Food chemical contaminants are a major factor in the cause of foodborne diseases and can do harm to human health. Hence, it is highly desirable to develop robust, easy, and sensitive methods for rapid evaluation of food chemical contaminants. Nanomaterial-based optical aptasensors combined with the advantages of the high selectivity of optical detection techniques, excellent stability of aptamer, and the unique properties of nanomaterials have been recognized as useful tools for routine biosensing applications. The recent progress in nanomaterial-based optical aptamer assays to determine food chemical contaminants including heavy metals, toxins, pesticides, and antibiotics are presented in this paper. Furthermore, the major challenges and future prospects in this field are discussed to provide ideas for further research.Keywords: antibiotics; aptasensor; heavy metals; nanomaterials; optical; pesticides; toxins;

•DNA probes for Hg2+ covalently conjugated to graphene oxide.•Systematically compared for the covalent and non-covalent sensors using the same DNA.•Covalent probes have much higher stability against non-specific displacement.Graphene oxide (GO) has attracted extensive research interest as a platform for DNA adsorption and biosensor development. While most researchers use simple physisorption of fluorescently labeled DNA, covalent sensors are less susceptible to non-specific probe displacement and minimize false positive results. In this work, three thymine-rich DNA probes of different lengths are modified on their 3′-end with an amino group for covalent conjugation to GO. They also each contain an internally labeled fluorophore so that Hg2+ binding can lead to a large distance increase between the fluorophore and the GO surface for fluorescence signaling. Hg2+-dependent fluorescence signaling from the covalent sensors are compared with that from the non-covalent sensors in terms of sensitivity, selectivity, signaling kinetics, and continuous monitoring. The covalent sensors are much more stable and resistant to non-specific probe displacement, while still retaining high sensitivity and similar selectivity. The detection limits are 16.3 and 20.6 nM Hg2+, respectively, for the covalent and non-covalent sensors, and detection of spiked Hg2+ in Lake Ontario water is demonstrated.

Fluorescently labeled DNA adsorbed on graphene oxide (GO) is a well-established sensing platform for detecting a diverse range of analytes. GO is a loosely defined material and its oxygen content may vary depending on the condition of preparation. Sometimes, a further reduction step is intentionally performed to decrease the oxygen content, and the resulting material is called reduced GO (rGO). In this study, DNA adsorption and desorption from GO and rGO is systematically compared. Under the same salt concentration, DNA adsorbs slightly faster with a 2.6-fold higher capacity on rGO. At the same time, DNA adsorbed on rGO is more resistant to desorption induced by temperature, pH, urea, and organic solvents. Various lengths and sequences of DNA probes have been tested. When its complementary DNA is added as a model target analyte, the rGO sample has a higher signal-to-background and signal-to-noise ratio, whereas the GO sample has a slightly higher absolute signal increase and faster signaling kinetics. DNAs adsorbed on GO or rGO are still susceptible to nonspecific displacement by other DNA and proteins. Overall, although rGO adsorbs DNA more tightly, it allows efficient DNA sensing with an extremely low background fluorescence signal.

•Impedimetric microfluidic bioassay based on magnetic separation was present.•Gold interdigitated array microelectrode was used to fabricate microfluidic system.•Aptamer modified magnetic beads were used to separate and concentrate thrombin.•Wide liner range, low detection limit, excellent specificity and short assay time.An aptamer-based impedimetric bioassay using the microfluidic system and magnetic separation was developed for the sensitive and rapid detection of protein. The microfluidic impedance device was fabricated through integrating the gold interdigitated array microelectrode into a flow cell made of polydimethylsiloxane (PDMS). Aptamer modified magnetic beads were used to capture and separate the target protein, and concentrated into a suitable volume. Then the complexes were injected into the microfluidic flow cell for impedance measurement. To demonstrate the high performance of this novel detection system, thrombin was employed as the target protein. The results showed that the impedance signals at the frequency of 90 kHz have a good linearity with the concentrations of thrombin in a range from 0.1 nM to 10 nM and the detection limit is 0.01 nM. Compared with the reported impedimetric aptasensors for thrombin detection, this method possesses several advantages, such as the increasing sensitivity, improving reproducibility, reducing sample volume and assay time. All these demonstrate the proposed detection system is an alternative way to enable sensitive, rapid and specific detection of protein.

