•The gold nanotriangles (GNTs)-DTNB@Ag-DTNB nanotriangles (GDADNTs) were synthesized and used as the SERS-active substrate.•The DTNB was labeled not only on the surface of GTNs but also on the surface of Ag shell as Raman signal molecule.•Under the optimized condition, the platform shows a distinguished sensitivity with the LOD as low as 0.54 pg/mL.A novel universal Surface-enhanced Raman Spectroscopy (SERS) based aptasensor platform for the trace detection of Aflatoxin B1 (AFB1), a common food contaminating mycotoxin, has been constructed, with the aid of the specific interaction between AFB1 and aptamers. The amino-terminal aptamer conjugated magnetic-bead (CS-Fe3O4) and the gold nanotriangles (GNTs)-DTNB@Ag-DTNB nanotriangles (GDADNTs) were used as the capturer and the reporter of AFB1, respectively. Under the optimized assay condition, the platform shows a distinguished sensitivity with the LOD as low as 0.54 pg/mL and the linear range from 0.001 to 10 ng/mL, a high stability of the SERS substrate activity remained three months at least, a decent reproducibility with RSD of ca. 5%, and a good selectivity to the general coexisted interferences. The distinguished sensitivity and selectivity for trace AFB1 detection has been achieved mainly due to the strong Raman enhancement effect of GNTs as the kernel for GDADNTs from the double-layer of the reporter molecules, the specificity of aptamer and superparamagnetic CS-Fe3O4 respectively. Furthermore, the proposed SERS aptasensor is universal to other trace molecules detection with the specific aptamers.Download high-res image (202KB)Download full-size image

Fermentation level is a key bean quality indicator in the cocoa industry. A colorimetric sensor e-nose (CS e-nose) and an innovatively designed near infrared chemo-intermediary-dyes spectra technique (NIR-CDS) combined with four chemometric algorithms – extreme machine learning (ELM), support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) – were applied to classify 90 sampled cocoa beans into three quality grades – fully fermented, partially fermented and non-fermented. The CS e-nose (89% ≤ Rp ≤ 94%) and NIR-CDS (85% ≤ Rp ≤ 94%) achieved comparable classification rates, with the systems' data cluster analysis yielding cophenetic correlation coefficients of 0.85–0.89. Both systems combined with SVM and ELM achieved a high classification rate (Rp = 94%) and could be applied to cocoa bean quality classification on an in situ and nondestructive basis. This novel NIR-CDS technique proved a pragmatic approach for the selection of sensitive chemo-dyes used in the fabrication of e-nose colorimetric sensor arrays compared with the hitherto trial-and-error method, which is time-consuming and dye-wasteful. The technique could also be deployed in near-infrared systems for the detection of volatile (gaseous) compounds, which previously had been a limitation.

•Developed a portable and low-cost VIS-NIR spectroscopy system•Evaluation of color sensory quality of black tea samples using this spectra system•Comparing the performance of models from spectra and color informationInstrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.Download high-res image (206KB)Download full-size image

Total Volatile Basic-Nitrogen (TVB-N) content is one of core measures in evaluating chicken freshness. This study reported the feasibility to quantify Total Volatile Basic-Nitrogen (TVB-N) content in chicken meat by a low cost colorimetric sensor array with the help of chemometric analysis. We fabricated a colorimetric sensor array by printing 12 chemically responsive dyes (i.e. 9 porphyrins/metalloporphyrins and 3 pH indicators) on a C2 reverse silica-gel flat plate for the fast and non-destructive quantitative determination of TVB-N content in chicken. A colour change profile for each sample was obtained by differentiating the image of the sensor array before and after exposure to volatile organic compounds (VOCs). Linear algorithm; partial least squares regression (PLSR) and nonlinear algorithms; back propagation artificial neural network (BPANN), Adaptive Boosting BPANN (BP-AdaBoost) and support vector machine regression (SVMR) methods based on particle swarm optimization (PSO) were used to build the TVB-N prediction model. Experimental results showed that the predictive precision of the PSO-SVMR model was superior to linear and classic non-linear models. The optimum PSO-SVMR model was obtained with 4 support vectors and Rp of 0.8981, RMSEP of 5.5255. The overall results are encouraging for the application of low cost colorimetric sensors combined with an appropriate chemometric method in the poultry industry for quality assessment because it is practical, non-invasive, rapid and simple.

