Food Quality Detection Research Articles

Multifunctional performance of chitosan/soy protein isolation-based films impregnated carbon dots/anthocyanin derived from purple hull pistachio for tracking and extending the shelf life of fish

The present study aimed to develop a sustainable biopolymer film that exhibits active and intelligent properties. The chitosan/soy protein isolation (Cs/SPI) based film was prepared by blending purple hull pistachio anthocyanin (PHPA) with carbon dots (CDs), which were synthesized using a by-product from purple hull pistachio (PHP) extraction. PHPA and CDs were shown to be compatible with the Cs/SPI biopolymer films. The resulting matrix was a homogeneous film with improved physico-mechanical properties. The biopolymer films were characterized by their various desirable properties, including 100% UV protection, potent antibacterial activity against S.aureus and E. coli strains with inhibition zonoe about 22.1 ± 36±0.24 mm and 20.36 ± 0.21 mm, respectively, and strong antioxidant properties (DPPH; 82.3 ± 0.1 % and ABTS; 90.6 ± 0.1 %). The film was also found to exhibit distinguishable colorimetric responses to pH 2–12 buffers and volatile ammonia. The packaging test of CS/SPI/PHPA/PHP-CDs film in scenario one (intelligent assay) for evaluation through fish freshness monitoring showed that the film's color changed from light pink (fresh fish) to yellow/brownish (spoilage fish) during storage at 4 °C and 25 °C. Also, scenario two (active assay) at 4 °C showed the ability to increase the shelf life of fish up to 12 days. The study demonstrated that the biopolymer films have exhibited exceptional adaptability, making it a highly promising option for prompt and effective on-site detection of food quality. Furthermore, the film's capacity to prolong the shelf life of fish and reduce the rate of quality degradation during storage positions it as an outstanding contender for multifunctional film applications in the food industry.

Terahertz Spectroscopy in the Field of Food Safety: A Review

with the continuous improvement of people's living standards, food quality and safety problem has caused the wide attention of the whole society, the traditional food quality detection method has certain limitations, terahertz spectral technology (THZ) is a new spectral detection technology, its strong perspective, efficient spectral resolution ability, special fingerprint and nondestructive, in the field of food quality and safety detection has important application value. Due to the high efficiency of food quality and the need for accurate nondestructive testing, automated nondestructive testing for food quality and safety has aroused great interest from the food sector. Faster, accurate, reliable and simplified techniques are needed to classify good quality. With their unique low-energy, penetrating, rapid and non-destructive techniques are widely used as a non-destructive and cost-effective method in the food sector. This paper summarizes the principles and instruments of terahertz spectrum, and introduces the research progress of terahertz spectral detection in food, analyzes the problems in the field of food quality and safety detection, and discusses the future development trend of terahertz spectral technology.

A Novel Terahertz Metamaterial Microfluidic Sensing Chip for Ultra-Sensitive Detection.

A terahertz metamaterial microfluidic sensing chip for ultrasensitive detection is proposed to investigate the response of substances to terahertz radiation in liquid environments and enhance the molecular fingerprinting of trace substances. The structure consists of a cover layer, a metal microstructure, a microfluidic channel, a metal reflective layer, and a buffer layer from top to bottom, respectively. The simulation results show that there are three obvious resonance absorption peaks in the range of 1.5-3.0 THz and the absorption intensities are all above 90%. Among them, the absorption intensity at M1 = 1.971 THz is 99.99%, which is close to the perfect absorption, and its refractive index sensitivity and Q-factor are 859 GHz/RIU and 23, respectively, showing excellent sensing characteristics. In addition, impedance matching and equivalent circuit theory are introduced in this paper to further analyze the physical mechanism of the sensor. Finally, we perform numerical simulations using refractive index data of normal and cancer cells, and the results show that the sensor can distinguish different types of cells well. The chip can reduce the sample pretreatment time as well as enhance the interaction between terahertz waves and matter, which can be used for early disease screening and food quality and safety detection in the future.

