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Simple Summary: Bile acids have various physiological functions in the body. On the one hand, they can promote the digestion and absorption of fats as emulsifiers, and on the other hand, they participate in many metabolic pathways as signaling molecules. In addition, bile acids can shape the gut flora and improve gut health. This experiment aimed to investigate the effects of non-12α-hydroxylated bile acids on the growth and development, and intestinal flora of weaned piglets. This study showed that the addition of non-12α-hydroxylated bile acids could improve growth performance and nutrient digestibility in weaned piglets without negatively affecting the liver. In addition, the addition of non-12α-hydroxylated bile acids also improved the intestinal flora, increased the abundance of Lactobacillus_johnsonii, and increased the content of short-chain fatty acids. As an emulsifier and bioactive substance, bile acids (BAs) participate in the absorption of nutrients and in various physiological processes. The objective of this experiment was to investigate the effects of non-12α-hydroxylated BAs (including hyocholic acid, hyodeoxycholic acid and chenodeoxycholic acid, from now on referred to as NBAs) on growth performance, BAs metabolism and the intestinal flora of piglets. The experiment included four groups, with eight piglets per group. The four groups of pigs were fed 0, 60, 120 and 180 mg/kg of NBAs, respectively. The results show that adding NBAs significantly increased the final weight (FW), average daily feed intake (ADFI), average daily gain (ADG), and digestibility of crude fat (EE) and organic matter (OM) in piglets (p < 0.05). Adding NBAs significantly increased the villus height (VH) of the jejunum and ileum (p < 0.05). In addition, NBAs supplementation increased the content of urea nitrogen (BUN) and creatinine (CREA) as well as the ratio of urea nitrogen to creatinine (BUN/CREA) in serum (p < 0.05). Adding NBAs can affect the genes related to BAs enterohepatic circulation. Specifically, adding NBAs significantly decreased the relative mRNA abundance of FXR in the liver (p < 0.05), significantly increased the relative mRNA abundance of CYP27A1 (p < 0.05), and significantly increased the relative mRNA abundance of NTCP (p < 0.05). Adding NBAs also significantly decreased the relative mRNA abundance of FXR in the ileum (p < 0.05). In the full-length 16S rDNA sequencing analysis, ten biomarkers were found from the gate to the species level. NBAs mainly enriched Lactobacillus_Johnsonii and decreased the abundance of Streptococcus_alactolyticus. Short-chain fatty acids (SCFAs) content in the colon was significantly increased (p < 0.05). These results indicate that NBAs supplementation can improve the growth performance of piglets, promote the development of the bile acid replacement pathway and improve intestinal flora. [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]

Abstract The oxidation kinetics of uranium surfaces modified by pulsed laser nitriding in air at temperatures of 420, 440, 450 and 460 K has been investigated. X-ray diffraction (XRD), Auger electron spectroscopy (AES) and scanning electron microscopy (SEM) were used to characterize the structure, morphology and composition of the samples. The nitrided uranium was consisted of three parts: an outer thin uranium oxide (UO 2) layer in contact with air, an inner oxygen diffusion layer at the interface, which was formed by the diffusion of oxygen into the uranium mononitride (UN) and the quantity of oxygen descended gradually as the modified layer thickening, and an underlying UN layer mixed with metallic uranium. Oxide thickness measurements via in situ XRD indicate that a linear oxidation kinetics for the nitrided uranium was observed at 460 K. At temperature range 420–450 K, the oxidation kinetics can clearly be subdivided into two stages: a non-linear curve, initial region of slow oxide-film growth, which is followed by a much faster growth stage of a linear curve. The transformation critical thickness was about 300 nm. The average activation energy of oxidation was estimated to be 118.8 kJ/mol and 89.5 kJ/mol, when the oxide-film for new growth was below and above the critical thickness, respectively. Highlights • Oxidation kinetics of uranium surfaces modified by pulsed laser nitriding in air was investigated. • The oxidation kinetics can clearly be subdivided into two different stages: a non-linear curve and a linear curve. • The activation energies calculated for the oxide growth are Q non-linear = 118.8 kJ/mol and Q linear = 89.5 kJ/mol. [ABSTRACT FROM AUTHOR]

Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface–enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat. [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]

Adulteration and fraud for powdered foods and ingredients are rising food safety risks that threaten consumers' health. In this study, a newly developed line-scan macro-scale Raman imaging system using a 5-W 785-nm line laser as excitation source was used to authenticate food powders. The system was used to collect hyperspectral Raman images in a wave number range of 102-2865 cm from three representative food powders mixed with selected chemical adulterants with a concentration of 0.5 %, including milk powder and melamine, wheat flour and benzoyl peroxide, and corn starch and maleic anhydride. An acoustic mixer that generates high-intensity acoustic waves was used to create food-adulterant mixtures. Maximum penetration depths of the line laser through powders of milk, flour, and starch were all estimated at 3 mm using layered food-on-adulterant samples. All the mixed samples were placed in sample holders with a surface area of 50 mm × 50 mm and a depth of 3 mm to ensure the adulterant particles at the very bottom can still be detected. Raman spectral and image processing algorithms were developed based on fluorescence correction and single-band images at unique Raman peaks of the individual adulterants. Chemical images were created to show identification, spatial distribution, and morphological features of the adulterant particles mixed in the food powders. The potential of estimating mass concentrations of the adulterants using the percentages of the adulterant pixels in the chemical images (i.e., volume concentrations) was also demonstrated. [ABSTRACT FROM AUTHOR]

Background: Oxidized soybean oil (OSO) has been shown to impair growth and exacerbate inflammation, leading to intestinal barrier injury in animals. Recent evidence suggests important roles for resveratrol (RES) in the promoting growth performance, antioxidant capacity, anti-inflammatory, and regulate intestinal barriers in animals. Therefore, The objectives of this study are to investigate the effects of dietary RES (purity 98%) supplementation on the growth performance, antioxidant capacity, inflammatory state, and intestinal function of weaned piglets challenged with OSO. Methods: A total of 28 castrated weaned male piglets with a similar body weight of 10.19 ± 0.10 kg were randomly assigned to 4 dietary treatments for 28-d feeding trial with 7 replications per treatment and 1 piglet per replicate. Treatments were arranged as a 2 × 2 factorial with oil type [3% fresh soybean oil (FSO) vs. 3% OSO] and dietary RES (0 vs. 300 mg/kg). Results: The results showed that relative to the FSO group, OSO stress tended to decrease the average daily feed intake (ADFI), and decreased the activity levels of lipase, villus/crypt ratio (VCR), the mRNA expression of FABP1, SOD2, IL-10 and ZO-1 in the jejunum, and SOD2, GPX1, occludin and ZO-1 in the colon, the levels of acetic acid in the colonic digesta, whereas up-regulated the mRNA expression of IL-1β and TNF-α in the jejunum (P < 0.05). Moreover, dietary supplementation with RES increased ether extract (EE), the activity levels of sucrase, lipase, α-amylase, villus height (VH) and VCR, the mRNA expression of FABP1, SOD2, IL-10 and occludin in the jejunum, and FABP1, PPAR-γ, GPX1, occludin and ZO-1 in the colon, and the abundance of Firmicutes, acetic and propionic acid, but decreased the levels of D-lactic acid in the plasma, the abundance of Bacteroidetes in the colonic digesta of weaned piglets compared to the non-RES group (P < 0.05). Meanwhile, in the interaction effect analysis, relative to the OSO group, dietary RES supplementation in the diets supplemented with OSO increased the activity levels of trypsin, VH in the jejunum, the abundance of Actinobacteria, the levels of butyric acid of weaned piglets, but failed to influence the activity levels of trypsin and VH, Actinobacteria abundance, the levels of butyric acid when diets were supplemented with FSO (interaction, P < 0.05). Relative to the OSO group, dietary RES supplementation in the diets supplemented with OSO decreased the activity levels of DAO in the plasma of weaned piglets but failed to influence the activity levels of DAO when diets were supplemented with FSO (interaction, P < 0.05). Relative to the FSO group, dietary RES supplementation in the diets supplemented with FSO decreased the level of propionic acid, whereas RES supplementation failed to influence the level of propionic acid when the diet was supplemented with OSO (interaction, P < 0.01). Conclusions: Inclusion of OSO intensified inflammatory states and impaired the intestinal health characteristics of weaned piglets. Dietary RES supplementation improved the antioxidant capacity, anti-inflammatory activity, and intestinal morphology. Further studies showed that the protective effects of RES on gut health could be linked to the decreased abundance of Prevotella_1, Clostridium_sensu_stricto_6, and Prevotellaceae_UCG003 and increased levels of acetic and propionic acid. [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: The potential of Raman chemical imaging for simultaneously detecting multiple adulterants in milk powder was investigated. Potential chemical adulterants, including ammonium sulphate, dicyandiamide, melamine, and urea, were mixed together into skim dry milk in the concentration range of 0.1–5.0% for each adulterant. Using a 785-nm laser, a Raman imaging system acquired hyperspectral images in the wavenumber range of 102–2538cm−1 for a 25×25mm2 area of each mixture sample, with a spatial resolution of 0.25mm. Self-modelling mixture analysis (SMA) was used to extract pure component spectra, by which the four types of the adulterants were identified at all concentration levels based on their spectral information divergence values to the reference spectra. Raman chemical images were created using the contribution images from SMA, and their use to effectively visualise identification and spatial distribution of the multiple adulterant particles in the dry milk was demonstrated. [Copyright &y& Elsevier]

