The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.

Pickled frequently contains nitrosodimethylamine (NDMA), a mutagenic and carcinogenic substance that is dangerous for the general public's health. This study reports on the fabrication of a fluorescent biosensor using zein film and aptamer functionalized upconversion nanoparticles (UCNPs) for on-site monitoring of NDMA in meat. UCNPs were first prepared followed by aptamer binding and mixing with zein film, which was further conjugated with cDNA of dabcyl modified at 5 & PRIME;. The fluorescence resonance energy transfer (FRET) mechanism between the UCNPs and dabcyl was exploited. The fluorescence signals of the zein film recovered when NDMA was present because it was selectively collected by the particular aptamer and damaged the cDNA structure. The designed functionalized zein film was used for on-site and portable determination of NDMA with a lower limit of detection of 0.017 ng/mL, and possessed a satisfactory recovery ranging from 95.8% to 100.2% with no sig-nificant difference compared with the GC-MS method.

Chlorpyrifos (CPF) residues in food pose a serious threat to ecosystems and human health. Herein, we propose a three-dimensional folded paper-based microfluidic analysis device (3D-mu PAD) based on multifunctional metal-organic frameworks, which can achieve rapid quantitative detection of CPF by fluorescence-colorimetric dual-mode readout. Upconversion nanomaterials were first coupled with a bimetal organic framework possessing peroxidase activity to create a fluorescence-quenched nanoprobe. After that, the 3D-mu PAD was finished by loading the nanoprobe onto the paper-based detection zone and spraying it with a color-developing solution. With CPF present, the fluorescence intensity of the detection zone gradually recovers, the color changes from colorless to blue. This showed a good linear relationship with the concentration of CPF, and the limits of detection were 0.028 (fluorescence) and 0.043 (colorimetric) ng/mL, respectively. Moreover, the 3D-mu PAD was well applied in detecting real samples with no significant difference compared with the high-performance liquid chromatography method. We believe it has huge potential for application in the on-site detection of food hazardous substance residues.

The presence of fluoroquinolone (FQs) antibiotic residues in the food and environment has become a significant concern for human health and ecosystems. In this study, the background-free properties of upconversion nanoparticles (UCNPs), the high specificity of the target aptamer (Apt), and the high quenching properties of graphene oxide (GO) were integrated into a microfluidic-based fluorescence biosensing chip (MFBC). Interestingly, the microfluidic channels of the MFBC were prepared by laser-printing technology without the need for complex preparation processes and additional specialized equipment. The target-responsive fluorescence biosensing probes loaded on the MFBC were prepared by self-assembly of the UCNPs-Apt complex with GO based on π-π stacking interactions, which can be used for the detection of the two FQs on a large scale without the need for multi-step manipulations and reactions, resulting in excellent multiplexed, automated and simultaneous sensing capabilities with detection limits as low as 1.84 ng/mL (enrofloxacin) and 2.22 ng/mL (ciprofloxacin). In addition, the MFBC was integrated with a smartphone into a portable device to enable the analysis of a wide range of FQs in the field. This research provides a simple-to-prepare biosensing chip with great potential for field applications and large-scale screening of FQs residues in the food and environment.

Malachite green (MG) used in aquaculture is highly toxic and poses a serious risk to human health. Therefore, a novel solid-phase biosensor was developed for the detection of MG in this study. We loaded upconversion nanomaterials with a core/shell/shell structure (CSS-UCNPs) on polydimethylsiloxane as a fluorescence donor and Au@Ag nanoparticles as a fluorescence acceptor. The fluorescence quenching of CSS-UCNPs was caused by fluorescence resonance energy transfer. With MG present, the fluorescence of CSS-UCNPs was restored so that MG could be detected by fluorescence spectroscopy and smartphones. Under the optimal conditions, the limit of detection of MG based on image and fluorescence was 0.037 and 0.029 ng/mL, respectively. In addition, the dual-mode method had a broad linear range of 0.05-500 ng/mL and was not significantly different from the results of high-performance liquid chromatography. Therefore, the solid-phase biosensor developed in this study can be applied in the field of food safety and provides insight for rapid on-site detection.

