Evaluating Moisture Content in Immersion Vacuum-Cooled Sausages with Citrus Peel Extracts Using Hyperspectral Imaging.

The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares regression model, employing the full reflectance spectrum with pre-treatment of the standard normal variate, showed calibration coefficients of determination (Rc2) of 0.6160 and a root mean square error of calibration (RMSEC) of 2.8130%. For the first time, prediction maps developed via HSI visualized the distribution of moisture content in the immersion vacuum-cooled sausages with unique modified casings in response to fluctuating storage conditions. The prediction maps showed exact parts with high water content, which will help us to monitor and prevent mold growth. The combination of HSI with multivariate analysis not only quantifies changes in moisture content but also visually represents them in response to various casing treatments under different storage conditions, illustrating the significant potential for real-time inspection and early mold detection in sausages within the processed meat industry.

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Assessment of adenosine triphosphate content in sausages stuffed in different modified casing treatments added with orange extracts, utilising hyperspectral imaging combined with multivariate analysis.

Introduction: An investigation was conducted using a hyperspectral imaging (HSI) system to non-invasively estimate adenosine triphosphate (ATP) content in vacuum packaged sausages in different modified casing treatments added with orange extracts after a year of storage at 4°C. Methods: Various pre-processing combinations were applied to the spectra to enhance the performance of partial least squares regression (PLSR). Results and discussion: PLSR models, utilising the full absorbance spectrum with pre-treatment of standard normal variate combined with 1st derivative,exhibited prediction coefficients of determination (Rp2) reaching up to 0.6629. A distribution map developed through MATLAB was employed to display the location and concentration of ATP content in these unique sausages for the first time. The integration of HSI and multivariate analysis not only quantifies but also visually represents the changes in ATP content response to the different casing treatments, demonstrating the significant potential for real-time inspection in the processed meat industry.

Characterization and bioaccessibility assessment of bioactive compounds from camu-camu (Myrciaria dubia) powders and their food applications.

Camu-camu (Myrciaria dubia) is a tropical fruit known for its content of bioactive compounds. This study aimed to evaluate physicochemically, morphologically, andsensorialpowders from camu-camu obtained by spray-drying at two inlet temperatures (150 °C and 180 °C) with three encapsulating agents (maltodextrin, whey protein and a 50:50 mixture of both) and by freeze-drying of whole fruit. The use of maltodextrin protected bet anthocyanins (cyanidin-3-glucoside (C3G) and delphinidin-3-glucoside (D3G)), but whey protein showed a better protective effect on ascorbic and malic acids. These facts were confirmed during the storage stability test, finding that relative humidity is a critical variable in preserving the bioactive compounds of camu-camu powders. The powders with the highest content of bioactive compounds were added to a yogurt and a white grape juice, and then sensory evaluated. The bioaccessibility studies in gastric and intestinal phases showed better recovery percentages of bioactive compounds in camu-camu powders (up to 60.8 %) and beverages (up to 90 %) for C3G, D3G, ascorbic acid, and malic acid than in the fruit juice. Dehydration of camu-camu (M. dubia) is a strategy to increase the bioactive compounds stability, modulate the fruit sensory properties, and improve their bioavailability after incorporation in food matrices.

Hyperspectral Imaging Combined with Chemometrics Analysis for Monitoring the Textural Properties of Modified Casing Sausages with Differentiated Additions of Orange Extracts.

The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390-1100 nm. To improve the model performance, normalization, 1st derivative, 2nd derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied for spectral pre-treatments. The raw, pretreated spectral data and textural attributes were fit to the partial least squares regression model. The RSM results show that the highest R2 value achieved at adhesion (77.57%) derived from a second-order polynomial model, and the interactive effects of soy lecithin and orange extracts on adhesion were significant (p < 0.05). The adhesion of the PLSR model developed from reflectance after SNV pretreatment possessed a higher calibration coefficient of determination (0.8744) than raw data (0.8591). The selected ten important wavelengths for gumminess and adhesion can simplify the model and can be used for convenient industrial applications.

Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology.

The pH values of sausages stuffed in natural hog casings with different modifications (soy lecithin, soy oil, orange extracts (OE) from waste orange peels, lactic acid in slush salt, and treatment time) after 16-day 4 °C storage were evaluated for the first time by hyperspectral imaging (350−1100 nm) coupled with response surface methodology (RSM). A partial least squares regression (PLSR) model was developed to relate the spectra to the pH of sausages. Spectral pretreatment, including first derivative, second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), normalization, and normalization, with different combinations was employed to improve model performance. RSM showed that only soy lecithin and OE interactively affected the pH of sausages (p < 0.05). The pH value decreased when the casing was treated with a higher concentration of soy lecithin with 0.26% OE. As the first and second derivatives are commonly used to eliminate the baseline shift, the PLSR model derived from absorbance pretreated by the first derivative in the full wavelengths showed a calibration coefficient of determination (R2) of 0.73 with a root mean square error of calibration of 0.4283. Twelve feature wavelengths were selected with a comparable R2 value compared with the full wavelengths. The prediction map enables the visualization of the pH evolution of sausages stuffed in the modified casings added with OE.