•A novel and sensitive immunoassay method was used for detection of Biotoxin.•A new upconversion nanoparticle material was applied as signal probes.•An improved separation method is presented by combining magnetic nanobead.•Specific combination was developed based on adopting given antigen and antibody.•A good linear relationship was obtained between luminescence intensity and toxin concentration.Rare earth doped upconversion nanoparticles convert near-infrared excitation light into visible emission light. Compared to organic fluorophores and semiconducting nanoparticles, upconversion nanoparticles (UCNPs) offer high photochemical stability, sharp emission bandwidths, and large anti-Stokes shifts. Along with the significant light penetration depth and the absence of autofluorescence in biological samples under infrared excitation, these UCNPs have attracted more and more attention on toxin detection and biological labelling. Herein, the fluorescence probe based on UCNPs was developed for quantifying Aflatoxin B1 (AFB1) in peanut oil. Based on a specific immunity format, the detection limit for AFB1 under optimal conditions was obtained as low as 0.2 ng·ml− 1, and in the effective detection range 0.2 to 100 ng·ml− 1, good relationship between fluorescence intensity and AFB1 concentration was achieved under the linear ratios up to 0.90. Moreover, to check the feasibility of these probes on AFB1 measurements in peanut oil, recovery tests have been carried out. A good accuracy rating (93.8%) was obtained in this study. Results showed that the nanoparticles can be successfully applied for sensing AFB1 in peanut oil.

Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. This study attempts to quantify the TVC content in pork by combining two nondestructive sensing tools of hyperspectral imaging (HSI) and artificial olfaction system based on the colorimetric sensor array. First, data were acquired using HSI system and colorimetric sensors array, 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 two different sensor data for further multivariate analysis. In developing the models, linear (PLS and stepwise MLR) and nonlinear (BPANN and SVMR) pattern recognition methods were comparatively employed, and they were optimized by cross-validation. Compared with other models, the SVMR model achieved the best result, and the optimum results were achieved with the root mean square error of prediction (RMSEP) = 2.9913 and the determination coefficient (Rp) = 0.9055 in the prediction set. The overall results showed that it has the potential in nondestructive detection of TVC content in pork meat by integrating two nondestructive sensing tools of HSI and colorimetric sensors with SVMR pattern recognition tool.

•NIR spectroscopy was proposed for sensing the sensory quality of Chinese rice wine.•A novel algorithm of Si-BP-AdaBoost was used for modeling.•Si-BP-AdaBoost showed superiority in modeling when compared with other algorithms.The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp = 0.9180 and RMSEP = 2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine.

Near infrared multispectral imaging system (MSI) based on three wavebands—1280 nm, 1440 nm and 1660 nm—was developed for the non-destructive sensing of tenderness and water holding capacity (WHC) of pork. Multispectral images were acquired for pork samples, and the real tenderness (Warner-Bratzler Shear Force, WBSF) and WHC (cook loss, CL) of these samples were simultaneously determined using traditional destructive methods. The gray level co-occurrence matrix was used for the extraction of characteristic variables from multispectral images. Next, ant colony optimization combined with back propagation artificial neural network was used for modeling, which achieved good performance compared with the other two commonly used algorithms. The correlation coefficient and the root mean square error in the prediction set were achieved as follows: Rp = 0.8451 and RMSEP = 0.9087 for WBSF; Rp = 0.9116 and RMSEP = 1.5129 for CL. This work adequately demonstrates that the MSI technique has a high potential for non-destructive sensing of pork quality attributes combined with an appropriate algorithm, thus facilitating a simple and fast method of meat analysis.