The development of cost-effective, robust, and rugged biochemical sensing devices is of great interest for point-of-care testing and on-spot monitoring. Here we reported a facile approach to build high-performance flexible potentiometric sensing devices by using free-standing graphene paper (GNP) as electrode substrate. The transduction mechanism of the GNP-based electrode was investigated systematically, revealing that GNP acts as both electric conductor and ion-to-electron transducer of the potentiometric signals. Three GNP-based ion-selective electrodes (GNP–ISEs) were constructed, which exhibited excellent potentiometric performance. Furthermore, a paper-type reference electrode (RE) was presented for the first time in which the photo-polymerised reference membrane is directly coated onto the GNP. Results show that the GNP-based RE (GNP-RE) displayed low potential variability for a wide range of biochemical species in a wide calibration range. On the basis of these excellent results, integrated potentiometric sensing devices using GNP-ISE and GNP-RE were built that possess comparable performance with common types of potentiometric detection system. High-performance, easy fabrication, low cost, and excellent flexibility make the GNP-based potentiometric sensing devices very attractive as ‘vanguard analytical tools’ for routine sensing applications.

A novel surface plasmon resonance (SPR) biosensor using lectin as bioreceptor was developed for the rapid detection of Escherichia coli (E. coli) O157:H7. The selective interaction of lectins with carbohydrate components from bacterial cells surface was used as the recognition principle for the detection. Five types of lectins from Triticum vulgaris, Canavailia ensiformis, Ulex europaeus, Arachis hypogaea, and Maackia amurensis, were employed to evaluate the selectivity of the approach for binding E. coli O157:H7 effectively. A detection limit of 3 × 103 cfu mL−1 was obtained for determination of E. coli O157:H7 when used the lectin from T. vulgaris as the binding molecule. Furthermore, the proposed biosensor was used to detect E. coli O157:H7 in real food samples. Results showed that the lectin based SPR biosensor was sensitive, reliable and effective for detection of E. coli O157:H7, which hold a great promise in food safety analysis.Highlights► Lectin based surface plasmon resonance (SPR) biosensor was firstly constructed for Escherichia coli O157:H7 detection. ► This biosensor possesses high specificity, remarkable sensitivity, as well as high-throughput screening for bacteria. ► The developed sensor could be used to direct evaluation of E. coli O157:H7 levels in real food samples.

•Ammonia acts a key role in H2 production by anaerobic fermentation.•H2 yield increases 57% by 3.5 g/l additive TAN at F/M 3.9.•H2 yield increases 19% by 1.5 g/l additive TAN at F/M 8.0.•H2 production at F/M 3.9 is inhibited at TAN over 6.0 g/l.•H2 production at F/M 8.0 is inhibited at TAN over 2.5 g/l.The effect of different additive ammonia (0–10 g/l as nitrogen) on hydrogen production from the anaerobic batch mesophilic fermentation of food waste was studied at two feed-to-microorganism ratios (F/M), 3.9 and 8.0. Anaerobic sludge taken from an anaerobic digester was used as inoculum. The hydrogen yield at F/M 3.9 and 8.0 without additive ammonia was 77.2 and 51.0 ml-H2/gVS, respectively. At F/M 3.9, the hydrogen production was enhanced by adding additive ammonia in the system when the total ammonia nitrogen (TAN) concentration was no higher than 6.0 g/l. A maximum hydrogen yield of 121.4 ml-H2/gVS was obtained at a TAN concentration of 3.5 g/l. At F/M 8.0, the enhancement of hydrogen production was found in a narrower range of additive TAN concentrations, with a highest yield of 60.9 ml-H2/gVS at the TAN of 1.5 g/l. Hydrogen production was inhibited at higher additive TAN concentrations for both F/M ratios. This study provides a novel strategy for controlling ammonia for production of hydrogen from food waste via anaerobic fermentation.

A novel aptamer based dot-blot assay for the detection of immunoglobulin E (IgE) was developed. A biotinylated aptamer was employed as the bio-recognition element to specifically interact with the target protein immobilized onto a nitrocellulose membrane substrate. Avidin conjugated horseradish peroxidase was introduced onto the membrane through the biotin-avidin system to catalyze the hydrogen peroxide mediated oxidation of 3,3′,5,5′-tetramethylbenzidine, thereby producing the blue-colored insoluble product. The intensity of the dots increased as the concentration of IgE increased. The spot intensity was quantified using a simple portable instrument. A linear response relationship between the spot intensity and the concentration of IgE over the range of 50 nmol/L to 1 μmol/L was obtained. The detection limit for IgE using the aptamer-based assay was 2.89 nmol/L. This assay was found to discriminate IgE from non-target proteins such as thrombin, bovine serum albumin and immunoglobulin G.

