Optical Determination of Lead Chrome Green in Green Tea by Fourier Transform Infrared (FT-IR) Transmission Spectroscopy

Mg-Al Layered Double Hydroxide Doped Activated Carbon Composites for Phosphate Removal from Synthetic Water: Adsorption and Thermodynamics Studies

Increased phosphate concentration in water bodies has led to eutrophication, and its removal is an inevitable requirement of sustainable wastewater purification systems. In this study, MgAl layered doubled hydroxide (LDH) composites doped on the surface of activated carbon (AC/MgAl LDH) with various (Mg + Al) total metal loading (5 wt%, 10 wt%, and 15 wt%) were prepared by the co-precipitation method. The influence of (Mg + Al) total metal loading onto AC was examined to remove phosphate ions from aqueous solutions. The effect of adsorption parameters, including adsorbent dosage, initial solution pH, initial phosphate concentration, contact time, and experiment temperature, were investigated via batch adsorption experiments. The adsorption results demonstrated that the phosphate adsorption capacity significantly improved with increasing the (Mg + Al) metal loading on the surface of AC. The maximum Langmuir phosphate adsorption capacity was 337.2 mg phosphate per gram of AC/MgAl-3 LDH composite (15 wt% Mg + Al) composite at pH ~6.3, 22 °C, and 1 g/L of adsorbent. The kinetic data were best fitted with the pseudo-second order model. The initial solution pH notably influenced the phosphate removal by AC/MgAl-3 LDH composite with a maximum removal at pH 2.3. According to the spent adsorbent characterization results, the dominant mechanisms of phosphate removal by AC/MgAl-3 LDH were electrostatic interactions, ion exchange, and inner-sphere complexation. The phosphate adsorption capacity was gradually increased with increasing the experiment temperature, suggesting an endothermic adsorption process. Overall, the AC/MgAl LDH composites pave the way for an effective strategy for phosphate removal from aqueous solutions.

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

Application of quadrupole-Orbitrap high-resolution mass spectrometry in rapid screening and identification of synthetic dyes in herbal medicines

In this work, a method combining ultra-high performance liquid chromatography and hybrid quadrupole-Orbitrap high-resolution mass spectrometry (HR-MS) was developed and validated for use in the simultaneous screening, identification, and quantification of 21 synthetic dyes in herbal medicines. To optimize the chromatographic conditions, we used a combined Full mass scan and data-dependent MS/MS (Full MS/dd-MS²) approach in positive and negative ion mode. Under this mode, selected ions with given fragmentation energy were subjected to a dd-MS² scan following a Full MS scan. The selectivity of this method was effectively improved using 70,000 full width at half maximum mass resolution and narrow mass window (typically 5 ppm), and a single injection was sufficient for simultaneous identification and quantification of 21 synthetic dyes within 10 min. The combined method was fully validated and complies with all criteria for selectivity, sensitivity, calibration curve linearity, accuracy, precision, recovery, matrix effect, and stability. All analytes showed excellent linear relationships as all the coefficients of determination (r²) are greater than 0.9978 over wide ranges of concentrations (e.g. 1.0-500 ng/mL for sunset yellow). The validated method was employed to detect synthetic dyes in herbal medicines and was demonstrated to provide a reliable technical basis for drug regulation and public health protection.

Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology

Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.