In-vitro antioxidant and antibacterial activities of pumpkin,quince, muskmelon and bottle gourd seeds

Ethanol and chloroform extracts of pumpkin, quince, muskmelon, and bottle gourd seeds were studied for in-vitro antioxidant and antibacterial activities. Crude protein and fat contents of all the seeds compared favorably with high protein legumes and high oil containing oilseeds. Ethanol and chloroform extracts of bottle gourd seeds had highest phenolic content. A direct positive relationship between antioxidant activities and extract concentration was observed with the relationship being more pronounced in chloroform extract of quince seeds (R²?=?0.9685 and 0.9829) followed by ethanol extract of pumpkin seeds (R²?=?0.9666 and 0.9685) as per 1,1-diphenyl-2-picrylhydrazyl and hydrogen peroxide (H₂O₂) method, respectively. Seed extracts were also studied for in-vitro antibacterial activity against Salmonella typhii, Enterococcus sp., Escherichia coli, Bacillus subtilis and Staphylococcus aureus. Ethanol extract of pumpkin seeds inhibited the growth of all bacteria; however, both the extracts of quince seeds did not show any activity against S. typhii, E. coli and S. aureus.     

Non-destructive Assessment of Guava (<Emphasis Type="Italic">Psidium guajava</Emphasis> L.) Maturity and Firmness Based on Mechanical Vibration Response

Storage potential and eating quality of guava (Psidium guajava L.) fruit depend on its maturity. Segregation of guava according to maturity and firmness measured using non-destructive technologies would help the industry to designate ripe fruit to immediate market and less ripe fruit for distant market (e.g., exportation). This research was conducted to evaluate the potential of experimental resonant frequency (f _e) and elasticity index (EI) to estimate fruit firmness, which has been reported to be inversely correlated to its maturity. A maturity index (I _m) was calculated as the ratio of total soluble solids/titratable acidity (TSS/TA). It was proved that TSS, TA, and I _m were significantly correlated (P?<?0.05) to skin firmness (F _s), flesh firmness (F _f), stiffness (S), and analytical resonant frequency (ω _n ), being S the attribute best fitted to I _m (R ²?=?0.77). Since it was observed that f _e and EI were sensitive to changes in fruit firmness, both of them were explored as alternatives to predict F _s, F _f, S, and ω _n of guava fruit. In some cases, EI improved the models to predict guava firmness traits (e.g., F _s vs f _e had a coefficient of determination of R ²?=?0.58, whereas for F _s vs EI, it was R ²?=?0.62). The best model occurred when plotting ω _n vs f _e (R ²?=?0.86), followed by S vs EI (R ²?=?0.84), making these promising features for the development of a new practical application using frequency response measurement as a non-destructive method to assess guava maturity.     

Quality Assessment of Intact Chicken Breast Fillets Using Factor Analysis with Vis/NIR Spectroscopy

Factor analysis (FA) method was tested to assess quality of chicken breast fillets with the visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm. According to inherent correlation, three factors were extracted from the measured eight quality traits (L*, a*, b*, pH, moisture, drip loss, expressible fluid, and salt-induced water gain). The extracted “grade factor” (F ₁), “color factor” (F ₂), and “moisture factor” (F ₃) could respectively represent the characteristics and the variation tendency of the corresponding quality traits and were defined as three new quality assessment indexes. Furthermore, partial least squares regression (PLSR) models were established to quantitatively relate spectral information to eight individual quality traits and three factors. The results indicated that the models for predicting each factor performed better than those for individual quality traits. Key wavelengths of each quality trait were then selected, and the corresponding spectra were taken to build new PLSR prediction models. The selected key wavelengths showed obvious practical significance, and the new models had comparable predictive performance to those models developed based on the full spectra, among which the new models of F ₁ and F ₂ had acceptable and robust predictive abilities (R²p?=?0.73, RPD?=?1.91; R²p?=?0.74, RPD?=?1.97). Our results in the present study demonstrate the potential for FA and Vis/NIR spectroscopy as a useful method to assess the quality of chicken breast fillets.     

