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Antibiotics and chemical disinfectants usually have serious problems of drug resistance and biosafety. Metal-organic frameworks (MOFs), as a new alternative method, have been studied to inhibit bacteria attributed to their tunability of compositions and structures, ideal biocompatibility and great catalytic properties. In the current study, two trimetal-organic frameworks (Ni1Co1Fe1-MOFs and Ni6Co3Fe1-MOFs) with different atomic ratios of Ni/Co/Fe were synthesized by a simple one-pot room temperature method. The fabricated MOFs showed outstanding Fenton-like catalytic activities and Ni1Co1Fe1-MOFs exerted greater catalytic activity. Based on a Fenton-like reaction, a high-efficiency antibacterial system was constructed. The manufactured Ni1Co1Fe1-MOF/sodium alginate composite coating delayed the weight loss of apple chunks and effectively inhibited E. coli bacteria from polluted apple chunks after storage for 2 d, significantly improving the fruit storage. This study proposed a novel and effective antibacterial strategy, which would open up a promising idea for the exploitation of food antibacterial agents in the future. [ABSTRACT FROM AUTHOR]

Despite prohibition in many countries, malachite green (MG) and crystal violet (CV) are illegally used in aquaculture, endangering both human health and the environment. Therefore, it is crucial to develop a method to accurately detect MG and CV. Surface-enhanced Raman spectroscopy (SERS) technology based on noble metal materials has attracted considerable attention for detecting food contaminations. However, noble metal materials are of poor stability and have a very low affinity for organic molecules. Herein, in this study, covalent organic frameworks (COFs) modified with silver nanoparticles (Ag NPs) were used as SERS substrates for the adsorption and detection of fungicides. COFs prepared by the Schiff base reaction of 1,3,5-tris (4-aminophenyl) benzene (TAPB) and 2,5-dimethoxybenzene-1,4-dicarboxaldehyde (DMTA) had a huge specific surface area and could provide sufficient adsorption sites and Ag NPs grown on the surface of COFs not only generated dense SERS "hot spots" but also improved their stability. The results showed that COF@Ag NPs exhibited a high enhancement factor (1.4 × 106), high reproducibility (RSD = 6.18%), and good stability within 50 days (RSD = 8.88%). Regression analyses in the ranges of 0.0001–10.0 mg/L and 0.0002–10.0 mg/L showed that the limits of detection (LOD) for the standard solutions of MG and CV were 3.8 × 10−5 mg/L and 1.8 × 10−5 mg/L, respectively. Furthermore, for MG and CV in fish samples, significant recoveries of 90.83–107.91% and 98.60–116.69% were achieved. Hence, it is hoped that this work could expand the application of COF materials for trace detection in SERS analyses. [ABSTRACT FROM AUTHOR]

Aquaculture fungicide causes threats to the human community and their sensitive detection are thus essential for food safety. Herein, MoS2/g-C3N4@Au heterojunction-based recyclable SERS substrate was developed for fungicide detection. The optimal substrate exhibited good SERS performance with a detection limit as low as 9.908 × 10−5 mg L−1 for malachite green (MG). Furthermore, an MCN@Au-based photodegradation of crystal violet (CV) under simulated light irradiation was realized, resulting in a degradation rate of 95.6%. With the dual function of the as-synthesized materials, good reusability of the substrates for at least six "detection-degradation" cycles was achieved. In addition, the MG in prawn extract could be sensitively determined with a satisfactory recovery from 91.60 to 124.00%. This work paves an avenue for the fabrication of heterojunction-based recyclable SERS substrates and provides a novel weapon for food safety protection. [ABSTRACT FROM AUTHOR]

The Terahertz (THz) time domain computed tomography (CT) system was used for evaluating kernel weight and plumpness of sunflower seed in present study. Specifically, a series of THz projections of the sunflower seed on a rotation stage were captured by a customized transmission mode THz time domain imaging system from different angles. These projections were preprocessed and then modelled by the inverse Radon transform to reconstruct the three-dimensional (3D) sunflower seed. The shapes and structures of sunflower seeds in reconstructed volumetric images were well presented. The volume ratio (obtained by THz CT imaging) and the area ratio (obtained by RGB imaging) of the whole seed to the kernel were calculated as 3D and 2D plumpness, respectively. The comparison between these two different plumpness indicating that our 3D plumpness can better describe the sunflower seed structure. Correlation coefficient (R) between sum intensity values of kernel voxels and kernel weights is 0.89 at 1.2 THz. This is the first application of applying THz CT systems to agri-food research, and the results indicate that THz time domain CT can be a useful nondestructive tool for evaluating weight and plumpness of intact sunflower seed. In addition, there is potential to extend the THz time domain CT system to the analysis of other agri-food sample types. [ABSTRACT FROM AUTHOR]

