Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods.

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

The Application of Handheld Near-Infrared Spectroscopy and Raman Spectroscopic Imaging for the Identification and Quality Control of Food Products.

The following investigations describe the potential of handheld NIR spectroscopy and Raman imaging measurements for the identification and authentication of food products. On the one hand, during the last decade, handheld NIR spectroscopy has made the greatest progress among vibrational spectroscopic methods in terms of miniaturization and price/performance ratio, and on the other hand, the Raman spectroscopic imaging method can achieve the best lateral resolution when examining the heterogeneous composition of samples. The utilization of both methods is further enhanced via the combination with chemometric evaluation methods with respect to the detection, identification, and discrimination of illegal counterfeiting of food products. To demonstrate the solution to practical problems with these two spectroscopic techniques, the results of our recent investigations obtained for various industrial processes and customer-relevant product examples have been discussed in this article. Specifically, the monitoring of food extraction processes (e.g., ethanol extraction of clove and water extraction of wolfberry) and the identification of food quality (e.g., differentiation of cocoa nibs and cocoa beans) via handheld NIR spectroscopy, and the detection and quantification of adulterations in powdered dairy products via Raman imaging were outlined in some detail. Although the present work only demonstrates exemplary product and process examples, the applications provide a balanced overview of materials with different physical properties and manufacturing processes in order to be able to derive modified applications for other products or production processes.

Nontargeted Analytical Methods as a Powerful Tool for the Authentication of Spices and Herbs: A Review.

Food fraud in herbs and spices is an important topic, which has led to new technologies being studied as potential tools for fraud identification. Nontargeted technologies have proven to be a useful tool for the authentication of herbs and spices. The present review focuses on the use of near-infrared, hyperspectral imaging, Fourier-transform infrared, Raman, nuclear magnetic resonance, and electron spin resonance spectroscopy for the authentication of spices, which includes the determination of origin and irradiated spices and the identification of adulterants. The methods developed based on vibrational spectroscopy combined with chemometric techniques seem to be promising tools for determining the presence of adulterants and contaminants in herbs and spices. On the other hand, nuclear magnetic resonance seems to be the most efficient technology to determine the origin of herbs and spices although, for some cases, studies with near-infrared spectroscopy can be a viable substitute. Electron spin resonance spectroscopy is the technique par excellence used for the authentication of irradiated herbs and spices, so its use should be expanded to many more spices' varieties. Portable devices are preferred by those involved in the food industry, due to its manageability and low cost. Data fusion and big data are shown as promising tools for spice fraud control. In conclusion, spectroscopic techniques show a great efficiency to authenticate spices, although their evaluation must be expanded to other spice varieties, to new strategies of data analysis (as data fusion and big data), and to the use of portable devices.

Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging.

In this study a near-infrared (NIR) hyperspectral imaging technique was investigated for non-destructive determination of chemical composition of intact and minced pork. Hyperspectral images (900-1700 nm) were acquired for both intact and minced pork samples and the mean spectra were extracted by automatic segmentation. Protein, moisture and fat contents were determined by traditional methods and then related with the spectral information by partial least-squares (PLS) regression models. The coefficient of determination obtained by cross-validated PLS models indicated that the NIR spectral range had an excellent ability to predict the content of protein (R(2)(cv)=0.88), moisture (R(2)(cv)=0.87) and fat (R(2)(cv)=0.95) in pork. Regression models using a few selected feature-related wavelengths showed that chemical composition could be predicted with coefficients of determination of 0.92, 0.87 and 0.95 for protein, moisture and fat, respectively. Prediction of chemical contents in each pixel of the hyperspectral image using these prediction models yielded spatially distributed visualisations of the sample composition.

Meat quality evaluation by hyperspectral imaging technique: an overview.

During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread deployment.

Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging.

Many subjective assessment methods for fresh meat quality are still widely used in the meat industry, making the development of an objective and non-destructive technique for assessing meat quality traits a vital need. In this study, a hyperspectral imaging technique was investigated for objective determination of pork quality attributes. Hyperspectral images in the near infrared region (900-1700 nm) were acquired for pork samples from the longissimus dorsi muscle, and the representative spectral information was extracted from the loin eye area. Several mathematical pre-treatments including first and second derivatives, standard normal variate (SNV) and multiplicative scatter correction (MSC) were applied to examine the influence of spectral variations in predicting pork quality characteristics. Spectral information was used for predicting color features (L, a, b, chroma and hue angle), drip loss, pH and sensory characteristics by partial least-squares regression (PLS-R) models. Independent sets of feature-related wavelengths were selected for predicting each quality attribute. The results showed that color reflectance (L), pH and drip loss of pork meat could be predicted with determination coefficients (R(CV)(2)) of 0.93, 0.87 and 0.83, respectively. The regression coefficients from the PLS-R models at the selected optimal wavelengths were applied in a pixel-wise manner to convert spectral images to prediction maps that display the distribution of attributes within the sample. Results indicated that this technique is a potential tool for rapid assessment of pork quality.