Application of computer information technology in food quality safety and detection

Nowadays, food safety problems have occurred frequently, and how to ensure food quality safety has become the focus of attention of the whole world. While computer information technology has become a necessary means to control food quality safety along with the continuous improvement of information degree. Computer information technology is the general term mainly used for the management and processing of information, including acquisition, processing, expression, communication, management and evaluation of information. In this paper, the application of computer information technology applied in food industry at home and abroad was mainly summarized, including query and retrieval related information of food science, stimulation of the production process to effectively control the potential risk factors, fast calculation in the process of food production and consumption, network platform design of sensory evaluation instead of complex manual work, and realization the whole process of full traceability for food industrial chain, so as to provide effective supervision channels for the government, establish food industrial traceability system, improve the information degree in food management and play the role of information technology in food safety management and realize the value.

Modified Metal–Organic Framework (MOF-818) inspired by natural enzymes for intelligent detection of total antioxidants and bisphenol A in infant beverages

BackgroundAccurate and quick judgement of the food quality can protect the legitimate rights of consumers. Currently, nanozymes are widely employed in the rapid detection of food due to their stability and economy. The contents of bisphenol A and antioxidant can be used to measure the quality of beverages. However, due to the complexity of the actual samples, it is still challenging to achieve the sensitive detection of both at the same time. The development of nanozyme with high enzyme activity is essential for sensitive detection of targets in complex foods. ResultsIn this work, a novel nanomaterial (ZrTGA) was synthesized based on thioglycolic acid-modified Metal–Organic Framework (MOF-818). The interaction between Cu–S bonds and increase in the proportion of Cu1+ resulted in ZrTGA exhibiting higher peroxidase-like and polyphenol oxidase-like activities. These enzyme activities were 317 % and 200 % of the original values, respectively. With high enzyme activity can sensitively detect two important indicators of bisphenol A and antioxidants in beverages. The increased enzyme activity of ZrTGA enabled the content of both substances to be detected by smartphone extraction of RGB. Finally, through the output of the ''0″ and ''1″ signals of the logic gates, it is possible to quickly determine the level of the two substances and thus directly assess the quality of the beverages. SignificanceThe modification of nanozyme enables the detection of substances at low concentrations based on enhancing dual-enzyme activity. The combination of mobile phone photography and logic gate technology enables the continuous detection of two important indicators in beverages, overcoming the limitations of traditional large-scale instruments. It also provides an alternative strategy for food quality detection.

Detection of adulterants in rice grains by the characteristics of reflection coefficient of the incident microwave signal

Detection of food quality by identifying different adulterants in food products is a grave requirement for the food industry. Various methods of food analysis have been evolved to identify the quality and quantity of impurities but those methods are time-consuming, expensive and laborious. Our research aims at reducing the challenges experienced in these existing methods. Our procedure is to detect food adulterants using a microwave signal and observe the change in reflection coefficient and resonant frequency of the reflected microwave signal. The property of modification of reflection coefficient and resonant frequency of reflected microwave signal from pure rice grains in the presence of adulterants like small stones/pebbles and broken rice is exploited. In this work, the detection and proportion of adulterants, such as stones and low-quality broken rice in fresh rice in varying proportions, have been determined. Adulterants as low as 2% weight of rice are highly detectable utilizing microwave.

Rapid identification of moldy peanuts based on three-dimensional hyperspectral object detection

Hyperspectral imaging (HSI) has been effectively used in the nondestructive assessment of food quality in recent years. However, the identification of moldy objects using HSIs still faces challenges, including slow detection speed and poor identification accuracy. To address these challenges, this study proposes a three-dimensional hyperspectral mold detection (3D-HMD) approach. The model utilizes multiple 3D convolution (3DMC) modules as the backbone network for optimizing spectral-spatial feature extraction and introduces an attention mechanism to promote the feature information of different hyperspectral bands. A feature pyramid network (FPN) is then used to fuse classification features outputted from the backbone network for feature enhancement. To improve the recognition efficiency for moldy targets, a detection head module derived from the field of object detection is introduced to achieve HIS object-level classification. The experimental results indicate that the detection speed of the proposed model is nearly tenfold greater than that of traditional algorithms, such as 1D-RNN and 3D-CNN, with a mean average precision (mAP) of 81.63 %. Overall, the 3D-HMD model demonstrates remarkable efficiency and accuracy in recognizing moldy peanuts, leading to suitable applications for food quality detection.