A microscale modelling framework to compute effective properties related to flow, heat and mass transfer during vacuum cooling (VC) of porous foods was developed. A heterogeneous computational domain reconstructed from steamed bread (SB) was used for modelling, while the cellular water transport in SB investigated using nuclear magnetic resonance was applied for model validation. The computed porosity (63.47 ± 1.05%), effective permeability (1.91 ± 0.39 × 10−11 m2), effective thermal conductivity, (0.33 ± 0.08 W m−1 K−1), and effective diffusivity (5.56 × ± 0.24 10−8 m2 s−1) were in the same range as those measured from the experiment/literature. Also, the analysis revealed that microstructural variability significantly affected the estimated effective properties. The microscale model yielded results similar to the lumped formulation but provided details not visible in the latter. Therefore, the developed model provides a framework for multiscale modelling, which could lead to a better understanding of the underlying moisture loss mechanisms during VC. [ABSTRACT FROM AUTHOR]

Previous studies have shown the great potential of using plasma-activated water (PAW) on improving agriculture seed germination, however, information on the influence of PAW on crop plantlet juice remains scanty. In this research, the effect of PAW generated by atmosphere pressure Ar–O2 plasma jet for 1–5 min on wheat seed germination, seedling growth and nutritional properties of wheat plantlet juice was investigated. Results revealed that all PAWs could enhance wheat seed germination and seedling growth in 7 days by improving the germination rate, germination index, fresh weight, dry weight and vigour index, and especially that PAW activated for 3 min (PAW-3) showed the best overall performance. In addition, the application of PAWs enhanced the nutritional properties of wheat plantlet juice from those grown for 14 days by improving total soluble solids, protein content, photosynthetic pigments, total phenolic content, antioxidant activity, enzyme activity, free amino acids and minerals content, and the best enhancement was also observed in PAW-3. It was concluded that PAWs would be an effective technique to enhance the growth and nutritional properties of crop sprouts, which could be served as functional foods in many forms. [ABSTRACT FROM AUTHOR]

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]

Microstructures affect the properties of food products; accurate and relatively less complex microstructural representations are thus needed for modelling of transport phenomena during food processing. Hence, the present study aimed at developing computational microstructures of steamed bread using descriptor-based approach. Relevant information was extracted from the scanning electron microscope (SEM) images of the steamed bread and evaluated using seven classifiers. For the automatic classification and using all descriptors, bagged trees ensembles (BTE) had the highest accuracy of 98.40%, while Gaussian Naïve Bayes was the least with 92.10% accuracy. In the "step forward" analysis, five descriptors had higher classification accuracy (98.80%) than all descriptors, implying that increase in descriptors might or might not increase classification accuracy. Microstructural validation revealed that the ellipse fitting method with a p value of 0.7984 for the area was found to be superior to the Voronoi method with a corresponding p value of 1.4554 × 10−5, confirming that the ellipse developed microstructure was more suitable for microscale modelling of transport phenomena in steamed bread. [ABSTRACT FROM AUTHOR]

Plasmonic Au–Ag core–shell nanorods (Au@Ag NRs) have drawn great attention and are widely used in surface-enhanced Raman scattering (SERS) due to their distinctive local surface plasmon resonance peculiarities. To optimize the thickness of the Ag shell, the electromagnetic field distribution of Au@Ag NRs was firstly simulated using the Radiofrequency module of COMSOL. Then, Au@Ag NRs with different shell thicknesses were prepared by coating different amounts of silver nitrate, and the Raman signal of the molecule of 4-mercaptobenzoic acid was measured to evaluate the SERS activity of the prepared Au@Ag NRs. The experimental results showed that the Au@Ag NRs with the shell thickness of 7.51 nm performed the best SERS effect. Finally, the optimized Au@Ag NRs were used to detect thiram solution and a low limit of detection of 0.003 mg/L was achieved, indicating that the optimized Au@Ag NRs might be a promising substrate for pesticide residues detection. [ABSTRACT FROM AUTHOR]

Coffee silverskin (CSS) is a coffee by-product obtained during the coffee roasting process and a potential source of proteins (14–19% protein content). In this study, conventional extraction (CE) and novel extraction methods were investigated for protein extraction from CSS. Optimum extraction pH and concentration for conventional acid and alkali extraction were studied. In addition, ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE), which have been widely recognised as efficient and economic novel extraction techniques, were employed to enhance the protein extraction from CSS using alkaline and acid extraction. Among the extraction techniques studied, MAE was found to generate the highest yield of protein recovery, 43.53% of recovery with an average molecular weight of 6.64 kDa. Comparing with the conventional sequential extraction using NaOH followed by HCl at two different concentrations (0.2 M and 0.6 M), the recovery yields were significantly increased by 2.8-fold and 2.3-fold by applying UAE, and by 5.8-fold and 7-fold by applying MAE. The present work shows that UAE and MAE have potential to be rapid and effective tools for protein extraction from coffee silverskin. In this study, extracted proteins and raw material were characterised considering the molecular weight and the amino acid profile as well as its nutritional composition and its mineral contents. [ABSTRACT FROM AUTHOR]

