Prediction of postharvest dry matter, soluble solids content, firmness and acidity in apples (cv. Elshof) using NMR and NIR spectroscopy: a comparative study

Dry matter (DM), soluble solids content (SSC), firmness and acidity by proton nuclear magnetic resonance (NMR) T₂ relaxometry and near infrared (NIR) spectroscopy were investigated on a total of 390 apples (cv. Elshof). The fruit came from four different pre- or postharvest treatments and covered a large range of DM (11.4–20.0 %) and SSC values (10.5–18.3 °Brix). NIR was superior in predicting DM (R ² = 0.82) and SSC (R ² = 0.80), compared to NMR (R ² = 0.50 and R ² = 0.58). However, NMR relaxometry was able to detect multiple water populations assigned to different water pools in the apples and variation in the water distribution between different pre- and postharvest treatments. Differences in the mobility of the vacuole water (population T₂₄) were consistent with changes in fruit firmness. In conclusion, even though NIR is superior in predicting DM and SSC, NMR provides useful information about the intrinsic water state and its distribution in the fruit.     

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.     

Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D¹ and second derivative, D²) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R²?=?0.957 and RMSEP?=?3.386) using MSC, Savitzky–Golay and D¹, compressive energy (R²?=?0.907 and RMSEP?=?15.757) using the combination of MSC, Wavelet and D¹, moisture content (R²?=?0.907 and RMSEP?=?0.179) and apparent modulus of elasticity (R²?=?0.921 and RMSEP?=?2.366) employing combination of SNV, Wavelet and D¹, respectively. Moreover, Vis–NIR data correlated well with hardness (R²?=?0.876 and RMSEP?=?5.216) using SNV, Wavelet and D². These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.     

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.     

Comparison of Visible and Long-wave Near-Infrared Hyperspectral Imaging for Colour Measurement of Grass Carp (Ctenopharyngodon idella)

This study was conducted to investigate the potential of hyperspectral imaging technique in a rapid and non-invasive manner for measuring colour distribution of grass carp fillets during cold storage. The quantitative calibration models were established between the spectral data extracted from the hyperspectral images and the measured colour reference values by partial least squares regression (PLSR) and least squares support vector machines (LS-SVM). The performance of two spectral ranges of 400–1,000 and 1,000–2,500 nm was compared to select the best spectral range for further colour analysis of grass carp fillets. The LS-SVM model using the whole spectral range possessed better performance than the PLSR model for predicting colour components of L* and a* with higher coefficients of determination (R ² _P) of 0.916 and 0.905 and lower root-mean-square errors of prediction (RMSEPs) of 2.876 and 2.253, respectively. Seven (466, 525, 590, 620, 715, 850 and 955 nm) and five (465, 585, 660, 720 and 950 nm) optimal wavelengths carrying the most important and sensitive information were recognized and selected using successive projections algorithm (SPA) for predicting L* and a*, with R ² _P values of 0.912 and 0.891 being obtained from the optimized SPA-LS-SVM models established based on the selected valuable wavelengths. In addition, the visualization maps of colour distribution of the examined fish fillets were acquired. The overall results of this study demonstrated that hyperspectral imaging technique in the spectral range of 400–1,000 nm has the potential to be used as an objective and promising tool for rapid and non-destructive measurement of colour distribution of grass carp fillets.     

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.     

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).     

Nondestructive Determination of Soluble Solids Content of ‘Fuji’ Apples Produced in Different Areas and Bagged with Different Materials During Ripening

To investigate the feasibility of hyperspectral imaging technique in nondestructive determination of soluble solids content (SSC) of fruits produced in different places and bagged with different materials during ripening, the near infrared hyperspectral reflectance images were acquired on 196 ‘Fuji’ apples picked from four orchards in different areas and bagged with polyethylene film or light-impermeable paper. Mean reflectance spectrum from the regions of interest in the hyperspectral image of each apple was extracted. Standard normal variate (SNV) was used to eliminate the effect of instrument and environment on spectra. The sample set partitioning based on joint x–y distances method was applied to divide the samples into calibration set and prediction set as the ratio of 3:1. Successive projection algorithm (SPA) and uninformative variable elimination (UVE) method were used to select effective wavelengths (EWs) from the full spectra. Partial least squares (PLS), least squares support vector machine (LSSVM), and extreme learning machine (ELM) were used to develop SSC determination models. The results showed that 24 and 122 EWs were selected by SPA and UVE, respectively. The selection of EWs was helpful to SSC determination performance improvement. The optimal SSC prediction model was LSSVM based on selected EWs by SPA, with the correlation coefficient and root-mean-square error of prediction set of 0.878 and 0.908 °Brix, respectively. This study indicates that hyperspectral imaging technique could be used to determine SSC of intact apples produced in different places and bagged with different materials during ripening.     

