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In conclusion, this study has successfully developed a novel SERS substrate using Fe3O4@UiO-66(Zr)@Ag nanoparticles for the detection of OPs in food samples. In this work, a new SERS substrate, Fe3O4@UiO-66(Zr)@Ag nanoparticles (FUAs), was developed and applied for trace detection of organophosphorus pesticides (OPs) in food. Surface-enhanced Raman spectroscopy (SERS) has attracted much attention as a rapid, sensitive, and non-destructive method for trace analysis, especially in food and biological samples (Yun et al., 2012). [Extracted from the article]

As novel processes such as ultrasound assisted heat transfer are emerged, new models and simulations are needed to describe these processes. In this paper, a numerical model was developed to study the freezing process of potatoes. Different thermal conductivity models were investigated, and the effect of sonication was evaluated on the convective heat transfer in a fluid to the particle heat transfer system. Potato spheres and sticks were the geometries researched, and the effect of different processing parameters on the results were studied. The numerical model successfully predicted the ultrasound assisted freezing of various shapes in comparison with experimental data of the process. The model was sensitive to processing parameters variation (sound intensity, duty cycle, shape, etc.) and could accurately simulate the freezing process. Among the thermal conductivity correlations studied, de Vries and Maxwell models gave closer estimations. The maximum temperature difference was obtained for the series equation that underestimated the thermal conductivity. Both numerical and experimental data confirmed that an optimum condition of intensity and duty cycle is needed for reducing the freezing time, as increasing the intensity, increased the heat transfer rate and sonically heating rate, simultaneously, that acted against each other. [ABSTRACT FROM AUTHOR]

The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures $$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) $$ and freezing rates $$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) $$ . The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. [ABSTRACT FROM AUTHOR]

Since the food cold chain produces large amounts of greenhouse gases (GHGs), its carbon footprint (CF) has caused widespread concern. The process of freezing, one of the most common operations, is used in numerous food processes, and new freezing methods appear constantly. Ultrasound-assisted immersion freezing is an emerging technique that can significantly improve product quality compared to the conventional immersion freezing. In the current study, the CFs of conventional immersion freezing and ultrasound-assisted immersion freezing were investigated according to the principle of life cycle assessment (LCA). The freezing time of 1 mL of deionized water in a flask was 264 ± 15 s for conventional freezing, while the time of 1 mL of deionized water in a flask for the ultrasonic-assisted freezing could be lowered to 188 ± 5, 182 ± 5, 193 ± 6, and 201 ± 8 s under the four ultrasonic frequencies of 28, 40, 50, and 80 kHz, respectively. By improving heat transfer and enhancing ice nucleation, the freezing time of ultrasonic-assisted freezing was reduced significantly ( P < 0.05) compared to conventional immersion freezing. In addition, electricity use and carbon emissions from electricity use at four frequency levels were reduced significantly ( P < 0.05) compared to conventional immersion freezing. The final CFs of the freezing process for 1 mL of frozen water were 11.65 ± 0.52, 9.16 ± 0.24, 8.95 ± 0.17, 9.33 ± 0.22, and 9.60 ± 0.27E−5 kgCOeq (equivalent) for the control, 28, 40, 50, and 80 kHz, respectively. Most of the final product CF was related to electricity and the flask used, contributing 78.1 and 19.7 % respectively for the conventional freezing, and about 70 and 25 % respectively for the ultrasound-assisted freezing. This research demonstrated that ultrasonic-assisted freezing as an emerging technology could not only improve the food quality but can also reduce the CF of a product. In addition, sensitivity analysis showed that the use of flask and improving the electricity use efficiency could decrease the carbon emissions of the product significantly. [ABSTRACT FROM AUTHOR]

Microbial contamination during fish flesh spoilage process can easily induce food-borne outbreaks and consumer health problems. Hyperspectral imaging in the spectral range of 400-1000 nm was developed to measure the Escherichia coli ( E. coli) loads in grass carp fish for evaluation and visualization of microbial spoilage. Partial least square regression (PLSR) model was conducted to build prediction models between the spectral data and the reference E. coli loads estimated by classical microbiological plating method. The PLSR model based on full wavelengths showed good performance on predicting E. coli loads with the residual predictive deviation (RPD) of 5.47 and determination coefficient of R = 0.880. Six characteristic wavelengths were selected by the weighted regression coefficients from PLSR analysis and used to simplify the models. The simplified PLSR and multiple linear regression (MLR) models also presented good prediction capability. The better simplified MLR model (RPD = 5.22 and R = 0.870) was used to transfer each pixel in the image for visualizing the spatial distribution of E. coli loads. The results demonstrated that hyperspectral imaging technique with multivariate analysis has the potential to rapidly and non-invasively quantify and visualize the E. coli loads in grass carp fish flesh during the spoilage process. [ABSTRACT FROM AUTHOR]

