Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Evaluation and monitoring of the quality of sausages by different analytical techniques over the last five years

Sausages are among the most vulnerable and perishable products, although those products are an important source of essential nutrients for human organisms. The evaluation of the quality of sausages becomes more and more required by consumers, producers, and authorities to thwarter falsification. Numerous analytical techniques including chemical, sensory, chromatography, and so on, are employed for the determination of the quality and authenticity of sausages. These methods are expensive and time consuming, and are often sensitive to significant sources of variation. Therefore, rapid analytical techniques such as fluorescence spectroscopy, near infrared (NIR), mid infrared (MIR), nuclear magnetic resonance (NMR), among others were considered helpful tools in this domain. This review will identify current gaps related to different analytical techniques in assessing and monitoring the quality of sausages and discuss the drawbacks of existing analytical methods regarding the quality and authenticity of sausages from 2015 up to now.

Iron supplementation and iron-fortified foods: a review

About one-third of the world population is suffering from iron deficiency. Delivery of iron through diet is a practical, economical, and sustainable approach. Clinical studies have shown that the consumption of iron-fortified foods is one of the most effective methods for the prevention of iron deficiency. However, supplementing iron through diet can cause undesirable side-effects. Thus, it is essential to develop new iron-rich ingredients, iron-fortified products with high bioavailability, better stability, and lower cost. It is also essential to develop newer processing technologies for more effective fortification. This review compared the iron supplementation strategies used to treat the highly iron-deficient population and the general public. We also reviewed the efficacy of functional (iron-rich) ingredients that can be incorporated into food materials to produce iron-fortified foods. The most commonly available foods, such as cereals, bakery products, dairy products, beverages, and condiments are still the best vehicles for iron fortification and delivery.Scope of reviewThe manuscript aims at providing a comprehensive review of the latest publications that cover three aspects: administration routes for iron supplementation, iron-rich ingredients used for iron supplementation, and iron-fortified foods.

Effect of Multiple Freeze-Thaw Cycles on Myoglobin and Lipid Oxidations of Grass Carp (Ctenopharyngodon idella) Surimi with Different Pork Back Fat Content

Fresh grass carp was used to produce surimi and 50 g/kg, 100 g/kg, or 150 g/kg pork back fat was added. The water distribution, thiobarbituric acid reactive substances (TBARS), myoglobin oxidation, color parameter (L*, a*, and b*), heme and non-heme iron content of samples were determined to analyze the effects of different fat content on the oxidation of myoglobin and lipids during multiple freeze-thaw cycles of grass carp surimi. Both multiple freeze-thaw cycles and increased fat content lead to an increase in TBARS, a blue shift in the absorption peak of myoglobin porphyrin, a decrease in heme iron content, and an increase of non-heme iron content. Repeated freeze-thaw caused a decrease in immobilised water content and L*, and caused an increase in a* and b*. Increased fat content caused an increase in immobilised water content, L* and a*, and caused a decrease in b*.

Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R² test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R²P = 0.99 and RMSEP = 0.103 g × 100 g⁻¹ DM.

Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy

The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPD_CV = 1.767; SE_CV = 545.745; bias_CV = -3.107; slope_CV = 0.698) and SNV (RPD_CV = 1.768; SE_CV = 545.337; bias_CV = -3.201; slope_CV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPD_CV = 1.679; SE_CV = 572.669; bias_CV = -7.046; slope_CV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPD_CV = 1.482; SE_CV = 648.672; bias_CV = -3.805; slope_CV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications.

Multispectral imaging for predicting the water status in mushroom during hot-air dehydration

In-depth understanding of the shifting of water status during dehydration is crucial for obtaining better quality of dried food. In this work, we report a nondestructive method to measure the water status in hot-air dried mushroom via multispectral imaging (MSI) technology combined with chemometric methods. The low-field nuclear magnetic resonance (LF-NMR) measurements were performed as reference. During drying process, the moisture content changed dramatically with notable migration and conversion of different water phases. Partial least squares (PLS), back propagation neural network (BPNN), and least squares-support vector machine (LS-SVM) models were applied to develop quantitative models. Among all, BPNN model showed considerably better performance of prediction with coefficient of determination R² c = 0.9829, R² p = 0.9639. The results demonstrated that MSI technology combined with chemometric methods is an impressive approach for determination of the water status in hot-air dried mushrooms, which would facilitate infield of food processing by providing applicable and appropriate platform. PRACTICAL APPLICATION: Experimental investigation of different water status during food processing. Assessment of the potential of multispectral imaging to predict water status. Usage of novel measurement method for food processors.

