Potentiometric Determination of Biogenic Amines Using a Cucurbit[6]uril-PVC Sensing Membrane

Traditional Quality Control in the Food Industry consists mainly of microbiological and chemical techniques performed on samples of food extracted from production lines. Traditionally, the goal of the Quality Control department in the Food Industry has been the application of the minimal number of microbiological and chemical techniques to the samples of food, so as to have a decision on the quality of the production as quickly as possible. In this paper we are introducing a new computerized method to perform an automatic evaluation of the quality of the production, based on Artificial Intelligence (AI) techniques and Fractal Theory. The main idea in this paper is that, combining the use of the fractal dimension as a measure of classification of microorganisms and chemicals, and the use of AI to simulate the expert evaluation/decision process of the quality of the production, we obtain a computer program that is able to perform automated quality control in the food industry. We can conclude then, that the use of AI and Fractal Theory increases quality control efficiency (in accuracy and in time) because it eliminates the need of the application of a long sequence of microbiological and chemical techniques.

Recent advances in the application of molecularly imprinted polymers (MIPs) in food analysis

To effectively extract biogenic amines (BAs) from complex food matrices is an important issue. In this study, a stop-and-go-extraction tip (StageTip)-based method for simultaneous enrichment of six BAs including histamine, tyramine, putrescine, cadaverine, tryptamine, and β-phenylethylamine in fish samples was developed and optimized. This StageTip-based BAs extraction method required short extraction time, small sample size, and minimal consumption of organic solvents. In addition, an ultrahigh-performance liquid chromatography coupled with triple quadrupole mass spectrometer was used to identify and quantify BAs without derivatization. Target recovery efficiencies of BAs from spiked fish samples ranged from 84% to 110%. Linear regression models were established to determine BAs in the range of 0.4 to 3.0 mg/L (R2 > 0.995). Good repeatability was obtained with relative standard deviation of peak areas (1.17 to 5.61%) and retention time (0.09 to 0.20%). High sensitivity was achieved for the detection of six BAs (0.064 to 1.00 ng/L for the limit of detection, 0.10 to 5 ng/L for the limit of quantification). The proposed method had an obvious advantage in the analysis of BAs in complex food matrices such as fish tissues since it could reflect the subtle distribution of BAs from one fish fillet. PRACTICAL APPLICATION: A StageTip-based BAs enrichment combined with UPLC-MS analysis method was developed and optimized, which was low-cost, environmentally friendly, and with good analytical performances. As such, it has the potential to become a powerful tool for the analysis of BAs in different complex food matrices.

Surface plasmon resonance (SPR) based biosensor is a gold standard optical sensor for biological protein interaction in life science. In this paper, we firstly discuss how the SPR based sensor can give unique advantages over other sensing techniques for food safety and food quality control in food industry. We discuss the differences in sample preparation process for the SPR system and other screening methods and point out that the SPR can reduce the food screen quality control cost and time. A brief review of food analysis that has been tested under SPR system. Key requirements for building up a surface plasmon resonance based sensor for food industry especially in Thailand are highlighted. An SPR based sensor has been recently developed and constructed based on the requirements. We also discuss practical issues and how to possibly get around them.

Food industry is continuously innovating its capability of analysis by incorporating new technologies aimed to meet increasing quality and safety requirements, concurrence and consumers demands. In this context, non-destructive and non-invasive techniques are demanded in different stages of the production chain for quality control and verification. Air-coupled ultrasound is a fully non-invasive and non-destructive technique that has already been successfully implemented in other industries (aeronautical, aerospace, etc.) with fast scan velocities, and compatibility with industrial environment. This technique is capable to determine mechanical and viscoelastic properties of different materials as well as the presence of discontinuities, cavities, and foreign objects. Clearly, these capabilities are extremely interesting for inline quality control in the food industry. Being the main challenge of air-coupled applications the performance of air-coupled transducers (bandwidth and sensitivity), the objective of this work is to show a transducer design approach to simultaneously optimize both sensitivity and frequency band that make possible to operate in through transmission. In addition, food industry requirements in terms of materials and working conditions are also reviewed and included in the transducers design. Transducers with centre frequency at 300 and 500 kHz has been designed, built and tested. A first verification of the use of this technology in food products is shown revealing the possibility to measure transmitted signal above the noise level.

