On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods.

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.

Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models.

Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (RP2) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10-4 and 7.24 × 10-4 µg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.

A lead-based room-temperature phosphorescent metal-organic framework sensor for assessing the peroxide value of edible oils.

The peroxide value (PV) is an important indicator to assess quality of edible oils. However, traditional methods for determining PV are complicated for operating and lack sensitivity. In this paper, we report a fast, reusable, selective and sensitive room-temperature phosphorescence (RTP)-based sensor to determine the PV of edible oils. The sensor comprises a lead-based metal-organic framework (Pb-MOF, Pb4O(TPA)3, TPA: (terephthalic acid). Luminescence studies reveal that bright RTP of Pb-MOF quenched significantly by iodide ions (I-), a classic reductant for peroxides in edible oils, thus the determination of the PV is possible. Crucially, the proposed method yields responses within 10 min and has a wide linear range of 0.35-25.62 mmol/kg, a low detection limit of 30 µmol/kg, and high selectivity for PV detection. The sensing system was successfully applied to determine the PVs of edible oils and monitor the PV of rapeseed oil during storage.

Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering.

In this study, a novel analytical approach is proposed for the identification of pesticide residues in tea by combining surface-enhanced Raman scattering (SERS) with a deep learning method one-dimensional convolutional neural network (1D CNN). First, a handheld Raman spectrometer was used for rapid on-site collection of SERS spectra. Second, the collected SERS spectra were augmented by a data augmentation strategy. Third, based on the augmented SERS spectra, the 1D CNN models were established on the cloud server, and then the trained 1D CNN models were used for subsequent pesticide residue identification analysis. In addition, to investigate the identification performance of the 1D CNN method, four conventional identification methods, including partial least square-discriminant analysis (PLS-DA), k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF), were also developed on the basis of the augmented SERS spectra and applied for pesticide residue identification analysis. The comparative studies show that the 1D CNN method possesses better identification accuracy, stability and sensitivity than the other four conventional identification methods. In conclusion, the proposed novel analytical approach that exploits the advantages of SERS and a deep learning method (1D CNN) is a promising method for rapid on-site identification of pesticide residues in tea.

Signal optimized rough silver nanoparticle for rapid SERS sensing of pesticide residues in tea.

Trace detection of toxic chemicals in foodstuffs is of great concern in recent years. Surface-enhanced Raman scattering (SERS) has drawn significant attention in the monitoring of food safety due to its high sensitivity. This study synthesized signal optimized flower-like silver nanoparticle-(AgNP) with EF at 25 °C of 1.39 × 106 to extend the SERS application for pesticide sensing in foodstuffs. The synthesized AgNP was deployed as SERS based sensing platform to detect methomyl, acetamiprid-(AC) and 2,4-dichlorophenoxyacetic acid-(2,4-D) residue levels in green tea via solid-phase extraction. A linear correlation was twigged between the SERS signal and the concentration for methomyl, AC and 2,4-D with regression coefficient of 0.9974, 0.9956 and 0.9982 and limit of detection of 5.58 × 10-4, 1.88 × 10-4 and 4.72 × 10-3 µg/mL, respectively; the RSD value < 5% was recorded for accuracy and precision analysis suggesting that proposed method could be deployed for the monitoring of methomyl, AC and 2,4-D residue levels in green tea.

Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares.

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 108 was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10-4 to 1.0 × 103 µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10-4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).

Bioinspired morphology-controlled silver nanoparticles for antimicrobial application.

Phytochemicals sources have been extensively used as reducing and capping agents for synthesis of nanoparticles (NPs). However, morphology-controlled synthesis and shape/size dependent applications of these NPs still need to be explored further, and there is a need to develop a way in which particular and optimized phytochemicals result in the desired NPs in lesser time and cost with higher reproducibility rate. The present study is focused on morphology-controlled synthesis and shape/size dependent application of silver NPs based on the fractionated phytochemicals of Elaeagnus umbellata extract (EU). Unlike other approaches, in this study the reaction parameters such as time, temperature, pH, stirring speed and concentration of the precursor solutions were not altered during the optimization process. The fractionated phytochemicals were used separately for the synthesis of AgNPs, and the synthesized NPs were characterized by UV-visible, FT-IR, atomic force microscopy (AFM) and scanning electron microscopy (SEM). Our findings suggested that the constituents of the extract fractions varied with the selection of the extraction solvent, and the shape/size, bactericidal properties and toxicity of the NPs have a strong correlation with the phytochemicals of the plant extract. The fractionated phytochemicals present in the water fractions (EUW) resulted in monodispersed spherical AgNPs in the size about 40 nm. The NPs have significant stability in physiological conditions (i.e. temperature, pH and salt), have good antibacterial activity, and were found to be non-toxic. Furthermore, AFM and SEM analysis exposed that the NPs killed the bacteria by disturbing the cellular morphology and releasing the cellular matrix. Our results justify the use of different fractions of plant extract to obtain detail implications on shape, size, antibacterial potential and toxicity of AgNPs. This is the first step in a controllable, easy and cheap approach for the synthesis of highly stable, uniform, non-toxic and bactericidal AgNPs using five fractions of EU. The findings suggested that the synthesized NPs, particularly from EUW, could be used in pharmaceutical and homeopathic industry for the development of antibacterial medications.