A food quality detection method based on electronic nose technology

Food quality detection is of great importance for human health and industrial production. Currently, the common detection methods are difficult to achieve the need for fast, accurate, and non-destructive detection. In this work, an electronic nose (E-nose) detection method based on the combination of convolutional neural network combined with wavelet scattering network (CNN-WSN) and improved seahorse optimizes kernel extreme learning machine (ISHO-KELM) is proposed for identifying the quality level of a variety of food products. In the feature extraction part, the abstract features of CNN are fused with the scattering features of WSN, and the obtained CNN-WSN fusion features can characterize the original information of the food quality effectively. In the classifier design and decision-making section, chaotic mapping is used to initialize the population in the seahorse optimisation algorithm (SHO), avoiding the problem that SHO may fall into local optimal solutions. The kernel parameters and regularisation coefficients of the KELM model were then optimized by improving the locomotion, predation, and reproduction behaviors of the hippocampal populations, which solved the problem of the difficult selection of the key parameters in the model, and thus improved the accuracy and generalization of the overall model. To validate the effectiveness of the proposed food quality detection model, the E-nose system was first built and milk quality data were collected independently, and then tested on two publicly available food quality datasets as well as a self-collected milk quality dataset, respectively. The experimental results show that the food quality detection method proposed in this work has good quality assessment effect on different datasets.

Visible-blind near-infrared organic photodetectors

The presently available commercial photodetectors have a broadband photoresponse and require different external optical filters to block the undesired light outside the detection spectrum window, e.g., near-infrared (NIR) detection. The use of an NIR bandpass has technical limitations in curved or flexible large-area photodetectors, as an NIR bandpass depends critically on the difference in the interference of the optical path in the filter. This work reports the effort to develop a high-performance filter-free organic photodetector (OPD) with a double bulk heterojunction (BHJ) structure. The visible-blind NIR photodetection is realized by eliminating the photocurrent generated in the front visible light-absorbing BHJ optical depletion layer, due to the suppression of electron transport by incorporating a copper thiocyanate-based electron-blocking layer in the OPD. Only excitons generated in the rear NIR-absorbing BHJ contribute to photocurrent in the OPD, thereby achieving visible-blind NIR photodetection. The double BHJ OPD has a high responsivity of 0.38 A/W at 1050 nm and a specific detectivity of >1013 Jones over the wavelength range from 800 to 1050 nm, making it an ideal candidate for applications in optical communication, food quality detection, wellness monitoring, and NIR image sensors.

Application of lightweight YOLOv5 for walnut kernel grade classification and endogenous foreign body detection

The appearance quality grading of walnut kernels is extremely important in the deep processing of walnuts. Foreign body pollution in food is also a challenging problem that has been troubling food companies. Therefore, it is necessary to propose an efficient and non-destructive detection method for walnut kernels and their endogenous foreign bodies. To improve the quality and safety of walnut deep-processed products, this paper proposes an improved detection model based on the original YOLO V5. The improved model achieves a reduction in memory consumption by 38% and computational workload by 34%. Additionally, it increases the average precision of the model by 1.1% and improves CPU speed by 52%. When compared to other lightweight networks, our model demonstrates optimal overall performance. Moreover, our algorithm significantly enhances the precision of detecting small targets and target selection, as compared to the original model.By reducing the input image size of the model, it is able to meet the requirements of real-time detection of walnut kernels and their endogenous foreign bodies on the edge device Raspberry Pi. This provides a technical reference for the non-destructive detection of food quality and its endogenous foreign bodies.

An Electronic Nose Food Quality Detection Method Based on CNN-WSN and SHO-KELM

An Electronic Nose Food Quality Detection Method Based on CNN-WSN and SHO-KELM

(Invited) Therapeutic Drug Monitoring Using Quantitative Surface Enhanced Raman Scattering

We have for the past ten years developed nanopillar-based substrates for Surface Enhanced Raman Scattering (SERS) and have applied them in e.g. food quality monitoring and detection of toxins. In an ongoing project financed by the Danish Bio Innovation Institute, we are pursuing quantitative detection of small drug molecules in patient blood samples. The aim is to be able to detect e.g. concentration of anti-cancer medication within half an hour, hereby allowing rapid and personalized dose adjustment. I will show examples of recent findings and results within sensor development, instrumentation and blood analysis.