Development and characterization of reconstituted hydrogel from <Emphasis Type="Italic">Aloe vera</Emphasis> (<Emphasis Type="Italic">Aloe barbadensis</Emphasis> Miller) powder

Structural and rheological characterization of reconstituted hydrogels developed from A. vera non-fibrous alcohol insoluble residue (NFAIR) powder using different methods [viz., shaking (S), heating-shaking (HS), and heating (H)] and concentrations (viz., 0.2–1.6 %, w/v) was carried out. Functional group distribution by FTIR spectroscopy and Congo red (CR) method revealed the presence of acetylated acemannan in A. vera powder. Dynamic oscillation studies of A. vera (NFAIR) fluids at all concentrations of 0.2–1.6 %, w/v, showed gel strength in the order of H > HS > S method. However, in H method, increase in concentration from 0.2 to 1.6 %, w/v showed the conformational transition from semi-diluted solution to weak gel nature. Rheological models described the effect of heating temperatures (HT); 30–90 °C, and times (Ht); 15–60 min on viscoelastic behavior in reconstituted A. vera fluids. The reconstituted A. vera hydrogel prepared with a concentration of 1.6 %, w/v using 50 °C (HT) and 30 min (Ht) condition showed a good agreement with the Power law (storage modulus, G′) and Weak gel model (complex modulus, G*) fitted data (R² > 0.94) resulting higher viscoelastic moduli intercepts; G′₀ (71.5 Pa s ^n′), G″₀ (33.5 Pa s ^n″), lower slopes; n′ (0.22), n″ (0.06), higher network strength (A _F , 121.3 Pa s^1/z ) and number of network (z, 5.3) values. The obtained results suggested that heating at 50 °C/30 min can develop aqueous weak gel networks of A. vera with enhanced gel strength which may be utilized as a novel gelling agent for wide variety of targeted applications in food and pharmaceutical sectors.     

Combined Application of Fluorescence Spectroscopy and Chemometrics Analysis in Oxidative Deterioration of Edible Oils

Combined methods of fluorescence spectrometry with chemometrics were used to monitor oxidation deterioration of edible oil. Synchronous and three dimensional fluorescence spectroscopy techniques were proposed for monitoring palm oil, camellia oil, sunflower oil and perilla oil during oven accelerated oxidation. Principal component analysis plot of fluorescence intensity (λex = 320–700 nm) clearly showed oxidative evolution of oils over heating time. High saturated or monounsaturated oils exhibited high regression coefficients between peroxide values and fluorescence intensity (R ²  = 0.973 for 400 nm in palm oil; R ²  = 0.956 for 370 nm in camellia oil). High diunsaturated oil exhibited high regression coefficient between nonpolar carbonyl compounds and fluorescence intensity (R ²  = 0.970 for 370 nm in sunflower oil). High triunsaturated oil exhibited high regression coefficient between p-anisidine value and fluorescence intensity (R ²  = 0.938 for 665 nm in perilla oil). In conclusion, Fluorescence spectroscopy is a rapid and green nondestructive method for oxidation monitoring. Differences of fatty acid compositions played key rules in formation of oxidation products and evolution of fluorescence spectra.     

A mathematical model for moisture movement during continous and intermittent drying of <Emphasis Type="Italic">Eucalyptus saligna</Emphasis>

A modified diffusion-based mathematical model is proposed to describe the moisture movement during continuous and intermittent drying of Eucalyptus saligna. This model includes the temperature change, the surface drying coefficient (β _n ) and 2 diffusion coefficients [from green to FSP (D _f ) and from FSP to dry condition (D _o )] as important parameters. The final model expression obtained was M?=?exp (??25 β _n ² D _t /l²) with the β _n used was 1.5807 kg m^?2 s^?1, the D _f was 2.26?×?10^?11 m² s^?1, and the D _o was 5.85?×?10^?12 m² s^?1. The range of temperature change between heating and non-heating phases in the intermittent drying regimes was from 24.9 to 31.8 °C. The R² values obtained when the model was fitted into the drying data of different intermittent regimes ranged from 71.5 to 85.9%. The R² value was 87.4% when the model was fitted into continuous trial data. The high values of R² indicate that the model can be used to understand the moisture reduction both in intermittent and continuous regimes.     