This study aimed to propose a novel surface-enhanced Raman spectroscopy (SERS)-based aptasensor by using 4-mercaptobenzoic acid (4-MBA) modified gold@silver core-shell nanoparticles (Au@Ag NPs) for detecting kanamycin for the first time with outstanding sensitivity and selectivity in the real milk samples. To achieve a high SERS activity substrate, the Au@Ag NPs with uniform shapes in a diameter of 32.2 ± 3.4 nm were synthesized successfully by seed-mediated growth and then modified with the Raman reporter 4-MBA. Under the optimal conditions, this aptamer sensing system exhibited a linear range between 6.67 × 10− 10 g/mL and 2 × 10− 7 g/mL with the limit of detection (142 pg/mL) of kanamycin. Moreover, the KANA-specific aptamer endowed a good specificity to this biosensor for the negative interference from other analogues (streptomycin sulfate, gentamicin sulfate, and penicillin). Finally, the aptasensor was applied in the artificially contaminated milk (1–100 ng/mL) samples and compared with the HPLC-MS technique, with the result indicating great practicality of the aptasensor. This paper has provided a potential aptasensor for simple and rapid trace-sensing of the harmful antibiotic residue in milk. [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]

Surface-enhanced Raman spectroscopy (SERS) with cysteamine modified silver-coated gold nanoparticles (Au@Ag-CysNPs) was presented for fast screening of oxamyl and thiacloprid in liquid samples of milk. Concentrations at different levels were detected ranging from 0.5 to 10 ppm by choosing 500–1600 cm−1 spectral range. A strongest Raman peak at 679 cm−1 was ascribed to oxamyl, whereas a band at 1095 cm−1 was associated with thiacloprid in milk samples. The morphology and average size of core-shell nanoparticles (Au@AgNPs) and Au@Ag-CysNPs were acquired by High-resolution transition electron microscopy images and dynamic light scattering. Images showed that Au@AgNPs which had a core size of 28 nm and a shell of 6 nm thickness was modified with 6 nm cysteamine hydrochloride successfully. High coefficient of determination (R2) values of 0.9968 and 0.9875 was calculated for oxamyl and thiacloprid with the limits of detection of 0.031 ppm and 0.023 ppm, respectively. The current novel nanoparticle was easy to prepare and was suitable to serve as a sensitive substrate for SERS detection applications, which could be employed for assessing other accidental contaminants in other food matrices in future studies. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) technique for classifying commercial Cheddar cheeses from different brands. Three classification models including a probability based partial least squares discriminant analysis (PLSDA), linear discriminant analysis (LDA) and successive projections algorithm (SPA)–LDA were used to discriminate four brands of Cheddar cheeses. A simple sample ranking method was used to improve the performance of these models. The generalisation abilities of the PLSDA, LDA and SPA–LDA models for the classification of new batch cheeses were investigated. The optimal number of PLS components was 24 for PLSDA, while the optimal SPA selected wavelengths was 105 for SPA–LDA. The results showed that PLSDA built by hyperspectral data was the most suitable model for brands classification with correct classification rate of 86.67%, and SPA–LDA had better performance than LDA with corresponding correct classification rates of 83.33% and 76.67%, respectively. As a comparison, models built by physical data (texture and colour) achieved poor classification results (< 65% for test set). The current result revealed that HSI technique is better and faster than conventional cheese classification methods. [ABSTRACT FROM AUTHOR]

Nowadays, progresses in data processing software have promoted the application of infrared (e.g., FT-IR, NIR, MIR), Raman, and hyperspectral imaging (HSI) techniques for quantitative analysis of biological material and/or aroma compounds in foods. In this review, applications of vibrational spectroscopy combined with chemometrics are summarized including analysis of total polyphenol, individual polyphenols, vitamins, and aromatic compounds in raw and some processed products. Laboratory-based and online application of vibrational spectroscopies monitoring for analysis of phenolic compounds have been described. In addition, technical challenges and future trends have been covered. Based on the literature, the near-infrared technique often has an advantage over other spectroscopy approaches and the expensive and time-consuming chemical methods such as high-performance liquid chromatography and gas chromatography. Overall, the current review suggests that vibrational spectroscopies are promising and powerful techniques that can be used for rapid and accurate determinations of food nutraceuticals and volatile compounds in both academic and industrial contexts. [ABSTRACT FROM AUTHOR]