Surface enhanced Raman scattering (SERS), as a non-destructive spectroscopic detection tool, has been widely utilized for the detection of potential toxins. Over the past few years, SERS research has been focusing on the development of substrates that have self-cleaning properties and strong SERS enhancement qualities. This study reports the design of a recyclable and tailorable SERS substrate based on three-dimensional (3D) nanofibers film of TiO2 loaded with silver nanoparticles (Ag NPs). The uniform TiO2 nanofibers film (TiO2 NFSF) was fabricated on the surface of Ti flakelet using hydrothermal method and Ag NPs were loaded via photocatalytic reduction to form TiO2 NFSF/Ti@Ag NPs. The designed TiO2 NFSF/Ti@Ag NPs were cut and carved to an appropriate size, and the malachite green (MG) evaluation demonstrates that the substrate has excellent uniformity, stability, reusability (at least five times), as well as high SERS enhancement factor (EF) (7.11 × 108). The developed SERS sensor has been successfully applied to detect MG in fish by using the new nanostructure. Moreover, the fabricated TiO2 NFSF/Ti@Ag NPs substrate could be employed as a promising and eco-friendly SERS enhancer for ultrasensitive detection of toxic substances found in food samples.

Low-cost and portable detection system for foodborne pathogens is crucial in assurance of food quality and public health. Therefore, a multiple signal amplified system with the advantages of easy operation, multiple signal detection mode and low-cost was developed to detect foodborne pathogens. In this study, Staphylococcus aureus (S. aureus) was selected as detection object to capture by the magnetic probe. Fe-MIL-88 enzyme-catalyzed leuco malachite green (LMG, white color with no SERS signal and absorbance properties) to malachite green (MG, green color with strong SERS signal and UV-Vis absorbance in 620 nm). However, the catalytic activity of the artificial Fe-MIL-88 enzyme was lost after adsorbing S. aureus aptamer. Consequently, a system was built to yield the catalytic product (MG) equivalent to the amount of S. aureus. SERS spectroscopy and spectrophotometer were applied to quantitatively detect S. aureus over the range of 10(1) -10(6) CFU/mL via detecting the signals of MG. SERS based method contributed a higher correlation coefficient (0.987) and a lower detection limit (1.95 CFU/mL) compared with UV-Vis method. T-test results showed no significant differences between the SERS based method and plate-counting method for detecting the amount of S. aureus, which indicated the proposed method had excellent accuracy to quantify pathogens in real samples.

Trace detection of toxic chemicals in foodstuffs is of great concern in recent years. Surface-enhanced Raman scattering (SERS) has drawn significant attention in the monitoring of food safety due to its high sensitivity. This study synthesized signal optimized flower-like silver nanoparticle-(AgNP) with EF at 25 degrees C of 1.39 x 10(6) to extend the SERS application for pesticide sensing in foodstuffs. The synthesized AgNP was deployed as SERS based sensing platform to detect methomyl, acetamiprid-(AC) and 2,4-dichlorophenoxyacetic acid-(2,4-D) residue levels in green tea via solid-phase extraction. A linear correlation was twigged between the SERS signal and the concentration for methomyl, AC and 2,4-D with regression coefficient of 0.9974, 0.9956 and 0.9982 and limit of detection of 5.58 x10(-4), 1.88 x10(-4) and 4.72 x10(-3) mu g/mL, respectively; the RSD value < 5% was recorded for accuracy and precision analysis suggesting that proposed method could be deployed for the monitoring of methomyl, AC and 2,4-D residue levels in green tea.