Smart NMR Method of Measurement of Moisture Content of Vegetables During Microwave Vacuum Drying

Microwave drying is usually combined with vacuum environment in conjunction with hot air flow to draw the moisture rapidly. The moisture content of the vegetables undergoing drying is hard to measure online. This research designed a microwave vacuum drying (MVD)-low-field nuclear magnetic resonance (NMR) smart device and investigated the feasibility of NMR method for online measurement of state of moisture during MVD. The relation between the signal amplitude (A ₂) and the true moisture content (M ₁) of six kinds of vegetables (mushroom, carrot, potato, lotus, edamame, vegetable corn) was fitted to estimate if NMR can measure the M ₁ of vegetables directly. Results showed that A ₂ and M ₁ of different fresh vegetables had no single empirical mathematical model to fit. However, for each kind of these vegetables, the A ₂ and corresponding M ₁ in different MVD stages showed a significant linear relationship. The predicted moisture content (M ₂) of mushroom: M ₂ = 5.25351 × 10^?4 A ₂ ? 0.34042, R = 0.996; carrot: M ₂ = 5.78756 × 10^?4 A ₂ ? 0.14108, R = 0.998; potato: M ₂ = 3.10019 × 10^?4 A ₂ ? 0.10612, R = 0.991; lotus: M ₂ = 2.32415 × 10^?4 A ₂ ? 0.01573, R = 0.998; edamame: M ₂ = 3.13310 × 10^?4 A ₂ ? 0.4198, R = 0.996; vegetable corn: M ₂ = 1.69461 × 10^?4 A ₂ ? 0.09063, R = 0.995. The linear models between M ₂ and A ₂ were able to estimate the end point (M ₁ < 8%) of MVD with a high accuracy (P > 0.950).     

Effect of Tannin Extracts on Biofilms and Attachment of <Emphasis Type="Italic">Escherichia coli</Emphasis> on Lettuce Leaves

Lettuce is often involved in foodborne outbreaks caused by pathogenic Escherichia coli. Current control strategies have often proved ineffective to ensure safe food production. For that reason, the present study compared the efficacy of tannin extracts and chlorine treatments on the reduction of E. coli ATCC 25922 adhered to lettuce leaves. E. coli was inoculated artificially on leaf surfaces of fresh crisp lettuce. Effectiveness of water, chlorine (200 mg/L), and three commercial available tannin extracts from Acacia mearnsii De Wild. (tannin AQ (2 %, w/v), tannin SG (1 %, v/v) and tannin SM (1 %, v/v)) treatments was evaluated using the viable plate count method and scanning electron microscopy (SEM). SEM results revealed that bacterial cells are attached as individual cells and in clusters to the leaf surface after 2 h of incubation. Biofilm formation was observed after 24 h of incubation. The tannin SM treatment was able to reduce counts in approximately 2 log CFU/cm² on leaf segments. However, treatment was less effective in the reduction of E. coli counts after 24 h of incubation when compared to 2 h incubation of the same extract. The results suggest that the tannin SM extract diminishes E. coli counts adhered to and under biofilm formation on lettuce leaves and its effect is similar to the use of chlorine solutions.     

Modeling the Inactivation of <Emphasis Type="Italic">Listeria innocua</Emphasis> and <Emphasis Type="Italic">Escherichia coli</Emphasis> in Fresh-Cut Tomato Treated with Pulsed Light

The effectiveness of pulsed light (PL) treatments to inhibit microorganisms on fresh-cut tomatoes (Lycopersicon esculentum Mill., cv. Daniela) was investigated. Tomato slices inoculated with Escherichia coli or Listeria innocua were exposed to PL treatments (4, 6, or 8 J cm^?2 fluence) and kept cold at 4 °C for 20 days. L. innocua and E. coli counts, gases in the headspace of the containers (O₂ and CO₂), pH, titratable acidity, and soluble solid content were monitored throughout the cold storage. The PL treatments reduced significantly (p < 0.05) initial loads of both microbes. The effect of the PL fluence on the survival number of microoganisms was described by a log-linear model (R ² = 0.849–0.999). At any fixed time within the cold storing, the microbial counts for untreated samples were always higher than those cut tomatoes that had been previously PL-treated. The behavior of L. innocua and E. coli during the storage were well adjusted (R ² > 0.930) by Gompertzian models; the studied microorganisms exhibited different patterns during the storage period. On the other hand, O₂ and CO₂ partial pressures in containers with fresh-cut tomatoes were also significantly affected by PL treatments (p < 0.05). The highest PL fluence caused the greatest changes of O₂ and CO₂ contents. In addition, the application of PL triggered an acceleration of the O₂ consumption during the cold stage. PL treatments might be used to effectively extend the safety of fresh-cut tomatoes over 12 days of storage against E. coli and L. innocua growth.     

