•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.
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
An olfaction system based on colorimetric sensor array was developed for fish freshness evaluation. Nine chemically responsive dyes were selected according to their sensitivity to volatile compounds ...typically occurring during spoilage in fish. The colorimetric sensor array was made by printing selected dyes on a reverse phase silica gel plate. Detection on fish of chub was made every 24
h within seven days. A color change profile for each sample was obtained by differentiating the images of the sensor array before and after exposure to the odor of sample. The digital data representing the color change profiles for the fish samples were analyzed using principal component analysis. The chub samples were classified into three freshness groups using a radial basis function neural network, with an overall classification accuracy of 87.5%. This research suggests that the system is useful for quality evaluation of fish and perhaps other food containing high protein.
Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and ...biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), “Univariate” and “Sequential” selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (
iPLS), windows PLS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given.
•Fabricating an odor imaging sensors array using dyes printing on a plate.•A portable E-nose system based on an odor imaging sensor array was developed.•PCA and LDA were comparatively used for data ...analysis.•Successfully used it for classification of tea category with different fermentations.
A developed portable electronic nose (E-nose) based on an odor imaging sensor array was successfully used for classification of three different fermentation degrees of tea (i.e., green tea, black tea, and Oolong tea). The odor imaging sensor array was fabricated by printing nine dyes, including porphyrin and metalloporphyrins, on the hydrophobic porous membrane. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to tea's volatile organic compounds (VOCs). Multivariate analysis was used for the classification of tea categories, and linear discriminant analysis (LDA) achieved 100% classification rate by leave-one-out cross-validation (LOOCV). This study demonstrates that the E-nose based on odor imaging sensor array has a high potential in the classification of tea category according to different fermentation degrees.
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•NIR models for soluble solids content of apple were developed.•Both short wave (SWNIR) and long wave (LWNIR) wavelength ranges were considered.•Color compensation significantly ...improves prediction accuracy for SWNIR.•Nonlinear calibration models were better than linear ones.•Wavelength selection and latent variable construction algorithms were investigated.
Shortwave near infrared (SWNIR) and long wave near infrared (LWNIR) spectroscopy with a novel color compensation method were compared to predict soluble solids content of apple. Linear and nonlinear regression models were considered. Eventually, independent component analysis-support vector machine (ICA-SVM) models proved to be superior to other nonlinear models. Rp was 0.9398 and RMSEP was 0.3870% for the optimal model of SWNIR, while Rp was 0.9455 and RMSEP was 0.3691% for that of LWNIR. Moreover, the results showed that color compensation could significantly improve the prediction performance of SWNIR model. Our work implies that SWNIR with color compensation has an obvious prospect in practical industrial use for real-time monitoring apple quality.
This paper attempted to evaluate chicken freshness using a low-cost colorimetric sensor array with the help of a classification algorithm. We fabricated a novel and low-cost colorimetric sensors ...array, with a specific colorific fingerprint to volatile compounds, using printing chemically responsive dyes on a C2 reverse silica-gel flat plate. In addition, we proposed a novel classification algorithm for sensors data classification – orthogonal linear discriminant analysis (OLDA) and adaptive boosting (AdaBoost) algorithm, namely AdaBoost–OLDA. And we compared it with two classical classification algorithms – linear discriminant analysis (LDA) and back propagation artificial neural network (BP-ANN). Experimental results showed classification results by AdaBoost–OLDA algorithm is superior to BP-ANN and LDA algorithms, the classification results by which are both 100% in the calibration and prediction sets. This study sufficiently demonstrated that the colorimetric sensors array with a classification algorithm has a high potential in evaluating chicken freshness, and AdaBoost–OLDA algorithm has a strong performance in solution to a complex data classification.
•Developed a low-cost colorimetric sensor array using chemical dyes printed on a plate.•Chicken freshness was successfully evaluated by the colorimetric sensor array.•A novel AdaBoost + OLDA algorithm was used for sensors data classification.
► Rapid measurement of total acid content (TAC) in Chinese vinegar using NIR spectroscopy technique. ► Selection of the efficient spectra variables by Si-PLS. ► Construction of nonlinear regression ...models based on the efficient spectra variables selected Si-PLS. ► Comparing, diagnosing, and discussing the different models for measurement of TAC in Chinese vinegar.
Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486g/100mL, and the correlation coefficient (Rp) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.
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