•A low-cost but high-performance impedance biosensor was present.•Gold nanoparticles modified free-standing graphene paper was fabricated.•Paper type immunosensor possesses greatly enhanced sensing performance.•Gaphene paper based sensing device shows high tolerability to mechanical stress.In this study, a low-cost and robust impedimetric immunosensor based on gold nanoparticles modified free-standing graphene paper electrode for rapid and sensitive detection of Escherichia coli O157:H7 (E. coli O157:H7) was developed. Graphene paper was prepared by chemical reduction of graphene oxide paper obtained from vacuum filtration method. Scanning electron microscope, Raman spectroscopy and X-ray diffraction techniques were employed to investigate the surface morphology and crystal structure of the prepared graphene paper. The gold nanoparticles were grown on the surface of graphene paper electrode by one-step electrodeposition technique. The immobilization of anti-E. coli O157:H7 antibodies on paper electrode were performed via biotin-streptavidin system. Electrochemical impedance spectroscopy was used to detect E. coli O157:H7 captured on the paper electrode. Results show that the developed paper immunosensor possesses greatly enhanced sensing performance, such as wide linear range (1.5×102–1.5×107 cfu mL−1), low detection limit (1.5×102 cfu mL−1), and excellent specificity. Furthermore, flexible test demonstrate the graphene paper based sensing device has high tolerability to mechanical stress. The strategy of structurally integrating metal nanomaterials, graphene paper, and biorecognition molecules would provide new insight into design of flexible immunosensors for routine sensing applications.

•A one-step and label-free optical biosensor based on competitive dispersion of gold nanorods was developed for aflatoxin B1.•The increase of AFB1 concentration in samples will increase dispersion of nanorods in solution.•False results caused by undesirable aggregation could be effectively reduced based on dispersion of gold nanorods.•The changes in absorption intensity of UV–vis spectra and the average hydrodynamic size of nanorods were employed as sensing indicator.•Sensitivity, selectivity and feasibility of proposed biosensor were evaluated.This report illustrates a promising one-step and label-free optical biosensor for determination of aflatoxin B1 (AFB1) that is most commonly found in foods and highly dangerous even at very low concentrations. In this research, gold nanorods (GNRs) were employed as a sensing platform, which showed high stability under high ionic strength conditions without addition of any stabilizing agent. GNR-AFB1–BSA (bovine serum albumin) conjugates aggregated after mixing with free antibodies, resulting in significant changes in absorption intensity. At the same time the existence of AFB1 molecules in samples caused dispersion of nanorods, as a result of competitive immune-reaction with antibodies. By taking advantages of the competitive dispersion of GNRs, the developed method could effectively reduce false results caused by undesirable aggregation, which is a big problem for spherical gold nanoparticles. Absorption intensity of UV–vis spectra served as the sensing indicator, with dynamic light scattering (DLS) measurement as another sensing tool. The designed biosensing system could detect AFB1 in a linear range from 0.5 to 20 ng mL−1, with a good correlation coefficient of 0.99. And the limit of detection (LOD) was 0.16 ng mL−1, indicating an excellent sensitivity with absorbance result. The recoveries of the spiked AFB1 in real peanut samples ranged from 94.2% to 117.3%. Therefore the proposed nano-biosensor was demonstrated to be sensitive, selective, and simple, providing a viable alternative for rapid screening of toxins in agriculture products and foods.

A disposable and sensitive screen-printed electrode using an ink containing graphene was developed. This electrode combined the advantages of graphene and the disposable characteristic of electrode, which possessed wide potential window, low background current and fast electron transfer kinetics. Compared with the electrodes made from other inks, screen-printed graphene electrode (SPGNE) showed excellent electrocatalytic activity for the oxidation of ascorbic acid (AA), dopamine (DA), and uric acid (UA). Three well-defined sharp and fully resolved anodic peaks were found at the developed electrode. Differential pulse voltammetry was used to simultaneous determination of AA, DA, and UA in their ternary mixture. In the co-existence system of these three species, the linear response ranges for the determination of AA, DA, and UA were 4.0–4500 μM, 0.5–2000 μM, and 0.8–2500 μM, respectively. The detection limits (S/N = 3) were found to be 0.95 μM, 0.12 μM, and 0.20 μM for the determination of AA, DA, and UA, respectively. Furthermore, the SPGNE displayed high reproducibility and stability for these species determination. The feasibility of the developed electrode for real sample analysis was investigated. Results showed that the SPGNE could be used as a sensitive and selective sensor for simultaneous determination of AA, DA, and UA in biological samples, which may provide a promising alternative in routine sensing applications.Highlights► A disposable electrode made from graphene-based screen-printing ink was proposed. ► Large potential window, low background current and fast electron transfer kinetics. ► Three well-defined and fully resolved peaks for oxidation of three species. ► High sensitivity, selectivity, stability, reproducibility and easy fabrication.