Total volatile basic nitrogen (TVB-N) content is an important indicator for evaluating meat's freshness. This study attempts to quantify TVB-N content non-destructively in chicken using a colorimetric sensors array with the help of multivariate calibration. First, we fabricated a colorimetric sensor array by printing 12 chemically responsive dyes on a C2 reverse silica-gel flat plate. A color change profile was obtained by differentiating the images of the sensor array before and after exposure to volatile organic compounds (VOCs) released from a chicken sample. In addition, we proposed a novel algorithm for modeling, which is a back propagation artificial neural network (BPANN), and an adaptive boosting (AdaBoost) algorithm, namely, AdaBoost–BPANN, and we compared it with the commonly used algorithms. Experimental results showed that the optimum model was achieved by the AdaBoost–BPANN algorithm with RMSEP = 7.7124 mg/100 g and R = 0.8915 in the prediction set. This study demonstrated that the colorimetric sensors array has a high potential in the non-destructive sensing of chicken's freshness and that the AdaBoost–BPANN algorithm performs well as a solution to a complex data calibration.

•NIR spectroscopy was used for measuring non-sugar solids in Chinese rice wine.•A new algorithm of Si-CARS-PLS was proposed for modeling.•Si-CARS-PLS showed superiority in modeling when compared with other algorithms.The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc = 0.95 and RMSEC = 1.12 in the calibration set, Rp = 0.95 and RMSEP = 1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine.

•To develop a novel instrumental intelligent test methodology for food sensory analysis.•A novel data fusion was used in instrumental intelligent test methodology.•Linear and nonlinear tools were comparatively used for modeling.•The instrumental test methodology can be imitative of human test behavior.Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers.

•This work developed a portable and low-cost optical sensors system.•The optical sensors system was used for real-time monitoring tea quality.•Si-PLS algorithm was used to select spectral intervals of interest.•CARS and GA was comparatively used to select spectral variables of interest.•The independent samples were used to test the performance of this system.A portable and low-cost optical sensors system consisting of hardware and software was developed and used for real-time monitoring total polyphenols content in tea in this work. This developed system was used for data acquisition. Partial least square (PLS) with several variable selection algorithms was used for modeling. Synergy interval partial least square (Si-PLS) was first used to select spectral subintervals of interest, and then competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were comparatively employed to select the variables of interest from the subintervals of interest. The optimum model was achieved and stored in the developed software. Next, 20 independent samples were used to test the performance of this system. And the coefficient of variation (CV) of the final results was used to state the stability and reliability of this system. The results also showed that GA–Si-PLS performed better than CARS–Si-PLS model and the CVs for most of the samples were <5%. This study demonstrated this developed optical sensors system as a promising tool that could be used for real-time monitoring tea quality.

An improved classification of vinegar with different marked ages is presented using a combination of gustatory sensors and olfactory sensors. Herein, the gustatory sensor system is developed using four electrodes (gold, copper, platinum and glassy carbon) in a standard three-electrode configuration, and the olfactory system is developed based on a colorimetric sensor array. Initially, the data obtained from the two sensor systems were analyzed separately. Then, the potential of the combination of the two sensor systems for classification is investigated. Principal component analysis (PCA) and linear discriminant analysis (LDA), as two classification tools, are used for data classification. The results show that the capability of discrimination of the combined system is superior to that obtained with the two sensor systems separately, and eventually LDA achieved 100% classification rate by cross-validation. This work indicates that the combination of gustatory sensor systems and olfactory sensor systems can be a useful tool for the classification of vinegar with different marked ages.

