Protein Quantification and Imaging by Surface-Enhanced Raman Spectroscopy and Similarity Analysis

Protein quantification techniques such as immunoassays have been improved considerably, but they have several limitations, including time-consuming procedures, low sensitivity, and extrinsic detection. Because direct surface-enhanced Raman spectroscopy (SERS) can detect intrinsic signals of proteins, it can be used as an effective detection method. However, owing to the complexity and reliability of SERS signals, SERS is rarely adopted for quantification without a purified target protein. This study reports an efficient and effective direct SERS-based immunoassay (SERSIA) technique for protein quantification and imaging. SERSIA relies on the uniform coating of gold nanoparticles (GNPs) on a target-protein-immobilized substrate by simple centrifugation. As centrifugation induces close contact between the GNPs and target proteins, the intrinsic signals of the target protein can be detected. For quantification, the protein levels in a cell lysate are estimated using similarity analysis between antibody-only and protein-conjugated samples. This method reliably estimates the protein level at a sub-picomolar detection limit. Furthermore, this method enables quantitative imaging of immobilized protein at a micrometer range. Because this technique is fast, sensitive, and requires only one type of antibody, this approach can be a useful method to detect proteins in biological samples.

Current analytical methods for porcine identification in meat and meat products

Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.

Quantification of Ash and Moisture in Wheat Flour by Raman Spectroscopy

Wheat flour is widely used on an industrial scale in baked goods, pasta, food concentrates, and confectionaries. Ash content and moisture can serve as important indicators of the wheat flour’s quality and use, but the routinely applied assessment methods are laborious. Partial least squares regression models, obtained using Raman spectra of flour samples and the results of reference gravimetric analysis, allow for fast and reliable determination of ash and moisture in wheat flour, with relative standard errors of prediction of the order of 2%. Analogous calibration models that enable quantification of carbon, oxygen, sulfur, and nitrogen, and hence protein, in the analyzed flours, with relative standard errors of prediction equal to 0.1, 0.3, 3.3, and 1.4%, respectively, were built combining the results of elemental analysis and Raman spectra.

Amine Functionalized Metal-Organic Framework Coordinated with Transition Metal Ions: d-d Transition Enhanced Optical Absorption and Role of Transition Metal Sites on Solar Light Driven H2 Production

Design and development of efficient photocatalysts for H₂ production from water and sunlight have gained significant attention as the solar assisted approach is considered to be a promising approach for the generation of clean fuel. However, the poor charge carrier separation and light harvesting ability of existing photocatalysts limits the efficiency of photoconversion of water. In this work, the synthesis of transition metal ions (M²⁺ = Co²⁺ , Cu²⁺ , and Ni²⁺ ) coordinated with Ti-metal organic frameworks (Ti-MOFs) through a simple post-synthetic coordination method for efficient solar light-driven H₂ production is reported. Notably, coordination of M²⁺ ions with Ti-MOF significantly improves the optical absorption by d-d transitions and the multimetal sites facilitate the fast charge carrier separation, thereby enhancing the solar light-driven H₂ production activity. Very interestingly, the rate of solar light-driven H₂ production is varied with respect to different metal ions coordination due to the position of d-d bands absorption in the solar spectrum, and the complexing tendency of M²⁺ ions with sacrificial electron donors. A maximum solar H₂ production rate of 1583.55 µmol h^-1 g^-1 is achieved with a Cu²⁺ coordinated Ti-MOF, which is ≈13 fold higher than that of the pristine Ti-MOF.

Biocatalytic green alternative to existing hazardous reaction media: synthesis of chalcone and flavone derivatives via the Claisen–Schmidt reaction at room temperature

Herein, two novel agro-food waste products from banana peels were used to synthesize chalcone and flavone derivatives at room temperature under solvent free conditions.

Use of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy in Combination with Multivariate Methods for the Rapid Determination of the Adulteration of Grape, Carob and Mulberry Pekmez

Pekmez, a traditional Turkish food generally produced by concentration of fruit juices, is subjected to fraudulent activities like many other foodstuffs. This study reports the use of Fourier transform infrared spectroscopy (FTIR) in combination with chemometric methods for the detection of fraudulent addition of glucose syrup to traditional grape, carob and mulberry pekmez. FTIR spectra of samples were taken in mid-infrared (MIR) range of 400-4000 cm-1 using attenuated total reflectance (ATR) sample accessory. Partial least squares-discriminant analysis (PLS-DA) and PLS chemometric methods were built for qualitative and quantitative analysis of pekmez samples, respectively. PLS-DA models were successfully used for the discrimination of pure pekmez samples and the adulterated pekmez samples with glucose syrup. Sensitivity and specificity of 100%, and model efficiency of 100% were obtained in PLS-DA models for all pekmez groups. Detection of the adulteration ratio of pekmez samples was also accomplished using ATR-FTIR spectroscopy in combination with PLS. As a result, it was shown that ATR-FTIR spectroscopy along with chemometric methods had a great potential for determination of pekmez adulteration with glucose syrup.