Micro/nanobeads with different materials (magnetic, silica and polymer) and different sizes (diameters from 30 nm to 970 nm) were investigated for their use as amplifiers in a quartz crystal microbalance (QCM) immunosensor for more sensitive detection of Escherichia coli O157:H7. The micro/nanobeads were conjugated with anti-E. coli antibodies. E. coli O157:H7 cells were first captured by the first antibody immobilized on the electrode surface, and then micro/nanobeads labeled secondary antibodies attached to the cells, and finally the complexes of antibody–E. coli–antibody modified beads were formed. The results showed that antibody-labeled beads lead to signal amplification in both the change in frequency (ΔF) and the change in resistance (ΔR). Since the penetration depth of the oscillation-induced shear-waves for a ∼8 MHz crystal is limited to 200 nm, the interpretation of how the signal is amplified by the adsorbed particles was represented in terms of the coupled-oscillator theory. The amplification is not sensed in terms of increase in mass on the sensor surface. Amplification is sensed as a change in bacterial resonance frequency when the spheres adsorb to the bacteria. The change in the values of ΔF caused by different micro/nanobeads (amplifiers) attaching on target bacterial cells is indicative of the ratio between the resonance frequency of the absorbed bacterial-particle complex (ωs), and the resonance frequency of the crystal (ω).

The feasibility of visible and near infrared (Vis–NIR) spectroscopy and least-squares support vector machines (LS-SVM) for on-line determination of rice wine composition was investigated. A circle-light fibre spectrometer system was designed to collect transreflectance spectra of rice wine samples in round brown glass bottles with the bottle sealed and the labels removed. Statistical equations were established between reference data and Vis–NIR spectra by LS-SVM. Compared to partial least squares regression (PLSR), the performance of LS-SVM was slightly better, with higher correlation coefficients for validation (rval) of 0.915, 0.888 and 0.872, and lower root mean square error of validation (RMSEP) of 0.168 (%(V V−1)), 0.146 (g L−1) and 0.033 for alcohol content, titratable acidity, and pH, respectively. Based on the results, it was concluded that the Vis–NIR spectrometer system was suitable for on-line wine quality determination, and LS-SVM was a reliable multivariate method for NIR analysis.

Bayberry plays an important role in the nutrition and is a very important fruit-product. It has a high economic and officinal value. In this study, glucose, fructose and sucrose in bayberry juice were detected and quantified using near-infrared (NIR) spectroscopy. The HPLC method was assumed to provide the reference value of the analyte for calibration. Partial least-squares regression (PLSR) was used to construct calibration models with different pre-processing methods. The number of PLS factors was optimised. The results show PLS models are good for predicting glucose, fructose and sucrose concentrations in bayberry juice, and their prediction accuracy can be improved by using derivative process with the exception of the glucose. The best models were mostly given by the second derivative processed spectra, especially for sucrose with the determination coefficient, R2 of 0.9931. This demonstrates the potential of NIR spectroscopy to quickly detect these components simultaneously in bayberry juice with the reference method of HPLC.

Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that NIR spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.

The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (Rc)=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction= 0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

Fruits provide nutrients for human body and are able to prevent sorts of non-communicable diseases. The fruit quality test is an area that both technology and market section concern about. Near infrared spectroscopy (NIR) is a rapid, precise, and non-destructive technique which can be well utilized in determination of fruit quality. This review paper summarizes the theory of NIR analysis, and the fundamental structure of instruments based on NIR for fruit quality assessment. Chemometrics for NIR spectroscopy involving analysis methods of data pre-processing, calibration, model transfer and evaluation, is also included. In recent 11 years, significant progresses were achieved in fruit quality assessment via NIR spectroscopy, which is the main focus in this review. Furthermore, urgent problems in this research field are discussed, expecting to be solved in the near future.