•3DFS technique was attempted to detect the presence of adulterants in honey.•We attempted to extract characteristic parameters from 3D fluorescence spectra.•Two algorithms of PLS and BP-ANN were comparatively used for modeling.To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.Graphical abstract

This paper attempted a portable colorimetric sensor array to monitor vinegar acetic fermentation. We fabricated a colorimetric sensor array by printing 15 chemically responsive dyes (i.e. 9 porphyrins/metalloporphyrins and 6 pH indicators) on a C2 reverse silica-gel flat plate. The colorimetric sensor array, with specific colorific fingerprint to the VOCs in vinegar, was successfully used to monitor vinegar acetic fermentation with the help of multivariate calibration. Principal component analysis (PCA) and linear discriminant analysis (LDA), as two multivariate calibration tools, were used to classify vinegar samples at different acetic fermentation stages. Experimental results show that LDA achieved a better result and its classification rate is 100% by leave-one-out cross-validation (LOOCV). Besides, we systemically studied the change of VOCs during vinegar acetic fermentation with the help of hierarchical cluster analysis (HCA). This research work shows that the colorimetric sensor technique has a potential in monitoring vinegar acetic fermentation.

•Current situation in the analysis of food quality and safety is reviewed.•Applications of emerging imaging tools to food quality and safety are reviewed.•Advantages of emerging imaging tools to food quality and safety are reviewed.•Limitations of emerging imaging tools to food quality and safety are pointed out.•Observed trends of emerging imaging tools to food quality and safety are provided.Food quality and safety issues are increasingly attracting attention. Emerging imaging techniques have particular advantages in non-destructive detection of food quality and safety. This review looks at the trends in applying these emerging imaging techniques to analysis of food quality and safety, in particular, hyperspectral imaging, magnetic resonance imaging, soft X-ray imaging, ultrasound imaging, thermal imaging, fluorescence imaging, and odor imaging. On the basis of the observed trends, we also present the technical challenges and future outlook for these emerging imaging techniques.

This paper attempted to show the feasibility of measuring the antioxidant activity in dark soy sauce by NIR spectroscopy technique. Chemometrics on spectral intervals selection and nonlinear regression tools were systematically studied in the calibrating model. First, the optimal spectral intervals were selected by synergy interval-partial least square (Si-PLS). Then, kernel PLS (KPLS) and back propagation artificial neural network (BPANN), as two nonlinear regression tools, were performed comparatively to calibrate models based on optimal spectral intervals, called Si-KPLS and Si-BPANN models, respectively. These models were optimized by cross-validation, and the performance of the final model was evaluated according to correlation coefficient (Rp2) and root mean square error of prediction (RMSEP) in the prediction set. The results showed that the Si-BPANN model was superior to other models, and the optimal result was achieved with Rp2 = 0.9769 and RMSEP = 0.0221 in the prediction set. This work demonstrated that total antioxidant capacity in dark soy sauce could be measured by NIR spectroscopy technique, and Si-BPANN showed its superiority in model calibration.

The acid value (AV) is an essential parameter for the quality and safety evaluation of peanut oil. In this study, for efficiently and real-time monitor of acid value (AV) in peanut oil, a portable spectroscopy system was first developed and combined with variables selection algorithms to measure acid value (AV) in peanut oils. Developed portable spectroscopy system was applied for transmittance spectrum data acquisition after which partial least squares (PLS) and several variables selection algorithms synergy interval partial least square (Si-PLS), genetic algorithm (GA), genetic algorithm combined with Si-PLS namely GA-Si-PLS, ant colony optimization (ACO) algorithms were systemically studied and comparatively used for modeling. The performances of these models were evaluated according to correlation coefficients squared in the prediction set (RP) and root mean square error of prediction (RMSEP). The results showed that the variables selection methods could select more significant variables and improve the model performance, especially for the GA-Si-PLS model with the best performance than other variables selection algorithms with RP = 0.9426 and RMSEP = 0.2980. Finally, the paper draws a conclusion that the developed portable spectroscopy system combined with a suitable variables selection methods could be used for the simultaneous and rapid measurement of acid value in peanut oil.