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: Inspection of citrus canker is crucial due to its fast spread, high damage potential, and massive impact on export and domestic trade. This research was aimed to develop a prototype for real-time citrus canker detection. An inspection module was developed on a one-line commercial fruit sorting machine. Twenty tungsten halogen spotlights coupled with an aluminum dome painted with white diffuse paint provided reflectance illumination to the fruits in the detection chamber. The camera unit was a two-band spectral imaging system, which mainly consisted of a beamsplitter, two bandpass filters with central wavelengths at 730 and 830nm, and two identical monochrome cameras. Using an exposure time of 10ms, the imaging system can capture narrowband images without blurring from samples moving at a speed of 5 fruits/s. Spatial resolution of the acquired images was 2.3pixels/mm. Real-time image processing and classification algorithms were developed based on a two-band ratio approach (i.e., R830/R730). The system was tested using 360 grapefruits with normal surface, canker lesions, and other peel diseases and defects. The overall classification accuracy was 95.3%, demonstrating that the methodology as well as the hardware and the software are effective and suitable for real-time citrus canker detection. Greasy spot, melanose, and sooty mold could generate false positive errors for the fruits without canker. The current system setup was limited to a single perspective view of the fruits. Future work will be conducted with an emphasis on whole surface inspection of each fruit. [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]

Abstract: Light interaction with turbid biological materials involves absorption and scattering. Quantitative understanding of light transport process and features in the fruit is critical to designing better optical systems for inspection of food quality. This paper reports on the quantification of light transport in the apple fruit in the visible and short-wavelength near-infrared region using Monte Carlo simulations. The absorption and reduced scattering coefficients (μ a and , respectively) of 600 ‘Golden Delicious’ apples were determined over the spectral range of 500–1000nm using a spatially resolved hyperspectral imaging method coupled with a diffusion theory model. The μ a and values were used in Monte Carlo (MC) models to simulate light transport in the fruit tissue. MC simulation models were validated against the diffusion theory model and experimental data. The patterns of diffuse reflectance, internal absorption, and light penetration depth were determined using typical values of μ a and for the apples. Simulation results showed that up to 96.4% of the photons were absorbed under the maximum absorption condition, while 75.9% photons exited as diffuse reflectance for the maximum scattering case. The optimum sensing range under our imaging system setup was found to be 1–11mm for ‘Golden Delicious’ apples. Fruit tissue with a larger μ a value absorbed light energy rapidly in short depth and radial distances, whereas light in the tissue with small values tended to propagate forward to the deeper area of the sample. Light penetration depths in ‘Golden Delicious’ apples, defined as the depths at which the incident light was reduced by 99%, were in the range of 0.43–8.67cm over the 500–1000nm spectral range, with a majority of the samples (approximately 68%) in the range of 0.81–4.48cm. Pigments and water in the fruit tissue greatly influenced light penetration depth. [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]