The sensitivity of surface-enhanced Raman spectroscopy (SERS) extremely relies on experimental parameters including pH and aggregating agents. Using Au@Ag nanoparticles (NPs) with 26 nm Au core size and 6 nm Ag shell thickness as the substrate, the effect of cationic (Ca2+, K+, Na+) and anionic (Cl−, Br−) aggregating agents was investigated on the SERS detection of phosalone. The optimum concentrations of the aggregating agents in relation to the maximum SERS intensity differed broadly from 1 × 10−2 mol/L for CaBr2 to 1 × 101 mol/L for KCl. Both anions and cations greatly affected the SERS enhancement. With employing Br− as the anion, Ca2+ and K+ showed the maximum SERS intensities for phosalone, while using Ca2+ as the cation, Br− produced the maximum SERS enhancement. Among these aggregating agent combinations, the maximum SERS enhancement was achieved by employing 1 × 10−2 mol/L CaBr2 at pH 3.0. The detection limits of phosalone in standard solution and peach were 0.02 mg/L and 0.2 mg/kg, respectively. This study demonstrated that the Au@Ag NP-based SERS approach could be used as a sensitive, rapid, and simple method for detecting trace contaminants in food matrices. [ABSTRACT FROM AUTHOR]

Liquid foods play important roles in the development of human culture due to their own peculiarities. Mid-infrared (MIR) spectroscopy combines several advantages such as non-invasive operation and high-efficiency detection and is thus proposed to be a prospective alternative to conventional techniques for food quality assessment. However, no reviews on MIR spectroscopic analysis of liquid food products are reported. This review summarizes the recent research progress of MIR chemical sensing methods for determinations of the authenticity and related quality attributes of liquid foods (such as milk, edible oils, alcoholic beverages, honeys, and vegetable and fruit juices). The characteristics and applications of MIR spectroscopic technique in tandem with chemometrics, along with the major challenges and future prospects, are discussed. MIR spectroscopy has great potential to fulfill the need of food industry for quality and authenticity analysis of liquid foods. Nevertheless, further measurements and analyses based on the existing results of food inspection are still required. It is believed that this review will be an effective signpost for scholars in related research fields to study the comprehensive quality of liquid food products. [ABSTRACT FROM AUTHOR]

Bananito (Musa acuminata, AA) fruit in three maturity stages (stages 2, 4 and 6) were investigated in this study. Correlation analysis was conducted between fruit firmness, soluble solids content (SSC) and several colour parameters. Point-measured spectroscopy (Vis-point) and spatial-measured hyperspectral imaging (Vis-HSI) were applied to collect visible spectra (400–740 nm) from the fruit peel. Three classification methods, k-nearest neighbour (k-NN), soft independence modelling of class analogy (SIMCA) and partial least square discriminate analysis (PLSDA), were applied for maturity stage classification. Results showed that a strong correlation was found between SSC and peel yellowness index (r = 0.92). Ripeness classification models developed using Vis-HSI data performed better than using Vis-point data. The best model based on PLSDA achieved a total correct classification rate of 93.3%. A simplified PLSDA model established on three wavelengths (650 nm, 705 nm and 740 nm) derived from the regression vector provided an equivalent model performance. This study demonstrated the use of hyperspectral imaging for accurate and non-destructive ripeness classification of bananito fruit based on the visible peel spectra, and the potential of using the three feature wavelengths to develop a multispectral imaging system for industrial application. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy were used to identify potato varieties and detect potato doneness degree. The varieties of potato tubers can be successfully classified by hierarchical cluster analysis (HCA). The partial least squares regression (PLSR) model exhibited good prediction result for the doneness degree evaluation. Principal component and first-derivative iteration algorithm (PCFIA) was introduced to select feature variables instead of using the full wavelength spectra for modelling. Based on two sets of feature variables selected from NIR and MIR regions, both NIR–PCFIA–HCA and MIR–PCFIA–HCA showed higher performances of hierarchical clustering. Moreover, NIR–PCFIA–PLSR and MIR–PCFIA–PLSR models were effectively used to predict tuber doneness degree, yielding the RP as high as 0.935 and the RMSEP as low as of 0.503. It is concluded that the PCFIA is an effective approach for feature variable selection, and both NIR and MIR spectroscopic techniques are capable of classifying potato varieties and determining potato doneness degree. [ABSTRACT FROM AUTHOR]

In this work, surface-enhanced Raman spectroscopy (SERS) was used with silver colloid substrate for rapid detection of omethoate (an organophosphate pesticide) in standard solution and peach extract. The findings demonstrated that the characteristic wavenumber of the pesticide could be precisely identified using the SERS method. The calibration curve was plotted between concentrations and Raman intensities of the target peak at 1649 cm−1 for the peach extract and at 1647 cm−1 for the standard solution. The coefficients of determination (R2) of 0.9829 and 0.98 were obtained for standard solution and for peach extract, respectively. The calculated limits of detection for omethoate in standard solution and in peach extracts were 0.001 mg L−1 and 0.01 mg kg−1, respectively. This study revealed that the proposed method could be used for the analysis of trace contaminants like omethoate in multifaceted food matrices. [ABSTRACT FROM AUTHOR]