This paper demonstrates the application of surface-enhanced Raman scattering (SERS) using positive charged gold nanorods (Au NRs) as an enhancement substrate to classify Pseudomonas spp. coupled with multivariate methods. Four species of Pseudomonas as dominant spoilage bacteria of food were isolated from rotten chicken, namely, Pseudomonas gessardii (P9), Pseudomonas psychrophila (P8), Pseudomonas psychrophila (S2) and Pseudomonas fluorescens (T3). Au NRs were synthesized with positive charge by seed-mediated growth method which can be adsorbed onto the surface of the bacteria by electrostatic adsorption. SERS spectra were collected individually for four types of Pseudomonas and pretreated by mean centering (MC), then principal component analysis (PCA) and hierarchical clustering analysis (LDA) were used to achieve data dimensionality reduction and visualize the result of differentiation for the species of Pseudomonas. Particularly, the classification accuracy of LDA was reached to 100%. Following we applied hierarchical clustering analysis (HCA) to cluster each species of Pseudomonas and the results of HCA consistent with the results of 16S rRNA. This study has shown that SERS combined with LDA and HCA can be used as a reliable method to classify Pseudomonas.

In this study, surface-enhanced Raman spectroscopy (SERS) coupled with multivariate calibrations were employed to develop a rapid, simple and sensitive method for determination of mercury ions residues in dairy products. Initially, spherical Au@SiO2 core shell nanoparticles with highly enhancement effect were synthesized to serve as the SERS substrate. Afterwards, an optical sensor system, namely micro-Raman spectroscopy system, was constructed for rapid acquisition of Au@SiO2-mercury ions spectra. Then, ant colony optimization (ACO) and genetic algorithm (GA) were applied comparatively for selecting the characteristic variables from the Savitzky Golay-First derivative (SG-FD) processing data for subsequent quantitative analysis. Eventually, both linear (PLS and SW-MLR) and nonlinear (BPANN and BP-AdaBoost) methods were used for modeling. Experimental results showed that the variables selection methods significantly improved the model performance. Especially for the ACO algorithm, and the ACO-BP-AdaBoost model achieved the best results with the higher correlation coefficient of determination (R2 = 0.997), and lower root-mean-square error of prediction (RMSEP = 0.092) than other quantification models. Paired sample t-test exhibited no statistically significant difference (sig > 0.05) between the reference concentrations determined by inductively coupled plasma mass spectrometry (ICP-MS) and the predicted concentrations by ACO-BP-AdaBoost model in adulterated foodstuffs.

The study claims to exploit nanomaterials based fast sensing strategy at liquid-liquid interface for pesticide residues in short analytical time. Herein, a rapid extractive spectrometric platform for the trace levels determination of methamidophos (MET) in water has been established. It was based on the room temperature formation of nano-gold particles (AuNPs((org))) in organic phase (CHCl3) by ascorbic acid as a reducing agent and hexadecyl trimethyl ammonium bromide (HTAB) as a phase transfer agent at pH 10-11. The method employs response surface methodology (RSM) based on central composite design (CCD) for selecting appropriate conditions for optimal reagent functioning and liquid microextraction pretreatment factors. Traces of MET influences the surface plasmon absorbance of the formed AuNPs((org)) at lambda(max) = 520 nm with consequent lowering of the color. The stability for the formed AuNPs((org)), influence of temperature (298-338 K), TEM and EDS analysis were carefully performed. Under the optimized CCD experimental conditions, the obtained linear calibration range was 0.01-0.25 mu g mL(-1) with a limit of detection (LOD) 3.30 x 10(-3) mu g mL(-1) and limit of quantification (LOQ) 0.011 mu g mL(-1). A relative standard deviation (RSD) of 2.29 (n = 5) at 0.20 mu g mL(-1) of MET was obtained. The developed method was successfully applied for the analysis of trace concentrations of MET in environmental water samples with satisfactory recovery percentages (95.14 +/- 3.99-103.00 +/- 2.78). The suitability of the proposed method was further validated in terms of student t- and F tests at 95% confidence.

A novel wavelength selection method named ICPA-mRMR coupled SERS was employed for the detection of CPS residues in tea samples.