This paper reports on the measurement of the optical properties of fresh fruits and vegetables over the visible and short-wave near-infrared region (500–1000nm) using a spatially resolved steady-state diffuse reflectance technique. A hyperspectral imaging system in line scan mode was used to acquire spatially resolved diffuse reflectance images from the samples of apple (three varieties), peach, pear, kiwifruit, plum, cucumber, zucchini squash, and tomato (at three ripeness stages) over the spectral range of 500–1000nm. The absorption and reduced scattering coefficients of the samples were determined from the spatially resolved scattering profiles using inverse algorithms for a diffusion theory model. Spectra of the absorption coefficient were featured by major pigments (chlorophyll, anthocyanin, and carotenoid) and water in the samples, whereas spectra of the reduced scattering coefficient generally decreased with the increase of wavelength. Values of the absorption and reduced scattering coefficients varied greatly among the test samples. Large differences in the absorption spectra were observed for the tomatoes of three ripeness stages (green, pink, and red), and their ripeness was correctly classified using the ratio of the absorption coefficient at 675nm (for chlorophyll) to that at 535nm (for anthocyanin). Values of the reduced scattering coefficient positively correlated with the firmness of tomatoes at individual wavelengths of 500–1000nm, with the maximum correlation of 0.66 being obtained at 790nm. Light penetration depths, defined as the depths at which the incident light was reduced by 99%, were estimated to be between 0.97 and 6.52cm for the fruit and vegetable samples over the wavelength range of 500–1000nm; they were influenced by major pigments in the plant tissue. The spatially resolved steady-state diffuse reflectance technique provides a convenient and efficient means for measuring the optical properties of turbid food and agricultural products. [Copyright &y& Elsevier]

Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling. • Multimode hyperspectral imaging techniques were used to authenticate fish fillets. • Different fish species and freshness showed differences in four types of spectra. • Fish species and freshness can be classified using spectral machine learning methods. • Visible-near-infrared reflectance can be used to develop a low-cost detection device. [ABSTRACT FROM AUTHOR]

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes. [ABSTRACT FROM AUTHOR]

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future. [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] • Angiogenesis impairment caused by GBHs is related to mitochondrial dysfunction. • GBHs have negative impact on placental function. • Maternal GBHs exposure during pregnancy impairs postnatal progeny health. • Betaine protects placenta and postnatal progeny against GBHs-induced damage. • The limit level of glyphosate residue by governments needs to be reappraised. Glyphosate-based herbicides (GBHs) are the most widely used pesticide worldwide and can provoke placental injury. However, whether and how GBHs damage angiogenesis in the placenta is not yet known. This work evaluated the safety of glyphosate on pregnant sows based on the limit level by governments and investigated the effects and mechanism of Low-GBHs (20 mg/kg) and High-GBHs (100 mg/kg) exposure on placental angiogenesis. Results showed that gestational exposure to GBHs decreased placental vessel density and cell multiplication by interfering with the expression of VEGFA, PLGF, VEGFr2 and Hand2 (indicators of angiogenesis), which may be in relation to oxidative stress-induced disorders of mitochondrial fission and fusion as well as the impaired function of the mitochondrial respiratory chain. Additionally, GBHs destroyed barrier function and nutrient transport in the placenta, and was accompanied by jejunum oxidative stress in newborn piglets. However, GBHs exposure had no significant differences on sow reproductive performance. As a natural antioxidant, betaine treatment protected placenta and newborn piglets against GBHs-induced damage. In conclusion, GBHs impaired placental angiogenesis and function and further damaged the health of postnatal progeny, these effects may be linked to mitochondrial dysfunction. Betaine treatment following glyphosate exposure provided modest relief. [ABSTRACT FROM AUTHOR]

Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan. [ABSTRACT FROM AUTHOR]

