Food Quality Inspection Research Articles

Rapid Detection of Spoiled Apple Juice Using Electrical Impedance Spectroscopy and Data Augmentation-Based Machine Learning

T he demand for fresh fruit juices is increasing, however, some food processors are using spoiled fruit for juice production in order to seek higher profits. Electrical impedance spectroscopy (EIS), in combination with machine learning, enables wide applicability in food quality inspection, as it does not necessitate expensive instruments or complex sample processing. However, EIS data vary with temperature changes. The measurement of EIS data requires consistent temperature conditions. To overcome this limitation, data augmentation was applied to train the model based on EIS to improve the performance and robustness of the model. The training set consists of 200 EIS data measured under consistent temperature conditions and 200 EIS data with added noise and a recognition model for detecting spoiled apple juice was established. Under inconsistent temperature conditions, the model’s accuracy in identifying spoiled fruit juice reached 98% on the test set, while the accuracy dropped to 50% without data augmentation. This study demonstrates that the application of data augmentation on the training set reduces the need for consistent temperature conditions during the collection of EIS data, thereby improving the model’s robustness and eliminating the waiting time for data stability. Therefore, applying data augmentation to EIS and machine learning provides a rapid, practical, and reliable method for assessing the quality of liquid products

Research progress on the detection of water-soluble vitamins in food using liquid chromatography

Currently, liquid chromatography is widely used for the analysis and determination of water-soluble vitamins (WSVs) in functional foods. However, due to the diversity of sample characteristics and the requirement for purification accuracy, the chromatographic conditions and specific methods used vary. This paper reviews the chromatographic conditions of liquid chromatography techniques for detecting WSVs in food, as well as the selection of liquid chromatography methods in different application fields. The chromatographic conditions include chromatographic columns, column temperature, mobile phase, flow rate, and elution gradient, and the application scope of different chromatographic conditions is compared. Liquid chromatography methods include high-performance liquid chromatography and liquid chromatography-tandem mass spectrometry, and discussions are made on the applications and prospects of these two methods under different chromatographic conditions. Therefore, this review provides research insights into different chromatographic conditions and methods for food quality inspection and biochemical analysis research.

Simultaneous determination of pigments of spinach (Spinacia oleracea L.) leaf for quality inspection using hyperspectral imaging and multi-task deep learning regression approaches

Rapid and accurate determination of pigment content is important for quality inspection of spinach leaves during storage. This study aimed to use hyperspectral imaging at two spectral ranges (visible/near-infrared, VNIR: 400–1000 nm; NIR: 900–1700 nm) to simultaneously determine the pigment (chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids) content in spinach stored at different durations and conditions (unpackaged and packaged). Partial least squares (PLS), back propagation neural network (BPNN) and convolutional neural network (CNN) were used to establish single-task and multi-task regression models. Single-task CNN (STCNN) models and multi-task CNN (MTCNN) models obtained better performances than the other models. The models using VNIR spectra were superior to those using NIR spectra. The overall results indicated that hyperspectral imaging with multi-task learning could predict the quality attributes of spinach simultaneously for spinach quality inspection under various storage conditions. This research will guide food quality inspection by simultaneously inspecting multiple quality attributes.