Steady- and Unsteady-State Determination of the Water Vapor Permeance (<Emphasis Type="Italic">WVP</Emphasis>) of Polyethylene Film to Estimate the Moisture Gain of Packed Dry Mango

The water vapor permeance (WVP; g m^?2 d^?1 Pa^?1) of packaging films quantifying the water vapor transfer rate between foods and its surroundings is usually determined in units operating under steady-state conditions that do not necessarily reflect food handling scenarios. This study evaluated the determination of the WVP of a polyethylene (PE) film by steady-state method ASTM F1249-06 using a permeability cell and unsteady-state method ASTM E96/E96M in which 102 vacuum-sealed PE bags containing silica gel were stored (37.8 °C, 75% relative humidity) and weighed over 25 days. Average steady-state WVP (2.935 ± 0.365 × 10^?3, n = 4) fell within the 95% quantiles of unsteady-state WVP values (1.818–3.183 × 10^?3, n = 2142). Moisture uptake of dehydrated mango stored at 37.8 °C and 75% relative humidity was predicted with WVP values obtained by both methods. Predictions were validated by monitoring over 25 days the weight gain of 100 PE bags with dry mango. Experimental moisture averages during storage fell within one standard deviation of predictions using the unsteady-state WVP (R ² = 0.974). The same was observed only until day 15 for predictions obtained with the steady-state WVP. Calculations for days 20–25 overestimated the moisture uptake by 6.0–7.2%, resulting in registered R ² = 0.924. The unsteady-state WVP determination is low-cost, uses large numbers of film samples, and allowed more accurate predictions of dry mango moisture uptake. Knowledge of the moisture uptake controlled by the film WVP is essential when predicting the safety and quality changes limiting the shelf-life of foods.     

Limitations of the Log-Logistic Model for the Analysis of Sigmoidal Microbial Inactivation Data for High-Pressure Processing (HPP)

This study identified limitations of the log-logistic model to evaluate microbial inactivation kinetics by high-pressure processing (HPP) including the need to assign a numerical value to “approximate” the undefined expression log₁₀ t?=?0 and the misinterpretation of its parameters due to a derivation flaw. Peer-reviewed HPP microbial inactivation data were adjusted to a sigmoidal equation (SIG), the original “vitalistic” log-logistic models (VIT-1, VIT-6), and two functions that did not follow the original derivation procedure (LOG-1, LOG-6). Their goodness of fit was determined utilizing the coefficient of determination (R ² ) and Akaike information criteria (AIC). The shape of the survival curve greatly influenced the performance of log-logistic models. VIT and LOG models performed equally when the kinetic curve showed a sigmoidal shape, and the numerical values of their parameter estimates were identical regardless of the log₁₀ (t?=?0) approximation. Conversely, most concave curves yielded inaccurate parameter estimates for all models. LOG-1 and VIT-1 performed best when log₁₀ t?=?0 was ?1 or ?2, whereas LOG-6 and VIT-6 yielded best results for values of ?3 to ?9. SIG ranked last for most datasets but occasionally performed best (Akaike weight factor w_AICi ?=?0.40–1.00) when microbial survival counts showed clear sigmoidal shapes. VIT models consistently displayed R ² ?≥?0.98, and their parameters can be interpreted within a “biological” context using the corrected derivation shown for LOG models. However, concave curves are more frequently observed for HPP microbial inactivation, and fitting the experimental data to log-logistic models deems unnecessary.     