The effect of different food to microorganism ratios (F/M) (1–10) on the hydrogen production from the anaerobic batch fermentation of mixed food waste was studied at two temperatures, 35 ± 2 °C and 50 ± 2 °C. Anaerobic sludge taken from anaerobic reactors was used as inoculum. It was found that hydrogen was produced mainly during the first 44 h of fermentation. The F/M between 7 and 10 was found to be appropriate for hydrogen production via thermophilic fermentation with the highest yield of 57 ml-H2/g VS at an F/M of 7. Under mesophilic conditions, hydrogen was produced at a lower level and in a narrower range of F/Ms, with the highest yield of 39 ml-H2/g VS at the F/M of 6. A modified Gompertz equation adequately (R2 > 0.946) described the cumulative hydrogen production yields. This study provides a novel strategy for controlling the conditions for production of hydrogen from food waste via anaerobic fermentation.

The feasibility of rapid analysis for oligosaccharides, including isomaltose, isomaltotriose, maltose, and panose, in Chinese rice wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least-squares regression (PLSR) was studied in this work. Forty samples of five brewing years (1996, 1998, 2001, 2003, and 2005) were analyzed by NIR transmission spectroscopy with seven optical path lengths (0.5, 1, 1.5, 2, 2.5, 3, and 5 mm) between 800 and 2500 nm. Calibration models were established by PLSR with full cross-validation and using high-performance anion-exchange chromatography coupled with pulsed amperometric detection as a reference method. The optimal models were obtained through wavelength selection, in which the correlation coefficients of calibration (rcal) for the four sugars were 0.911, 0.938, 0.925, and 0.966, and the root-mean-square errors of calibrations were 0.157, 0.147, 0.358, and 0.355 g/L, respectively. The validation accuracy of the four models, with correlation coefficients of cross-validation (rcv) being 0.718, 0.793, 0.681, and 0.873, were not very satisfactory. This might be due to the low concentrations of the four sugars in Chinese rice wine and the influence of some components having structures similar to those of the four sugars. The results obtained in this study indicated that the NIR spectroscopy technique offers screening capability for isomaltose, isomaltotriose, maltose, and panose in Chinese rice wine. Further studies with a larger Chinese rice wine sample should be done to improve the specificity, prediction accuracy, and robustness of the models.

The use of least-squares support vector machines (LS-SVM) combined with near-infrared (NIR) spectra for prediction of enological parameters and discrimination of rice wine age is proposed. The scores of the first ten principal components (PCs) derived from PC analysis (PCA) and radial basis function (RBF) were used as input feature subset and kernel function of LS-SVM models, respectively. The optimal parameters, the relative weight of the regression error γ and the kernel parameter σ2, were found from grid search and leave-one-out cross-validation. As compared to partial least-squares (PLS) regression, the performance of LS-SVM was slightly better, with higher determination coefficients for validation (Rval2) and lower root-mean-square error of validation (RMSEP) for alcohol content, titratable acidity, and pH, respectively. When used to discriminate rice wine age, LS-SVM gave better results than discriminant analysis (DA). On the basis of the results, it was concluded that LS-SVM together with NIR spectroscopy was a reliable and accurate method for rice wine quality estimation.

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800∼2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i, ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.

Immunosensors are biosensors that use antibodies or antigens as the specific sensing element and provide concentration-dependent signals. There is great potential in the applications of immunosensing technologies for rapid detection of pesticide residues in food and environment. This paper presents an overview of various transduction systems, such as electrochemical, optical, piezoelectric, and nanomechanics methods, which have been reported in the literature in the design and fabrication of immunosensors for pesticide detection. Various immobilization protocols used for formation of a biorecognition interface are also discussed. In addition, techniques of regeneration, signal amplification, miniaturization, and antibodies are evaluated for the development and applications of these immunosensors. It can be concluded that despite some limitations of the immunosensing technologies, these immuosensors for pesticide monitoring are becoming more and more relevant in environmental and food analysis.

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

In the current studies a miniature silicon wafer fuel cell (FC) using L-ascorbic acid as fuel was developed. The cell employs L-ascorbic acid and air as reactants and a thin polymer electrolyte as a separator. Inductively coupled plasma (ICP) silicon etching was employed to fabricate high aspect-ratio columns on the silicon substrate to increase the surface area. A thin platinum layer deposited directly on the silicon surface by the sputtering was used as the catalyst layer for L-ascorbic acid electro-oxidation. Cyclic voltammetry shows that the oxidation of L-ascorbic acid on the sputtered platinum layer is irreversible and that the onset potentials for the oxidation of L-ascorbic acid are from 0.27 V to 0.35 V versus an Ag/AgCl reference electrode. It is found that at the room temperature, with 1 mol/L L-ascorbic acid/PBS (phosphate buffered solution) solution pumped to the anode at 1 ml/min flow rate and air spontaneously diffusing to the cathode as the oxidant, the maximum output power density of the cell was 1.95 mW/cm2 at a current density of 10 mA/cm2.