Interrelationship between myoglobin oxidation and lipid oxidation during the processing of Cantonese sausage with d-sodium erythorbate

BACKGROUND

Pork is used as raw material to produce Cantonese sausage, and 0.5 or 1 g kg^-1 of d-sodium erythorbate is added to the pork meat. In this study the myoglobin oxidation rate, relative metmyoglobin content, heme iron content, redness, pH, free radical content and thiobarbituric acid reactive substance (TBARS) value were measured at different processing times and different content of d-sodium erythorbate.

RESULTS

It was found that d-sodium erythorbate significantly reduced the free radical content and myoglobin and lipid oxidation rates and increased heme iron levels. When d-sodium erythorbate was added to the sausage, the absorption peak of myoglobin porphyrin shifted left, migrating from 414 to 405 nm. At 72 h, with an increase in the d-sodium erythorbate content, a significant negative correlation was identified between heme iron and the degree of redness (P < 0.01).

CONCLUSION

During sausage processing, there are strong correlations among TBARS values, free radical content, metmyoglobin levels, heme iron levels, a* and pH at the same d-sodium erythorbate level. At the same processing time, adding d-sodium erythorbate can slow the rate of myoglobin and lipid oxidation and prevent the discoloration of sausage. © 2019 Society of Chemical Industry.

Potential use of multispectral imaging technology to identify moisture content and water-holding capacity in cooked pork sausages

BACKGROUND

The traditional detection methods for moisture content (MC) and water-holding capacity (WHC) in cooked pork sausages (CPS) are destructive, time consuming, require skilled personnel and are not suitable for online industry applications. The goal of this work was to explore the potential of multispectral imaging (MSI) in combination with multivariate analysis for the identification of MC and WHC in CPS.

RESULTS

Spectra and textures of 156 CPS treated by six salt concentrations (0-2.5%) were analyzed using different calibration models to find the most optimal results of predicting MC and WHC in CPS. By using the fused data of spectra and textures, partial least squares regression models performed well for determining the MC and WHC, with a correlation coefficient (r) of 0.949 and 0.832, respectively. Additionally, their spatial distribution in CPS could be visualized via applying prediction equations to transfer each pixel in the image.

CONCLUSION

Results of satisfactory detection and visualization of the MC and WHC showed that MSI has the potential to serve as a rapid and non-destructive method for use in sausage industry. © 2017 Society of Chemical Industry.

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

The potential of hyperspectral imaging with wavelengths of 380 to 1000 nm was used to determine the pH of cooked sausages after different storage conditions (4 °C for 1 d, 35 °C for 1, 3, and 5 d). The mean spectra of the sausages were extracted from the hyperspectral images and partial least squares regression (PLSR) model was developed to relate spectral profiles with the pH of the cooked sausages. Eleven important wavelengths were selected based on the regression coefficient values. The PLSR model established using the optimal wavelengths showed good precision being the prediction coefficient of determination (R_p² ) 0.909 and the root mean square error of prediction 0.035. The prediction map for illustrating pH indices in sausages was for the first time developed by R statistics. The overall results suggested that hyperspectral imaging combined with PLSR and R statistics are capable to quantify and visualize the sausages pH evolution under different storage conditions.

PRACTICAL APPLICATION

In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high R_p² (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems.

Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy

In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20°C) and stored for 7 and 32days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively.

Rapid and non-destructive identification of water-injected beef samples using multispectral imaging analysis

Water-injected beef has aroused public concern as a major food-safety issue in meat products. In the study, the potential of multispectral imaging analysis in the visible and near-infrared (405-970 nm) regions was evaluated for identifying water-injected beef. A multispectral vision system was used to acquire images of beef injected with up to 21% content of water, and partial least squares regression (PLSR) algorithm was employed to establish prediction model, leading to quantitative estimations of actual water increase with a correlation coefficient (r) of 0.923. Subsequently, an optimized model was achieved by integrating spectral data with feature information extracted from ordinary RGB data, yielding better predictions (r = 0.946). Moreover, the prediction equation was transferred to each pixel within the images for visualizing the distribution of actual water increase. These results demonstrate the capability of multispectral imaging technology as a rapid and non-destructive tool for the identification of water-injected beef.