Characterization and evaluation of antimicrobial activity of actinonin against foodborne pathogens

This study revealed the antimicrobial properties of actinonin against major foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella Typhimurium, Staphylococcus aureus, and Vibrio vulnificus. Among them, actinonin caused growth defect in S. Typhimurium and V. vulnificus. Minimal inhibitory concentration (MIC) values of actinonin were determined by broth microdilution methods. The MICs of actinonin were ≤0.768 μg/ml for S. Typhimurium and ≤0.192 μg/ml for V. vulnificus. Susceptibility to actinonin in both pathogens was measured by colony-forming ability and disc diffusion test. The results showed actinonin had antimicrobial activity against S. Typhimurium and V. vulnificus in a dose-dependent manner. The inhibitory effects on swarming motility were determined, and cytotoxicity of each pathogen against HeLa cells was decreased significantly by actinonin treatment. Furthermore, actinonin showed an antimicrobial efficacy in food models infected with these pathogens. These results demonstrate that actinonin is potentially an effective agent for food sanitization or preservation.     

Application of Curve Fitting and Wavelength Selection Methods for Determination of Chlorogenic Acid Concentration in Coffee Aqueous Solution by Vis/NIR Spectroscopy

Coffee is considered as a functional food due to its being rich in bioactive compounds, mainly chlorogenic acid (CGA). CGA concentration in coffee aqueous solution was investigated based on visible/near-infrared (Vis/NIR) spectroscopy in this research. To enhance the spectral difference among different samples and increase the signal to noise ratio, Lorentz function curve fitting was applied to fit raw Vis/NIR spectra of samples. Then, the fitting parameters were used to correct raw full spectra. Partial least squares (PLS) regression method was used to develop calibration models of CGA concentration. Full-spectrum models were built with raw and fitting parameter-corrected spectra, respectively. Further, wavelength selection methods, such as genetic algorithms (GAs) and success projection algorithms (SPAs), were applied to eliminate redundancy information and identify relevant information from full spectra. Calibration models based on the effective wavelengths selected by GA and SPA methods were developed. The overall results showed that LFPs a/b-corrected spectra had a better performance compared with other processing methods. Performance of the selected wavelength model was better than that of the full-spectrum model. Final results indicated that the SPAs-PLS method provided a more precise prediction model of CGA concentration with R _c of 0.913 and R _cv of 0.795.     

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R ²) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.     

Determination of optimal machining parameters of massive wooden edge glued panels which is made of Scots pine (<Emphasis Type="Italic">Pinus sylvestris</Emphasis> L.) using Taguchi design method

In this paper, the optimization of CNC machining parameters was conducted using the Taguchi design method on the surface quality of massive wooden edge glued panels made of Scots pine (Pinus sylvestris L.). Five machining parameters and their effects on surface roughness were evaluated. These parameters included cutters type, tool clearance strategy, spindle speed, feed rate and depth of cut. An analysis of variance (ANOVA) was performed to identify significant factors affecting the surface roughness (R _a and R _z ). Optimum machining parameter combinations were acquired by conducting an analysis of the signal-to-noise (S/N) ratio. Optimal cutting performance for R _a and R _z was obtained for Cutter 1, at a tool clearance strategy of a raster 16,000 rpm spindle speed, 1000 mm/min feed rate and 4 mm depth. Based on the results of the confirmation tests, R _a decreased 2.8 times and R _z decreased 2.0 times.     

Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy

The worldwide consumption of dried carrot (Daucus carota L.) is on a growing trend. Conventional methods for drying carrots include hot-water blanching followed by hot-air drying, which is usually uncontrolled and therefore prone to product quality deterioration. Thus, there is a need for innovative drying systems that yield high-value end products. In this study, the efficacy of NIR spectroscopy for the non-destructive monitoring of physicochemical changes and drying behaviour in organic carrot slices during 8-h hot-air drying at 40 °C was demonstrated using Partial least squares (PLS) regression and PLS discriminant analysis (PLS-DA). The impact of hot-water blanching pre-treatment (at 95 °C for 1.45 min) for enzyme inactivation on performances of both regression and classification models was also evaluated. PLS regression models were successfully developed to monitor changes in water activity (R ² = 0.91–0.96), moisture content (R ² = 0.97–0.98), total carotenoids content (R ² = 0.92–0.96), lightness for unblanched carrots (R ² = 0.80–0.83) and hue angle for blanched samples (R ² = 0.85–0.87). Soluble solids content prediction was poor for both treatments (RMSEP = 3.43–4.40). Classification models were developed to recognise dehydration phases of carrot slices on the basis of their NIR spectral profile using K-means and PLS-DA algorithms in sequence. The performance of each PLS-DA model was defined based on its accuracy, sensitivity and specificity rates. All of the selected models provided from good (> 0.85) to excellent (> 0.95) sensitivity and specificity for the predefined drying phases. Feature selection procedures yielded both regression and classification models with performances very similar to models computed from the full spectrum.     