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 (R2P = 0.869, RMSEP = 1.304, and RPD = 2.724) and the SG-RC-MLR model for a* (R2P = 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. [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]

The non-uniformity of microwave sterilization usually causes incomplete inactivation of microorganism (especially foodborne pathogens) and potential food safety issue. Detecting the non-uniformity of load distribution using hyperspectral imaging (HSI) (400-1050 nm) in tandem with multivariate analysis was investigated and infrared (IR) thermal imaging technique was used to assist the evaluation. The best simplified model of least-squares support vector machines (LS-SVM) obtained based on ten optimal wavelengths showed an R2p of 0.905, RMSEP of 0.404 log CFU/mL and RPD of 2.62, while the analysis of temperatures detected by IR thermal camera showed that the temperatures of mid-way along each edge were obviously higher than the center, and heating time of microwave had a significant effect on mean temperature (p < 0.01) and temperature coefficient of variation (COV) values (p < 0.01). Moreover, the visualization of L. monocytogenes load distribution was acquired by transferring the quantitative model of LS-SVM to each pixel in the image at different heating times, with the results being well consistent with those of temperature distribution by IR thermal camera. Therefore HSI technique could be used to monitor the non-uniformity of microwave sterilization processing. [ABSTRACT FROM AUTHOR]

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]

Salmon muscle gaping will lead to the irregular voids or undesirable lace-like appearance in the final product. This study was carried out to develop an automatic imaging analysis method for rapid, accurate, and non-invasive detection of gaping blemishes on salmon carcasses. Salmon fillets could be classified as wholesome or defective samples based on the number of candidate gaping regions in the preliminary step applying local adaptive thresholding. Supervised classification results were compared between using histograms of oriented gradients (HOG) and convolutional neural network (CNN) feature extractors. It was shown that CNN features outperformed HOG features with correct classification rates (CCRs) of 0.927 and 0.916 for cross validation and test data set, respectively. Relieff was then applied to select important feature attributes by reducing the 4096-dimensional to 239-dimensional vector. Simplified CNN model also yielded good classification performance with CCR of 0.925 for cross validation. Therefore, CNNs were used to extract features from candidate regions and then reduced features to the 239-dimensional vector. The resultant vector was fed to the simplified CNN model to make a final decision. The prediction maps for visualizing the classification result on salmon fillet were subsequently generated. The overall results confirmed that this proposed method is effective and suitable for the muscle gaping detection. Future work will be focused on applying this method in packing plants where fish fillets are progressing rapidly, and promising results will allow the identification of critical points in the supply chain that impact upon product quality. [ABSTRACT FROM AUTHOR]

Correction: Microstructural Classification and Reconstruction of the Computational Geometry of Steamed Bread Using Descriptor-Based Approach The original article can be found online at https://doi.org/10.1007/s11242-022-01796-2. Correction to: Transport in Porous Media https://doi.org/10.1007/s11242-022-01796-2 In the original article, the Fig. [Extracted from the article]

Total volatile basic nitrogen (TVB-N) is one of the most important indicators for evaluation of fish protein degradation and freshness loss. A novel algorithm of Physarum network (PN) combined with genetic algorithm (GA) was developed to select optimal wavelengths from hyperspectral images for enhancing the TVB-N level prediction in grass carp fish fillet. Partial least squares regression (PLSR) and least squares support vector machines (LS-SVM) calibration models were built using six optimal wavelengths selected by the PN-GA method and the PN-GA-PLSR model showed better performance for predicting the TVB-N value with determination coefficient in prediction ( R ) of 0.956 and root mean square errors in prediction (RMSEP) of 1.737 mg N/100 g. The PN-GA-PLSR model established using the optimal wavelengths and image texture variables extracted by gray-level gradient co-occurrence matrix (GLGCM) algorithm showed higher R of 0.981 and lower RMSEP of 1.435 mg N/100 g. The results indicated that the PN-GA method was a good technique for selecting optimal wavelengths for enhancing prediction ability of hyperspectral imaging, which also demonstrated the efficiency and usefulness of this method for monitoring the freshness degree during fish cold storage. [ABSTRACT FROM AUTHOR]