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The rc, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices.

Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats (‘Dahongpao’ and ‘Jiajiaozhong’) picked from two producing areas of ‘Tangxi’ and ‘Cunan’ in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA–PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA–PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA–PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties.

Watermelon is a popular fruit in the world with soluble solids content (SSC) being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350–1000 nm. Spectra data were analyzed by two multivariate calibration techniques: partial least squares (PLS) and principal component regression (PCR) methods. Two experiments were designed for two varieties of watermelons [Qilin (QL), Zaochunhongyu (ZC)], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration (RMSEC), root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 (QL); r=0.954 (ZC)], low RMSEP [0.65 °Brix (QL); 0.58 °Brix (ZC)], low RMSEC [0.48 °Brix (QL); 0.34 °Brix (ZC)] and small difference between the RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra (Savitzky-Golay filter smoothing method) did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a nondestructive way.

A novel approach for fruit shape detection in RGB space was proposed, which was based on fast level set and Chan-Vese model named as Modified Chan-Vese model (MCV). This new algorithm is fast and suitable for fruit sorting because it does not need re-initializing. MCV has three advantages compared to the traditional methods. First, it provides a unified framework for detecting fruit shape boundary, and does not need any preprocessing even though the raw image is noisy or blurred. Second, if the fruit has different colors at the edges, it can detect perfect boundary. Third, it processed directly in color space without any transformations that may lose much information. The proposed method has been applied to fruit shape detection with promising result.

The feasibility of near infrared spectroscopy (NIRS) for discrimination between Chinese rice wine of different geographical origins (Shaoxing and Jiashan, China) is presented in this research. NIR spectra were collected in transmission mode in the wavelength range of 800–2500 nm. Qualitative analysis models were developed based on partial least squares regression (PLSR). The prediction performance of calibration models in different wavelength range was also investigated. The best models gave a 100% classification of wines of the two geographical origins in the range of 1300–1650 nm. The content of trace metals (potassium, magnesium, zinc, and iron) was also investigated to classify wines of the two categories by atomic absorption spectroscopy (AAS). The AAS results were in agreement with NIRS, with 100% classification for wines of the two categories. In addition, the correlation between NIRS and AAS was also investigated by PLSR. Potassium and magnesium were well predicted by quantitative models based on NIR spectra and AAS data. The correlation coefficient of calibration (Rcal) for potassium and magnesium were 0.958 and 0.885, respectively, and the correlation coefficient of validation (Rval) were 0.861 and 0.700, respectively. The results demonstrated that NIRS technique could be used as a rapid method for classification of geographical origin of Chinese rice wine, and AAS could be used as an alternative technique or to validate the discrimination results.

In this paper, the discharge ignited in a capillary connecting two beakers filled with electrolyte solution is investigated. During the experiment, an external electrical voltage is applied through two platinum electrodes dipped in the beakers. A gas bubble forms inside the capillary when the applied voltage is higher than 1000 V. Since the beakers are tilted slightly, after generation, the bubble moves slowly to the uphill outlet of the capillary due to buoyancy. When the bubble reaches the end of the capillary, it cracks and a bright discharge is ignited. The emission spectra of the discharge plasma are related to the metal ions dissolved in the solution and thus can be used for metal ion detection. An application of the system to measurement of water hardness is shown.

This research was to study the potential of Fourier transform near-infrared (FT-NIR) diffuse reflectance spectroscopy as a nondestructive way for firmness measurement of “Qinmei” kiwifruit. NIR spectra were acquired from 800 nm to 2500 nm with different combinations of resolution (2, 4, 8, 16, and 32 cm−1) and scan number (32, 64 and 128). Original spectra, along with first and second derivative treatments, were analyzed with and without preprocessing. Two preprocessing methods were investigated, including multiplicative scattering correction (MSC) and standard normal variate (SNV). Partial least square (PLS) model for original spectra of 2 cm−1 resolution and 64 scan number without preprocessing indicated good results for calibration: correlation coefficient (r) of 0.89, root mean square error of calibration (RMSEC) of 4.09 N. The results showed that NIR diffuse reflectance spectroscopy was feasible to predict the firmness of “Qinmei” kiwifruit.