Apples Nutraceutic Properties Evaluation Through a Visible and Near-Infrared Portable System

Non-destructive and rapid tools are required for predicting the optimum harvest window and for monitoring fruit quality during postharvest period. This study tested a portable, experimental visible/near-infrared (vis/NIR) spectrophotometer, more versatile and handy than traditional vis/NIR instruments, to measure phytonutrients active in human health and important in fruit storability. Parameters determining sensorial and quality properties of the fruit were also analyzed. The vis/NIR measurement was carried out in field using apples of “Golden Delicious” and “Stark Red Delicious” on tree. Calibration models were developed using PLS regression based on second derivative spectra. For “Golden Delicious” apple, the cross-validation R ² for soluble solids content (SSC), chlorophyll, titratable acidity (TA), flesh firmness, total phenols, carotenoids, and ascorbic acid were 0.72, 0.86, 0.52, 0.44, 0.09, 0.77, and 0.50, respectively. The corresponding RMSECV were 0.78 °Brix, 0.50 nmol/cm², 0.59 g/L, 6.08 N, 0.10 mg/g, 0.08 nmol/cm², and 0.83 mg/100 g, respectively. For “Stark Red Delicious” similar calibration statistics were found for SSC, TA, flesh firmness, chlorophyll, and ascorbic acid content. A better calibration performance was achieved for total phenols, while for carotenoids it was less accurate. Cross-validation R ² for “Stark Red Delicious” total anthocyanins, total flavonoids, and non-anthocyanic flavonoids were 0.67, 0.86, and 0.77, respectively. The corresponding RMSECV were 0.12, 0.14, and 0.15 mg/g, respectively. It was concluded that the portable vis/NIR instrument performed similarly to bench top or portable vis/NIR instruments reported in the literature.     

Rapid Determination of Total Dietary Fiber and Minerals in <Emphasis Type="Italic">Coix</Emphasis> Seed by Near-Infrared Spectroscopy Technology Based on Variable Selection Methods

The feasibility of near-infrared (NIR) spectroscopy for determining total dietary fiber (TDF) and mineral elements (mainly K, Mg, P, and S) in Coix seed was investigated. Partial least squares regression (PLSR) was applied to establish quantitative models. Norris derivative smoothing (NDS) was used as pretreatment method. A comparison of three variable selection methods, namely competitive adaptive reweighted sampling (CARS), genetic algorithms (GA), and random frog (RF), showed that CARS obtained the best performances of PLSR models with the effective wavelengths mainly concentrated on around 12,000–11,000 cm^?1 and 6500–3600 cm^?1. For the quantitative determination models of TDF, K, Mg, P, and S, the optimal root mean square error of prediction (RMSEP) values were 0.0923, 182.7224, 75.4987, 162.6993, and 36.6278; the r values were 0.95, 0.88, 0.80, 0.96, and 0.96; the residual predictive deviation (RPD) values were 2.68, 2.05, 1.70, 3.24, and 3.04, respectively. It is concluded that the NIR spectral technique has a potential to determine TDF, K, Mg, P, and S in Coix seed.     

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.     

Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear

Soluble solid content (SSC) in fruit is one of the most crucial internal quality factors, which could provide valuable information for commercial decision-making. Near-infrared (NIR) technique has effective potentials for determining the SSC since NIR was sensitive to the concentrations of organic materials. In this study, a novel NIR technique, long-wave near infrared (LWNIR) hyperspectral imaging with a spectral range of 930–2548 nm, was investigated for measuring the SSC in pear, which has never been examined in the past. A new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) was proposed to select most effective variables from LWNIR hyperspectral data. The selected variables were used as the inputs of partial least square (PLS) to build calibration models for determining the SSC of ‘Ya’ pear. The results indicated that calibration model built using MC-UVE-SPA-PLS on 18 effective variables achieved the optimal performance for prediction of SSC comparing with other developed PLS models (MC-UVE-PLS and SPA-PLS) by comprehensively considering the accuracy, robustness, and complexity of models. The correlation coefficients between the predicted and actual SSC were 0.88 and 0.88 and the root mean square errors were 0.49 and 0.35 °Brix for calibration and prediction set, respectively. The overall results indicated that long-wave near infrared hyperspectral imaging incorporated to MC-UVE-SPA-PLS model could be applied as an alternative, fast, accurate, and nondestructive method for the determination of SSC in pear.     

Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging

Currently, blueberries are inspected and sorted by color, size and/or firmness (or softness) in packing houses, using different inspection techniques like machine vision and mechanical vibration or impact. A new inspection technique is needed for effectively assessing both external features and internal quality attributes of individual blueberries. This paper reports on the use of hyperspectral imaging technique for predicting the firmness and soluble solids content (SSC) of blueberries. A pushbroom hyperspectral imaging system was used to acquire hyperspectral reflectance images from 302 blueberries in two fruit orientations (i.e., stem and calyx ends) for the spectral region of 500–1000 nm. Mean spectra were extracted from the regions of interest for the hyperspectral images of each blueberry. Prediction models were developed based on partial least squares method using cross validation and were externally tested with 25% of the samples. Better firmness predictions (R = 0.87) were obtained, compared to SSC predictions (R = 0.79). Fruit orientation had no or insignificant effect on the firmness and SSC predictions. Further analysis showed that blueberries could be sorted into two classes of firmness. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting blueberries for firmness and possibly SSC to enhance the product quality and marketability.     

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.     

Nondestructive determination of soluble solids content and pH in tomato juice using NIR transmittance spectroscopy

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The r _c, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices.     

Antioxidant Activity and Determination of Phenolic Compounds from <Emphasis Type="Italic">Eugenia involucrata</Emphasis> DC. Fruits by UHPLC-MS/MS

Fruits have been the focus of several studies aimed at finding new antioxidant sources for protection against the damage caused by reactive species. In this study, the antioxidant activity and the presence of phenolic compounds in all parts (peel, pulp, and seeds) of Eugenia involucrata DC. fruits were evaluated. DPPH^·, ABTS^·+, and ORAC methods were used to determine the antioxidant activity, and an UHPLC-MS/MS method was developed for determining the phenolic compounds (gallic, chlorogenic, ferulic, p-coumaric and ellagic acids, quercetin, and myricetin). In the determination of both antioxidant activity and phenolic composition, the efficiency of solvents with different polarities—methanol/H₂O (80:20, v/v), ethanol/H₂O (80:20, v/v), methanol/acidified water with phosphoric acid pH 3.00 (80:20, v/v), and ethyl acetate—for the extraction of the phenolic compounds, was also evaluated. All parts of E. involucrata fruits showed antioxidant activity, in the range of 36.68 ± 1.44 to 873.87 ± 18.24 μmol TE g^?1, being the highest values found in the seeds and peel when more polar extraction solvents were used. Six, five, and three phenolic compounds were identified and quantified in the pulp, peel, and seeds, respectively, with the highest abundance as p-coumaric acid (14 ± 2 mg kg^?1) in the pulp, quercetin (47 ± 5 mg kg^?1) in the peel, and gallic acid (74 ± 4 mg kg^?1) in the seeds, also when more polar solvents were used. Although antioxidant activity methods suggested that the peel and seeds have more antioxidant potential, a wider variety of compounds were determined in the pulp.     

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.     

Speciation Determination of Selenium in Seafood by High-Performance Ion-Exchange Chromatography-Hydride Generation-Atomic Fluorescence Spectrometry