ANALIZA FUNKCJONOWANIA GOSPODARSTW EKOLOGICZNYCH W POLSCE W LATACH 2005–2020

The objective of the present work is to evaluate the performance of organic farms, their production potential, the size of crop and livestock production and the opportunities for their development in Poland in the years 2005–2020. The work draws on statistical data published by the Agricultural and Food Quality Inspection (IJHARS) from the years 2005–2021. The data was characterised by calculating the dynamics index. Moreover, 18 organic farms located in the Mazowieckie Voivodship were surveyed. The primary data was collected using the direct interview method at the end of 2020. The results were used to calculate the number and percentage of organic farms. In the studied period, that is in the years 2005–2020, a variety of changes took place in organic farming in Poland. Until 2013, there occurred a steady increase in the number and area of organic farms. From 2014 to 2020, the number dropped by 30% whereas the organically farmed area decreased by 24%. What is more, a decline in the number of medium-sized farms (5–20 ha) was accompanied by a rise in the number of larger farms (whose area exceeded 20 ha). There were also noticeable changes in the structure of land use, organic production being abandoned in grasslands (meadows and pastures) in favour of cereals and other crops, orchards, berries and field vegetables. In the years 2005–2020, there was a positive trend of growing numbers of livestock, that is dairy cows, sheep, goats, it being particularly high for young beef cattle and laying hens. By contrast, a decline was observed in the number of fattening swine. The causes of the declining numbers of organic farms and organically managed area are economic in character and include scattered production, difficulties with selling organic products, strong reliance of organic production, animal production in particular, on public support, high costs and low profitability of this type of agricultural activity and low incomes earned by organic farms. The future performance of organic holdings and the development of this type of agriculture will be conditioned by both economic and organisational factors, as well as sufficient support of organic production, integration and cooperation among entities operating in this sector, well organised market as well as availability and promotion of organic products, which will hopefully increase demand on and marketisation of this production. It is important that organic farms should rely on balance between crop and livestock production so as to expand mixed production, which, in turn, will let them gain competitive advantage and become part of the concept of sustainable development.

A High Throughput Tunable Filter Module for Multispectral Imaging

AbstractTunable filters are vital not only for dynamic display but also in the miniaturized spectral imaging system. Yet, the present tunable devices possess low optical throughput, bulky volume, slow response, or complex configuration. In this paper, the study proposed and experimentally demonstrated a compact tunable filter module based on metagratings combined with liquid crystals (LC). By the synergetic effect between the birefringent LC and the dichroic metagratings, the module possesses distinct transmissive features by electrically tuning the LC material, resulting from the polarization interference effect. A simple nanoimprinting method, which is suitable for the mass production of nanostructures is adopted for low‐cost fabrication. These unique features endow the present tunable filter module with low cost, high optical throughput, and compact volume. As a proof of concept, a multispectral imager is built with this filter based on a commercial microscope. Experimental results show the system response is five times higher in optical throughput across the whole visible region than the traditional liquid crystal tunable filter and the signal‐to‐noise ratio can be improved by 7 dB. Considering the feasibility of nanoimprinting, this module paves the way for spectral imaging in low‐light conditions and on‐site scenarios such as tongue diagnosis and food quality inspection.

Research on Quality Control and Problems of Food Inspection and Testing

At present, the food quality inspection industry has become an important foundation of the national quality development strategy. Combined with China's current food inspection and testing work to implement the status quo analysis, there are still problems in the development stage, the need to grasp the actual situation based on the factors affecting the targeted solution to ensure that the food inspection and testing of the work of the importance of the need for a strong.

Research on Quality Control and Problems of Food Inspection and Testing

At present, the food quality inspection industry has become an important foundation of the national quality development strategy. Combined with China's current food inspection and testing work to implement the status quo analysis, there are still problems in the development stage, the need to grasp the actual situation based on the factors affecting the targeted solution to ensure that the food inspection and testing of the work of the importance of the need for a strong.

Application of Multispectral Imaging Technology in Food Quality and Safety Inspection

Application of Multispectral Imaging Technology in Food Quality and Safety Inspection

Non-destructive internal disorder segmentation in pear fruit by X-ray radiography and AI

A particular challenge for food quality inspection remains the quantification of internal defects. This work addresses the challenge of internal defects segmentation in pear fruit (cv. Conference) based on high-throughput inline X-ray radiography images in combination with deep learning. Unlike previous approaches, which focused on healthy vs defect classification, the current segmentation approach is able to determine the type of defect, its dimensions, and location. To this end, a novel simulation method was designed to obtain input-target image pairs of radiography data, and more diverse defect pears were generated using a conditioned generator model. The obtained data contributed to the design of a segmentation model that labeled every pixel in the X-ray radiographs of pear fruit as ‘external air’, ‘healthy tissue’, ‘core’, ‘browning’, or ‘cavity’. We demonstrated that additional synthetic data in the learning process drastically improved the model performance. For instance, the mean IoU increased from 0.781 ± 0.112 to 0.883 ± 0.088 for consumable pears with minor cavities and browning. In terms of utility, our segmentation maps provide more detailed information about the type, size, and location of the disorders compared to the heatmaps produced by existing benchmark classifiers.