Proteomic and computational characterisation of 11S globulins from grape seed flour by-product and its interaction with malvidin 3-glucoside by molecular docking.

Grape seed flour by-product (GSBP) is an economic and renewable source of proteins, increasingly being explored due to interesting technological application such as colour protection in rich-anthocyanins beverages. Globulin-like proteins from GSBP were characterised by proteomic and computational studies. MALDI TOF/TOF analysis revealed the presence of two 11S globulins (acid and basic), whose 3D structures have been elucidated for the first time in Vitis vinifera L. grape seeds by using homology models and molecular dynamics. The secondary structure showed 11 α-helices and 25 ß-sheets for acid and 12 α-helices and 24 ß-sheets for basic 11S globulins. Molecular docking results indicate that both grape seed 11S globulins could establish different types of non-covalent interactions (π-π) with malvidin 3-O-glucoside (wine anthocyanin), which suggest a possible colour protection similar to that occurring in copigmentation phenomenon. These findings provide valuable information of globulin family proteins that could be relevant in food industry applications.

Research Progress in Imaging Technology for Assessing Quality in Wine Grapes and Seeds.

The chemical composition of wine grapes changes qualitatively and quantitatively during the ripening process. In addition to the sugar content, which determines the alcohol content of the wine, it is necessary to consider the phenolic composition of the grape skins and seeds to obtain quality red wines. In this work, some imaging techniques have been used for the comprehensive characterisation of the chemical composition of red grapes (cv. Tempranillo and cv. Syrah) grown in a warm-climate region during two seasons. In addition, and for the first time, mathematical models trained with laboratory images have been extrapolated for using in field images, obtaining interesting results. Determination coefficients of 0.90 for sugars, 0.73 for total phenols, and 0.73 for individual anthocyanins in grape skins have been achieved with a portable hyperspectral camera between 400 and 1000 nm, and 0.83 for total and individual phenols in grape seeds with a desktop hyperspectral camera between 900 and 1700 nm.

Assessment of Sensory and Texture Profiles of Grape Seeds at Real Maturity Stages Using Image Analysis.

The usefulness of digital image analysis in estimating sensory attributes of grape seeds in relation to maturation level was evaluated for the first time. Seeds from Syrah grapes harvested throughout the ripening period were grouped according to maturity using the DigiEye® system. The discriminant ability, homogeneity, repeatability, and uniformity of a sensory panel were assessed after training on grape seeds. The aim was to evaluate the use of digital image techniques in order to accurately establish the maturity level of grape seeds, based on sensory and textural features. All sensory attributes (color, hardness, cracking, vegetal, bitterness and astringency) showed significant (p < 0.05) correlations with the chemical maturity stage. Color and vegetal (sensory attributes), together with deformation energy (instrumental texture parameter) (De), allowed for the classification of the seeds into four real maturity stages, hence their usefulness as grape seed ripening indicators. Significant (p < 0.05) and high-correlation factors were also found between instrumental and sensory attributes. Therefore, digital analysis can be a useful tool to better define the maturity stage in the vineyard, and to dispose of grape seeds with well-defined sensory profiles for specific oenological applications. This could help to determine the optimal harvest date to manage winemaking, in order to produce high quality wines in warm climates.

A Study of Overripe Seed Byproducts from Sun-Dried Grapes by Dispersive Raman Spectroscopy.

Overripe seeds from sun-dried grapes submitted to postharvest dehydration constitute a scarcely investigated class of vinification byproduct with limited reports on their phenolic composition and industrial applications. In this study, Raman spectroscopy was applied to characterize a selection of overripe seed byproducts from different white grapes (cv. Moscatel, cv. Pedro Ximénez and cv. Zalema) submitted to postharvest sun drying. The Raman measurements were taken using a 1064 nm excitation laser in order to mitigate the fluorescent effect and the dispersive detection scheme allowed a compactness of the optical system. Spectroscopic data were processed by a principal component analysis to reduce the dimensionality and partner recognition. The evolution of the Raman spectrum during the overripening process was compared with the phenolic composition of grape seeds, which was determined by rapid resolution liquid chromatography/mass spectrometry (RRLC/MS). A multivariate processing of the spectroscopic data allowed the classification of overripe seeds according to the grape variety and the monitoring of stages of the postharvest sun drying process.