Bulk Protein and Oil Prediction in Soybeans Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy

Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15 ± 0.04 (R²= 0.87) and 0.80 ± 0.02 (R²= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to threefold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ∼5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models; using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.

Approaching Authenticity Issues in Fish and Seafood Products by Qualitative Spectroscopy and Chemometrics

The intrinsically complex nature of fish and seafood, as well as the complicated organisation of the international fish supply and market, make struggle against counterfeiting and falsification of fish and seafood products very difficult. The development of fast and reliable omics strategies based on spectroscopy in conjunction with multivariate data analysis has been attracting great interest from food scientists, so that the studies linked to fish and seafood authenticity have increased considerably in recent years. The present work has been designed to review the most promising studies dealing with the use of qualitative spectroscopy and chemometrics for the resolution of the key authenticity issues of fish and seafood products, with a focus on species substitution, geographical origin falsification, production method or farming system misrepresentation, and fresh for frozen/thawed product substitution. Within this framework, the potential of fluorescence, vibrational, nuclear magnetic resonance, and hyperspectral imaging spectroscopies, combined with both unsupervised and supervised chemometric techniques, has been highlighted, each time pointing out the trends in using one or another analytical approach and the performances achieved.

Raman spectroscopy for the differentiation of Arabic coffee genotypes

The objective of this study was to evaluate the ability of Raman spectroscopy to identify the genotype of green coffee beans. Four genotypes of Arabic coffee: one Mundo Novo line (G1) and three Bourbon lines (G2, G3, and G4). The harvest was selected using a wet processing method. Raman spectra of the samples were obtained using a FT-Raman RFS/100 spectrometer in the spectral range of 3500-400 cm^-1. The data were treated using chemometric unsupervised classification tools and supervised analysis. Using the unsupervised analysis (PCA), the apparent tendency of agglomeration between samples G1 and G3 was verified. These differences were present in the spectral bands that are characteristic of fatty acids and kahweol. Based on this information, a classification model to discriminate (PLS-DA) the Mundo Novo and Bourbon samples was utilized. Raman spectroscopy allowed the building of an adequate model to differentiate between coffee genotypes.

Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (R_p) of 0.9260 and RMSEP of 87.9132 μg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage

Raman spectroscopy was used to identify chemical changes associated sensory quality of coffee beans, for natural and pulped natural coffee stored in different packaging. The green beans of natural coffee and pulped natural coffee were stored in three types of packaging materials in a commercial warehouse. Sensory analyses were performed, and Raman spectra were collected after 0, 3, 6, 9, 12, and 18 storage months. Raman spectra were used to construct multivariate control charts. The charts, which were constructed using principal component analysis, can only be used to identify chemical changes in the green beans from pulped natural coffee stored in different packaging materials. Raman spectroscopy is more sensitive than sensory analysis for detecting chemical changes in stored pulped natural coffee. The measured changes ultimately affect the quality of the beverage because samples stored for six months in paper packaging were determined to no longer meet the quality control requirements.

3‑Mercapto‑propanoic acid modified cellulose filter paper for quick removal of arsenate from drinking water

This paper reports a simple, facile and rapid preparation of 3‑mercapto‑propanoic acid (MPA) modified cellulose filter paper (MPA-Cell paper) for arsenate removal from drinking water. The MPA was covalently grafted to the cellulose filter paper (Cell) by esterification process through the formation of O‑acylisourea intermediate and characterized by the FTIR, SEM, EDS and XPS analyses. The arsenate adsorption efficiency was studied for batch and semi-continuous systems while exploring the adsorption kinetics, isotherm and the effect of pH for the former. The experimental data fitted well with Langmuir, Dubinin-Radushkevich (DR) and pseudo second order kinetic models. The mechanism of adsorption was studied by FTIR spectroscopy utilizing the adsorption isotherm, kinetic model and XPS results. The modified filter paper performed well at nearly neutral pH in arsenate removal through adsorption and demonstrated a significant arsenate uptake capacity of 92.59 mg/g. The DR and FTIR results indicated that the adsorption of arsenate ion occurred through ion exchange process. The MPA-Cell paper could have a potential use as low-cost but efficient commercial adsorbent for arsenate abatement from contaminated drinking water by both batch as well as semi-continuous operating systems. The MPA-Cell paper could purify ground water containing high level of arsenate.

Applications of Raman spectroscopic techniques for quality and safety evaluation of milk: A review of recent developments

Milk is a complete nutrient source for humans. The quality and safety of milk are critical for both producers and consumers, thereby the dairy industry requires rapid and nondestructive methods to ensure milk quality and safety. However, conventional methods are time-consuming and laborious, and require complicated preparation procedures. Therefore, the exploration of new milk analytical methods is essential. This current review introduces the principles of Raman spectroscopy and presents recent advances since 2012 of Raman spectroscopic techniques mainly involving surface-enhanced Raman spectroscopy (SERS), fourier-transform (FT) Raman spectroscopy, near-infrared (NIR) Raman spectroscopy, and micro-Raman spectroscopy for milk analysis including milk compositions, microorganisms and antibiotic residues in milk, as well as milk adulterants. Additionally, some challenges and future outlooks are proposed. The current review shows that Raman spectroscopic techniques have the promising potential for providing rapid and nondestructive detection of milk parameters. However, the application of Raman spectroscopy on milk analysis is not common yet since some limitations of Raman spectroscopy need to be overcome before making it a routine tool for the dairy industry.