Organic Milk Production Sector in Poland: Driving the Potential to Meet Future Market, Societal and Environmental Challenges

During the pandemic, health and environmental issues were re-evaluated, stimulating interest in organic food. Organic milk is more beneficial than conventional milk in terms of the health-promoting substances (e.g., vitamins, fatty acids), particularly regarding its protein and lipid fractions. Poland is the main producer of cow’s milk in the EU, but only 0.2% of this production is organic. To identify the antecedents and obstacles to increasing organic milk production in Poland, the aim of this study is to explore the low level of organic cow’s milk output in Poland compared with sector leaders in the EU. We analyzed statistics on milk production, including unpublished data collected by the Agricultural and Food Quality Inspection, Poland, with the use of descriptive statistics and frequency distributions. Over the period 2010–2020, the number of farms producing and selling organic milk and the number of farms with organic pastures and meadows decreased significantly in Poland. A limited number of certified farms sell their milk as organic. If all production of organic milk was sold as organic, the supply would have increased by 56% in 2020. Organic milk processing is highly concentrated which is an obstacle to the development of organic milk farming. Due to its high instability and the spatial differences in the locations of sellers (producers) and buyers (processors) and inconsistent production volumes, the supply of organic cow’s raw milk does not meet the demand from Polish dairies. Cooperation between producers and processors is essential. Providing organic dairy farms with production and market advice, as well as technical support, is crucial for the development of farms and the organic milk sector as a whole, as is, to a certain extent, evidenced by Austria. The specific nature of the raw milk market requires the procurement of organic milk on a lasting basis regarding guarantees of purchase and farmgate prices. In this way, the continuity of organic raw milk supply would be secured and the sector of organic dairy products should grow.

Artificial Intelligence Based Predictive Analysis of Customer Churn

Deep learning has been evidenced to be a cutting-edge technology for big data scrutiny with a huge figure of effective cases in image processing, speech recognition, object detection, and so on. Lately, it has also been acquainted with in food science and business. In this paper, a fleeting overview of deep learning and detailly labelled the structure of some prevalent constructions of deep neural networks and the method for training a model is provided. Various techniques that used deep learning as the data analysis tool are analyzed to answer the complications and challenges in food sphere together with quality detection of fruits & vegetables. The precise difficulties, the datasets, the pre-processing approaches, the networks and frameworks used, the performance attained, and the evaluation with other prevalent explanations of each research are examined. We also analyzed the potential of deep learning to be used as a cutting-edge data mining tool in food sensory and consume explores. The outcome of our review specifies that deep learning outclasses other approaches such as physical feature extractors, orthodox machine learning algorithms, and deep learning as a capable tool in food quality and safety inspection. The cheering outcomes in classification and regression problems attained by deep learning will fascinate more research exertions to apply deep learning into the arena of food in the forthcoming. The main aim of this work is to facilitate our learning and implement that in real life. Food quality and food security are always issues which are always overlooked. In modern times, this has morphed into more significant concerns relating to optimization of on- demand supply chains and profitability of agri-businesses. But now with the advanced systems and technology, it is possible to resolve this issue efficiently using the power of AI.

Efficient Near-Infrared Organic Photodetectors with Spectral Response up to 1600 nm for Accurate Alcohol Concentration Detection

The development of near-infrared organic photodetectors (NIR-OPDs) in 1000-1700 nm is essential for medical monitoring, food quality inspection, machine vision, and biomedical imaging. However, when solving the high dark current density (JD) in bulk-heterojunction (BHJ) NIR-OPDs based on narrow-bandgap systems, it is often accompanied by photocurrent loss, which is a great challenge in achieving high-performance NIR-OPDs. Here, an ideal hybrid pseudo-PHJ (planar-heterojunction)/BHJ structure is proposed to overcome this challenge, which is introducing the N2200 layer between the cathode and BHJ. The introduction of the N2200 raises the external charge injection barrier and reduces the trap density, thus achieving significant suppression of JD (6.22 × 10-7 A cm-2 at -0.2 V bias, about 2 orders of magnitude lower compared to the BHJ NIR-OPDs). Meanwhile, the hybrid structure combines the advantages of PHJ and BHJ, thus maintaining a high photocurrent, resulting in responsivity and detectivity of 18.71 mA W-1 and 4.19 × 1010 Jones, respectively, at 1400 nm at -0.2 V bias, which is superior to the performance of BHJ NIR-OPDs. And the hybrid structured NIR-OPDs are proven to rapidly quantify the alcohol content of mixtures to within 2% accuracy, which exhibits great potential for application in the food and pharmaceutical industries.

