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bis-Phenol A (BPA), bis-Phenol S (BPS), and bis-Phenol F (BPF) are important polymer industry plasticizers. Regulatory measures have restricted the use of BPA in plastic formulations, especially for those which come in contact with food products. Rapid, accurate spectroscopic measurements are required for distinguishing which of the three are present. The bis-phenol groups are structurally identical. The second set of bis-groups (CH3-C-CH3, O=S=O, and H-C-H, respectively) are discretely different chemically, but vibrational modes corresponding to these groups are not unique identifiers, routinely overlapping with wavenumbers present in other members of the set. The dual modality method identifies the specific wavenumbers in which the Infrared (IR) signal is near zero and the Raman relative intensity is maximum, and those in which the Raman signal is minimum and the IR signal is maximum. The normalized intensity ratio between IR and Raman enhances the signal [BPA 10.6 (1508 cm−1); BPS 7.4 (751 cm−1); BPF 5.1 (1100 cm−1)]. The ratio between Raman and IR in BPF is also enhanced: 6.3 (845 cm−1). Discerning which specific wavenumbers are most enhanced is experimentally feasible, though not necessarily at present theoretically predictable. This study demonstrates that IR and Raman spectra are not just complimentary, but together they are confirmatory even when the normalized intensity ratios of corresponding wavenumbers are most different. [ABSTRACT FROM AUTHOR]

In the study, a new correction method was applied to reduce error during Raman spectral detection on mixed pesticide residue in apples. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples could be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which affected the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method was proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 100 mg·kg−1 to 10−3 mg·kg−1 and Lagrangian interpolation were applied in the correction method. And a smooth surface was applied to describe the correction coefficient of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction coefficient would be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide would be acquired by the correction method. Correlation coefficient of model validation exceeded 0.88 generally and Root Mean Square Error also decreased obviously after correction, which proved the reliability of the method. [ABSTRACT FROM AUTHOR]

Highlights • Raman technologies in the area of horticultural product evaluation are reviewed. • Raman principles, techniques, instruments, and data analysis methods are presented. • Applications for quality and safety of horticultural products are discussed. Abstract This paper gives an overview of Raman technology for inspecting quality and safety of horticultural products. Emphasis is put on introduction and demonstration of Raman spectroscopy and imaging techniques for practical uses to assess the horticultural products. Raman scattering principles are presented first, followed by introduction and comparison to Raman measurement techniques, such as backscattering Raman spectroscopy, transmission Raman spectroscopy, spatially offset Raman spectroscopy, Raman chemical imaging, surface-enhanced Raman spectroscopy, etc. Raman measurement instruments, including excitation sources, wavelength separation devices, detectors, commercial integrated and custom-developed systems, and system calibration methods, are discussed and compared. Raman data analysis methods, such as data preprocessing, spectral mixture analysis, and quantitative analysis, are presented with examples for analyzing Raman spectral and image data of selected horticultural products including tomato and carrot. Raman spectroscopy and imaging applications for quality and safety evaluation of the horticultural products are also reviewed. [ABSTRACT FROM AUTHOR]

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]

Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector. [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]

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]

Abstract: This research explored the use of spatially offset Raman spectroscopy (SORS) for nondestructive evaluation of internal maturity of tomatoes. A Raman system using a 785-nm laser was developed to collect spatially offset spectra in the wavenumber range of 200–2500cm−1. The SORS measurements were conducted using a source-detector distance ranging from 0 to 5mm with a step size of 0.2mm. One hundred and sixty tomatoes at seven ripeness stages (i.e., immature green, mature green, breaker, turning, pink, light red, and red) were tested. The feasibility of the SORS for subsurface detection was examined by using a Teflon slab placed under outer pericarp slices of 5-mm and 10-mm thicknesses cut from green and red tomatoes. Raman signals from the outer pericarp layer and the Teflon layer were effectively separated by self-modeling mixture analysis of the offset spectra after fluorescence correction. Three Raman peaks due to carotenoids inside the tomatoes started showing at the mature green stage. Two peaks appeared consistently at 1001 and 1151cm−1, and the third peak was gradually shifted from 1525cm−1 (lutein at mature green stage) to 1513cm−1 (lycopene at red stage) owing to the loss of lutein and β-carotene and the accumulation of lycopene during tomato ripening. The Raman peak changes were evaluated by spectral information divergence (SID) with pure lycopene as the reference. The SID values decreased as the tomatoes ripened, and thus these values can be used to evaluate the internal ripeness of tomatoes. [Copyright &y& Elsevier]