BACKGROUND: The application of optical sensing technology in food adulteration detection has been extensively studied. However, due to the impact of packaging materials on the penetration depth of photons in foods and the interference from the optical properties of the packaging materials themselves, the use of optical sensing technology to detect packaged foods adulteration is still a well‐known problem. RESULTS: The line‐scan Raman imaging system was used to collect Raman hyperspectral images of adulterated sugars, made by mixing soft sugar and cheap glucose in seven different ratios. With the 0 and 3 mm (optimal offset distance) between line‐laser source and scanning line, the Raman hyperspectral images of adulterated sugars covered by packaging plastic were acquired respectively. Using adulterated samples un‐covered by packaging plastic as training samples, the Random Forest prediction model was developed, and excellent prediction performance was achieved for adulterated samples un‐covered by packaging plastics. Compared with Raman data acquired with 0 mm offset distance, the performance of the prediction model was significantly improved, with 0.957 for coefficient of determination (R2), 0.413 for root mean square error of prediction (RMSEP), and 4.846 for residual predictive deviation (RPD), for adulterated samples with plastic packaging acquired with the 3 mm offset distance. CONCLUSIONS: The novel non‐destructive method based on spatially offset Raman imaging technology, which can reduce the interference of packaging materials and enhance the signal of internal interesting materials, was proposed for detection of adulterated sugar with plastic packaging. The experiment results show that spatially offset imaging technology provides a candidate method for detecting adulteration of packaged foods. © 2021 Society of Chemical Industry [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]

• A sample collection component was designed to detect clenbuterol. • The problem of poor reproducibility of SERS detection was solved. • The optimal particle size of gold colloid for detection of clenbuterol was found. • This method can be used for the rapid crude screening of clenbuterol in pork. Clenbuterol is often used as a feed additive to increase the percentage of lean meat in livestock. Meat containing clenbuterol can cause many illnesses and even death for people. In this paper, the particle growth method was used to prepare gold colloids of different sizes, and the enhanced effectiveness of gold colloids of different sizes on clenbuterol in pork was investigated. The results showed that the gold colloid with the best enhanced effectiveness for clenbuterol had a particle size of approximately 90 nm. Second, a sample collection component was designed to detect clenbuterol from bottom to top, solving the problem of poor reproducibility of Surface-enhanced Raman scattering (SERS) detection caused by different droplet sizes and shapes. Then, the influence of different volumes of samples and concentrations of aggregating compounds on the enhanced effectiveness was optimized. The results showed that, based on the sample collection components designed in this article, 5 µL of enhanced substrate, 7.5 µL of clenbuterol and 3 µL of 1 mol/L mixed detection of NaCl solution had the best enhanced performance. Finally, 88 pork samples (0.5, 1, 1.5,..., 10, 12, 14 µg/g) with different concentrations were divided into correction sets and prediction sets in a ratio of 3:1. Unary linear regression models were established between the concentration of clenbuterol residue in the pork and the intensity of the bands at 390, 648, 1259, 1472, and 1601 cm−1. The results showed that the unary linear regression models at 390, 648, and 1259 cm−1 had lower root mean square errors than those at 1472 and 1601 cm−1. The intensity of the three bands and the concentration of clenbuterol residue in the pork were selected to establish a multiple linear regression model, and the concentration of clenbuterol residue in the pork was predicted. The results showed that the determination coefficients (R2) of the correction set and the prediction set were 0.99 and 0.99, respectively. The root mean square errors (RMSE) of the correction set and the prediction set were 0.169 and 0.184, respectively. The detection limit of clenbuterol in pork by this method is 42 ng/g, which can realize the crude screening of pork containing clenbuterol in the market. [ABSTRACT FROM AUTHOR]

In recent years, fruit harvesting robots have become a research hotspot for agricultural machinery. To be effective, robots should quickly and accurately detect fruits on trees in the complex environments. In this study, a method using an RGB-D (Red, green, blue, and depth space) camera combining colour data and 3D contour features for the detection of juicy peach was developed based on clustering and model segmentation. The point cloud of fruits was acquired in realistic field conditions. The pre-processed point cloud was divided into clusters by the conditional Euclidean clustering algorithm, and the 3D contour features were then used to extract fruits. The random sample consensus (RANSAC) spherical segmentation method was applied to clusters considered to contain multiple fruits that have close distance with one another. The fruit point cloud obtained is incomplete due to the limited range of camera view and actual occlusions in orchard, these clusters are considered to contain only one fruit. The progressive sample consensus (PROSAC) circular segmentation method was exploited to improve accuracy. Experiments were conducted under three situations with different occlusions. The total recognition accuracy reached 88.68%, and the average recognition processing time for a single fruit was about 292.4 ms, it can meet the requirements for fruit detection considering that the harvesting robot mainly takes the ripe peaches as object. • A detection method for juicy peach by clustering and segmentation was proposed. • Colour difference and position between points were combined when clustering. • Cluster was dealt separately by model segmentation based on 3D contour features. • Experiments in an orchard reached a total recognition accuracy with 88.68%. [ABSTRACT FROM AUTHOR]