Tendencies and Perspectives of Organic Farming Development in the EU – the Significance of European Green Deal Strategy

Organic system of agricultural production is particularly important in the context of agriculture sustainability. Taking into consideration sustainability premises, agricultural sector should deliver environmental, social and economic profits. One of the most important form of agriculture in this context is organic production. In 2019, the European strategy of Green Deal underlined the need of further and faster development of organic farms in the EU. The purpose of the paper is establishing current tendencies, as well as perspectives of organic farming development, including its market environment in the EU. The accent was focused on Poland`s situation and experiences. The period of the analysis covered the years 2012-2020. The data used in the research came from national institution Agricultural and Food Quality Inspection, Statistics Poland reports and papers, as well as database of Eurostat. Indicator analysis methods were used and trends were established.
 The results of the study revealed, that during the analyzed period, organic farming developed in the EU. The main statistics, such as the number of organic farms and organic agricultural land improved significantly. The current trends in individual European countries in the field of development of the organic management system indicate, that this segment of the agricultural market has different significance in individual countries, which may also result from local economic and market conditions. Based on the research results, it was stated, that organic farming development in the EU – in the nearest further – will be challenging, especially in the context of the european strategic purposes, presented in Green Deal documents. 
 Keywords: organic farming, organic farms, agricultural land in organic system of production, organic processors, European Green Deal strategy, EU, Poland

Component identification of subsurface food additives by spatially offset Raman spectroscopy

AbstractSpatially offset Raman spectroscopy (SORS) is an important branch derived from conventional Raman spectroscopy. It has the characteristics to realize the concealed component identification covered by opaque and translucent media nondestructively and rapidly. This technology gradually plays an important role in basic and applied research of numerous areas. This paper focuses on the SORS potential application in food quality and safety inspection, and a SORS detection system has been setup. The ingredients of various concealed food additives have been identified by SORS and the optimum offset distance for detection has been obtained. Meanwhile, the identification effectiveness of the system for low‐concentration additives in real foods has also been evaluated.

Experimental investigation of supercontinuum generation in a birefringence tellurite microstructured optical fiber.

A four-hole birefringence tellurite microstructured optical fiber (BTMOF) was designed and fabricated based on 76.5T e O 2-6Z n O-11.5L i 2 O-6B i 2 O 3 glass, and its core (slow and fast axes were) measured to be approximately 4.74µm and 4.29µm, respectively. The experimentally measured results demonstrated that the maximum supercontinuum (SC) spectra extended from ∼914.1n m to ∼1885.1n m when the polarization state of the pump pulse was parallel to the fast axis at 1400nm with an average power of 460mW. We performed numerical simulations based on the nonlinear Schrödinger equation, which support the experimentally measured results. The SC generation in birefringent silica microstructured fiber with the same geometric parameters was simulated, and the results showed that the enhanced nonlinear refractive index of the BTMOF yielded a spectrum with a significantly larger bandwidth. Furthermore, the two polarization states along the fast axis and slow axis exhibit different dispersion characteristics, which provide a convenient way of tuning the properties of the generated SC. This work highlights BTMOF as a promising platform for the development of a SC light source, which can be widely used in food quality inspection, early cancer diagnostics, gas sensing, and high-spatial-resolution imaging.

Recent advances in electrochemical-based sensors amplified with carbon-based nanomaterials (CNMs) for sensing pharmaceutical and food pollutants.

Foodborne-related infections due to additives and pollutants pose a considerable task for food processing enterprises. Therefore, the competent, cost-effective, and quick investigation of nutrition additives and contaminants is essential to reduce the threat of public fitness problems. The electrochemical sensor (ECS) shows facile and potent analytical approaches desirable for food protection and quality inspection over traditional methods. The consequence of a broad display of nanomaterials has paved the path for their relevance in designing high-performance ECSs appliances for medical diagnostics and conditions and food protection. This review article has discussed the importance of electrochemical-based sensors amplified with carbon-based nanomaterials (CNMs). Initially, we have demonstrated the types of pharmaceutical and food/agriculture pollutants (such as pesticides, heavy metals, antibiotics and other medical drugs) present in water. Subsequently, we have compiled the information on electrochemical techniques (such as voltammetric and electrochemical impedance spectroscopy) and their crucial parameters for detecting pollutants. Further, the applications of CNMs for sensing pharmaceutical and food pollutants have been demonstrated in detail. Finally, the topic has been concluded with existing challenges and future prospects.