Abstract: Lycopene is a major carotenoid in tomatoes and detecting changes in its content can be used to monitor the ripening of tomatoes. Raman chemical imaging is a new technique that shows promise for mapping constituents of interest in complex food matrices. In this study, a benchtop point-scan Raman chemical imaging system was developed to detect and visualize internal lycopene distribution during postharvest ripening of tomatoes. Tomato samples at different ripeness stages (i.e., green, breaker, turning, pink, light red, and red) were selected and cut open for imaging. Hyperspectral Raman images were acquired from fruit cross-sections in the wavenumber range of 200–2500cm−1 with a spatial resolution of 1mm. A polynomial curve-fitting method was used to correct for the underlying fluorescence background in the original spectra. A hyperspectral image classification method was developed based on spectral information divergence to identify lycopene in the tomato cross-sections. Raman chemical images were created to visualize the spatial distribution of the lycopene for different ripeness stages. The system was also configured to test the feasibility of utilizing spatially offset Raman spectroscopy (SORS) technique for subsurface detection of a Teflon slab placed under samples of outer pericarp cut in 5-mm and 10-mm thick slices from green and breaker tomatoes. The results showed that the Raman spectrum of Teflon can be extracted from the SORS measurements of the pericarps placed over the Teflon, demonstrating the potential of the future development of a Raman-based nondestructive approach for subsurface detection of lycopene as an indicator of tomato maturity. [Copyright &y& Elsevier]

Background: Gradient temperature Raman spectroscopy (GTRS) applies the continuous temperature gradients utilized in differential scanning calorimetry (DSC) to Raman spectroscopy, providing a new means for rapid high throughput material identification and quality control. Methods: Using 20 Mb three-dimensional data arrays with 0.2 °C increments and first/second derivatives allows complete assignment of solid, liquid and transition state vibrational modes. The entire set or any subset of the any of the contour plots, first derivatives or second derivatives can be utilized to create a graphical standard to quickly authenticate a given source. In addition, a temperature range can be specified that maximizes information content. Results: We compared GTRS and DSC data for five commercial fish oils that are excellent sources of docosahexaenoic acid (DHA; 22:6n-3) and eicosapentaenoic acid (EPA; 20:5n-3). Each product has a unique, distinctive response to the thermal gradient, which graphically and spectroscopically differentiates them. We also present detailed Raman data and full vibrational mode assignments for EPA and DHA. Conclusion: Complex lipids with a variety of fatty acids and isomers have three dimensional structures based mainly on how structurally similar sites pack. Any localized non-uniformity in packing results in discrete "fingerprint" molecular sites due to increased elasticity and decreased torsion. [ABSTRACT FROM AUTHOR]

Raman imaging can analyze biological materials by generating detailed chemical images. Over the past decade, significant advancements in Raman imaging and data analysis techniques have overcome problems such as long data acquisition and analysis times and poor sensitivity. In this review article, Raman spectroscopy and imaging are introduced and the corresponding computational methods for image data analysis are discussed. We provide an overview of the applications of this method in areas such as food, pharmaceutical, and biomedical sectors, with emphasis on recent developments that have helped industrialize its applications in various sectors. Finally, the current limitations and trends for future Raman imaging are outlined and discussed with a view toward new research practices for applying this technique more efficiently and adaptably in numerous sectors. [ABSTRACT FROM AUTHOR]

[Display omitted] • Phospholipids containing sn1 saturated and sn2 polyunsaturated fatty acids are the most common in biological membranes. • 1- 18:0, 2- 20:4n-6; 1- 18:0 2- 22:6n-3; 1- 18:0, 2- 18:0 neat/molecularly hydrated were analyzed with gradient temperature Raman. • Phase transitions and numerous spectral differences resulting from hydration and double bonds were observed. • Molecular models showed minimal water makes large structural changes; hydrated 1- 18:0 2- 22:6n-3 is strikingly compact. Mixed chain phospholipids containing a saturated fatty acid at sn 1 and a polyunsaturated fatty acid in sn 2 are common in the specialized biological membranes prevalent in neural, retinal and organ tissues. Particularly important are mixed lipids containing palmitic or stearic acid and arachidonic or docosahexaenoic acid. Gradient temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry to Raman spectroscopy, providing a straightforward technique to identify molecular rearrangements and phase transitions. Herein we utilize GTRS for 1- 18:0, 2 -20:4n-6 PC; 1- 18:0 2- 22:6n-3 PC; and 1 -18:0, 2 -18:0 PC from −80 to 50 °C temperatures. 20 Mb three-dimensional data arrays with 0.2 °C increments and first/second derivatives allowed detailed vibrational mode assignment and analysis. Samples were analyzed neat and with molecular hydration. Previously reported phase transitions for hydrated 18:0−20:4PC and 18:0−22:6PC and numerous spectral differences resulting from hydration and the double bond structure were clearly observed. Molecular models showed that the addition of minimal water molecules results in significant structural differences compared to the neat molecules; 18:0−22:6PC is strikingly compact with water when viewed from the hydrophilic end. This precise Raman data cannot be observed in typically utilized fully hydrated vesicle samples, however the improved GTRS will allow for more precise analysis in fully hydrated vesicles because the underlying modes in the unavoidably broadened spectra can be identified. [ABSTRACT FROM AUTHOR]

