. This study used a newly developed 1064 nm dispersive point-scan Raman system for detection of metanil yellow and Sudan-I contamination in curry powder. Curry powder was mixed with metanil yellow and (separately) with Sudan-I, at concentration levels of 1%, 3%, 5%, 7%, and 10% (w/w). Each sample was packed into a nickel-plated sample container (25 mm × 25 mm × 1 mm). One Raman spectral image of each sample was acquired across the 25 mm × 25 mm surface area. Intensity threshold value was applied to the spectral images of Sudan-I mixtures (at 1593 cm−1) and metanil yellow mixtures (at 1147 cm−1) to obtain binary detection images. The results show that the number of detected adulterant pixels is linearly correlated with the sample concentration (R2 = 0.99). The Raman system was further used to obtain a Raman spectral image of a curry powder sample mixed together with Sudan-I and metanil yellow, with each contaminant at equal concentration of 5% (w/w). The multi-component spectra of the mixture sample were decomposed using self-modeling mixture analysis (SMA) to extract pure component spectra, which were then identified as matching those of Sudan-I and metanil yellow using spectral information divergence (SID) values. The results show that the 1064 nm dispersive Raman system is a potential tool for rapid and nondestructive detection of multiple chemical contaminants in the complex food matrix. [ABSTRACT FROM AUTHOR]

Raman spectral imaging is an effective method to analyze and evaluate the chemical composition and structure of a sample, and has many applications for food safety and quality research. This study developed a 1064 nm dispersive Raman spectral imaging system for surface and subsurface analysis of food samples. A 1064 nm laser module is used for sample excitation. A bifurcated optical fiber coupled with Raman probe is used to focus excitation laser on the sample and carry scattering signal to the spectrograph. A high throughput volume phase grating disperses the incoming Raman signal. A 512 pixels Indium-Gallium-Arsenide (InGaAs) detector receives the dispersed light signal. A motorized positioning table moves the sample in two-axis directions, accumulating hyperspectral image of the sample by the point-scan method. An interface software was developed in-house for parameterization, data acquisition, and data transfer. The system was spectrally calibrated using naphthalene and polystyrene. It has the Raman shift range of 142 to 1820 cm-1, the spectral resolution of 12 cm-1 at full width half maximum (FWHM). The spatial resolution of the system was evaluated using a standard resolution glass test chart. It has the spatial resolution of 0.1 mm. The application of the system was demonstrated by surface and subsurface detection of metanil yellow contamination in turmeric powder. Results indicate that the 1064 nm dispersive Raman spectral imaging system is a useful tool for food safety and quality evaluation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Subsurface inspection of food and agricultural products is challenging for optical-based sensing techniques due to complex interactions between light and heterogeneous or layered samples. In this study, a method for subsurface food inspection was presented based on a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm point laser was used as a Raman excitation source. The line-shape SORS data from the sample was collected in a wavenumber range of 0–2815 cm −1 using a detection module consisting of an imaging spectrograph and a CCD camera. Two layered samples, one by placing a 1 mm thick plastic sheet cut from original container on top of cane sugar and the other by placing a 5 mm thick carrot slice on top of melamine powder, were created to test the subsurface food inspection method. For each sample, a whole set of SORS data was acquired using one CCD exposure in an offset range of 0–36 mm (two sides of the incident laser point) with a spatial interval of 0.07 mm. Raman spectra from the cane sugar under the plastic sheet and the melamine powder under the carrot slice were successfully resolved using self-modeling mixture analysis (SMA) algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging and evaluating internal food safety and quality attributes. The line-scan SORS measurement technique provides a rapid and nondestructive method for subsurface inspection of food safety and quality. [ABSTRACT FROM AUTHOR]