Rapid and ultra-sensitive detection of pork DNA with surface enhanced Raman spectroscopy for onsite food quality inspection

Determination of pork DNA is essential to avoid the serious allergens imposed by it as well as for halal authentication for certain community. The needed sensor should be able to identify the pork DNA in rapid way and sensitive to ultra-low molarity detection. In this work we developed a surface enhanced Raman spectroscopy sensor (SERS) using silver nanoparticles (AgNPs) on Si matrix. A deep investigation of the effect of the laser wavelength on the sensing efficiency has been performed. Wavelength dependent sensing mechanisms are deliberately studied in the current research. Further the selectivity of sensor towards Pork DNA discrimination is exhibited with Chicken DNA analysis. The developed SERS sensor identifies the Pork DNA down to 13.6 fg/ μL proving its excellent sensitivity. The statistical outfit studies with regard to the developed sensor shows the low values of RSD (∼3%) adding advantage of the developed SERS sensor biosensor. The SERS sensor will definitely serve to be an excellent fast, selective and sensitive tool for onsite screening of meat products to cease the adulteration.

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Food safety is a major concern that has an impact on the national economy and people's lives. The food industry has grown in quality and innovation in tandem with the rapid development of the economy and society. The emergence of new food technologies, as well as changes in dietary habits, has increased public concern about food safety. With the emergence of various counterfeit and substandard products, food quality and safety testing have become even more important. Traditional testing methods rely on sensory analysis and physical and chemical analysis. This approach is subjective and poorly adapted to the general public. It requires a high level of technical operation and is difficult to carry out on a large scale. To address this situation, this paper proposes an intelligent approach to food safety quality testing. The core idea is, first, to use sensors to collect data on the various components of the sample to be tested. Second, the random forest (RF) model used in this paper is trained. Third, the trained model is used to classify and identify the test samples. Based on the classification results, a conclusion is drawn as to whether the food product is a variant or a counterfeit. The advantage of this study is that the training model used is a weighted RF algorithm based on mutual information. The correlation between any two decision trees is calculated using mutual information, and for the more correlated decision trees, only the one with the highest evaluation accuracy is retained to form a new RF, and the evaluation accuracy is converted into voting weights, resulting in an RF model with less redundancy and higher evaluation accuracy. The experimental results show that the method used in this paper can successfully identify spoiled or counterfeit products and has good practicality.

DEVELOPMENT OF ORGANIC FARMING AND ITS ENVIRONMENT IN POLAND IN THE LIGHT OF THE EUROPEAN GREEN DEAL CHALLENGES

The aim of European Green Deal (EGD) is to implement a pro-environmental and pro-climate policy, which should be translated into economic practice. EGD concerns agriculture, including organic, hence the importance of recognizing the current path of development of this management system and future prospects. The aim of this paper is illustration of current challenges in organic farming and its environment in Poland, taking into account European strategic and market goals. The population of organic farms and other organic entities operating in this segment were analyzed. The period covered 2004-2020, and data of Agricultural and Food Quality Inspection and Statistics Poland. Indicator analysis methods were used and trends were established. A comparison of the statistics for 2020 and 2004 showed the development of organic farming in Poland, taking into account the farms’ number and the agricultural land in organic system. The period 2004-2020 was not homogeneous, and the last years can be considered as a period of stagnation. In the adopted perspective, the production profile of organic farms changed, which more and more often carried out only crop production, omitting livestock production. The further development of organic farming will be an important challenge, also taking into account the level of development of the market environment of organic farms, in particular the number of processors and operators involved in the supply of certified seed. The main determinant of the development of organic farming in Poland was, and in the next few years probably will be, the institutional factory.

Food Quality Inspection Using Uncertain Rank Data

Food Quality Inspection Using Uncertain Rank Data