Food Quality Assessment Research Articles

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review.

The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco-friendly, and non-destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation-emission matrix (EEM) has been extensively explored in the food industry, but on-site detection of EEMs remain a challenge. To address this limitation, laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED-IF devices for non-destructive food measurement and recent studies that focus on the development of LIF/LED-IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre-application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low-concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

Future Potential of E-Nose Technology: A Review

Electronic Nose (E-Nose) technology unlocks the fascinating world of electronic detection, identification, and analysis of scents and odors, paving the way for innovative research and promising applications. E-Nose mimics the human sense of smell and has gained significant attention and is applied in various fields, including the food, health and drug industries, safety and crime, and the environmental and agricultural sectors. This technology has the potential to improve quality control, medical diagnostics, and hazardous material detection processes. The E-Nose consists of a combination of gas sensors that mimic the olfactory receptors of the human nose. These sensors detect and respond to different scent molecules, resulting in unique response patterns that can be interpreted and analyzed. E-Nose has found application in the food industry to assess food quality, detect contamination, and monitor fermentation processes. In the health field, it has been used for disease diagnosis, monitoring patient health, and detecting cancerous tissue. In addition, E-Nose has been used for security purposes, such as detection of explosives and prohibited substances, as well as identification of counterfeit products. In addition, it has been used in environmental monitoring for air quality assessment and agriculture for disease detection in crops. Despite its promising potential, widespread adoption of E-Nose faces challenges related to sensor sensitivity, data analysis algorithms (complex data interpretation), response diversity, regulatory considerations, implementation complexity, and cost. This article reviews the latest developments in E-Nose technology, explores its applications and future potential, and highlights challenges that need to be addressed. This is considered important because E-Nose opens up a world of electronic scent identification, and analysis with the potential to improve quality control, diagnosis, and detection.

Exploring Causes and Potential Solutions for Food Waste among Young Consumers.

Young consumers are often described as innovative and concerned about the environment. However, their practices sometimes are not strong enough, which are described as the attitude-behavior gap and are seen in significant amounts of food waste. The objective of this study is to focus on food waste among young consumers in high-income countries and to outline the main determinants of food waste generation. Qualitative data gathered from nine focus groups in Lithuania, Finland and Denmark (2021-2022) contribute to formulating potential intervention to decrease food waste behavior within this segment. The article provides a substantial literature review on food waste and discusses recommendations for possible interventions and further research to solve the attitude-behavior gap. The findings show four specific fields for potential solutions, related to (1) special occasions, (2) assessing food quality, (3) kitchen habits, and (4) shopping habits. Our contribution is discussed at the end of the article.

Canna-inspired superhydrophobic and icephobic regenerated cellulose based hierarchical intelligent colorimetric sensing film for food real-time visual monitoring

Unsafe food cause serious threat to public health and the economy, exploiting effective food quality assessment strategies has attracted concerns worldwide. Herein, inspired by Canna micro-nano hierarchical structure, a generic, facile, and highly processable roll-to-roll and solvent evaporation-induced self-assembly fabrication method is introduced to fabricate regenerated cellulose based superhydrophobic and icephobic hierarchical colorimetric sensing film for real-time visual monitoring of food freshness. The synergistic effect from macromolecular chains attraction and nanoscaled aggregation renders the colorimetric sensing film with excellent mechanical strength of 105.07 MPa and superhigh shear modulus of 17.28 GPa. Upon applied tension, the vigorous intermolecular interactions make the film extraordinary structure stability. The optimized colorimetric sensing film exhibits microscaled pores for sensing performance and three-dimensional nanoflower morphologies for superhydrophobic (water contact angle of 155.1 °and roll-off angle of 7.8°) and icephobic that delay the freezing time of 40 μL water droplets (45 s to 265 s) and reduce the bonding strength of ice (599.72 kPa to 45.53 kPa). What counts is the first time bestowed with icephobicity to food freshness colorimetric sensing film, which mitigating the interference of the covered ice layer against the colorimetric, and providing a more reasonable monitoring in food freshness. These results benefit for stimulating new design principles of colorimetric sensing films, and also provide promising applications in extremely cold environments.