Both surface and subsurface food inspection is important since interesting safety and quality attributes can be at different sample locations. This paper presents a multipurpose line-scan Raman platform for food safety and quality research, which can be configured for Raman chemical imaging (RCI) mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In the RCI mode, macro-scale imaging was achieved using a 785 nm line laser up to 24 cm long with a push-broom method. In the SORS mode, a 785 nm point laser was used and a complete set of SORS data was collected in an offset range of 0–36 mm with a spatial interval of 0.07 mm using one CCD exposure. The RCI and SORS modes share a common detection module including a dispersive imaging spectrograph and a CCD camera, covering a Raman shift range from −674 to 2865 cm −1 . A pork shoulder and an orange carrot were used to test large-field-of-view (230 mm wide) and high-spatial-resolution (0.07 mm/pixel) settings of the RCI mode for food surface evaluation. Fluorescence-corrected images at selected Raman peak wavenumbers were used to view Raman-active analytes on the whole sample surfaces (e.g., fat on the pork shoulder and carotenoids over the carrot cross section). Also, three layered samples, which were created by placing carrot slices with thicknesses of 2, 5, and 8 mm on top of melamine powder, were used to test the SORS mode for subsurface food evaluation. Raman spectra from carrot and melamine were successfully resolved for all three layered samples using self-modeling mixture analysis. The line-scan Raman imaging and spectroscopy platform provides a new tool for surface and subsurface inspection for food safety and quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spatially offset Raman spectroscopy (SORS) is a technique that can obtain subsurface layered information by collecting Raman spectra from a series of surface positions laterally offset from the excitation laser. Currently optical fiber probes are used as major tools in SORS measurement, which are either slow (single fiber probe with mechanical movement) or restricted in selecting offset range and interval (fiber probe array). This study proposes a new method to conduct SORS measurement based on a newly developed line-scan hyperspectral Raman imaging system. A 785-nm point laser was used as an excitation source. A detection module consisting of an imaging spectrograph and a charge-coupled device camera was used to acquire line-shape SORS data in a spectral region of −592 to 3015 cm−1. Using a single scan, the system allowed simultaneous collection of a series of Raman spectra in a broad offset range (e.g. 0-36 mm in two sides of the incident laser) with a narrow interval (e.g. 0.07 mm). Four layered samples were created by placing butter slices with thicknesses of 1, 4, 7, and 10 mm on top of melamine powder, providing different individual Raman characteristics to test the line-scan SORS technique. Self-modeling mixture analysis (SMA) was used to analyze the SORS data. Raman spectra from butter and melamine were successfully retrieved for all four butter-on-melamine samples using the SMA method. The line-scan SORS measurement technique provides a flexible and efficient method for subsurface evaluation, which has potential to be used for food safety and quality inspection. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] Nondestructive inspection can be implemented for high speeds and product volumes. [•] Food inspection applications can include external and internal attributes of food. [•] Automated online imaging has great potential for the food processing industry. [Copyright &y& Elsevier]

Abstract: Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. This research was aimed to develop a hyperspectral imaging approach for detecting canker lesions on citrus fruit. A hyperspectral imaging system was developed for acquiring reflectance images from citrus samples in the spectral region from 450 to 930nm. Ruby Red grapefruits with cankerous, normal and other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. Spectral information divergence (SID) classification method, which was based on quantifying the spectral similarities by using a predetermined canker reference spectrum, was performed on the hyperspectral images of the grapefruits for differentiating canker from normal fruit peels and other citrus surface conditions. The overall classification accuracy was 96.2% using an optimized SID threshold value of 0.008, which was determined under the condition that the errors of false negative and false positive were weighted equally. Considering the high economic impact of missing a cankerous fruit, zero false negative error was achieved by using a threshold value of 0.009, under which the classification accuracy was 95.2%. This research demonstrated that hyperspectral imaging technique coupled with the SID based image classification method could be used for discriminating citrus canker from other confounding diseases. [Copyright &y& Elsevier]

Packaged food safety has gained increasing attention worldwide. Existing analytical methods pose difficulties in accurately measuring food quality without destroying the packaging. In this study, a nondestructive detection method for packaged food was proposed based on the generalized Gaussian model for Raman scattering images. The Raman peaks of the scattering image were extracted, and the attenuation information of the peaks far from the laser point were imported into the established generalized Gaussian model. Analysis of the histogram of residual distribution revealed that the difference in residual distribution was enhanced, and an appropriate threshold was selected to separate the Raman baseline correction spectrum of the internal materials. Food-grade polyethylene sheets with thicknesses of 1, 2, and 3 mm were used as packaging materials for comparison experiments. The proposed model can accurately separate the Raman peak of the subsurface material when 1 mm-thick polyethylene was used as the packaging. Food-grade plastic sheets of polyethylene, polypropylene and high-density polyethylene were covered with pure substances such as melamine, sodium nitrite, and maleic anhydride. This model was considered suitable for most food-grade plastic packaging, and the subsurface materials did not influence the separation effect. Finally, evaluation of premium white granulated sugar demonstrated that the model effectively separated the Raman peak produced by packaged food and detected the packaged food without conferring damage. • This method can be applied to nondestructive testing with packaged foods. • Generalized Gaussian model separated food and package Raman peaks. • Two-layer sample experiments verified that this method is effective. • Analyzed the scope of application by changing the sample materials. [ABSTRACT FROM AUTHOR]