•Multispectral imaging system based on near infrared bands was developed.•Pork freshness was successfully evaluated by NIR multispectral imaging.•A novel AdaBoost + BPANN algorithm was used for model calibration.Total volatile basic nitrogen (TVB-N) content is one of core indicators for evaluating pork’s freshness. This paper attempted to non-destructively sensing TVB-N content in pork meat using near infrared (NIR) multispectral imaging technique (MSI) with multivariate calibration. First, a MSI system with 3 characteristic wavebands (i.e. 1280 nm, 1440 nm and 1660 nm) was developed for data acquisition. Then, gray level co-occurrence matrix (GLCM) was used for characteristic extraction from multispectral image data. Next, we proposed a novel algorithm for modeling-back propagation artificial neural network (BP-ANN) and adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, and we compared it with two commonly used algorithms. Experimental results showed that the BP-AdaBoost algorithm is superior to others with the root mean square error of prediction (RMSEP) = 6.9439 mg/100 g and the correlation coefficient (R) = 0.8325 in the prediction set. This work sufficiently demonstrated that the MSI technique has a high potential in non-destructively sensing pork freshness, and the nonlinear BP-AdaBoost algorithm has a strong performance in solution to a complex data processing.

This paper attempted to show the feasibility of measuring the antioxidant activity in dark soy sauce by NIR spectroscopy technique. Chemometrics on spectral intervals selection and nonlinear regression tools were systematically studied in the calibrating model. First, the optimal spectral intervals were selected by synergy interval-partial least square (Si-PLS). Then, kernel PLS (KPLS) and back propagation artificial neural network (BPANN), as two nonlinear regression tools, were performed comparatively to calibrate models based on optimal spectral intervals, called Si-KPLS and Si-BPANN models, respectively. These models were optimized by cross-validation, and the performance of the final model was evaluated according to correlation coefficient (Rp2) and root mean square error of prediction (RMSEP) in the prediction set. The results showed that the Si-BPANN model was superior to other models, and the optimal result was achieved with Rp2 = 0.9769 and RMSEP = 0.0221 in the prediction set. This work demonstrated that total antioxidant capacity in dark soy sauce could be measured by NIR spectroscopy technique, and Si-BPANN showed its superiority in model calibration.

An improved classification of vinegar with different marked ages is presented using a combination of gustatory sensors and olfactory sensors. Herein, the gustatory sensor system is developed using four electrodes (gold, copper, platinum and glassy carbon) in a standard three-electrode configuration, and the olfactory system is developed based on a colorimetric sensor array. Initially, the data obtained from the two sensor systems were analyzed separately. Then, the potential of the combination of the two sensor systems for classification is investigated. Principal component analysis (PCA) and linear discriminant analysis (LDA), as two classification tools, are used for data classification. The results show that the capability of discrimination of the combined system is superior to that obtained with the two sensor systems separately, and eventually LDA achieved 100% classification rate by cross-validation. This work indicates that the combination of gustatory sensor systems and olfactory sensor systems can be a useful tool for the classification of vinegar with different marked ages.

Total volatile basic nitrogen (TVB-N) content is an important indicator for evaluating meat's freshness. This study attempts to quantify TVB-N content non-destructively in chicken using a colorimetric sensors array with the help of multivariate calibration. First, we fabricated a colorimetric sensor array by printing 12 chemically responsive dyes on a C2 reverse silica-gel flat plate. A color change profile was obtained by differentiating the images of the sensor array before and after exposure to volatile organic compounds (VOCs) released from a chicken sample. In addition, we proposed a novel algorithm for modeling, which is a back propagation artificial neural network (BPANN), and an adaptive boosting (AdaBoost) algorithm, namely, AdaBoost–BPANN, and we compared it with the commonly used algorithms. Experimental results showed that the optimum model was achieved by the AdaBoost–BPANN algorithm with RMSEP = 7.7124 mg/100 g and R = 0.8915 in the prediction set. This study demonstrated that the colorimetric sensors array has a high potential in the non-destructive sensing of chicken's freshness and that the AdaBoost–BPANN algorithm performs well as a solution to a complex data calibration.