Image 1 • C14H2 -(H–C13 = C12–H)– C11H2 -(H–C10 = C9–H)– C8H2 is exactly the same structure in both Linolenic Acid isomers. • In γ-LN, chemical shifts for sites C14H2 and C8H2 are the same (1H = 2.65 ppm; 13C = 24.9 ppm). • In α-LN, 1H chemical shifts for sites C14H2 and C8H2 are the same (2.65 ppm) whereas the 13C chemical shifts are discretely different (24.9 ppm versus 24.7 ppm). • Chemical shifts for C11H2 are similar and unequal (1H = 0.78 ppm, 13C = 13.3 ppm α-LN; 1H = 0.72 ppm 13C = 13.5 ppm γ-LN). • Chemical shifts for C2H2 are similar and unequal (1H = 2.12 ppm, 13C = 33.4 ppm α-LN; 1H = 2.11 ppm 13C = 33.2 ppm γ-LN). • Moiety C17H2-(H–C16 = C15–H) is molecular site among redundant structures in α-LN at which spectroscopic differences are the most different and most important. [ABSTRACT FROM AUTHOR]

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]

Raman imaging has been proven to be a powerful analytical technique for the characterization and visualization of chemical components in a range of products, particularly in the food and pharmaceutical industries. The conventional backscattering Raman imaging technique for the spatial analysis of a deep layer suffers from the presence of intense fluorescent and Raman signals originating from the surface layer which mask the weaker subsurface signals. Here, we demonstrated the application of a new reflection amplifying method using a background mirror as a sample holder to increase the Raman signals from a deep layer. The approach is conceptually demonstrated on enhancing the Raman signals from the subsurface layer. Results show that when bilayer samples are scanned on a reflection mirror, the average signals increase 1.62 times for the intense band at 476 cm−1 of starch powder, and average increases of 2.04 times (for the band at 672 cm−1) for a subsurface layer of high Raman sensitive melamine powder under a 1 mm thick teflon sheet. The method was then applied successfully to detect noninvasively the presence of small polystyrene pieces buried under a 2 mm thick layer of food powder (a case of powdered food adulteration) which otherwise are inaccessible to conventional backscattering Raman imaging. In addition, the increase in the Raman signal to noise ratio when measuring samples on a mirror is an important feature in many applications where high-throughput imaging is of interest. This concept is also applicable in an analogous manner to other disciplines, such as pharmaceutical where the Raman signals from deeper zones are typically, substantially diluted due to the interference from the surface layer. [ABSTRACT FROM AUTHOR]

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]

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]

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]

[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]

Determination of the optical properties of biological materials based on steady-state spatially resolved diffuse reflectance imaging is a complicated inverse problem-solving process. An effective inverse algorithm is first necessary and validated. This article proposed a Fourier series expansion (FSE) coupled with least squares support vector machine (LS-SVM) as an inverse algorithm for determining the absorption coefficient (μa) and the reduced scattering coefficient () of biological materials. A hyperspectral imaging system was used to acquire scattering images and steady-state spatially resolved diffuse reflectance profiles of liquid phantoms. Experiment results demonstrated that the hyperspectral imaging system coupled with this inverse algorithm effectively improved prediction accuracy of both μa and of liquid phantoms. Tests on liquid phantoms showed that the mean relative errors of this inverse algorithm to be 11.03% for the absorption coefficient and 7.16% for the reduced scattering coefficient when corresponding Fourier coefficients of liquid phantoms were used to develop the prediction model. To further study the method, the Fourier coefficients calculated from normalized Monte Carlo simulation data were used to develop the prediction model for determining the optical properties of 36 liquid phantoms. The prediction errors were 15.96 and 10.91% for μa and , respectively. For all liquid phantoms, it was found that the prediction values of both μa and were generally in good agreement with their actual values. Therefore, the FSE-LS-SVM method provides an effectively means for improving the prediction accuracy of optical properties of biological materials. [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]