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) 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 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. [ABSTRACT FROM AUTHOR]

In this study, wavelet textural analysis was applied to hyperspectral images in the visible and near-infrared (VIS/NIR) region (400-1,000 nm) for differentiation between fresh and frozen-thawed pork. The spectral data of acquired hyperspectral images were analyzed using partial least squares (PLS) regression and five wavelengths (462, 488, 611, 629, and 678 nm) were selected as the feature wavelengths by the regression coefficients from the PLS model. The fourth-order daubechies wavelet ('db4') was used to serve as the wavelet mother function for wavelet textural extraction of the feature images at the above selected feature wavelengths with the wavelet decomposition level from 1 to 4. Four textural features were calculated in the horizontal, vertical, and diagonal orientations at each level. Forty-eight textural features were extracted from each feature image and used to differentiate between fresh and frozen-thawed pork samples by least-squares support vector machine (LS-SVM) model. Wavelet texture extracted from all five feature images at first decomposition level was identified as optimal wavelet texture combination, resulting in the highest classification accuracy for the LS-SVM models (98.48 % for the training set and 93.18 % for the testing set). Based on the texture combination, the quality attributes of pork meat could be predicted with correlation coefficients of calibration ( r) of 0.982 and 0.913, and correlation coefficients of prediction ( r) of 0.845 and 0.711 for pH and thawing loss, respectively. The results showed the possibility of developing a fast and reliable hyperspectral system for discrimination between fresh and frozen-thawed pork samples based on wavelet texture in the VIS/NIR wavelength range. [ABSTRACT FROM AUTHOR]

Plasma-functionalized liquids (PFL) emerge as an effective sanitizer with great potential to be against a variety of microorganisms but their applications on seafood products are limited. In the current study, the physicochemical properties of plasma-functionalized water (PFW) and plasma-functionalized buffer (PFB), and their antimicrobial activities on grass carp, were investigated under different conditions of applied voltage, plasma exposure time and immersion time. Results indicated that increasing voltage and exposure time led to an increase in levels of reactive species in PFW and PFB, while the presence of citric acid in the buffer accelerated possible reactions of active species and enhanced acidification, electrical conductivity (EC) and oxidation-reduction potential (ORP) as compared with PFW. Results also showed that the decontamination efficiency depended on voltage and exposure time, which could be up to 1.21 and 1.52 log reductions for L. monocytogenes, and 1.44 and 1.75 log reductions for S. Typhimurium for PFW and PFB, respectively. Immersing fish fillet samples in both solutions also led to a reduced pH and increased total acidity level in the samples with no significant difference (p > 0.05) between PFW and PFB, while PFB greatly affected the colour change in fish fillets. This study provided a basis for the potential development of novel sanitizers to decontaminate microorganism in fish and seafood products. Key points • Cold plasma induced a time-dependent change of active species in water and buffer. • Ionic equilibria of conjugate base and weak acid in buffer enhanced RNS and ROS. • Decontamination depended on voltage and exposure time of liquids to cold plasma. • Reduced pH, increased acidity and colour change were noticed in treated fish. • A basis for developing potential sanitizers for seafood products is provided. [ABSTRACT FROM AUTHOR]

Application of wavelet analysis to near-infrared (NIR) hyperspectral imaging data was exploited for categorization of lamb muscles in this study. A variety of common wavelet transforms was investigated to identify the best wavelet features for categorization of lamb muscles. The fifth-order Daubechies wavelet ('db5') was found to be the best wavelet function for decomposition of lamb spectral signal. Features of wavelet coefficients extracted from db5 wavelet at the fifth decomposition level were then used as the inputs of least-squares support vector machine (LS-SVM) for developing classification models. Principal component analysis (PCA) was used for dimensionality reduction. Classification performance of LS-SVM classifiers in tandem with wavelet transform and PCA was compared with the LS-SVM models based on original, first derivative, second derivative, smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC) spectral data; then, the overall correct classification performance for the training and test sets using combination with wavelet approximation and detail coefficients in fifth decomposition scale and PCA was 100 and 96.15 %, respectively. In addition, the developed classification models were successfully applied to the hyperspectral images for obtaining classification maps and the kappa coefficient of 0.83 was obtained for the visual classification. The results revealed that the application of wavelet analysis has a great potential for categorization of lamb muscles in tandem with multivariate analysis and image processing. [ABSTRACT FROM AUTHOR]

