Feasibility study for the use of colorimetric sensor arrays, NIR and FT-IR spectroscopy in the quantitative analysis of volatile components in honey

Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review

Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.

Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIR_LL, CSA-MIR_LL, and NIR-MIR_LL) and mid-level (CSA-NIR_ML, CSA-MIR_ML, and NIR-MIR_ML) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R²p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R²p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R²p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIR_ML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice.

Acacia Honey-derived Bioactive Compounds Exhibit Induction of p53-dependent Apoptosis in the MCF-7 Human Breast Cancer Cell Line

Background

Research studies have focused on discovering new anti-proliferative and pro-apoptotic agents derived from natural products from which honey constitutes a prominent candidate. The Acacia honey (AH) is known to display anticancer activity, but the mechanisms of action are still not well defined.

Objectives

Using in vitro and computational approaches, we aimed to assess the interaction among selected bioactive compounds derived from AH, with the apoptotic protein p53, which could trigger apoptosis.

Methods

The phytocompounds of AH were investigated via gas chromatography–mass spectrophotometry analysis. The cytotoxic effect and induced apoptosis on the MCF-7 breast cancer cell line were assessed by 3-(4,5-dimethylthiazolyl-2)-2,5 diphenyltetrazolium bromide and acridine orange-ethidium bromide staining approaches. The molecular docking analysis between AH compounds and p53 was carried out.

Results

The drug-likeness prediction revealed that most of the identified compounds meet Lipinski’s rules. We demonstrate that AH exerts an interesting cytotoxic effect in a dose-dependent manner against the MCF-7 cell line with IC50 5.053µg/mL. Significant cell alterations and notable induced apoptosis were detected when cells were treated with AH. The molecular docking analysis revealed that melezitose is among the most important potential bioactive compounds that interact with p53 leading to apoptosis. The binding affinity was −8.1 kcal/mol, and the closest molecular interactions in the active site included 10 residues, which could explain the potential biological activity.

Conclusion

This work sheds light on AH as a significant source of bioactive chemicals with potential for promoting apoptosis that may be exploited as an alternative therapy for breast cancer.

Smart films fabricated from natural pigments for measurement of total volatile basic nitrogen (TVB-N) content of meat for freshness evaluation: A systematic review

Major databases were searched from January 2012 to August 2021 and 54 eligible studies were included in the meta-analysis to estimate the overall mean of total volatile basic nitrogen (TVB-N) in meat. The mean of TVB-N was 24.96 mg/100 g (95 % CI:23.10-26.82). The pooled estimate of naphthoquinone, curcumin, anthocyanins, alizarin and betalains were 25.98 mg/100 g (95 %CI:19.63-32.33), 30.03 mg/100 g (95 %CI: 24.15-35.91), 24.92 mg/100 g (95 %CI: 22.55-27.30), 23.37 mg/100 g (95 %CI:19.42-27.33) and 19.50 mg/100 g (95 %CI:17.87-21.12), respectively. Meanwhile, subgroups based on meat types showed that smart film was most used in aquatic products at 27.19 mg/100 g (95 %CI:24.97-29.42), followed by red meat at 19.69 mg/100 g (95 %CI:17.44-21.94). Furthermore, 4 °C was the most storage temperature used for testing the performance of smart films at 25.48 mg/100 g (95 %CI:23.05-27.90), followed by storage at 25 °C of 25.65 mg/100 g (95 %CI:22.17-29.13). Substantial heterogeneity was found across the eligible studies (I² = 99 %, p = 0.00). The results of the trim-and-fill method demonstrated publication bias was well controlled.

An efficient differential sensing strategy for phenolic pollutants based on the nanozyme with polyphenol oxidase activity

To realize the efficient differential sensing of phenolic pollutants in sewage, a novel sensing strategy was successfully developed based on one nanozyme (GMP-Cu) with polyphenol oxidase activity. Phenolic pollutants can be oxidized by GMP-Cu, and the oxidation products reacts subsequently with 4-aminoantipyrine to produce a quinone-imine compound. The absorption spectra of final quinone-imine products resulted from different phenolic pollutants showed obvious differences, which were due to the interaction difference between GMP-Cu and phenolic pollutants, as well as the different molecular structures of the quinone-imine products from different phenolic pollutants. Based on the difference of absorption spectra, a novel differential sensing strategy was developed. The genetic algorithm was used to select the characteristic wavelengths at different enzymatic reaction times, HCA and PLS-DA algorithms were utilized for the discriminant sensing of seven representative phenolic pollutants, including hydroquinone, resorcinol, catechol, resorcinol, phenol, p-chlorophenol, and 2,4-dichlorophenol. Scientific wavelength selection algorithm and recognition algorithm resulted in the successful identification of phenolic pollutants in sewage with a discriminant accuracy of 100%, and differentiation of the phenolic pollutants regardless of their concentration. These results indicate that sensing strategy can be used as an effective tool for the efficient identification and differentiation of phenolic pollutants in sewage.