Total volatile basic nitrogen (TVB-N) content is an important index used to evaluate the freshness of pork. In this paper, a strategy for measurement of TVB-N content in pork through hyperspectral imaging (HSI) (400–1000 nm) was developed. Firstly, image textural features based on Gabor filter and spectral features were obtained from the hyperspectral image after determining the region of interest. Then, nine feature wavelengths were selected using partial least-squares projection algorithm. And, major components were obtained from the 2D principal component analysis (2DPCA). Finally, a calibration model was established based on major components using least-squares support vector machine to predict TVB-N values. The results of two methods for data fusion, which are 2DPCA and principal component analysis (PCA), are compared. The correlation coefficients of prediction ( R P ) and root-mean-square errors of prediction (RMSEP) obtained through 2DPCA were 0.955 and 1.86 mg/100 g respectively, which was superior to the results based on PCA ( R P = 0.944, RMSEP = 2.07 mg/100 g). Compared to PCA, the residual prediction deviations (RPD) based on 2DPCA was raised from 3.01 to 3.35. Results demonstrated that the proposed model based on 2DPCA exhibited potential for nondestructive detection of TVB-N content in pork. [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]

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique that has gained increasing attention because of many applications. The production of continuous background in LIBS is inevitable because of factors associated with laser energy, gate width, time delay, and experimental environment. The continuous background significantly influences the analysis of the spectrum. Researchers have proposed several background correction methods, such as polynomial fitting, Lorenz fitting and model-free methods. However, less of them apply these methods in the field of LIBS Technology, particularly in qualitative and quantitative analyses. This study proposes a method based on spline interpolation for detecting and estimating the continuous background spectrum according to its smooth property characteristic. Experiment on the background correction simulation indicated that, the spline interpolation method acquired the largest signal-to-background ratio (SBR) over polynomial fitting, Lorenz fitting and model-free method after background correction. These background correction methods all acquire larger SBR values than that acquired before background correction (The SBR value before background correction is 10.0992, whereas the SBR values after background correction by spline interpolation, polynomial fitting, Lorentz fitting, and model-free methods are 26.9576, 24.6828, 18.9770, and 25.6273 respectively). After adding random noise with different kinds of signal-to-noise ratio to the spectrum, spline interpolation method acquires large SBR value, whereas polynomial fitting and model-free method obtain low SBR values. All of the background correction methods exhibit improved quantitative results of Cu than those acquired before background correction (The linear correlation coefficient value before background correction is 0.9776. Moreover, the linear correlation coefficient values after background correction using spline interpolation, polynomial fitting, Lorentz fitting, and model-free methods are 0.9998, 0.9915, 0.9895, and 0.9940 respectively). The proposed spline interpolation method exhibits better linear correlation and smaller error in the results of the quantitative analysis of Cu compared with polynomial fitting, Lorentz fitting and model-free methods, The simulation and quantitative experimental results show that the spline interpolation method can effectively detect and correct the continuous background. [ABSTRACT FROM AUTHOR]

The use of hyperspectral imaging technology combined with chemometrics is an effective nondestructive method for sorting seed varieties. However, the performance of the method is susceptible to the influence of time and depends on the training set used in the modeling process. The accuracy of classification models maybe deteriorate when they are used to differentiate the same variety of seeds harvested in different years, due to new variances in the test set are introduced by changes in the cultivation conditions, soil environmental conditions and climatic changes from one year to another. To maintain the accuracy and robustness of model, a model-updating algorithm for differentiating maize seed varieties from different years based on hyperspectral imaging coupled with a pre-labeling method was proposed in this work. The pre-label of each unlabeled sample was obtained using the original classification models developed by the least squares support vector machine classifier. The representative unlabeled samples, which had reliable pre-labels, were selected for updating classification models based on Pearson correlation coefficients. After model updating, the average classification accuracies were improved by 8.9%, 35.8% and 9.6%, compared with those of non-updated models for three test sets, respectively. This shows the effectiveness of the proposed method for classifying maize seeds of different years. [ABSTRACT FROM AUTHOR]