Recent food safety incidents and public health concerns related to food adulteration drive the need for fast, sensitive, and reliable methods for the detection of food hazards and adulteration. Although Raman microscopy imaging has been used for quality and authenticity analysis of food products previously, the application of line-scan Raman imaging has emerged only recently. Here, we assess the applicability of line-scan Raman hyperspectral imaging (RHI) for simultaneous detection of three potential chemical adulterants in wheat flour (0.05–1.5% w/w). RHI of wheat flour samples were collected (0.2-mm step size, 1 s exposure time) in an aluminum sample holder using a 785-nm line laser to generate Raman scattering. Spectral angle mapping (SAM) was applied to the preprocessed data to distinguish adulterants' pixels from the flour background using the pure endmember as input extracted by independent component analysis. SAM images for each adulterant were converted to binary images to effectively visualise and quantitatively detect the adulterant pixels in wheat flour. The pixel-based calculated proportions of adulterants in wheat flour agreed with the concentrations added. The reproducibility of the developed technique was assessed for same samples measured at different times and the results demonstrated that RHI in combination with SAM provided a novel, elegant tool with potential for noninvasive quality and authenticity analyses of powdered foods. • Line-scan Raman imaging was used for detection of adulterants in wheat flour. • Independent component analysis was used for endmember selection. • SAM modeling of corrected data allows for quantification of adulterants. • The two major advantages of Raman imaging are good reproducibility and fast. [ABSTRACT FROM AUTHOR]

Since 2008, the detection of the adulterant melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the features of vibrational spectroscopy and digital imaging. In this study, NIR hyperspectral imaging was investigated for quantitative evaluation of melamine particles in nonfat and whole milk powders. Melamine was mixed into milk powders in a concentration range of 0.02–1.00% (w/w). A NIR hyperspectral imaging system was used to acquire images (938–1654 nm) of melamine powder, whole milk powder, nonfat milk powder, and mixtures of melamine and each of the milk powders. Two optimal bands (1447 nm and 1466 nm) were selected by a linear correlation algorithm with pure milk and pure melamine. Band ratio (B 1447/1466 ) images coupled with a single threshold were used to create resultant images to visualize identification and distribution of the melamine adulterant particles in milk powders. The identification results were verified by spectral feature comparison between separated mean spectra of melamine pixels and milk pixels. Linear correlations (r) were found between the number of pixels identified as containing melamine and melamine concentration in nonfat milk and whole milk powders, which were 0.980 and 0.970 or higher, respectively. The study demonstrated that the combination of NIR hyperspectral imaging and simple band ratioing was promising for rapid quantitative analysis of melamine in milk powders. [ABSTRACT FROM AUTHOR]

Highlights: [•] Hyperspectral near-infrared imaging to detect low-concentration melamine in dry milk. [•] Visualization of melamine at 200ppm in milk powder without sample pretreatment. [•] Potential detection technique to screen food ingredients for multiple adulterants. [Copyright &y& Elsevier]

• IR and Raman spectra were acquired from turmeric-adulterant samples. • Vibration modes were assigned and interpreted for turmeric-adulterant samples. • False positive detection of adulterants was observed in binary Raman images. • DD-SIMCA models were developed using IR spectra to authenticate yellow turmeric samples. • IR and Raman spectra can provide a more complete diagnostic fingerprint of samples. Deliberate chemical contamination of food powders has become a major food safety concern worldwide. This study used Raman imaging and FT-IR spectroscopy to detect Sudan Red and white turmeric adulteration in turmeric powder. While Sudan Red Raman spectral peaks were identifiable in turmeric-Sudan Red samples, Sudan Red false positive detection was observed in binary Raman images, limiting effective quantitative detection. In addition, white turmeric Raman spectral peaks were unidentifiable in turmeric-white turmeric mixtures. However, IR spectra of turmeric-Sudan Red and turmeric-white turmeric samples provided discrete identifier peaks for both the adulterants. Partial least squares regression models were developed using IR spectra for each mixture type. The models estimated Sudan Red and white turmeric concentrations with correlation coefficients of 0.97 and 0.95, respectively. Priority should be given to developing an IR imaging system and incorporating it with Raman system to simultaneously measure of food samples for detection of adulterants. [ABSTRACT FROM AUTHOR]

• Raman spectral analysis is useful for identifying internal and external parameters of fake eggs. • Visualization of egg image maps using Raman hyperspectral imaging can differentiate fake eggs from real eggs. • Discrimination analyses using Raman signal provide a clear classification between real and fake eggs non-invasively. Cases of imitation or fake food materials are sometimes produced and sold for purposes of economic fraud. However, while some imitation or fake food materials merely incorporate lower quality or cheaper alternative ingredients that are safe to eat, others fakes are produced using non-edible or hazardous ingredients that are unsafe for consumption. The latter group includes fake eggs which are often difficult to identify by eye. Such fakes have been found in various parts of Asia, made from harmful ingredients such as sodium alginate, tartrazine dye, gypsum powder, and paraffin wax. The objective of this study is to evaluate the use of Raman spectral analysis for nondestructive, noninvasive identification of fake eggs. In this study, fake eggs were prepared and then Raman spectroscopic and imaging data were collected from both the fake eggs and real chicken eggs. Classification of the fake and real eggs was tested using both Raman spectroscopy (1800–600 cm−1) with multivariate analysis methods and Raman hyperspectral imaging (1500–390 cm−1) with waveband optimization. The results demonstrated that both techniques are able to differentiate fake eggs from real eggs. [ABSTRACT FROM AUTHOR]