Beyond traditional hyperspectral imaging: exploring spectral DNA encoding and spectral velocity for enhanced food quality detection.

This study examines the use of hyperspectral imaging for the identification of stale food items by analyzing minute changes in their spectral signatures. An algorithm is proposed for the detection of subtle alterations in spectral signatures and is validated through intra-class classification comparisons among various stages of adulterating food samples acquired using a spectroradiometer. The analysis reveals that the spectral angle mapper proves effective for inter-class classification of consumable food items but faces challenges in classifying slight changes in spectral signatures within the same category. In contrast, DNA encoding demonstrates reliability, despite the generated code-words being independent of the actual intensity of received reflectance at each band. DNA encoding can provide insights into the nature of absorbance or reflectance at each band, making it a valuable tool for intra-class classification. Additionally, a novel concept called spectral velocity is introduced for subclass pattern matching. This method of single-pixel analysis relies on artificially constructed nD-vectors derived from spectral signatures. The findings suggest that the combination of hyperspectral imaging and DNA encoding offers a valuable tool for the quality assurance of consumable food items and demonstrates its potential for ensuring food safety and quality, ultimately contributing to human health.

Applications of Gas Sensing in Food Quality Detection: A Review.

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Waste citrus pectin/garlic bionanohybrids for edible food preservation

Two bioactive molecules derived from citrus peel and garlic (pectin and carbon quantum dot), were used to develop pectin-based edible films for food packaging and coating applications. The interaction between pectin molecules and carbon quantum dots (CDs) resulted in a composite film (CDs/pectin) with improved antioxidant and antibacterial activity, as well as better mechanical properties. The changes brought about by the addition of CDs to the film and their suitability for food packaging were verified through fundamental characterization. The intrinsic fluorescence properties of CDs with pH sensitivity will be further investigated to explore their potential application in food quality detection. Besides, the use of active ingredients from food sources and excellent degradability suggest that the bio-nanocomposite film is a potential edible food packaging material. A formulation based on citrus peel and garlic was used for the surface coating of strawberries to extend their shelf life. The strawberries treated with the active coating did not show any significant signs of decay or dehydration after 5 days. This active packaging coating derived from food ingredients offers a promising natural alternative to conventional materials, with potential applications in extending food product shelf life, promoting human health, and detecting spoilage.

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness.

Rapid nondestructive detection of fish freshness is essential to ensure food safety and nutrition. In this study, we demonstrate a conformal temperature/impedance sensing patch for temperature monitoring, as well as freshness classification during fish storage. The optimization of the flexible laser-induced graphene electrodes is studied based on both simulation and experimental validation, and dimensional accuracy of 5‰ and high impedance reproducibility are obtained. A laser-assisted thermal reduction technology is innovatively introduced to directly form a reduced graphene oxide-based temperature-sensitive layer on the surface of a flexible substrate. The comprehensive performance is superior to that of most reported temperature-sensitive devices based on graphene materials. As an application demonstration, the fabricated flexible dual-parameter sensing patch is conformed to the surface of a refrigerated fish. The patch demonstrates the ability to accurately sense low temperatures in a continuous 120 min monitoring, accompanied by no interference from high humidity. Meanwhile, the collected impedance data are imported into the support vector machine model to obtain a freshness classification accuracy of 93.07%. The conformal patch integrated with crosstalk-free dual functions costs less than $1 and supports free customization, providing a feasible methodology for rapid nondestructive detection or monitoring of food quality.

A clustering-optimized segmentation algorithm and application on food quality detection

For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms.