This study aimed to develop a visible and near-infrared (Vis/NIR) chemical imaging (400-1000 nm) technique to provide rapid prediction of the contents of sodium humate dissolved in aquaculture environment. Gray reference image with 5% reflectance value was first used to correct the obtained raw images in order to promote the reflectance values as compared to that with 99% reflectance for further spectral analysis. Successive projection algorithm (SPA) was introduced to extract four optimal wavelengths, which were then used for the establishment of back-propagation artificial neural network (BP-ANN) models. The results revealed that the BP-ANN model based on the selected four optimal wavelengths better performed ( $$ {R}_C^2 $$ = 0.986, $$ {R}_{CV}^2 $$ = 0.985, $$ {R}_P^2 $$ = 0.993, RMSEC = 0.329 mg/L, RMSECV = 0.433, RMSEP = 0.734 mg/L) than that based on the whole 381 wavelengths ( $$ {R}_C^2 $$ = 0.978, $$ {R}_{CV}^2 $$ = 0.996, $$ {R}_P^2 $$ = 0.977, RMSEC = 0.388 mg/L, RMSECV = 0.625, RMSEP = 0.734 mg/L). Finally, a series of chemical images were developed to clearly display the concentration distribution of the sodium humate dissolved in water, demonstrating that Vis/NIR chemical imaging technique was feasible to quantify the contents of sodium humate in the aquatic environment and could be further used for real-time monitoring the quality of aquaculture water. [ABSTRACT FROM AUTHOR]

Inland waters represent complex and highly variable ecosystems, which are also of immense recreational and economic values to humans. The maintenance of high quality of inland water status necessitates development of means for rapid quality monitoring. Imaging spectrometry techniques are proven technology that can provide useful information for the estimation of inland water quality attributes due to fast speed, noninvasiveness, ease of use, and in situ operation. Although there have been many studies conducted on the use of imaging spectrometry for marine water quality monitoring and assessment, relatively few studies have considered inland water bodies. The aim of this review is to present an overview of imaging spectrometry technologies for the monitoring of inland waters including spaceborne and airborne and field or ground-based hyperspectral systems. Some viewpoints on the current situation and suggestions for future research directions are also proposed. [ABSTRACT FROM AUTHOR]

Partial least squares regression (PLSR) is a classical and widely used linear method for modeling of spectral data. Measurement of fish chemical properties has been playing an important role in providing superior quality products for human health and international trade. This review focuses on the PLSR applied to near-infrared (NIR) and hyperspectral imaging (HSI) spectral data for rapid and chemical-free modeling and predicting chemical properties of fish muscle, including moisture content, lipid content, protein content, pH, and freshness indicators, such as total volatile basic nitrogen, thiobarbituric acid reactive substances, and K index value. Furthermore, the commonly used spectral preprocessing methods and variable selection algorithms are mentioned and discussed for the enhancement of PLSR analysis. The limitations and future trends of NIR and HSI techniques with PLSR analysis are also presented. In a word, NIR and HSI technique in tandem with PLSR method have been developed to be suitable and trustworthy alternatives to the traditional chemical analytical methods such as Kjeldahl, Soxhlet, and chromatography methods for detecting chemical information of fish muscle in an objective, rapid, noninvasive, and chemical-free manner. [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]

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]

Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimension reduction algorithms, including principal component analysis (PCA), local preserving projections (LPP), neighborhood preserving embedding (NPE), and linear discriminant analysis (LDA), were used to reduce hyperspectral dimensions, and their classification performances on the algae concentration levels in water samples using hyperspectral imaging were compared. The LPP model showed satisfactory classification accuracy of 94.296 %, which was superior to the results based on reducing spectral dimensions with LDA (94.118 %), NPE (93.353 %), and PCA (90.588 %). The results demonstrated the potential of hyperspectral imaging coupled with dimension reduction methods in classifying water bodies with different algae concentration levels. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the relationship between the intestinal Lactobacillus species and diet of elderly subjects in a longevity area in Southern China. Healthy elderly subjects ranging from 80 to 99 years old were respectively selected from the regions of Bama and Nanning, Guangxi, China. The nested polymerase chain reaction and denaturing gradient gel electrophoresis (PCR-DGGE) technology was used to analyze the intestinal Lactobacillus community structure. Results showed that Weissella confusa, L. mucosae, L. crispatus, L. salivarius, and L. delbrueckii were the representative Lactobacillus of elderly subjects. Among them, L. crispatus and L. delbrueckii were the dominant Lactobacillus of all species. In comparison to Nanning elderly subjects, the detection frequencies of W. confusa and L. salivarius were significantly increased in Bama elderly subjects ( P < 0.01), whereas L. mucosae was significantly decreased ( P < 0.01). Interestingly, it was also found that there were 4 kinds of representative Lactobacillus, which were significantly correlated with dietary fiber. W. confusa ( P < 0.01) and L. salivarius ( P < 0.05) were significantly positively correlated with the intake of dietary fiber, while L. mucosae ( P < 0.01) and L. crispatus ( P < 0.05) were significantly negatively correlated with the intake of dietary fiber, respectively. Results confirmed that different diets had obvious effects on the intestinal Lactobacillus community structure of elderly subjects in Southern China, which may provide a certain theoretical basis for the elderly's healthy food strategic design and probiotics product development. [ABSTRACT FROM AUTHOR]

