Comparison of various chemometric analysis for rapid prediction of thiobarbituric acid reactive substances in rainbow trout fillets by hyperspectral imaging technique

Physicochemical properties of Kakol (Suaeda aegyptiaca) essential oil nanoemulsion and its effect on the storage quality of rainbow trout (Oncorhynchus mykiss) during cold storage

The study aims to analyze the chemical composition of Suaeda aegyptiaca essential oil (PSAE) by GC-MS, produce the nanoemulsified essential oil (NSAE) using ultrasound, and compare the antimicrobial and antioxidant activity of the PSAE and NSAE in laboratory medium and rainbow trout fish (Oncorhynchus mykiss). Geranyl-acetone (30.52%) and p-Vinylguaiacol (10.66%), and (e)-β-ionone (7.79%) were the main PSAE chemical compounds. The mean droplet size diameter, polydispersity index, and viscosity of NSAE were 179.67 nm, 0.255, and 0.96 cP, respectively. PSAE and NSAE showed a moderate antiradical potential against DPPH- and ABTS-free radicals (50 < IC50 < 250 μg mL-1). There was no significant difference between antiradical scavenging of PSAE and NSAE (p > .05). E. faecalis and K. pneumonia were the most and lowest sensitive bacteria to PSAE and NSAE, respectively. Examining different treatments on the shelf-life of minced fish showed that Kakol essential oil could improve the shelf-life of fish between 12.5% and 60% (depending on quality index). There was no significant difference between the bioactivity of PSAE and NSAE, which means that the nanoemulsion showed acceptable performance at lower essential oil concentrations.

Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging

Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.

Preparation of Colorimetric Sensor Array System to Evaluate the Effects of Alginate Edible Coating on Boiled-Dried Anchovy

The colorimetric sensor array (CSA) is a simple, rapid, and cost-effective system widely used in food science to assess food quality by identifying undesirable volatile organic compounds. As a prospective alternative to conventional techniques such as total volatile basic nitrogen, peroxide value, and thiobarbituric acid reactive substance analysis, the CSA system has garnered significant attention. This study evaluated the quality of edible-coated food products using both conventional and CSA methods in order to demonstrate that the CSA approach is a feasible alternative to conventional methods. Boiled-dried anchovies (BDA) were selected as the model food product, and the sample's quality was assessed as a function of storage temperature and incubation period using conventional techniques and the CSA system. The surface of BDA was coated with an edible alginate film to form the surface-modified food product. The conventional methods revealed that an increase in storage temperature and incubation time accelerated the lipid oxidation process, with the uncoated BDA undergoing lipid oxidation at a faster rate than the coated BDA. Utilizing multivariate statistical analysis, the CSA approach essentially yielded the same results. In addition, the partial least square regression technique revealed a strong correlation between the CSA system and conventional methods, indicating that the CSA system may be a feasible alternative to existing methods for evaluating the quality of food products with surface modifications.

Effects of substance use disorder on oxidative and antioxidative stress markers: A systematic review and meta-analysis

Recently, it has been suggested that central and peripheral toxicities identified in persons with substance use disorder (SUD) could be partially associated with an imbalance in reactive oxygen species and antioxidant defenses. We conducted a systematic review and meta-analysis to investigate whether SUD is associated with oxidative stress and to identify biomarkers possibly more affected by this condition. We have included studies that analysed oxidant and antioxidant markers in individuals with SUD caused by stimulants, alcohol, nicotine, opioids, and others (cannabis, inhalants, and polysubstance use). Our analysis showed that persons with SUD show higher oxidant markers and lower antioxidant markers than healthy controls. SUD was associated specifically with higher levels of oxidant markers malondialdehyde, thiobarbituric acid reactive substances and lipid peroxidation. Conversely, the antioxidant superoxide dismutase and the total antioxidant capacity/status were lowered in the SUD group. A meta-regression analysis revealed that persons with alcohol use disorder had higher oxidative stress estimates than those with stimulant use disorder. Moreover, individuals evaluated during abstinence showed smaller antioxidant effect sizes than non-abstinent ones. Our findings suggest a clear oxidative imbalance in persons with SUD, which could lead to cell damage and result in multiple associated comorbidities, particularly accelerated aging.

