Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy

Smartphone-based electrochemical sensor for cost-effective, rapid and on site detection of chlorogenic acid in herbs using biomass-derived hierarchically porous carbon synthesized by a soft-hard dual template method

To achieve excellent germplasm resource screening and ensure the quality control of herbal tea raw material, it is important to establish a cost-effective, rapid, and on site quantitative detection method for their bioactive constituents. We developed a smartphone-operated sensor for electrochemical detection of chlorogenic acid (CGA) using hierarchically porous carbon (DSiFPC), synthesized through a soft-hard dual template strategy with tannin acid as a carbon source, silica colloid as a hard template, and Pluronic F127 as a soft template. The DSiFPC modified glassy carbon electrode sensor showed excellent electrocatalytic ability towards CGA, with a wide linear range of 0.03-1 μM and a low limit of detection of 6.2 nM. It was successfully applied for detecting CGA in dried flowers of Lonicera japonica. Furthermore, a portable sensor utilizing a DSiFPC modified screen-printed electrode was employed for on site detection of CGA in fresh Eucommia ulmoides leaves, yielding satisfactory recoveries.

On-Site Multisample Determination of Chlorogenic Acid in Green Coffee by Chemiluminiscent Imaging

The potential of antioxidants in preventing several diseases has attracted great attention in recent years. Indeed, these products are part of a multi-billion industry. However, there is a lack of scientific information about safety, quality, doses, and changes over time. In the present work, a simple multisample methodology based on chemiluminiscent imaging to determine chlorogenic acid (CHLA) in green coffee samples has been proposed. The multi-chemiluminiscent response was obtained after a luminol-persulfate reaction at pH 10.8 in a multiplate followed by image capture with a charge-coupled device (CCD) camera as a readout system. The chemiluminiscent image was used as an analytical response by measuring the luminescent intensity at 0 °C with the CCD camera. Under the optimal conditions, the detection limit was 20 µM and precision was also adequate with RSD < 12%. The accuracy of the proposed system was evaluated by studying the matrix effect, using a standard addition method. Recoveries of chlorogenic acid ranged from 93-94%. The use of the CCD camera demonstrated advantages such as analysis by image inspection, portability, and easy-handling which is of particular relevance in the application for quality control in industries. Furthermore, multisample analysis was allowed by one single image saving time, energy, and cost. The proposed methodology is a promising sustainable analytical tool for quality control to ensure green coffee safety through dosage control and proper labelling preventing potential frauds.

Application of Chlorogenic acid as a substitute for antibiotics in Multidrug-resistant Escherichia coli-induced mastitis

Mastitis affects animal welfare and causes economic losses in the dairy industry. It is caused mainly by bacterial pathogens, among which Escherichia coli (E. coli) is one of the prominent causative agents. To treat bovine mastitis, antibiotics were widely used. However, their extensive and uncontrolled use has led to the emergence of multi-antibiotic-resistant strains. Indeed, a superbug of E. coli was successfully isolated from a mastitis-suffering cow and found resistant to at least 10 antibiotics. Therefore, the development of a universal therapeutic agent used as a replacement for the antibiotic is an immediate need in the dairy industry. To do so, we examined whether chlorogenic acid (CGA), a natural and herbal extract, could be a perfect alternative in mastitis treatment. In this study, we observed that the combination of CGA and antibiotic had an additive or synergistic effect; CGA fought against the superbug by directly targeting bacterial cell wall and membrane; CGA can significantly alleviate the mastitis caused by the superbug E. coli via its antimicrobial, antioxidant and anti-inflammatory activities. Collectively, these data indicated that CGA had a true potential to replace antibiotics during mastitis treatment.

Electrochemiluminescence resonance energy transfer system between ruthenium-based nanosheets and CdS quantum dots for detection of chlorogenic acid

A new strategy is proposed for ultrasensitive detection of chlorogenic acid (CGA) by fabricating an electrochemiluminescence resonance energy transfer (ECL-RET) sensing platform. The novel system designed by introducing ruthenium-based 2D metal-organic framework nanosheets (Ru@Zn-MOF) as ECL acceptor and L-cysteine capped CdS quantum dots (L-CdS QDs) as ECL donor, exhibited good ECL response. The possible mechanism of the modified electrode surface reaction was discussed. Modifying of the electrode surface by application of L-CdS QDs directly on ultrathin MOF nanosheets greatly shortened the electron-transfer distance and reduce energy loss, therefore significantly improving the ECL efficiency. The prepared sensor demonstrated good stability and highly selective detection of the target molecule. Under optimal conditions, the constructed sensor for the detection of CGA exhibited a wide linear range from 1.0 × 10^-10 to 1.0 × 10^-4 mol·L^-1 and a low detection limit of 3.2 × 10^-11 mol·L^-1 with a correction coefficient of 0.995. The recovery for spiked samples was calculated to be 94.4-109% and the RSD was 1.07-1.72% in real samples. The obtained sensor is considered to be a promising platform for CGA detection. Electrochemiluminescence resonance energy transfer (ECL-RET) sensing platform is used for the detection for chlorogenic acid.

The Biological Activity Mechanism of Chlorogenic Acid and Its Applications in Food Industry: A Review

Chlorogenic acid (CGA), also known as coffee tannic acid and 3-caffeoylquinic acid, is a water-soluble polyphenolic phenylacrylate compound produced by plants through the shikimic acid pathway during aerobic respiration. CGA is widely found in higher dicotyledonous plants, ferns, and many Chinese medicine plants, which enjoy the reputation of “plant gold.” We have summarized the biological activities of CGA, which are mainly shown as anti-oxidant, liver and kidney protection, anti-bacterial, anti-tumor, regulation of glucose metabolism and lipid metabolism, anti-inflammatory, protection of the nervous system, and action on blood vessels. We further determined the main applications of CGA in the food industry, including food additives, food storage, food composition modification, food packaging materials, functional food materials, and prebiotics. With a view to the theoretical improvement of CGA, biological activity mechanism, and subsequent development and utilization provide reference and scientific basis.

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

A Review on Electrochemical Sensors and Biosensors Used in Chlorogenic Acid Electroanalysis

Chlorogenic acid (5-O-caffeoylquinic acid) is a phenolic compound from the hydroxycinnamic acid family. Epidemiological, biological, and biochemical studies concur to support the beneficial role of chlorogenic acid in human health, along with other dietary phenolic compounds. Thus, chlorogenic acid has been reported to exert inhibitory effects on carcinogenesis in the large intestine, liver, and tongue, and a protective action on oxidative stress in vivo, together with anti-inflammatory, antidiabetic and antihypertensive activities. It is also claimed to have antifungal, antibacterial and antiviral effects with relatively low toxicity and side effects, alongside properties that do not lead to antimicrobial resistance. Due to its importance, numerous methods for determining chlorogenic acid (CGA), as well as for its derivatives from coffee beans and other plants, were elaborated. The most frequently used methods are infrared spectroscopy, high performance liquid chromatography (HPLC), capillary electrophoresis, liquid chromatography-mass spectrometry and chemiluminescence. Although these methods proved to be efficient for quantifying CGA and its derived products, a number of deficiencies were identified: they are time consuming, laborious, and require expensive instruments. Therefore, electrochemical methods have been developed and used in the determination of CGA in different nutraceuticals or food products. The present review aims to present the main progresses and performance characteristics of electrochemical sensors and biosensors used to detect CGA, as it is reported in a high number of relevant scientific papers published mainly in the last decade.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.