Muscle foods play an important role in providing a vital source of high-quality protein, amino acids and vitamin for human health. Chemical composition is one of the most vital information of muscle foods, which directly relates to the quality of pork, beef, chicken, fish and other meats. Therefore, it is significant to identify the chemical information of muscle foods for the purpose of controlling the quality and safety of meat. Hyperspectral imaging can obtain spectral and spatial information of targets simultaneously and has been developed for rapid and nondestructive determination and identification of chemical information of muscle foods. This review focuses on recent applications of hyperspectral imaging technology for the measurement and analysis of chemical composition of muscle foods, including moisture content, fat and fatty acid, pH, protein content, pigment, salt content and freshness attributes. The fundamentals of hyperspectral imaging as well as future development trends are also presented and discussed. [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]

The quality of fruit is a very important measure in determining the market value of the fruit. Good fruit quality provides great potential benefits for antioxidant properties of abundant bioactive compounds such as flavonoids, anthocyanins, polyphenols, and ascorbic acid. Therefore, giving enough attention to fruit quality and its changes is important for assuring high quality of fruits. Hyperspectral imaging (HSI) technique has showed great potential for evaluating quality attributes of fruit due to its nature of simple, continuous noninvasiveness and economy. However, for inherent peculiarity of fruit, some problems arose in the practice of estimating fruit quality by HSI, including focusing, determination of scanning parameters, image processing, data mining, and data analysis. The purpose of this paper is to review the use of HSI in the estimation of physical-chemical attributes, detection of common defects and contaminants, and evaluation of maturity stage of fruit. Remaining problems are also identified, along with suggested solutions and possible future trends in the field. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging technique was utilized as a rapid and nondestructive tool to classify different varieties of maize seeds in the current study. The feasibility of combining spectral data with texture features to improve the classification accuracy for maize seeds was analyzed. Average and pretreated spectra with detrending were extracted from the region of interest of hyperspectral images over the wavelength region of 400-1000 nm, and six optimal spectral wavelengths were selected by successive projection algorithm. Meanwhile, five textural feature variables were extracted by gray-level run-length matrix analysis. Least-square support vector machine was developed to classify different varieties of maize seeds based on spectral, textural, or fusion data. The least-square support vector machine model based on full pretreated spectral data (91.667 %) achieved better results than those based on full spectral data (90.741 %) and optimal spectral data (87.037 %). On the other hand, an accuracy of 88.889 % based on data fusion was achieved, which was superior to the results based on spectra (87.037 %) or texture (85.185 %) alone. At last, the resulting classification maps were developed to visualize different varieties of maize seeds. The current study indicated that combining spectral data with textural features was an effective method to improve the classification accuracy for maize varieties. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) technique has shown promise as a rapid and nondestructive tool to evaluate various internal quality attributes of fruits and vegetables. The objective of this study was to investigate the nondestructive prediction of soluble solids content (SSC) and pH of lychees and maturity discrimination. Two hyperspectral imaging systems of visible/short-wave near infrared range (600-1000 nm, Spectral Set I) and long-wave near infrared range (1000-2500 nm, Spectral Set II) were employed. Results showed that Spectral Set II (SSC: r = 0.877, RMSEP = 0.911 °Brix; pH: r = 0.745, RMSEP = 0.291) performed better than Spectral Set I (SSC: r = 0.790, RMSEP = 1.279 °Brix; pH: r = 0.701, RMSEP = 0.308) for the internal quality prediction of litchi and maturity discrimination. The partial least square discriminant analysis (PSL-DA) model had a discrimination rate of 90.63 % for Spectral Set I and 96.88 % for Spectral Set II. β-Coefficients of partial least squares regression (PLSR) models were used to choose optimal wavelengths for quality predictions. The performance of optimized PLSR in both spectral sets were comparable to the models developed using the whole spectral range. [ABSTRACT FROM AUTHOR]

Enterobacteriaceae is one kind of harmful microorganisms commonly presented in raw fish products, and detection of Enterobacteriaceae plays a very important role in evaluating microbial contamination. This work was carried out to exploit the potential of emerging hyperspectral imaging technique to determine the Enterobacteriaceae contamination of salmon flesh during cold storage. The spectral information ranging from 900 to 1700 nm (239 wavelengths) was extracted to relate to the Enterobacteriaceae loads (recorded as log 10 CFU/g) using partial least square (PLS) regression, developing a PLS model with correlation coefficient of prediction ( r) of 0.94 and root mean square error of prediction (RMSEP) of 0.53 as well as residual predictive deviation (RPD) of 2.97. By applying successive projection algorithm (SPA), eight wavelengths at 924, 931, 964, 1068, 1262, 1373, 1628 and 1668 nm among the 239 wavelengths were selected as informative wavelengths to reduce the information redundancy and optimise the PLS model. With the eight informative wavelengths, a simplified PLS model defined as SPA-PLS was established with r of 0.95, RMSEP of 0.47 and RPD of 3.23. To visualise the contamination degree of salmon flesh caused by Enterobacteriaceae, the SPA-PLS model was transferred to each pixel of images, and colourful distribution maps were produced with different colour represented different numbers of Enterobacteriaceae colonies. The results showed that hyperspectral imaging operating in 900-1700 nm is promising in evaluating Enterobacteriaceae contamination of salmon products. More studies are still required to further refine the multispectral imaging system to achieve online application. [ABSTRACT FROM AUTHOR]