The potential of visible/near-infrared (Vis/NIR) diffuse transmittance spectroscopy, in combination with a variety of chemometrics techniques, was investigated to examine the feasibility to non-destructively distinguish transgenic tomatoes from non-transgenic tomatoes. One hundred tomatoes inserted with antisense ethylene receptor gene LeETR1 and the same number of their parents were scanned in the Vis/NIR regions. Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant partial least squares (DPLS) regression based on PCA scores were applied to classify tomatoes with different genes into two groups. The results show that differences between transgenic and non-transgenic tomatoes do exist and excellent classification can be obtained after optimizing spectral pre-treatment. The correct classifications of the calibration as well as the validation data set for transgenic and non-transgenic tomatoes were 100% using DPLS after second derivative spectral pre-treatment. The results in the present study show that Vis/NIR spectroscopy together with chemometrics techniques could be a rapid tool to be used for differentiating transgenic tomatoes from conventional tomatoes.

Visible (Vis)/near infrared (NIR) spectroscopy is an excellent technique for non-destructive fruit quality assessment. This research was focused on evaluating the use of Vis/NIR spectroscopy for measuring soluble solids content (SSC) of intact ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) on-line. Also, the effect of fruit moving speed on SSC measurements was investigated. Diffuse transmission spectra were collected using a fiber spectrometer equipped with a 3648-element linear silicon CCD array detector in the wavelength range of 345–1040 nm, and all sample spectra were collected three times at different fruit moving speeds of 0.3 m s−1, 0.5 m s−1 and 0.7 m s−1. Spectral pre-processing such as derivative, standard normal variate transformation (SNV) and multiplicative scatter correction (MSC) was used before calibration. Partial least squares (PLS) and least squares support vector machines (LS-SVM) were used to develop calibration models for SSC. The results show that fruit moving speed has few effects on spectra and model performance at a fruit moving speed of 0.3–0.7 m s−1. At 0.5 m s−1, the best model for SSC was PLS regression coupled with original spectra, its coefficient of determination (R2) and root mean square error of prediction (RMSEP) being 0.916% and 0.530%, respectively.

An improved genetic algorithms (GAs) is used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression. The methods also allow the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. Studies are performed to characterize the signal and noise characteristics of the spectral data, and optimal configurations for the GAs are found for each data set through experimental design techniques. The experiments tested in this method were sugar content (SC), titratable acidity (TA) and valid acidity (pH).Despite the complexity of the spectral data, the GAs procedure were found to perform well (RMSEP = 0.395, 0.0195, 0.0087 for SC, TA and pH respectively), leading to calibration models that significantly outperform those based on full-spectrum analyses (RMSEP = 0.512, 0.0198, 0.0111 for SC, TA and pH respectively). In addition, a significant reduction in the number of spectral points required to build the models is realized and all of the numbers of wavelengths for building the calibration models can reduce by 84.4%. This work proved that the GA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization.

The objective of this research was to compare transmission and reflectance modes of visible (VIS)/near infrared (NIR) spectroscopy for detecting brown heart in pears. Transmission spectra were collected by a fiber spectrometer in the range of 400–1028 nm with two fruit-orientations: stem-calyx axis vertical (T1) and stem-calyx axis horizontal (T2). Diffuse reflectance spectra were acquired using a FT–NIR spectrometer with two detectors (Si: 670–1110 nm; InGaAs: 800–2630 nm). Discriminant analysis (DA) method was employed to classify the pears with brown heart and without brown heart. Better results were obtained based on transmission spectra than those on reflectance spectra. The classifying correctness was 91.2% by using transmission spectra acquired in fruit-orientation T2. Three defective pears and one sound pear were misclassified during calibration process and for validation, no defective pear was predicted incorrectly and only one sound pear was predicted as defect. The results indicate that VIS–NIR spectroscopy is feasible for detecting brown heart in pear and transmission mode is better than reflectance mode for internal disorder detection.