An automated method for maize and weed detection is very important to efficiently remove weeds and precisely calculate the quantity of maize. Color features were used in this study to investigate a simple maize-detection method using a color machine-vision system. Conventional image segmentation methods based on RGB values cannot separate maize from weeds because of the highly similar image RGB values of these plants. Thus, a post-processing algorithm was developed to distinguish maize from weeds after image preprocessing. Color indices were used to develop a classification model. The nine optimal features were selected by principal component analysis to reduce the effect of illumination. Finally, support vector data description was used as a classifier to differentiate maize from the mixes of different species of weeds. Pictures were taken by a commercial camera and used to verify the stability of the algorithm. Results show that the overall accuracy for three years is 90.19%, 92.36% and 93.87%, respectively. And the color indices used in this work were stable under various weather conditions and over time. [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]

Since the 1950's, the broad-spectrum, organochlorine insecticide endosulfan (6,7,8, 9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiepine-3-oxide) has been used on numerous crops. Due to its persistence, bioaccumulation, long-range transport, and adverse effects to human health and ecosystems, it was officially identified as a persistent organic pollutant (POP) in 2011. The last uses in the United States were phased out in 2016. Endosulfan consists of two diastereomers, α-endosulfan and β-endosulfan, and while the α-isomer exists as two asymmetrical, twist-chair enantiomers which interchange, the β-isomer is a symmetrical-chair conformation. In addition, the β-isomer was found to isomerize to the α-isomer. Gradient Temperature Raman Spectroscopy (GTRS) enables identification the molecular sites at which bending vibrational modes becomes twisting or wagging vibrational modes. Previous studies using GTRS and chemical calculations afforded evidence for specific bond movements and the irreversibility of the isomerization mechanism. However, not all of the vibrational modes observed in the spectra could be explained. Thus, new analyses of the GTRS data were conducted to examine the effects of torsional forces on the bond movement, which allowed for the identification of all the peaks. These newly-identified torsional forces provide further confirmation of the isomerization mechanism and its irreversibility. Finally, this isomerization explains why β-endosulfan is rarely detected in the atmosphere. [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]

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]

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]

Illegal use of nitrogen-rich melamine (C 3 H 6 N 6 ) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography–mass spectrometry (GC–MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990–1700 nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders. [ABSTRACT FROM AUTHOR]

The objectives of this study were to investigate the feasibility of hyperspectral scattering imaging to predict the bacterial contamination in meat nondestructively, and propose an optimal approach for detecting low levels of total viable count (TVC) contamination in beef. Fresh beef samples were obtained from a commercial slaughtering plant, and stored at 4 °C for 0–12 days. The visible/near-infrared (VIS/NIR) hyperspectral images in the backscattering mode were acquired from 3–5 beef samples on each day of the experiment, in parallel with microbiological analysis to enumerate the TVC population. Lorentzian function was used to resolve the light scattering information within the hyperspectral image and consequently Lorentzian parameters, which represented different hyperspectral scattering characteristics were extracted. In this study, not only the individual Lorentzian parameters but also the parameter combinations were used to establish the multivariate statistical models for predicting beef TVC, based on the modeling methods of principal component regression (PCR), partial least squares regression (PLSR), and back propagation neural network (BPNN), respectively. The models established using individual Lorentzian parameters did not perform well in predicting low levels of TVC contamination in beef, and the best prediction result could only achieved with the correlation coefficient of prediction set ( R P ) and root mean squared error of prediction set (RMSEP) of 0.81 and 1.27 log CFU/g, respectively. Based on the parameter combinations, the best modeling results were achieved with R P and RMSEP of 0.86 and 0.93 log CFU/g, 0.87 and 0.79 log CFU/g, 0.90 and 0.88 log CFU/g by PCR, PLSR, and BPNN methods, respectively, which confirmed the superiority of the parameter combination method. The results of this study demonstrated for the first time that hyperspectral scattering imaging combined with Lorentzian function and the proposed parameter combination method could be used to detect low levels of bacterial contamination in beef nondestructively. [ABSTRACT FROM AUTHOR]