Quantitative determination of sunset yellow concentration in soft drinks via digital image processing

Pyridine ionic liquid functionalized bimetallic MOF solid-phase extraction coupled with high performance liquid chromatography for separation/analysis sunset yellow

An effective method based on the pyridine ionic liquid functionalized bimetallic MOF solid-phase extractant (Cu/Co-MOF@[PrPy][Br]) coupled with high performance liquid chromatography (HPLC) for the separation/analysis sunset yellow was established. Cu/Co-MOF@[PrPy][Br] was characterized by FTIR, XRD, SEM and TEM. Several important factors, such as pH, amount of extractant, extract time, and types of eluents were investigated in detail. Under the optimal conditions, linear range of the method was 0.05–40.00 μg mL⁻¹, the detection limit was 0.02 μg mL⁻¹, and the linear correlation was good (R² = 0.9992). The analysis of sunset yellow in soda, effervescent tablet and jelly proved that the method was simple and effective.

An effective method based on the pyridine ionic liquid functionalized bimetallic MOF solid-phase extractant (Cu/Co-MOF@[PrPy][Br]) coupled with high performance liquid chromatography (HPLC) for the separation/analysis sunset yellow was established.

Current progress in the utilization of smartphone-based imaging for quality assessment of food products: a review

The cell phone has been merely used for image acquisition and transmission in the last decades. Owing to the recent technological progress, its new generation, i.e., the smartphone, draws remarkable attention to food quality assessment with versatile applications. Smartphones possess high-resolution cameras, enabling them to be used instead of digital cameras in the computer vision system. Furthermore, their programmability and portability have recently encouraged researchers to introduce smartphone-based image processing in food analytical studies. This promising approach has advantages such as high sensing capability, being user friendly, and cost-effective over the conventional method, and therefore might be considered an emerging nondestructive technique for quality control purposes. However, there is a great effort to tackle implementation, calibration, as well as industrialization issues. In this context, this review aims to highlight the most recent studies of smartphone-based imaging systems in various food systems such as dairy, meat, fruit, and vegetables. Besides, the existing challenges and future trends for applying smartphones in food quality control are discussed. Although moving the computer vision systems toward a portable tool like a smartphone improves its versatility, more research works are needed to resolve its set-up weakness and limitations.

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Simultaneous Spectrophotometric Determination of Brilliant Blue and Tartrazine in Diverse Sample Matrices after Solid Phase Extraction

Background

Brilliant blue (BB) and tartrazine (TZ) are manufactured from petroleum and its products. These are the most popular consumed food dyes and are widely used in foodstuffs. Therefore, overuse of these dyes in foodstuffs and consumption of excessive amounts of these dyes can lead to health problems in humans.

Objective

The aim of this study was to develop a simple separation and preconcentration method for simultaneous spectrophotometric determination of BB and TZ dyes.

Methods

A column solid-phase separation extraction method combined with UV-Vis spectrophotometry was preferred and developed for single and simultaneous determination of BB and TZ dyes.

Results

The preconcentration factor was obtained as 80. Relative standard deviations were below than 4%. Detection limits of the method were determined as 0.29 and 1.21 µg/L for BB and TZ, respectively. Recovery values were obtained between 95–99% and 96–100% for BB and TZ, respectively. 10.9–235.7 µg/g and 1.7–8.0 µg/mL of BB contents of real samples were determined for solid and liquid samples, respectively. TZ concentrations of solid and liquid samples were ranged between 18.7–220.7 µg/g and 5.9–7.5 µg/mL, respectively.

Conclusions

Quantitative extraction results and satisfactory recovery values showed that method was successful and applicable for determination of BB and TZ concentrations in real pharmaceutical, industrial, and foodstuff samples.

Highlights

The method has exhibited a high preconcentration factor and effective separation against to matrix ions. The method did not need an experienced operator with high operation experience. Elution solvent can be chosen according to the availability of the chemicals in the laboratory and cheapness of the chemicals.

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.