Encapsulation of CoS2 nanoparticles in 3D star-like N-doped graphitized carbon/carbon nanotubes nanocomposites for the detection of hydrazine in human urine and food

The design and construction of an efficient electrochemical hydrazine sensor is of great significance for food quality and safety detection. Herein, a novel three-dimensional (3D) hexagonal star-like carbon nanotubes modified N-doped graphitized carbon encapsulated CoS2 nanocomposites (CoS2 @NC/CNTs) was prepared using cobalt and zinc zeolitic imidazolate frameworks (CoZn-ZIF) as a precursor. The "tip effect" of the hexagonal star-like structure facilitates electron transfer and mass transfer. Carbon nanotube-modified N-doped graphitized carbon substrates (NC/CNTs) avoid agglomeration of CoS2 nanoparticles and have excellent electrical conductivity and abundant active sites. The above synergistic effects enable the CoS2 @NC/CNTs-based hydrazine electrochemical sensors to exhibit enhanced electrocatalytic activity compared to the conventional 3D ZIF-derived dodecahedral CoS2 @NC. As an electrochemical hydrazine sensor, CoS2 @NC/CNTs can provide a wide linear range of 1.0 μM - 6.0 mM, an excellent sensitivity of 2101 μA mM−1 cm −2, a detection limit as low as 96 nM (S/N = 3), a satisfactory stability and a promising selectivity. In addition, the sensor displays encouraging performance in the detection of hydrazine in biological and food samples, which lights up a new field for the application of such catalysts in electrochemical sensing.

Highly sensitive and selective gas sensors based on 2D/3D Bi2MoO6 micro-nano composites for trimethylamine biomarker detection

Metal oxide semiconductor based gas sensors have been verified to be an effective way for food quality detection. However, the high operating temperature, insufficient sensitivity, and selectivity limit their wide application. Herein, one simplest Aurivillius oxide, Bi2MoO6 microspheres and a series of exfoliated g-C3N4/Bi2MoO6 hetero-composites were developed using a facile solvothermal route, which are used to detect trimethylamine (TMA, a volatile biomarker of fish freshness) at 22 °C. The introduction of E-g-C3N4 contributes to modulating the grain size of Bi2MoO6 and tuning the Bi/Mo cations valence states, thus improving the surface reactivity and electron transfer efficiency. Benefiting from the synergetic engineering of 2D/3D micro-nanostructure, crystal defects, and well-defined n-n heterojunctions, 1 wt% E-g-C3N4/Bi2MoO6 attained superior TMA sensing performance at 22 °C, including the highest response (Ra/Rg = 10.6 @ 20 ppm) and accelerated response/recovery speed, fine selectivity, and low detection limit (1.3 ppm), which is far satisfied with the detection requirement. Furthermore, the comprehensive evaluation based on the fish color and tissue state, sensing characteristics, PCA, and pH testing results reveal the fabricated TMA sensor can achieve rapid and non-destructive fish freshness detection. This work provides insights into designing low-power consumption chemiresistive gas sensors and devices with valid practicality.

A Review of Quartz Crystal Microbalance for Chemical and Biological Sensing Applications.

Humans are fundamentally interested in monitoring and understanding interactions that occur in and around our bodies. Biological interactions within the body determine our physical condition and can be used to improve medical treatments and develop new drugs. Daily life involves contact with numerous chemicals, ranging from household elements, naturally occurring scents from common plants and animals, and industrial agents. Many chemicals cause adverse health and environmental effects and require regulation to prevent pollution. Chemical detection is critically important for food and environmental quality control efforts, medical diagnostics, and detection of explosives. Thus, sensitive devices are needed for detecting and discriminating chemical and biological samples. Compared to other sensing devices, the Quartz Crystal Microbalance (QCM) is well-established and has been considered and sufficiently sensitive for detecting molecules, chemicals, polymers, and biological assemblies. Due to its simplicity and low cost, the QCM sensor has potential applications in analytical chemistry, surface chemistry, biochemistry, environmental science, and other disciplines. QCM detection measures resonate frequency changes generated by the quartz crystal sensor when covered with a thin film or liquid. The quartz crystal is sandwiched between two metal (typically gold) electrodes. Functionalizing the electrode's surface further enhances frequency change detection through to interactions between the sensor and the targeted material. These sensors are sensitive to high frequencies and can recognize ultrasmall masses. This review will cover advancements in QCM sensor technologies, highlighting in-sensor and real-time analysis. QCM-based sensor function is dictated by the coating material. We present various high-sensitivity coating techniques that use this novel sensor design. Then, we briefly review available measurement parameters and technological interventions that will inform future QCM research. Lastly, we examine QCM's theory and application to enhance our understanding of relevant electrical components and concepts.