Applications of Visible Spectroscopy and Color Measurements in the Assessments of Carotenoid Levels in Foods.

The color of the food can be evaluated visually or instrumentally. The instrumental measurement allows to measure the color objectively and can be carried out by different techniques, most of them fast, cheap, and simple. Carotenoids are responsible for the color of many plants and foods. The measurement of color can be an effective tool to determine indirectly the concentration of carotenoids in plants and foods. In this chapter, we describe both traditional and recent methodologies for color measurement.

Analysis of Multifloral Bee Pollen Pellets by Advanced Digital Imaging Applied to Functional Food Ingredients.

Bee pollen is a hive product, resulting from floral pollen agglutination by worker bees and it is characterized by its excellent bioactive and nutritional composition. Currently, research is focused on bee pollen applications on food industry, because this product has been considered an excellent source of compounds for human nutrition. It is also important in some industries, where color and particle size are important characteristics for production. Due to the granular nature of bee pollen, conventional colorimetry does not allow describing color correctly; thus, digital image analysis is a better alternative. This technique could also allow classifying bee pollen according to its appearance beyond the color. Consequently, the aim of this work was to develop a novel methodology for image data processing to classify bee pollen as ingredient in food industry. Seven color groups in samples were established regarding harvest month and particle size. It was possible to calculate the percentage of each color group in all samples. This methodology also allowed selecting each fraction for different applications in food industry using colorimetry, granulometry and the relationship between both of them.

Implications of the Red Beet Ripening on the Colour and Betalain Composition Relationships.

The evolution during ripening of Beta vulgaris (var. Pablo) on colour and betalain composition, not previously conducted in conjunction in red beets, has been examined. According to the results, it could be asserted that the ripening stage significantly (p < 0.05) influenced on all the studied parameters. On the basis of the betalain content, the optimum ripening stage corresponded to a medium weigh-to-calibre ratio, in the light of the significantly (p < 0.05) higher content of betalains, especially betanin and vulgaxanthin I. Moreover, colour attributes also differed during ripening, having the medium-ripened beetroots a significantly (p < 0.05) more reddish hue (hab) and lower lightness (L*), probably due to the higher content of betaxanthins in this stage. The colour differences among red beets in the stage II and the rest of stages were visually appreciable (ΔE*ab > 3) and mainly qualitative. A new range of opportunities for diversification of colorant market, from a nutritional and colorimetric point of view, could be possible by employing red beets with different stages of ripening.

Estimation of adenosine triphosphate content in ready-to-eat sausages with different storage days, using hyperspectral imaging coupled with R statistics.

A hyperspectral imaging (HSI) system (380-1000 nm) was investigated for non-invasively estimating adenosine triphosphate (ATP) content in ready-to-eat sausages during 5 days storage at 35 °C. A set of pretreated combinations were carried out on preprocessing the spectra to improve the performance of partial least squares regression (PLSR). According to the regression coefficient values, ten important wavelengths (385, 390, 395, 505, 580, 670, 745, 780, 855, and 955 nm) were selected in this study. PLSR models developed using full wavelengths and optimal wavelengths showed the prediction coefficient of determination (rp2) up to 0.8324 and 0.8606, respectively. The concentration and location of the ATP content in sausages were for the first time displayed via chemical imaging developed by R statistics. Combining HSI and multivariate analysis can quantify and visualize ATP dynamic changes during storage and a great potential in the processed meat industry for real-time inspection.

Real-time prediction of pre-cooked Japanese sausage color with different storage days using hyperspectral imaging.

BACKGROUND: Redness can greatly influence the freshness of sausages. A precise, rapid and noncontact analytical method or tool is needed to quantify the color. Hyperspectral imaging (HSI) is an emerging technique that integrates spectroscopy and imaging to obtain the spectral and spatial information simultaneously. In the present study, the redness of cooked sausages stored up to 57 days was predicted using HSI in tandem with multivariate data analysis. The mean spectra of the sausages were extracted from the hyperspectral images. Partial least squares regression (PLSR) and forward stepwise multiple regression (FSMR) models were used to develop the relavent spectral profiles with the redness of the cooked sausages. RESULTS: Ten important wavelengths were selected based on the regression coefficient values from the PLSR model. The PLSR model established using the full wavelengths presented a good performance, with Rc of 0.934 and a root mean square error of calibration of 0.642 (redness ranged between 14.99 and 21.48). The prediction maps for demonstrating evolution of redness in sausages were developed for the first time using R statistics (R Foundation for Statistical Computing) and Matlab (MathWorks Inc., Natick, MA, USA). CONCLUSION: HSI combined with PLSR and FSMR can be used to quantify and visualize evolution of sausage redness under different storage days. © 2017 Society of Chemical Industry.

Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques.

