•Nondestructive measurement of TVB-N in pork meat by integrating three techniques.•Correlation analysis of the three sensors data with TVB-N content of pork meat.•Extraction of the optimum feature ...variables from three sensors data.•Data fusion based on feature variables and BP-ANN model for measuring TVB-N content.
Total volatile basic nitrogen (TVB-N) content is an important reference index for evaluating pork freshness. This paper attempted to measure TVB-N content in pork meat using integrating near infrared spectroscopy (NIRS), computer vision (CV), and electronic nose (E-nose) techniques. In the experiment, 90 pork samples with different freshness were collected for data acquisition by three different techniques, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from 3 different sensors data. Back-propagation artificial neural network (BP-ANN) was used to construct the model for TVB-N content prediction, and the top principal components (PCs) were extracted as the input of model. The result of the model was achieved as follows: the root mean square error of prediction (RMSEP)=2.73mg/100g and the determination coefficient (Rp2)=0.9527 in the prediction set. Compared with single technique, integrating three techniques, in this paper, has its own superiority. This work demonstrates that it has the potential in nondestructive detection of TVB-N content in pork meat using integrating NIRS, CV and E-nose, and data fusion from multi-technique could significantly improve TVB-N prediction performance.
•Vis-NIR HSI was used to detect compound heavy metals content in lettuce leaves.•WT-SCAE is proposed to obtain the deep spectral features.•Deep learning has a great potential for the identification ...of compound heavy metals content.
The aim of this research was to develop a deep learning method which involved wavelet transform (WT) and stack convolution auto encoder (SCAE) for extracting compound heavy metals detection deep features of lettuce leaves. WT was used to decompose the visible-near infrared (400.68–1001.61 nm) hyperspectral image of lettuce sample in the multi-scale transform to acquire the optimal wavelet decomposition layers of cadmium (Cd) and lead (Pb) content prediction, and then using SCAE to perform deep feature learning on spectral data under optimal wavelet decomposition layer. Support vector machine regression (SVR) models established by the deep features obtained by WT-SCAE achieved reasonable performance with coefficient of determination for prediction (Rp2) of 0.9319, root mean square error for prediction (RMSEP) of 0.04988 mg/kg and the relative percent different (RPD) of 3.187 for Cd content, and with Rp2 of 0.9418, RMSEP of 0.04123 mg/kg and RPD of 3.214 for Pb content. The results of this study confirmed the great potential for detecting compound heavy metals by the combination of hyperspectral technique and deep learning algorithm.
This work applied the FT-NIR spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers ...were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS-PLS), Monte Carlo uninformative variable elimination PLS (MCUVE-PLS), and iteratively retaining informative variables PLS (IRIV-PLS), the BOSS-PLS model achieved better results, with the coefficient of determination (R
) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.
The use of Catechin as an antibacterial agent is becoming ever-more common, whereas unstable and easy oxidation, have limited its application. A simple and low-energy-consuming approach to synthesize ...highly stable and dispersive Catechin-Cu nanoparticles(NPs) has been developed, in which the stability and dispersivity of the NPs are varied greatly with the pH value and temperature of the reaction. The results demonstrate that the optimal reaction conditions are pH 11 at room temperature. As-synthesized NPs display excellent antimicrobial activity, the survival rates of bacterial cells exposed to the NPs were evaluated using live/dead Bacterial Viability Kit. The results showed that NPs at the concentration of 10 ppm and 20 ppm provided rapid and effective killing of up to 90% and 85% of S. aureus and E. coli within 3 h, respectively. After treatment with 20 ppm and 40 ppm NPs, the bacteria are killed completely. Furthermore, on the basis of assessing the antibacterial effects by SEM, TEM, and AFM, it was found the cell membrane damage of the bacteria caused by direct contact of the bacteria with the NPs was the effective mechanism in the bacterial inactivation.
► NIR spectroscopy technique was attempted to rapidly determine the pork quality. ► Improving the model performance by optimising spectra preprocessing and PLS factors. ► Spectra region selection by ...SI-PLS will improve the model quality.
Total volatile basic nitrogen (TVB-N) content is one of important index of pork’s freshness, and Warner–Bratzler shear force (WBSF) is seen as the important index of pork’s tenderness. This paper attempted the feasibility to determine TVB-N content and WBSF in pork by Fourier transform near infrared (FT-NIR) spectroscopy. Synergy interval partial least square (SI-PLS) algorithm was performed to calibrate regression model. The number of PLS factors and the number of intervals were optimised simultaneously by cross-validation. The performance of the model was evaluated according to two correlation coefficients (R) in calibration and prediction sets. Experimental results showed that the correlations coefficients in the calibration set (Rc) and prediction set (Rp) were achieved as follows: Rc=0.8398 and Rp=0.8084 for TVB-N content model; Rc=0.7533 and Rp=0.7041 for WBSF model. The overall results demonstrated that NIR spectroscopy combined with SI-PLS could be utilised to determinate TVB-N content and WBSF in pork.
Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. In this work, the TVC in pork meat was detected by hyperspectral imaging ...technology. First, the spectra were extracted from 3-D datacube of hyperspectral image and 100 characteristic variables were selected by synergy interval PLS (SI-PLS) algorithm. Meanwhile, principal component analysis (PCA) was implemented on the 3-D datacube to determine 3 characteristic pictures. And, 5 characteristic variables were extracted using texture analysis from each characteristic picture. PCA was implemented on 111 spectra variables, 15 image variables and data fusion (126 variables), and the top principal components (PCs) were extracted for developing the TVC prediction model, respectively. Experimental results show that the model based on data fusion is superior to others, which was achieved with RMSEP=0.243lgCFU/g and Rp2=0.8308 in the prediction set. This work demonstrates that HSI technique, as a nondestructive analytical tool, has the potential in nondestructive detection of TVC in pork meat.
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•Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging•Data fusion based on spectral and image information from hyperspectral data•Construction of nonlinear regression model based on data fusion
•Nonlinear correlation between thiabendazole (TBZ) and SERS data was diagnosed.•ACO-ELM, UVE-ELM and CARS-ELM were comparatively investigated for TBZ detection.•CARS-ELM model achieved optimum ...results when principal component was 7.•The recoveries of TBZ in spiked apple were ranged from 83.02 to 93.54%.•A robust SERS-based method for TBZ was developed with a LOD of 0.001 mg/L.
Thiabendazole (TBZ) is extensively used in agriculture to control molds; residue of TBZ may pose a threat to humans. Herein, surface-enhanced Raman spectroscopy (SERS) coupled variable selected regression methods have been proposed as simple and rapid TBZ quantification technique. The nonlinear correlation between the TBZ and SERS data was first diagnosed by augmented partial residual plots method and calculated by runs test. Au@Ag NPs with strong enhancement factor (EF = 4.07 × 106) of Raman signal was used as SERS active material to collect spectra from TBZ. Subsequently, three nonlinear regression models were comparatively investigated and the competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) achieved a higher correlation coefficient (Rp2 = 0.9406) and the lower root-mean-square-error of prediction (RMSEP = 0.5233 mg/L). Finally, recoveries of TBZ in apple samples were 83.02–93.54% with relative standard deviation (RSD) value < 10%. Therefore, SERS coupled CARS-ELM could be employed as a rapid and sensitive approach for TBZ detection in Fuji apples.
Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the ...data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600–1000 nm spectra with Rp, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 oBx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.
•Novel NIR transmittance spectroscopy quantitatively detected the degree of watercore in apple.•The characteristic spectral variables of apple watercore disease detection were studied.•Multiple variables selection simplified and improved the performance of models.•NIR spectroscopy is advantageous as the fast, non-destructive measurements.
In order to effectively realize the spectral detection of heavy metal content, a deep learning method which consisted of stacked auto-encoders (SAE) and partial least squares support vector machine ...regression (LSSVR) is proposed to obtain depth features and establish cadmium (Cd) detection model. The Vis-NIR hyperspectral images of 1120 lettuce leaf samples were obtained and the whole region of lettuce leaf sample spectral data was collected and preprocessed with different spectral pre-treatment methods. Successive projections algorithm (SPA), partial least squares regression (PLSR) and SAE were used to acquire the optimum wavelength, respectively. Besides, the characteristic wavelengths were used to build partial least squares support vector machine regression (LSSVR) models. Furthermore, the best prediction performance for detecting Cd content in lettuce leaves was obtained by Savitzky-Golay combined with first derivative (SG-1st) pre-processing method, with Rp2 of 0.9487, RMSEP of 0.01049 mg/kg and RPD of 3.330 using SAE-LSSVR method. The results of this study indicated that deep learning method coupled with hyperspectral imaging technique has great potential for detecting heavy metal content in lettuce leaves.
•Vis-NIR hyperspectral imaging was used to detect Cd content in lettuce leaves.•SAE-LSSVR is proposed to establish depth feature regression model.•Deep learning has a great potential for the identification of Cd content.
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•SERS signal optimized highly roughened silver nanoparticle (AgNP) was synthesized.•25 °C calculated highest enhancement factor value of 1.39 × 106.•Solid phase extraction was ...implemented to remove pigment from the extract.•AgNP SERS sensor for sensing methomyl, acetamiprid and 2,4-dichlorophenoxyacetic acid in tea.
Trace detection of toxic chemicals in foodstuffs is of great concern in recent years. Surface-enhanced Raman scattering (SERS) has drawn significant attention in the monitoring of food safety due to its high sensitivity. This study synthesized signal optimized flower-like silver nanoparticle-(AgNP) with EF at 25 °C of 1.39 × 106 to extend the SERS application for pesticide sensing in foodstuffs. The synthesized AgNP was deployed as SERS based sensing platform to detect methomyl, acetamiprid-(AC) and 2,4-dichlorophenoxyacetic acid-(2,4-D) residue levels in green tea via solid-phase extraction. A linear correlation was twigged between the SERS signal and the concentration for methomyl, AC and 2,4-D with regression coefficient of 0.9974, 0.9956 and 0.9982 and limit of detection of 5.58 × 10−4, 1.88 × 10−4 and 4.72 × 10−3 µg/mL, respectively; the RSD value < 5% was recorded for accuracy and precision analysis suggesting that proposed method could be deployed for the monitoring of methomyl, AC and 2,4-D residue levels in green tea.
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