In this study, hyperspectral imaging in the spectral range of 400-1,000 nm was applied for predicting texture changes of maize seeds by different storage times, mainly including hardness, springiness, and resilience. Before modeling, a novel denoising algorithm, namely orthogonal signal correction (OSC), was developed for spectral pre-treatment. Then, partial least squares regression (PLSR) algorithm was used to integrate the extracted spectral data with the reference values measured by a traditional method. The established PLSR models yielded good results for predicting hardness, springiness, and resilience ( R = 0.9012, RMSEP = 0.0502; R = 0.8744, RMSEP = 0.0038; R = 0.8477, RMSEP = 0.0057, respectively). In order to simplify the prediction models, successive projection algorithm (SPA) was employed to choose the most important wavelengths that had the greatest influence in predicting the textural properties. As a result, seven (400, 416, 450, 501, 517, 530, 980 nm), seven (400, 403, 420, 525, 552, 749, 835 nm), and six (436, 482, 532, 700, 735, 939 nm) optimal wavelengths were selected by SPA for hardness, springiness, and resilience prediction, respectively. Based on the optimal wavelengths, new OSC-SPA-PLSR models were built and showed good results for predicting hardness, springiness, and resilience of maize samples with high R of 0.8365, 0.8217, and 0.7930, respectively, and relatively low RMSEP of 0.2085, 0.0530, and 0.0595, respectively. Due to that no more than seven wavelengths were needed, the OSC-SPA-PLSR models were used for visualization of the values of textural properties in maize seeds as affected by different storage times. The results showed that hyperspectral imaging technique could be used as a fast, accurate, and non-destructive tool for predicting textural properties of maize as affected by different storage times. [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]

A hyperspectral imaging system was developed in the spectral region between 400 and 1,100 nm to investigate its potential for predicting the shelf life of bananas with different browning levels. Principal component analysis (PCA) was conducted for reducing data dimensionality and selecting optimal wavelengths. Five optimal wavelengths (454, 486, 559, 686, and 728 nm) were found. Among all principal component (PC) images, PC-4 was selected to segregate browning area from normal surface using a simple threshold algorithm for extracting image features of browning area. The average spectra were obtained by calculating the mean of the spectra of all browning areas in the hyperspectral images of bananas. Then, image features and average spectra in five optimal wavelengths were used to develop classification models for predicting the shelf life of banana samples by determining their browning levels using back propagation (BP), radial basis function (RBF), and self-organizing feature maps (SOM) networks. Results indicated that the classification models using both image features and average spectra were obviously superior to those models using image features or average spectra alone. Among all classifiers, BP classifier had the best performance with the best classification rates of 95.6 % for training set and 90.5 % for testing set, respectively. The results demonstrated that hyperspectral imaging has great potential in predicting banana shelf life based on combination of image features and average spectra. [ABSTRACT FROM AUTHOR]

This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', length-width ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice. [ABSTRACT FROM AUTHOR]

Springiness is an important quality characteristic of chicken meat, related with muscle structure and contents of biochemical components, and has great influence on eating quality of chicken meat. Traditional methods for springiness evaluation including manual inspection and instrumental measurement are tedious, time-consuming, and destructive. This study implemented a smart and promising nondestructive technique, i.e., hyperspectral imaging, for rapid prediction of springiness of fresh chicken meat. Hyperspectral images of tested samples with different springiness levels were acquired, and their spectral data were extracted in the spectral range of 400-1,000 nm. Two calibration methods, namely, partial least squares regression (PLSR) and artificial neural network (ANN), were respectively used to correlate the extracted spectra of chicken meat samples with the reference springiness values estimated by a twice-compression method. Successful projections algorithm (SPA) as a popular wavelength selection tool was applied, and ten optimal wavelengths (416, 458, 581, 637, 696, 722, 740, 754, 773, and 973 nm) were finally selected. Based on the selected optimal wavelengths, optimized SPA-PLSR and SPA-ANN model were established, respectively. By comparing with the results of two optimized models, the SPA-PLSR model showed better prediction results with high correlation coefficient ( R) of 0.84 and low root mean square error by prediction (RMSEP) of 0.159. Finally, an image processing algorithm was developed to transfer the SPA-PLSR model to each pixel in chicken meat for visualizing their springiness distribution. The results from the current study indicated that hyperspectral imaging could be a rapid and nondestructive tool for prediction of springiness of chicken meat. [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]

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]

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]