A high-performance ion-exchange chromatography coupled with hydride generation-atomic fluorescence spectrometry (HPIEC-HG-AFS) method was developed for simultaneous speciation of selenium in seafood. Three selenium species including of selenocystine (Se-Cys), selenome-thionine (Se-Met), and selenite Se(IV) were separated on an anion-exchange column (PRP-X100) with eluent of 30 mM NH₄H₂PO₄ and methanol (39:1, v/v) in 10 min at the flow rate of 1.0 mL min^?1. Variables affecting the HG-AFS detection of selenium species were optimized. The optimum conditions found were the following: reducing agent, 2.0 % of KBH₄, and 5.0 % of HCl; lamp current, 90 mA; photomultiplier tube voltage, 280 V; flow rate of carrier gas, 300 mL min^?1; and shielding gas, 800 mL min^?1. Under the optimized conditions, the good linearity of calibration curves (R ²?>?0.999) between signal of fluorescence and concentration of selenium species was obtained in the range of detection limits (DLs), 80 μg L^?1, and the DLs of Se-Cys, Se-Met, Se(IV) were 1.66, 0.990, 1.10 μg L^?1, respectively. The repeatability of the method, expressed as relative standard deviation, was less than 5.0 % (n?=?10), and the average recoveries for spiked test were from 87.3 to 103 % for three analytes in real seafood samples. The developed HPIEC-HG-AFS method was successfully applied for the speciation of selenium in seafood samples.     

Synergistic Processing of Skim Milk with High Pressure Nitrous Oxide,Heat, Nisin,and Lysozyme to Inactivate Vegetative and Spore-Forming Bacteria

Individual and combined effects of high pressure nitrous oxide (HPN₂O), heat, and antimicrobials on the inactivation of Escherichia coli, Listeria innocua, and Bacillus atrophaeus endospores in milk were all evaluated after 20-min treatments. Stand-alone milk treatments with HPN₂O (15.2 MPa), heat (45 and 65 °C), or nisin (50 and 150 IU mL^?1) resulted in log₁₀ reductions ranging only from 0.1 to 2.1 for E. coli and L. innocua. Combining HPN₂O (15.2 MPa) with heat (65 °C) inactivated 6.0 and 5.1 log₁₀ in the vegetative bacteria, respectively. Similarly, reductions of 5.9 and ≥ 6.0 log₁₀ of respective E. coli and L. innocua cells in milk were achieved through a combination of HPN₂O (15.2 MPa), heat (65 °C), and nisin (150 IU mL^?1). A 2.5 log₁₀ cycle inactivation of spores was obtained by HPN₂O, nisin (at both 50 and 150 IU mL^?1), and lysozyme (50 μg mL^?1) at 85 °C. Combining these processing techniques resulted in significantly greater microbial inactivation (p < 0.05) than the sum of individual reductions from each treatment alone, indicating synergistic effects. HPN₂O irrespective of processing temperatures did not cause any occurrence of sub-lethally injured cells or disruption in colloidal stability of milk at 65 and 85 °C (p ≥ 0.05). Color and pH changes in milk following the most demanding treatment conditions were minimal.     

A Comparative Biochemical Characterization of Microbial Transglutaminases: Commercial vs. a Newly Isolated Enzyme from <Emphasis Type="Italic">Streptomyces</Emphasis> Sp.

A new microbial transglutaminase (MTGase or MTG, EC 2.3.2.13) from a Streptomyces sp. strain isolated from Brazilian soil samples was characterized in crude and purified forms. The aim of this work is to provide relevant information about a new transglutaminase and to compare its characteristics with the well-known commercial transglutaminase from Ajinomoto Co. Inc. (Activa® TG-BP). The enzyme from Streptomyces sp., in both crude and pure forms, exhibited optimal activity in the 6.0–6.5 pH range and at 35–40°C. The results for the commercial enzyme were the same. A second maximum of activity was observed at pH 10.0 with both the crude Streptomyces sp. enzyme and the commercial enzyme. This interesting fact has not been reported in the literature previously. The fact that this second maximum of activity does not appear on the purified form of the enzyme may suggest the presence of an isoenzyme on the crude extract. All of the enzymes tested were stable over the pH range from 4.5 to 8.0 and up to 45°C. The decline in activity of the commercial transglutaminase above 45°C and pH 8.0 was more gradual. The activities of all the MTG samples were independent of Ca⁺² concentration, but they were elevated in the presence of K⁺, Ba²⁺, and Co²⁺ and inhibited by Cu²⁺ and Hg²⁺, which suggests the presence of a thiol group in the MTG’s active site. The purified enzyme presented a K _m of 6.37 mM and a V _max of 1.7 U/mL, while the crude enzyme demonstrated a K _m of 6.52 mM and a V _max of 1.35 U/mL.