Non-destructive Raman spectroscopy as a tool for measuring ASTA color values and Sudan I content in paprika powder

Raman Spectroscopy in the Characterization of Food Carotenoids: Challenges and Prospects

This paper presents an overview of the application of Raman spectroscopy (RS) in characterizing carotenoids, which have recently gained attention due to new findings on their health-promoting effects and rising demand in the food, pharmaceutical, and cosmetic industries. The backbone structure in the form of a polyene chain makes carotenoids sensitive to Raman spectroscopy, mainly due to the stretching vibrations of their conjugated double bonds. Raman spectroscopy is increasingly used in agricultural and food sciences and technologies as it is a non-preparative, environmentally friendly, fast and efficient method for characterizing target analytes. The application of RS in the qualitative and quantitative analysis of carotenoids requires the careful selection and adjustment of various instrument parameters (e.g., laser wavelength, laser power, spectral resolution, detector type, etc.) as well as performing complex chemometric modeling to interpret the Raman spectra. Most of the studies covered in this review focus more on qualitative than quantitative analysis. The most frequently used laser wavelengths are 1064, 785, and 532 nm, while 633 nm is the least used. Considering the sensitivity and complexity of RS, the present study focuses on the specific and critical points in the analysis of carotenoids by RS. The main methodological and experimental principles in the study of food carotenoids by RS are discussed and best practices recommended, while the future prospects and expectations for a wider application of RS, especially in food quality assessment, are emphasized. New Raman techniques such as Spatially Offset Raman Spectroscopy (SORS), Coherent Anti-Stokes Raman Spectroscopy (CARS) and Stimulated Raman Scattering Spectroscopy (SRS), as well as the application of artificial intelligence, are also described in the context of carotenoids analysis.

Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers

A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56 × 56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in Rv 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.

Effect of storage on bioactivity of an Algerian spice "paprika": optimization of phenolic extraction and study of antioxidant and antibacterial activities

The effect of different storage methods (ambient temperature (A), refrigeration at 4 °C (R) and freezing at - 18 °C (F)), on the phytochemistry of an Algerian spice (paprika powder), was assessed. The optimized extract was obtained under the optimum conditions of ultrasound-assisted extraction (UAE) using response surface methodology (RSM) coupled with a Box-Behnken Design (BBD). This extract was evaluated for its total phenolics content (TPC), total flavonoids content (TFC) and its antioxidant and antibacterial activities. Under the optimum conditions (5 min for the irradiation time, 40% for the amplitude, 80% for ethanol concentration and 50% for solid-liquid ratio) the TPC was 12.23 ± 1.01 mg Gallic Acid Equivalent/gram of Dried Powder (mg GAE/g DP) which is very close with experimental assay. The TPC are better preserved at A whereas TFC and the antioxidant activity at F, and the antibacterial activity depend on the storage methods and the strains tested.

Amplified electrochemical sensor employing Ag NPs functionalized graphene paper electrode for high sensitive analysis of Sudan I

In this study, a high-performance flexible reduced graphene oxide (rGO) paper electrode composed of silver nanoparticles (Ag NPs) for the detection of Sudan I was fabricated. Ag NPs were doped with rGO nanoheets by self-assemble and assembled into a paper electrode with layer-by-layer structure via vacuum filtration. Thanks to the highly efficient electrocatalysis of Ag NPs towards reduction of azo bond, the as-prepared hybrid paper can be used alone as a flexible sensor for the detection of Sudan I in chili powder, with the high sensitivity (22.93 μA μmol/L) and the low detection limit (41.3 nmol/L). The sensor also expressed good selectivity, repeatability, reproducibility, stability and recovery between 96.1% and 101.8% (RSD < 6%). With the advantages of low-cost and scalable production capacity, such Ag NPs/rGO functional papers can be used as flexible disposable sensors for electrochemical detection of Sudan I.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R²_CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R²_CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.

Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review

Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud.

Authentication of PDO paprika powder (Pimentón de la Vera) by multivariate analysis of the elemental fingerprint determined by ED-XRF. A feasibility study

Products with a Protected Denomination of Origin (PDO) are vulnerable to misdescription of their true geographical origin. In this work a method has been developed that allows the authentication of La Vera paprika powder (Pimentón de la Vera), a PDO product from the central-west Spanish region, Extremadura. The mass fractions of Br, Ca, Cr, Cl, Cu, Fe, K, Mn, Ni, P, Rb, S, Sr and Zn determined by energy dispersive X-ray fluorescence (ED-XRF) are used for classification purposes by multivariate analysis using Soft Independent Modelling of Class Analogy (SIMCA) (PCA-Class) and Partial Least Square-Discriminant Analysis (PLS-DA). Sixty-seven paprika samples purchased in supermarkets around Europe and on-line via the official web-site of Pimentón de La Vera, were used to build up the models for prediction purposes. The PCA-class model of La Vera paprika powder had a sensitivity of 82%, a specificity of 100% and an accuracy of 91%, whereas the PLS-DA model had a sensitivity of 100%, a specificity of 91% and an accuracy of 96%.

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Authentication of Pimentón de la Vera is achieved by ED-XRF elemental fingerprint.

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ED-XRF is fast and hardly requires any sample treatment.

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Hazardous reagents are not required and chemical waste is not generated.

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SIMCA and PLS-DA models are fit-for-the purpose of fighting food fraud.

The Detection of Substitution Adulteration of Paprika with Spent Paprika by the Application of Molecular Spectroscopy Tools

The spice paprika (Capsicum annuum and frutescens) is used in a wide variety of cooking methods as well as seasonings and sauces. The oil, paprika oleoresin, is a valuable product; however, once removed from paprika, the remaining spent product can be used to adulterate paprika. Near-infrared (NIR) and Fourier transform infrared (FTIR) were the platforms selected for the development of methods to detect paprika adulteration in conjunction with chemometrics. Orthogonal partial least squares discriminant analysis (OPLS-DA), a supervised technique, was used to develop the chemometric models, and the measurement of fit (R²) and measurement of prediction (Q²) values were 0.853 and 0.819, respectively, for the NIR method and 0.943 and 0.898 respectively for the FTIR method. An external validation set was tested against the model, and a receiver operating curve (ROC) was created. The area under the curve (AUC) for both methods was highly accurate at 0.951 (NIR) and 0.907 (FTIR). The levels of adulteration with 100% correct classification were 50–90% (NIR) and 40–90% (FTIR). Sudan I dye is a commonly used adulterant in paprika; however, in this study it was found that this dye had no effect on the outcome of the result for spent material adulteration.