Identifying meat species accurately is crucial for food safety, fraud prevention, and quality control in the food industry. Mislabeling or adulterating meat products can lead to economic losses and pose health risks to consumers. However, conventional methods for species identification, such as DNA analysis or spectroscopy, are often timeconsuming and expensive. In recent years, highly sensitive sensors have made estimating food product types and freshness possible. The BME688 sensor produced by Bosch Sensortec is one of the most sensitive gas measurement sensors today. In this study, various types of meat were classified using machine learning methods on the data obtained by the BME688 gas sensor. Each type of meat has a distinct microbiota composed of specific microorganisms that influence its spoilage process and the volatile compounds it releases over time. VOCs and VSCs released into the environment by microorganisms that develop over time in meat types can be detected with this sensor. In this study, Decision Tree,Gaussian Naive Bayes, Bagging Tree, Support Vector Machine, Xgboost, Logistic Regression, Multi-Layer Perceptron, And Bosch AI-Studio Neural Network models were trained and tested on data obtained from different meat types using BME688. The accuracy values of the trained models were compared, and it was determined that the GNB and BT models have the highest potential for possible usage scenarios. Considering the results obtained, it was revealed that the BME688 sensor could distinguish chicken, sheep, and cattle meat with near-perfect accuracy.

We describe a new method to perform automated quality control in the food industry, based on the use of a new fuzzy-fractal approach. Traditionally, the goal of the quality control department in the food industry has been the application of the minimal number of microbiological and chemical techniques to the samples of food, so as to have a decision on the quality of the production as quickly as possible. The main idea in this paper is to combine the use of the fractal dimension as a measure of classification of microorganisms and chemicals with the use of fuzzy logic to simulate the expert evaluation/decision process of productions quality. We have developed an intelligent system that is able to perform automated quality control in the food industry.

Food Safety and Quality Control in Food Industry

An automatic method for detecting mites in flour has been established using hyperspectral imaging (HSI) system coupled with chemometrics. Reflectance differences among flour, Tyrophagus putrescentiae, and Cheyletus eruditus were relatively distinct. Majority of the shape features, including area, perimeter, the major, and minor axis in C. eruditus were remarkably larger than flour and T. putrescentiae. Textural features reached the level of the maximum significance except for contrast with one‐way analysis of variance. Images under the ant colony optimization (ACO) wavebands in random forests (RF) classification showed at least 89% recognition rates, better than the successive projections algorithm wavebands. Artificial neural networks (ANN) with ACO wavebands gave higher recognition accuracies in training and validation sets than RF. Further analysis verified hyperspectrum with ACO‐PCA‐ANN (PCA, principal component analysis), which showed over 98% accuracy. This study revealed the promising potential of HSI coupled with ACO‐PCA‐ANN as an accurate and rapid method for detecting mites in flour.Practical applicationsMite infection in flour is a global problem and an important research area related to stored food safety and quality control in the food industry. An effective classification method for mite contaminants with automated detection has not been previously established in the literature. Therefore, the current study reports an automated hyperspectral imaging (HSI) system coupled with chemometrics to detect mites Tyrophagus putrescentiae and Cheyletus eruditus in flour. This study revealed the promising potential of HSI coupled with ACO‐PCA‐ANN as an accurate and rapid method for detecting mites T. putrescentiae and C. eruditus in flour. We believe the knowledge gained in this research could be incorporated in the food storage and processing industry to promote the accuracy of detecting and identifying mites by using HSI system combined with chemometric algorithms.

This study evaluated the efficacy of cooking processes in natural and forced convection systems, for ensuring microbiological safety in industrial‐scale ham production. The cold point of the tanks was determined by measuring the water temperature every 2 min, with a maximum variation of 1.4°C between different regions of the tanks. The calculation of the lethal value (Fcal) was carried out using Enterococcus faecalis as the target microorganism due to its high resistance to heat treatment. The cooking temperature was measured at the geometric center of the ham and, in both convection systems, it reached the Fref value (23.6 min), necessary to achieve a reduction of 8 log (8 D), with Fcal of 45.7 and 45 min of cooking in the natural and forced convection systems. Both convection systems achieved microbial reductions exceeding 15‐log cycles (15 D), surpassing regulatory requirements despite occasional core temperatures below standard thresholds. In both systems (natural and forced), the cold spot was found on the left side, at a lower height, and toward the front of the tank. After heat treatment, no microorganisms were detected in the ham cooked in natural or forced convection systems. Both systems (natural and forced convection) proved viable at an industrial scale, provided that appropriate cooking protocols are followed.Practical ApplicationsThe practical applications of this study are significant for the food industry and regulatory bodies involved in food safety. These include quality assurance in ham production, food safety compliance, process optimization, risk mitigation, and guidance for industrial practices. Overall, this study has practical implications for improving food safety, quality control, and production efficiency in the ham industry. It fills a gap in validation studies on industrial‐scale ham cooking, providing valuable information for industry stakeholders and regulatory bodies alike.