A regression model describing the effect of pH,NaCl and temperature on<Emphasis Type="Italic"> D</Emphasis> values of<Emphasis Type="Italic"> Bacillus stearothermophilus</Emphasis> spores

A new microbiological predictive model was developed, relating pH, NaCl concentration and temperature (T) with the decimal reduction coefficient D as dependent variable, using data previously reported by [1]. The model was obtained by multiple linear regression using only three predictor variables (T ², pH² and NaCl), easily computed from the basic variables pH, NaCl concentration and temperature (T). The fitted model is robust, stable and satisfies all the basic hypotheses of the regression models. It also provides simplicity when compared with previously published models based on multiple linear regression tools.     

Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans

Hyperspectral imaging covering the spectral range of 874–1734 nm was used to determine caffeine content of coffee beans. Spectral data of 958.24–1628.89 nm were extracted and preprocessed. Partial least squares regression (PLSR) model on the preprocessed full spectra obtained good performance with coefficient of determination of prediction (R ² _p ) of 0.843 and root mean square error of prediction (RMSEP) of 131.904 μg/g. In addition, 10 variable selection methods were applied to select the best optimal wavelengths. The PLSR models on the different optimal wavelengths obtained satisfactory results. The PLSR model on the wavelengths selected by random frog (RF) performed the best, with R ² _p of 0.878 and RMSEP of 116.327 μg/g. The RF wavelength selection combined with the PLSR model also achieved satisfactory visualization of caffeine content between different coffee beans. The overall results indicated that optimal wavelength selection was an efficient method for spectral data preprocessing, and hyperspectral imaging was illustrated as a potential technique for real-time online determination for caffeine content of coffee beans.     

Hyperspectral Imaging Sensing of Changes in Moisture Content and Color of Beef During Microwave Heating Process

Moisture content (MC) and color are two important quality parameters of beef during microwave heating process. This study examined the effects of microwave heating time (0–75 s) on MC, color, and myoglobins of beef samples. The results showed that heating time significantly influenced the MC, color (L*, a*), and percentage of related myoglobins. The suitability of hyperspectral imaging (HSI) (400–1000 nm) was investigated to correlate the mean spectra of beef samples and the color and MC values during microwave treatment. After the use of pre-processing methods and optimum wavelengths selection, the SG-SPA-LS-SVM prediction model for MC (R²_P?=?0.869, RMSEP?=?1.304, and RPD?=?2.724) and the SG-RC-MLR model for a* (R²_P?=?0.890, RMSEP?=?0.735, and RPD?=?2.733) were established. The models were then used to develop the distribution maps of MC and a* values, respectively, showing that both MC and a* at the center of the meat slices were higher than those at the edge, corresponding to the temperature distribution during microwave heating. The results demonstrated the ability of HSI system for monitoring the changes of some quality parameters during microwave heating.     

Application of Bismuth(III)-Entrapped XO Biosensing System for Xanthine Determination in Beverages

A xanthine biosensor was prepared by electrochemical immobilization of xanthine oxidize enzyme onto carbon paste electrode via entrapment of Bi³⁺. After the optimization of experimental parameters, analytical characteristics were investigated. Two linear ranges between 0.02 and 0.06 and 1–7.5 μM with the equation y?=?93.00x?+?0.12 and y?=?1.07x?+?18.03 with the correlation coefficients of R ²?=?0.9951 and R ²?=?0.9931, respectively, were obtained for this biosensing system. RSD value was calculated for 0.04 μM xanthine (n?=?5) and found as 3.84%. LOD and LOQ values were also calculated and revealed as 1.30?×?10^?8 and 4.3?×?10^?8 M, respectively. Then, this biosensor was applied for xanthine detection in real samples. As a sample treatment, only necessary dilutions were made. Four types of beverages including wine, energy drink, peach, and sour cherry juice were used for this purpose. Obtained recovery values demonstrate that this system is applicable for xanthine detection in real samples without needing any laborious sample pretreatment procedures.