Automatic detection of fruit peel defects by a computer vision system is difficult due to the challenges of acquiring images from the surface of spherical fruit and the visual similarity between the stem-ends and the true defects. In this study, oranges with wind scarring, thrips scarring, scale infestation, dehiscent fruit, anthracnose, copper burn, canker spot and normal surface were researched. A lighting transform method based on a low pass Butterworth filter with a cutoff frequency D0 = 7 was first developed to convert the non-uniform intensity distribution on spherical oranges into a uniform intensity distribution over the whole fruit surface. However, the stem-ends were easily confused with defective areas. In order to solve this problem, different color components (R, G and B) and their combinations were analyzed. It was found that a ratio method and R and G component combination coupled with a big area and elongated region removal algorithm (BER) could be used to differentiate stem-ends from defects effectively. Finally, a processing and classification algorithm based on a simple thresholding method was proposed. The result with 98.9% overall detection rate for the 720 independent sample images indicated that the proposed algorithm was effective in differentiation of normal and defective oranges. The method, however, could not discriminate between different types of defects.Highlights► A method was developed for correcting the non-uniform intensity distribution on the fruit. ► A stem-end identification algorithm based on a ratio method was also introduced in this study. ► A combination algorithm was presented to segment defects from normal fruit peels on orange. ► Experimental work was performed using 720 independent images of oranges and achieved a 98.9% detection rate.

Over the past 30 years, on/in-line near infrared (NIR) spectroscopy has proved to be one of the most efficient and advanced tools for continuous monitoring and controlling of process and product quality in food processing industry. A lot of work has been done in this area. This review focuses on the use of NIR spectroscopy for the on/in-line analysis of foods such as meat, fruit, grain, dairy products, beverage and other areas, and mainly looks at the literature published in the last 10 years. The topics covered emphasize the methods designed for on/in-line measurement of data, chemometric treatment, as well as interpretation of the experimental observations. Finally, problems relating to the successful applications of on/in-line NIR spectroscopy in production processes have been briefly outlined.

A label-free immunosensor was designed and fabricated for sensitive detection of alpha-fetoprotein (AFP) based on the localized surface plasmon resonance (LSPR) of gold nanorods (GNRs). GNRs were fabricated through seedmediated growth and surface activation by alkanethiols for the attachment of antibodies that could directly capture of the target. A novel strategy using the absorbance ratio of transverse and longitudinal LSPR peak of GNRs as a function of AFP contraction was performed for AFP quantification. The limit of detection of the sensor is 0.25 nM in the PBS, and the dynamic range spans 0.25 nM to 14.3 nM. It is expected that this simple and cost-effective LSPR biosensor can also be applicable to the detection of several other tumor markers that are present at low concentrations.

•An overview about current advances in nanomaterial-based biosensors for antibiotics detection was present.•Several nanomaterials that widely applied to the fabrication of biosensors were discussed in details.•Two main signal transducing mechanisms of biosensors including electrochemical and optical were discussed.•Some challenges and futures about the development of nanomaterial-based antibiotic biosensors were summarized.Antibiotics are able to be accumulated in human body by food chain and may induce severe influence to human health and safety. Hence, the development of sensitive and simple methods for rapid evaluation of antibiotic levels is highly desirable. Nanomaterials with excellent electronic, optical, mechanical, and thermal properties have been recognized as one of the most promising materials for opening new gates in the development of next-generation biosensors. This review highlights the current advances in the nanomaterial-based biosensors for antibiotics detection. Different kinds of nanomaterials including carbon nanomaterials, metal nanomaterials, magnetic nanoparticles, up-conversion nanoparticles, and quantum dots have been applied to the construction of biosensors with two main signal-transducing mechanisms, i.e. optical and electrochemical. Furthermore, the current challenges and future prospects in this field are also included to provide an overview for future research directions.

The development of cost-effective, robust, and rugged biochemical sensing devices is of great interest for point-of-care testing and on-spot monitoring. Here we reported a facile approach to build high-performance flexible potentiometric sensing devices by using free-standing graphene paper (GNP) as electrode substrate. The transduction mechanism of the GNP-based electrode was investigated systematically, revealing that GNP acts as both electric conductor and ion-to-electron transducer of the potentiometric signals. Three GNP-based ion-selective electrodes (GNP–ISEs) were constructed, which exhibited excellent potentiometric performance. Furthermore, a paper-type reference electrode (RE) was presented for the first time in which the photo-polymerised reference membrane is directly coated onto the GNP. Results show that the GNP-based RE (GNP-RE) displayed low potential variability for a wide range of biochemical species in a wide calibration range. On the basis of these excellent results, integrated potentiometric sensing devices using GNP-ISE and GNP-RE were built that possess comparable performance with common types of potentiometric detection system. High-performance, easy fabrication, low cost, and excellent flexibility make the GNP-based potentiometric sensing devices very attractive as ‘vanguard analytical tools’ for routine sensing applications.