Spectroscopic techniques for monitoring changes in the quality of milk and other dairy products during processing and storage.

The application of spectroscopic techniques can help in alleviating problems encountered during the processing of milk and dairy products. Indeed, traditional analytical methods (e.g., physicochemical measurements, sensory, chromatography) are relatively expensive, time-consuming, and require chemicals and sophisticated analytical equipment, and skilled operators. Hence, there is a need to develop faster and less costly methods for accurately monitoring changes in the quality of milk and other dairy products during processing and storage.Many nondestructive and noninvasive instrumental techniques are available for inline and online monitoring of food. These include fluorescence spectroscopy, mid-infrared (MIR), near-infrared (NIR), nuclear magnetic resonance (NMR), etc. These techniques are usually used in combination with chemometric tools a to explore the information present in spectral data.This review article will discuss the potential of the above-mentioned spectroscopic techniques for monitoring chemical modifications of dairy products and the prediction of their functional properties during processing. The advantages and disadvantages of each technique are also discussed in this review. Finally, some conclusions are drawn, and the future trends of these methods are presented.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years.

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Synchronous fluorescence spectroscopy for determination of tahini adulteration.

In this study, a quick and simple method was developed for detection of tahini adulteration with sunflower oil. The synchronous fluorescence spectroscopy (SFS) data of oil samples were collected by scanning the excitation and emission monochromators simultaneously with 20, 40, 60 and 80nm wavelength intervals within the range of 250-600nm. Three different multivariate calibration methods, namely partial least squares (PLS) analysis, principal component regression (PCR), and multiple linear regression (MLR) were used for data analysis. Wavelength selection feature of the chemometric software was also used in order to determine the optimum range of each dataset collected at 20, 40, 60 and 80nm wavelength intervals. All regression methods with and without wavelength selection mode were applied to these each dataset individually. Application of wavelength selection mode adversely affected the root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) values and other quality parameters of all calibration and validation models which were built by using each dataset collected at 20, 40, 60 and 80nm wavelength intervals. Taking all parameters into consideration, the best results were obtained through the application of PLS analysis without wavelength selection mode on the SFS data collected at all wavelength intervals. The lowest detection limits of adulteration, 0.09% and 0.15% were obtained through the use of 40 and 80nm as wavelength intervals, respectively. RMSECV and RMSEP values were calculated as 0.74 and 1.26 for 40nm, and 0.65 and 0.81 for 80nm wavelength intervals.

Use of Raman spectroscopy for determining erucic acid content in canola oil.

This study presents a novel method to determine erucic acid in canola oil samples by using Raman spectroscopy and chemometric analysis. The oil mixtures were prepared at various concentrations of erucic acid ranging from 0% to 33.56% (w/w) through binary combinations of different oils. In order to predict erucic acid content, Raman spectroscopy and GC results were correlated by means of partial least squares analysis. High coefficient of determination values was obtained for both calibration and validation data sets, which are 0.990 and 0.982, respectively. The results of the present study reveal the potential of Raman spectroscopy for rapid determination (45s) of erucic acid in canola oil. Further research would be useful to improve the method to put it forward as an alternative to GC in the erucic acid analysis.

Differentiation of fresh and frozen-thawed fish samples using Raman spectroscopy coupled with chemometric analysis.

The potential of Raman spectroscopy was investigated in terms of its capability to discriminate the species of the fish samples and determine their freshness according to the number of freezing/thawing cycles they exposed. Species discrimination analysis was carried out on sixty-four fish samples from six different species, namely horse mackerel (Trachurus trachurus), European anchovy (Engraulis encrasicolus), red mullet (Mullus surmuletus), Bluefish (Pomatamus saltatrix), Atlantic salmon (Salmo salar) and flying gurnard (Trigla lucerna). Afterwards, fish samples were exposed to different numbers of freezing/thawing cycles and separated into three batches, namely (i) fresh, (ii) once frozen-thawed (OF) and (iii) twice frozen-thawed (TF) samples, in order to perform the freshness analysis. Raman data collected were used as inputs for chemometric analysis, which enabled us to develop two main PCA models to successfully terminate the studies for both species discrimination and freshness determination analysis.

A novel method for discrimination of beef and horsemeat using Raman spectroscopy.

A new approach, based on the usage of Raman spectroscopy in combination with chemometrics, was developed for the rapid determination of beef adulteration with horsemeat. The data mining process of collected Raman spectra was performed with principal component analysis (PCA). Pure fat samples, extracted from forty-nine meat beef and horsemeat samples, were analysed using the Raman spectroscopy. All meat samples were classified successfully according to their origins. The presence of different concentrations (25%, 50%, 75%, w/w) of horsemeat in beef was also differentiated using the developed model system. This study offers a rapid assay for determination of meat adulteration by discriminating beef and horsemeat with high accuracy, a short analysis time (30s) and no requirement for time-consuming sample preparation procedures.

Surface enhanced Raman spectroscopy as a new spectral technique for quantitative detection of metal ions.

Four newly synthesized poly (propylene amine) dendrimers from first and second generation modified with 1,8-naphthalimide units in the dendrimer periphery have been investigated as ligands for the detection of heavy metal ions (Al(3+), Sb(2+), As(2+), Cd(2+) and Pb(2+)) by surface-enhanced Raman spectroscopy. Calibration curves were established for all metal ions between the concentration ranges of 1 x 10(-6) to 5 x 10(-4) M. It has been shown that these dendrimers can be coordinated, especially with different metal ions. Using dendrimer molecules and silver colloids at the same time allowed us to obtain an SERS signal from the abovementioned metal ions at very low concentrations. Principle component analysis (PCA) analysis was also applied to the collected SERS data. Four different PCA models were developed to accomplish the discrimination of five metal ions, which interacted with each of the four dendrimer molecules, separately. A detailed investigation was performed in the present study to provide the basis of a new approach for heavy metal detection.

Gold-Coated Iron Composite Nanospheres Targeted the Detection of Escherichia coli.

We report the preparation and characterization of spherical core-shell structured Fe3O4-Au magnetic nanoparticles, modified with two component self-assembled monolayers (SAMs) consisting of 3-mercaptophenylboronic acid (3-MBA) and 1-decanethiol (1-DT). The rapid and room temperature synthesis of magnetic nanoparticles was achieved using the hydroxylamine reduction of HAuCl4 on the surface of ethylenediaminetetraacetic acid (EDTA)-immobilized iron (magnetite Fe3O4) nanoparticles in the presence of an aqueous solution of hexadecyltrimetylammonium bromide (CTAB) as a dispersant. The reduction of gold on the surface of Fe3O4 nanoparticles exhibits a uniform, highly stable, and narrow particle size distribution of Fe3O4-Au nanoparticles with an average diameter of 9 ± 2 nm. The saturation magnetization value for the resulting nanoparticles was found to be 15 emu/g at 298 K. Subsequent surface modification with SAMs against glucoside moieties on the surface of bacteria provided effective magnetic separation. Comparison of the bacteria capturing efficiency, by means of different molecular recognition agents 3-MBA, 1-DT and the mixed monolayer of 3-MBA and 1-DT was presented. The best capturing efficiency of E. coli was achieved with the mixed monolayer of 3-MBA and 1-DT-modified nanoparticles. Molecular specificity and selectivity were also demonstrated by comparing the surface-enhanced Raman scattering (SERS) spectrum of E. coli-nanoparticle conjugates with bacterial growth media.