Food quality and safety had become an increasing issue for quality authorities, food scientists, manufacturers, producers and consumers as well. In the present work, food quality control using bacterial identification isolated from food samples was performed. Greek olives (raw drupes), currants (dry fruits), roe (fish eggs), bacon and “feta” cheese were collected for this purpose. The aim of this study was to investigate the quality of traditional Greek food types and to compare bacterial population observed of each food matrix according to analysis method. Therefore, culture based and molecular methods were performed in parallel. Total bacterial DNA isolated from food samples was verified by PCR and PCR-RAPD amplification and then analyzed by Next Generation Sequencing. Results showed that NGS analysis can provide an advanced and detailed look at food microbiota in comparison to culture-dependent techniques. Climate conditions, environmental factors, even production or processing procedures can result an abundant bacterial diversity isolated from each food product, so NGS approaches can be suitable to elucidate the whole picture of bacteria populations. NGS analysis regarding food safety management can be applied in food industries and therefore quality and safety of food products can be reassured.

This first of three volumes on quality control in the food industry was designed to cover aspects which are applicable to all branches of the food industry. The second and third volumes will be devoted to specific ingredients or products.The first section on the organization for quality control by J Hawthorn considers the needs and reasons for quality control as well as the organization of the program and personnel required to handle a program. The approach of considering the problems arising in the quality control of a hypothetical food product made from acorns is quite refreshing.The consideration of the health problems is divided into the logically separated chemical and microbiological aspects. The first of these was covered by Alastair C. Fraser while the latter was handled by Betty Hobbs. Under the chemical aspects, the approach of balancing of benefits and risks is introduced along with the factors that determined the potential hazards or safety.

Food safety is crucial to guarantee human life health safety and promote social stability and social stability. However, as various kinds of food increase in the market, food quality and safety accidents take place frequently, and food processing and production are faced with severe challenges. Thus, hygiene and quality control in food processing is particularly important. Meanwhile, it is the key to improving business market reputation and maintaining social harmony and stability. This paper will analyze hygiene and quality control methods in food processing to show the importance of food quality control. experience long-term development and evolution. Food industry has been very developed and food varieties are very rich. Hence, food industry becomes a pillar industry in China. The development of food industry is confronted with numerous problems and food challenges. Gross value of food industry is a key index to measure development level of food industry. The proportion of food industry is 1.7:2~3:1 in developed countries, while the proportion of total output value of food industry is 0.34:2 in China. There is still a gap with developed countries. Besides, food industry accounts for 10% of total food consumption in China, while this figure is 70% in developed countries and 50% in other developing countries. In 2005, total value of food industry in China was only 190 million, less than 1/3 in the world. It thus can be seen that China still has a significant gap with developed countries in food industry, and overall development level falls behind. Even if the reserve of grain, vegetable, fruit and meat is very abundant, and they rank top in the world, many shortcomings exist in food processing and production. Low processing degree and semi-finished products still account for a large proportion, and the number of finished products is small. In food deep processing, the amount of deep processing is very limited, and accounts for only 7% of total food deep processing, while that figure is above 60% in developed countries. In one word, seeing from current food processing level, many defects exist and industrial development is faced with numerous problems. To narrow the gap with developed countries and improve food processing quality, it is required to combine development status, formulate and optimize development strategy. Problems in food processing. Food industry is most closely related to people's life. China is a multinational state, and regional differences are significant. In particular, significant differences exist in various regions in terms of dietary habits and consumption level. Regional differences are more

A ratiometric dual-channel fluorescent probe for selective Zn2+/Cd2+ sensing: Applications in food quality control, real-time monitoring in living cells, and mice.

This review paper critically examines the current state of research concerning the analysis and derivatization of aldehyde, aromatic hydrocarbons and carboxylic acids components in foods and drinks samples, with a specific focus on the application of Chromatographic techniques. These diverse components, as vital contributors to the sensory attributes of food, necessitate accurate and sensitive analytical methods for their identification and quantification, which is crucial for ensuring food safety and compliance with regulatory standards. In this paper, High-Performance Liquid Chromatography (HPLC) and Gas Chromatographic (GC) methods for the separation, identification, and quantification of aldehydes in complex food matrices were reviewed. In addition, the review explores derivatization strategies employed to enhance the detectability and stability of aldehydes during chromatographic analysis. Derivatization methods, when applied judiciously, improve separation efficiency and increase detection sensitivity, thereby ensuring a more accurate and reliable quantification of aldehyde aromatic hydrocarbons and carboxylic acids species in food samples. Furthermore, methodological aspects encompassing sample preparation, chromatographic separation, and derivatization techniques are discussed. Validation was carried out in term of limit of detections are highlighted as crucial elements in achieving accurate quantification of compounds content. The discussion presented by emphasizing the significance of the combined HPLC and GC chromatography methods, along with derivatization strategies, in advancing the analytical capabilities within the realm of food science.