Rapid detection of carmine in black tea with spectrophotometry coupled predictive modelling

Electrochemical luminescence sensor for the detection of Allure Red: double luminescence cooperative amplification strategy of self-supporting material Zn3Cu2O2 and CdTe@MWNTs

The self-supporting material Zn3Cu2O2 was synthesized through the methods of electrospinning combined with high-temperature calcination. A novel sensitive electrochemiluminescence (ECL) sensor based on Zn3Cu2O2 and CdTe@MWNTs was constructed by the multi-step amplification strategy. First, Zn3Cu2O2 and CdTe served as the ECL groups to construct the sensor, yielding a larger signal of the sensor in the presence of the co-reactant K2S2O8. Multi-walled carbon nanotubes (MWNTs) were introduced as a carrier to increase the signal value of the sensor further. The synergistic action of CdTe@MWNTs and Zn3Cu2O2 made the system obtain the maximum initial signal. With the addition of Allure Red (AR), the ECL signal decreased. The quenching phenomenon was not only due to the large organic molecule AR occupying the active sites of Zn3Cu2O2 and CdTe, but also, more importantly, because the sensor existed as an ECL-RET mechanism between CdTe and AR. The response mechanism and experimental conditions of the system were also investigated. Under optimal conditions, the sensor showed a linear relationship between the ECL signal change and the logarithm of AR concentration in the range 1.0 × 10-14 to 1.0 × 10-6 mol L-1, the linear equation was ∆I = 15,295.54 + 956.42 log CAR, and the correlation coefficient was 0.9928. The lowest detection limit of (S/N = 3) was 1.67 × 10-15 mol L-1. Satisfactory results were obtained for the analysis  of beverages.

Rapid and real time detection of black tea rolling quality by using an inexpensive machine vison system

Rolling is a crucial step in black tea manufacturing. Rolling is achieved through a rolling machine, which achieves the processes of crushing, tearing, and curling. At present, the lack of intelligent detection for black tea rolling quality has limited the development of automation in the black tea industry. This study established qualitative and quantitative prediction models based on machine vision technology and chemometric methods for detecting black tea rolling quality. High-performance liquid chromatography (HPLC) was employed to quantify the contents of catechins and theaflavins (TFs). A qualitative discrimination model for rolling time was constructed using the DarkNet-53 convolutional neural network (DarkNet-53 CNN) with a dynamic learning rate, while a quantitative prediction model for TFs content was developed using the improved sparrow search algorithm optimized support vector regression (ISSA-SVR) during the black tea rolling process. The results indicated that, compared to other models, the DarkNet-53 CNN model presented in this study achieved superior discrimination of rolling time, attaining an overall accuracy of 97.82%. The ISSA-SVR model demonstrated considerable advantages in predictive performance, with an Rp value exceeding 0.9 and an RPD value surpassing 7.5. Therefore, this research introduces a low-cost and reliable method for rapid detection of black tea rolling quality for the first time.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Unlocking the elemental signature of European tea gardens: Implications for tea traceability

This study presents a comprehensive analysis of the elemental profiles of tea leaves coming from plants grown in several European gardens, with a focus on the bioaccumulation of essential and potentially toxic trace elements in relation to processing and location of tea garden. Samples were collected from various gardens across Europe, including Portugal, the Azores, Germany, the Netherlands, and Switzerland. Elemental analysis was conducted on fresh tea leaves, dried leaves, and leaves processed for the production of green and black tea, along with soil samples from the root zones of tea plants. The results reveal no significant differences in elemental content based on the processing of tea leaves. However, distinct elemental profiles were observed among tea leaves of plants grown in gardens from different European regions. Utilizing chemometric and machine learning tools, the study highlights the potential of these elemental profiles for enhancing the traceability of tea products.

Combining ion mobility spectrometry and chemometrics for detecting synthetic colorants in black tea: A reliable and fast method

Black tea (Camellia sinensis) is a widely consumed beverage and is subjected to adulteration. In this study, the combination of ion mobility spectrometry and machine learning techniques was employed to detect synthetic colorants in black tea. To accomplish our objective, six synthetic colorants (carmine, carmoisine, indigo carmine, brilliant blue, sunset yellow, and tartrazine) were added to pure tea at different concentrations. A qualitative model was built using partial least squares discriminant analysis (PLS-DA) for the collected data and exhibited 100% accuracy in identifying synthetic colorants in black tea. For quantitative analysis, a PLS regression model was employed. The R2 values obtained for the test set ranged from 0.986 to 0.997. The method developed in this study has proven to be reliable and effective in detecting synthetic colorants in black tea. Also, this method is a simple, rapid, and trustworthy tool for identifying adulteration in black tea.

A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration

Primary and secondary food processing had been identified as areas vulnerable to fraud. Besides the food processing area, other stages within the food supply chain are also vulnerable to fraud. This study aims to develop a Bayesian network (BN) model to predict food fraud type and point of adulteration i.e., the occurrence of fraudulent activity. The BN model was developed using GeNie Modeler (BayesFusion, LLC) based on 715 notifications (1979-2018) from Food Adulteration Incidents Registry (FAIR) database. Types of food fraud were linked to six explanatory variables such as food categories, year, adulterants (chemicals, ingredients, non-food, microbiological, physical, and others), reporting country, point of adulteration, and point of detection. The BN model was validated using 80 notifications from 2019 to determine the predictive accuracy of food fraud type and point of adulteration. Mislabelling (20.7%), artificial enhancement (17.2%), and substitution (16.4%) were the most commonly reported types of fraud. Beverages (21.4%), dairy (14.3%), and meat (14.0%) received the highest fraud notifications. Adulterants such as chemicals (21.7%) (e.g., formaldehyde, methanol, bleaching agent) and cheaper, expired or rotten ingredients (13.7%) were often used to adulterate food. Manufacturing (63.9%) was identified as the main point of adulteration followed by the retailer (13.4%) and distribution (9.9%).

Development of an ultrasensitive spectrophotometric method for carmine determination based on fluorescent carbon dots

A high-efficiency spectrophotometric method based on nitrogen-doped fluorescent carbon dots (N-FCDs) was developed for the ultrasensitive determination of carmine (CRM) in foodstuffs. The N-FCDs were fabricated via a one-pot hydrothermal method with m-phenylenediamine as the starting material. The detection principle was based on the fluorescence quenching effect of N-FCDs by CRM, where their interaction was due to the inner filter effect (IFE) and static quenching. A good linear relationship was established for CRM detection in a concentration range of 0.1-10.0 μM with a detection limit as low as 11.2 nM. The proposed method achieved satisfactory results for CRM determination in commercial food products with recoveries better than 98.6% and relative standard deviations (RSDs) less than 4.07%. The method established in this study was simple, ultrasensitive and reliable for rapid detecting CRM in a food matrix, which could be potentially used as a useful sensing agent for the analysis of additive food colourants.