This work includes the evaluation of 168 samples of raspberries 'Glen Lyon', representing whole maturation period, by colorimetric and near infrared imaging techniques, as well as the quantification of total phenols, total anthocyanins and antioxidant activity by chemical methods. Samples showed significant differences depending on the maturation stage using CIELAB colour parameters and total anthocyanins content. The application of partial least squares regression allowed predicting the chemical features from image analysis data, with coefficients of determination (R2) up to 0.75. The best prediction for total anthocyanins including colorimetric data was observed. The proposed methodology can be used as a reference method for assessing important quality attributes of raspberries. Moreover, it is useful, rapid and accurate automatic inspection method.

Trying to set up the flavanolic phases during grape seed ripening: A spectral and chemical approach.

Grape seeds were collected in ten different dates and classified in seven groups according to their individual hyperspectral imaging characteristics. Proanthocyanidin composition was studied using HPLC-MS for oligomers and acid catalyzed cleavage for polymers characterization. The combination of both analysis provided a complete description of the flavanols. Chemometric analysis was performed to summarize the analytical results. None of the considered variables presented statistical differences among all groups. From one to five groups were found for each variable, while three was the most frequent value, consequently three putative stages might be considered the real number of different analytical stages since it is the number of statistically significant groups for the majority of the compounds. This classification could be considered as the first step to optimize the use of seeds in winemaking to minimize the gap between sugar and phenolic maturities, consequence of the global climate change, mainly observed in warm climate.

Optimisation of an oak chips-grape mix maceration process. Influence of chip dose and maceration time.

Oak chips-related phenolics are able to modify the composition of red wine and modulate the colour stability. In this study, the effect of two maceration techniques, traditional and oak chips-grape mix process, on the phenolic composition and colour of Syrah red wines from warm climate was studied. Two doses of oak chips (3 and 6g/L) at two maceration times (5 and 10days) during fermentation was considered. Changes on phenolic composition (HPLC-DAD-MS), copigmentation/polymerisation (spectrophotometry), and colour (Tristimulus and Differential Colorimetry) were assessed by multivariate statistical techniques. The addition of oak chips at shorter maceration times enhanced phenolic extraction, colour and its stabilisation in comparison to the traditional maceration. On contrast, increasing chip dose in extended maceration time resulted in wines with lighter and less stable colour. Results open the possibility of optimise alternative technological applications to traditional grape maceration for avoiding the common loss of colour of wines from warm climate.

Application of Differential Colorimetry To Evaluate Anthocyanin-Flavonol-Flavanol Ternary Copigmentation Interactions in Model Solutions.

The combined effect of anthocyanin-flavanol-flavonol ternary interactions on the colorimetric and chemical stability of malvidin-3-glucoside has been studied. Model solutions with fixed malvidin-3-glucoside/(+)-catechin ratio (MC) and variable quercetin-3-ß-d-glucoside concentration (MC+Q) and solutions with fixed malvidin-3-glucoside/quercetin-3-ß-d-glucoside ratio (MQ) and variable (+)-catechin concentration (MQ+C) were tested at levels closer to those existing in wines. Color variations during storage were evaluated by differential colorimetry. Changes in the anthocyanin concentration were monitored by HPLC-DAD. CIELAB color-difference formulas were demonstrated to be of practical interest to assess the stronger and more stable interaction of quercetin-3-ß-d-glucoside with MC binary mixture than (+)-catechin with MQ mixture. The results imply that MC+Q ternary solutions kept their intensity and bluish tonalities for a longer time in comparison to MQ+C solutions. The stability of malvidin-3-glucoside improves when the concentration of quercetin-3-ß-d-glucoside increases in MC+Q mixtures, whereas the addition of (+)-catechin in MQ+C mixtures resulted in an opposite effect.

Assessment of the differences in the phenolic composition and color characteristics of new strawberry (Fragaria x ananassa Duch.) cultivars by HPLC-MS and Imaging Tristimulus Colorimetry.

The phenolic composition (by HPLC-DAD-MS) and color characteristics (by Imaging Tristimulus Colorimetry) of four strawberry cultivars that have shown good climate adaptation to subtropical area (Nikte, Zamorana, Jacona and Pakal) have been assessed. 24 monomeric phenolics were identified, including 15 anthocyanins, 5 phenolic acids, 1 flavanol and 4 flavonols. Nikte and Zamorana showed the highest phenolic potential mainly due to their higher content of anthocyanins, while Pakal was richer in phenolic acids. Regarding color, Nikte and Zamorana were the more similar cultivars having the lowest values of lightness and hue. On the contrary, the color of Pakal was quite different from all the rest, due to the specific distribution between pelargonidin and cyanidin. The inclusion of both phenolic and colorimetric information in the Linear Discriminant Analysis allowed reaching very good discriminations among cultivars.