Model systems for studying lipid oxidation associated with muscle foods: Methods, challenges, and prospects

Lipid oxidation is a complex process in muscle-based foods (red meat, poultry and fish) causing severe quality deterioration, e.g., off-odors, discoloration, texture defects and nutritional loss. The complexity of muscle tissue -both composition and structure- poses as a formidable challenge in directly clarifying the mechanisms of lipid oxidation in muscle-based foods. Therefore, different in vitro model systems simulating different aspects of muscle have been used to study the pathways of lipid oxidation. In this review, we discuss the principle, preparation, implementation as well as advantages and disadvantages of seven commonly-studied model systems that mimic either compositional or structural aspects of actual meat: emulsions, fatty acid micelles, liposomes, microsomes, erythrocytes, washed muscle mince, and muscle homogenates. Furthermore, we evaluate the prospects of stem cells, tissue cultures and three-dimensional printing for future model system development. Based on this reviewing of oxidation models, tailoring correct model to different study aims could be facilitated, and readers are becoming acquainted with advantages and shortcomings. In addition, insight into recent technology developments, e.g., stem cell- and tissue-cultures as well as three-dimensional printing could provide new opportunities to overcome the current bottlenecks of lipid oxidation studies in muscle.

Edible Xanthan/Propolis Coating and Its Effect on Physicochemical, Microbial, and Sensory Quality Indices in Mackerel Tuna (Euthynnus affinis) Fillets during Chilled Storage

Worldwide aquaculture production is increasing, but with this increase comes quality and safety related problems. Hence, there is an urgent need to develop potent technologies to extend the shelf life of fish. Xanthan gum is commonly used in the food industry because of its high-water solubility, stability of its aqueous solutions in a wide pH range, and high viscosity. One of its modern food applications is its use as a gelling agent in edible coatings building. Therefore, in this study, the effect of xanthan coating containing various concentrations (0, 1, 2%; w/v) of ethanolic extract of propolis (EEP) on physicochemical, microbial, and sensory quality indices in mackerel fillets stored at 2 °C for 20 days was evaluated. The pH, peroxide value, K-value, TVB-N, TBARS, microbiological and sensory characteristics were determined every 5 days over the storage period (20 days). Samples treated with xanthan (XAN) coatings containing 1 and 2% of EEP were shown to have the highest level of physicochemical protection and maximum level of microbial inhibition (p < 0.05) compared to uncoated samples (control) over the storage period. Furthermore, the addition of EEP to XAN was more effective in notably preserving (p < 0.05) the taste and odor of coated samples compared to control.

Evaluation of Different Models for Non-Destructive Detection of Tomato Pesticide Residues Based on Near-Infrared Spectroscopy

In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis/NIRS and prediction models such as PLSR and ANN. First, Vis/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350-1100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.

Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis

Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430-1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R²p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R²_p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.

A systematic review and meta-analysis of fish oil encapsulation within different micro/nanocarriers

Fish oil is one of the most important sources of omega 3 polyunsaturated fatty acids (PUFAs), especially eicosapentaenoic acid and docosahexaenoic acid which are the most important PUFAs with several health benefits. However, PUFAs are prone to oxidation and have a poor water solubility which limits the use of fish oils into food formulations. Encapsulation techniques can be applied to overcome these challenges. There is a large number of published micro/nanoencapsulation papers, where each of them contains a limited number of wall materials, feed formulation, encapsulation technique, and storage conditions. Therefore, without systematic evaluation of the data extracted from available studies, the design of functional foods containing fish oil would not be very successful. The objective of this systematic review is a meta-analysis of published researches on the nano/microencapsulation of fish oil. A comprehensive literature search was performed between 1 October and 31 December 2019 with encapsulation, fish oil, and oxidative stability keywords. Overall, 39 qualified articles were selected for the statistical analysis. Based on the technique used for encapsulation, the fish oil-loaded carriers were classified into four main groups: (a) spray-dried particles; (b) freeze-dried particles; (c) electrospun fibers and electrosprayed capsules; and (d) other carriers prepared by supercritical antisolvent, gelation, liposomes, spray-freeze drying, and transglutaminase catalyzed cross-linking. The three most frequent methods applied for fish oil encapsulation were spray drying (42.86%), freeze drying (21.43%), and electrohydrodynamic (19.04%) methods, respectively. Averagely, the best encapsulation efficiency was obtained for electrohydrodynamic processes. Also, the combination of polysaccharide-protein based wall materials provided the best performance in terms of fish oil encapsulation efficiency.

Prediction of various freshness indicators in fish fillets by one multispectral imaging system

In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R²_P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R²_p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R²_p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R²_p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R²_P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.