Amorphous Carbon Nanotubes-Nickel Oxide Nanoflower Hybrids: A Low Cost Energy Storage Material

Amorphous carbon nanotubes (a-CNTs) have been synthesized by a simple low-temperature process and have been grafted with chemically synthesized nickel oxide microflowers with different concentrations. The phase and morphology of the as-prepared pure and hybrid samples were characterized by X-ray diffraction and field emission scanning and transmission electron microscopes. Thermal properties of the samples were estimated by using thermal gravimetric and differential thermal analysis. The optical properties of the sample were characterized by UV-vis spectroscopic, Raman spectroscopic, and Fourier-transformed infrared spectroscopic analysis. The electrochemical performance of all hybrid samples has been done in detail for different scan rates as well as from charge-discharge analysis. It has been seen that because of the nickel oxide grafting, the electrochemical performance of pure a-CNTs gets enhanced significantly. The value of the specific capacitance of the hybrid comes out to be around 120 F/g for the best sample, which is almost 12 times higher compared to that of the pure a-CNTs. The result has been explained in terms of change in effective surface area as well as change in conductivity of the hybrid samples.

A simple and fast method for discrimination of phage and antibiotic contaminants in raw milk by using Raman spectroscopy

Phage and antibiotic in raw milk poses significant risks for starter culture activity in fermented products. Therefore, rapid detection of phage and antibiotic contaminations in raw milk is a crucial process in dairy science. For this purpose, a preliminary novel method for detection of phage and antibiotic was developed by using Raman spectroscopy. Streptococcus thermophilus phages and ampicillin which are quite important elements in dairy industry were used as model. The phage and antibiotic samples were added to raw milk separately, and Raman measurements were carried out. The obtained spectra were processed with a chemometric method. In this study, it has been demonstrated that the presence of phage has a titer sufficient to stop the fermentation (107 pfu/ml), and antibiotic in a concentration which inhibits the growth of starter cultures (0.5 µg/ml) in raw milk could be discriminated through Raman spectroscopy with a short analysis time (30 min).

Development of a quantitative approach using Raman spectroscopy for carotenoids determination in processed sweet potato

The orange-fleshed sweet potato is a vegetable-rich in carotenoids. The thermic treatment for sweet potato processing can decrease the content of these constituents in the foods, lowering their bioactive properties. Raman spectroscopy has been growing as a fast tool to food analysis, especially for detection of low concentrations of carotenoids and to the monitoring of its degradation profile over time. Therefore, in this work were evaluated two methods of drying, hot air and microwaving with rotary drum, combined with quantitative Raman spectroscopy. The results showed carotenoids degradation around 50% for both types of drying processes studied. PCA plot proved the potential of reproducibility of analyses for microwave drying samples. For samples heated with hot air, the best linear correlation achieved was R² = 0.90 and by microwave was R² = 0.88. Also, partial least squares (PLS) regression models were constructed obtaining a satisfactory coefficient of determination.

Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400–550 nm excitation range with Δλ of 10–100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

Automated decomposition algorithm for Raman spectra based on a Voigt line profile model

Raman spectra measured by spectrometers usually suffer from band overlap and random noise. In this paper, an automated decomposition algorithm based on a Voigt line profile model for Raman spectra is proposed to solve this problem. To decompose a measured Raman spectrum, a Voigt line profile model is introduced to parameterize the measured spectrum, and a Gaussian function is used as the instrumental broadening function. Hence, the issue of spectral decomposition is transformed into a multiparameter optimization problem of the Voigt line profile model parameters. The algorithm can eliminate instrumental broadening, obtain a recovered Raman spectrum, resolve overlapping bands, and suppress random noise simultaneously. Moreover, the recovered spectrum can be decomposed to a group of Lorentzian functions. Experimental results on simulated Raman spectra show that the performance of this algorithm is much better than a commonly used blind deconvolution method. The algorithm has also been tested on the industrial Raman spectra of ortho-xylene and proved to be effective.

Detection of Listeria innocua on roll-to-roll produced SERS substrates with gold nanoparticles

The rapid and accurate detection of food pathogens plays a critical role in the early prevention of foodborne epidemics. Combination of low cost sensing platforms and SERS detection can offer a solution for the pathogen detection.

Spectral normalisation by error minimisation for prediction of conversion in solvent-free catalytic chain transfer polymerisations

This work provides a robust method to determine spectral normalization points in reactions with no known constant responses.