[Display omitted] • Improved Monte Carlo simulates spatially offset Raman spectroscopy of packaged foods. • Evaluated the signal separation effect of MCR-ALS and FastICA on simulated SORS data. • This method can be applied to nondestructive testing with packaged foods. • Verified the effectiveness of FastICA by three commercial packaged foods. Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food. [ABSTRACT FROM AUTHOR]

Docosahexaenoic acid (DHA, 22:6n-3) is exclusively utilized in fast signal processing tissues such as retinal, neural and cardiac. N-3 docosapentaenoic acid (n-3DPA, 22:5n-3), with just one less double bond, is also found in the marine food chain yet cannot substitute for DHA. Gradient temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry (DSC) to Raman spectroscopy, providing a straightforward technique to identify molecular rearrangements that occur near and at phase transitions. Herein we apply GTRS and both conventional and modulated DSC to n-3DPA and DHA from −100 to 20 °C. Three-dimensional data arrays with 0.2 °C increments and first derivatives allowed complete assignment of solid, liquid and transition state vibrational modes. Melting temperatures n-3DPA (−45 °C) and DHA (−46 °C) are similar and show evidence for solid-state phase transitions not seen in n-6DPA (−27 °C melt). The C6H2 site is an elastic marker for temperature perturbation of all three lipids, each of which has a distinct three dimensional structure. N-3 DPA shows the spectroscopic signature of saturated fatty acids from C1 to C6. DHA does not have three aliphatic carbons in sequence; n-6DPA does but they occur at the methyl end, and do not yield the characteristic signal. DHA appears to have uniform twisting from C6H2 to C12H2 to C18H2 whereas n-6DPA bends from C12 to C18, centered at C15H2. For n-3DPA, twisting is centered at C6H2 adjacent to the C2-C3-C4-C5 aliphatic moiety. These molecular sites are the most elastic in the solid phase and during premelting. [ABSTRACT FROM AUTHOR]

Optical detection technology has been widely used in unpackaged food adulteration detection. However, due to the interference of packaging materials on the internal food optical signal, including signal occlusion, mixing and overlap precluded the accurate detection of internal food quality. In this study, a method of packaged butter adulteration evaluation based on spatially offset Raman spectroscopy (SORS) combined with fast independent component analysis (FastICA) was proposed. The adulterated butter from 0% to 100% w/w margarine at 10% intervals was covered with packaging sheets as test samples. A line-scan Raman hyperspectral imaging system was used to obtain a scattering spectral image of the packaged butter samples. The region of interest of the scattering image is extracted as the input of FastICA model to separate the internal butter signals. The extracted butter Raman features were input into four quantitative analysis models to assess the content of butter adulteration. The results showed that the ensemble model Extra-tree has the best performance with RMSE p , R p 2, and RPD values of 0.6, 0.93, and 4.73, respectively. Additionally, the applicability of the method was validated with four types of packaging materials. This rapid non-destructive testing method is beneficial to the effective testing method of packaged butter and other products industry. • This method can be applied to nondestructive testing with packaged butter. • Separation of Spatial Offset Raman Spectral Signals by FastICA. • t-SNE was used to visualize the signal separation effect. • Verified the effectiveness of this method by comparing the different processing of the four samples. [ABSTRACT FROM AUTHOR]