A quick, accurate, and reliable method for the evaluation of meat quality during salting stages is essential for quality control and management. This study was carried out to investigate the utility of hyperspectral imaging (HSI) techniques (400-1,000 nm) for predicting the color and pH of salted meat. Specifically, partial least squares regression (PLSR) was applied to the spectral data extracted from the images of the meat to develop statistical models for predicting color and pH. A subset of information-rich wavelengths was identified by principal component analysis (PCA) and used in a regression model. The results from the model with the reduced number of wavelengths generated L*, a*, and pH values with coefficients of determination ( R) of 0.723, 0.726, and 0.86 and root mean square errors estimated by cross-validation (RMSECV) of 2.898, 1.408, and 0.073, respectively. These values compared favorably with values generated by a PLSR model using all of the wavelengths investigated, illustrating the reasonable accuracy and robustness of the method. The overall results of this study demonstrate the potential of HSI to serve as an objective and nondestructive method for rapid determination of color and pH of porcine meat during the salting process. [ABSTRACT FROM AUTHOR]

Voids, filled with air, in plant tissues, can attenuate ultrasound, resulting in weakening the effectiveness of ultrasound during immersion freezing. The effect of voids on ultrasound-assisted immersion freezing (UF) in selected plant tissues, apple, radish, and potato was investigated in the present study. The freezing time and quality attributes of firmness, drip loss, total calcium content, and total phenolic content were investigated in apple, radish, and potato samples treated by normal immersion freezing (IF) and UF. The results showed that the more the percentage voids in the plant tissues, the lower the effectiveness of the ultrasound treatment. The total freezing time reduction (%) due to UF was a power function of the volume of voids (%): y = 0.018x−1.057 (R2 = 0.994). Ultrasound at 0.62 W/cm2 (28 kHz) resulted in the best firmness and lowest drip loss in potato, while no significant (p > 0.05) differences in quality attributes were observed between IF and UF in apple samples. These findings indicated that UF was more effective in freezing fruit or vegetables with a highly dense structure. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is built with the aggregation of imaging, spectroscopy and radiometric techniques. This technique observes the sample behaviour when it is exposed to light and interprets the properties of the biological samples. As hyperspectral imaging helps in interpreting the sample at the molecular level, it can distinguish very minute changes in the sample composition from its scatter properties. Hyperspectral data collection depends on several parameters such as electromagnetic spectrum wavelength range, imaging mode and imaging system. Spectral data acquired using a hyperspectral imaging system contain variations due to external factors and imaging components. Moreover, food samples are complex matrices with conditions of surface and internal heterogeneities, which may lead to variations in acquired data. Hence, before extracting information, these variations and noises must be reduced from the data using reference-dependent or reference-independent spectral pre-processing techniques. Using of the entire hyperspectral data for information extraction is tedious and time-consuming. In order to overcome this, exploratory data analysis techniques are used to select crucial wavelengths from the excessive hyperspectral data. Using appropriate chemometric techniques (supervised or unsupervised learning techniques) on this pre-processed hyperspectral data, qualitative or quantitative information from sample can be obtained. Qualitative information for analysing of the chemical composition, detecting of the defects and determining the purity of the food product can be extracted using discriminant analysis techniques. Quantitative information including variation in chemical constituents and contamination levels in food and agricultural sample can be extracted using categorical regression techniques. In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products. [ABSTRACT FROM AUTHOR]

This paper studied the effects of different cooking conditions (methods, temperatures and time) on the retention rate of 4-(methylthio)-3-butenyl isothiocyanate (MTBITC) in Chinese white radish by using a method of ultrasound extraction coupled with gas chromatography-flame ionization detector (GC-FID). Results showed that the retention rate of MTBITC decreased with increasing cooking time and temperatures. The retention rate of MTBITC declined dramatically after holding for 1 min at 85 or 95 °C. The fitted equations demonstrated that the degradation of MTBITC followed an exponential decay pattern for non-microwave (water bath or steam stove) cooking, while for microwave cooking, the degradation of MTBITC followed approximately a linear decay. [ABSTRACT FROM AUTHOR]