One of the great unanswered questions with respect to biological science in general is the absolute necessity of docosahexaenoic acid (DHA, 22:6n-3) in fast signal processing tissues. N-6 docosapentaenoic acid (n-6DPA, 22:5n-6), with just one less double bond, group, is fairly abundant in terrestrial food chains yet cannot substitute for DHA. Gradient temperature Raman spectroscopy (GTRS) applies the temperature gradients utilized in differential scanning calorimetry (DSC) to Raman spectroscopy, providing a straightforward technique to identify molecular rearrangements that occur near and at phase transitions. Herein we apply GTRS and DSC to n-6DPA and DHA from −100 to 20 °C. 20 Mb three-dimensional data arrays with 0.2 °C increments and first/second derivatives allowed complete assignment of solid, liquid and transition state vibrational modes, including low intensity/frequency vibrations that cannot be readily analyzed with conventional Raman. N-6DPA and DHA show significant spectral changes with premelting (−33 and −60 °C, respectively) and melting (−27 and −44 °C, respectively). The CH2 (HC CH) CH2 moieties are not identical in the second half of the DHA and DPA structures. DPA has bending (1450 cm −1 ) over almost the entire temperature range. In contrast, DHA contains major CH 2 twisting (1265 cm −1 ) with no noticeable CH 2 bending, consistent with a flat helical structure with a small pitch. Further modeling of neuronal membrane phospholipids must take into account torsion present in the DHA structure, which essential in determining whether the lipid chain is configured more parallel or perpendicular to the hydrophilic head group. [ABSTRACT FROM AUTHOR]

[Display omitted] • This method can be applied to nondestructive testing with packaged foods. • Mathematical model analysis of spatially offset Raman spectroscopy technique. • Improved FastICA separated food Raman signal with packaging. • Verified the effectiveness of this method by multi-sample experiments. Raman spectroscopy attempts to reflect food quality by characterizing molecular vibration and rotation. However, the blocking of optical signals by packaging materials and the interference of the optical signal generated by the packaging itself make the detection of internal food quality without destroying packaging highly difficult. In this regard, this paper proposes a novel packaged food internal signal separation based on spatially offset Raman spectroscopy (SORS) coupled with improved fast independent component analysis (FastICA). Firstly, the Raman scattering image of the packaged food with offset laser incident point was obtained. Then, the movable quadratic mean of information entropy was used to select the observation feature region of the image. Thirdly, the main independents decomposed by the optimized FastICA method were identified by spectral attenuation characteristics of the SORS peak signal. Finally, the non-negativity of the separated signal was ensured by baseline recognition and correction. The effectiveness of this method was verified by refactoring the similarity between the signal and the reference signal by testing three different packaging and four internal materials under standard experimental conditions. The applicability of the method was proved by the internal signal separation of three packaged foods on sale. The experimental results indicate that the proposed method can separate the Raman signal of packaged food and can be used as a pretreatment method and auxiliary analysis means for the detection of packaged food. [ABSTRACT FROM AUTHOR]

Highlights: [•] Optical instrument was developed for pesticide residue detection in apple surface. [•] Hardware and software system was self developed as prototype instrument. [•] The instrument can detect chlorpyrifos pesticide in apple surface nondestructively. [•] 6.69mg/kg of pesticide residue in apple surface is detectable by the instrument. [•] The system can detect the pesticide residue in apple surface within 4s. [ABSTRACT FROM AUTHOR]

Prawns are highly popular with consumers but present many technical difficulties for the evaluation of their internal quality when intact (in-shell prawns). This study proposed a nondestructive method to assess the internal quality of intact prawns (Fenneropenaeus chinensis) using spatially offset Raman spectroscopy (SORS) technique combined with data modeling analysis. This technique holds promise due to the capability of SORS to obtain chemical information nondestructively from below the surface of a sample material. Raman scattering image data for 100 fresh prawns (approximately 15 g each) were collected using a line-scan Raman imaging system over the course of seven days with 24 h measurement intervals. Measurement anomalies due to physical prawn irregularities were eliminated using a peak identification method. Twenty feature bands selected by Random Forest (RF) method were input to Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extremely Randomized Tree (ET) models to predict the freshness of prawns during the storage time. The prediction model based on SORS enhanced data and combining RF feature band selection with SVR demonstrated the best performance, with RMSEP, R2, and RPD values of 0.71, 0.88, and 2.63, respectively. This rapid and nondestructive method for quality evaluation may be feasible as a practical means of assessing internal quality of materials that demonstrate surface interference, such as in-shell prawns. • This method can be applied to nondestructive testing with intact prawns. • Spatially offset Raman spectroscopy technique enhanced internal signal. • Feature bands were extracted by Random Forest model. • Verified the effectiveness of this method by multi-model comparison. [ABSTRACT FROM AUTHOR]

• 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]