Simultaneous detection of molecules with the surface-enhanced infrared absorption sensor platform based on disk antennas with double spacer

Biomolecule detection has become important in many applications such as medical diagnosis, forensic analysis, basic biological studies, and food quality assessment. In particular, the Mid-infrared range offers an important opportunity for biomolecular sensing as it covers the molecular vibrational spectra of vital biochemicals such as Deoxyribonucleic acid, Ribonucleic Acid, and proteins. In this study, a double band absorbing plasmonic nanoantenna array with two gold disk resonators is proposed. The biosensing ability of this structure was investigated using the protein-goat anti-mouse immunoglobulin G model and Polymethyl methacrylate film. The basic structural bonds of protein monolayer, namely Amide-I, Amide-II, and Amide-III showed vibrational signatures at 6010 nm (∼1664 cm−1), 6496 nm (∼1539 cm−1), and 6989 nm (∼1431 cm−1) wavelengths, respectively. In addition, the spectral response of the proposed antenna structure was investigated using a Polymethyl methacrylate film by detecting the C=O and the C-H bonds. The strong dipole moment at C=O showed a strong absorption deep at 5782 nm (∼1730 cm−1) while the C-H bond has shown a relatively low absorption deep at 3350 nm (∼2985 cm−1) and 3395 nm (∼2946 cm−1). Our findings indicate that the double spacer disk configuration detects the spectral signature of the protein monolayer and Polymethyl methacrylate film in each band, simultaneously. The dual-band can be tuned independently by carefully engineering the radii of the double disks without making an effect on the other band. The proposed structure can be used as a characterization tool for identifying unknown complex molecules by simply detecting their spectral fingerprints in each mode of the dual-band, independently. Also, this design strategy can be insight to multi-mode SEIRA platforms, where more complex chemical molecules are needed to be detected or identified in biology, chemistry, and defense areas.

Postharvest Treatments of Hass Avocado (Persea americana Mill.) and Estimation of Its Quality Using Hyperspectral Imaging (HSI)

Avocados’ shelf life is limited and difficult to monitor. This study evaluated the performance of chitosan coatings (1.5 and 2% w/v, T1 and T2) on avocados’ quality and shelf life against samples untreated (C) and treated with an ethylene inhibitor (1-MCP, M). Hyperspectral imaging (HSI) coupled with machine learning (ML) techniques was also evaluated to estimate Hass avocados’ quality indicators. Sensorial, physicochemical, and metabolic characteristics were measured using standard procedures. While T2 samples exhibited undesirable changes (i.e., uneven color and heterogeneous firmness), T1 behaved similarly to C. However, neither treatment could delay senescence as much as 1-MCP (42 vs ≤ 33 days). In general, Bayesian regularization neural networks (BRNNs) outperformed the other tested ML techniques in estimating quality attributes from HSI features, allowing for real-time nondestructive assessment of food quality. Adverse effects of chitosan coatings on avocados’ physiology were identified, which can inform the development of films with improved performance.

Fusion-based strategy of CSA and mobile NIR for the quantification of free fatty acid in wheat varieties coupled with chemometrics

The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (RC and RP), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with RC = 0.8793, RMSECV = 7.91 mg/100 g, RP = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour.

Internet of Things, Big Data, and Artificial Intelligence in The Food and Agriculture Sector


 
 
 
 Big data often referred to as streaming data produced by the internet of things creates new options for monitoring food and agricultural processes. Along with sensors, social media big data is also playing a bigger role in the food business. The IoT, big data, and artificial intelligence are discussed in this review along with how they will impact the agri-food industry in the future. We begin with an introduction to the fields of IoT, big data, and AI before talking about how IoT and big data analysis are used in agriculture, including smart farm equipment, crop imaging using drones, and monitoring of greenhouses, food quality assessment using spectral techniques and sensor fusion, and eventually, food safety using gene sequencing and blockchain-based digital traceability. The commercial viability of applications and the outcomes of translational research are given significant consideration.
 
 
 

A Review on Quality Scanner Device for Radish and Carrot using Image Processing Technique

Abstract: Agriculture is the backbone of the Indian economy and productivity. The market value obtained from them is immense and effect both the export and quality valuation . Although grading and quality can be analysed by humans it is still inaccurate, time intensive, unreliable, subjective, and also hard which can be easily influenced by surrounding. Hence, there is an ultimate need for intelligent classification for vegetables. This paper focuses on Developing an analysis device for Radish (Raphanus raphanistrum ) and Carrot (Daucus carota subsp. sativus) which is an edible root vegetable grown in most parts of India. Radish and carrots are good sources of natural nitrates, minerals, calcium, and potassium. Thus, vegetables must be used as soon as possible to get a superior fresh character from them. External parameters such as size, shape, and quality factors of radish are important for finding out the total quality that is acceptable to the people. Various methods and algorithms are been put forwarded for the quality evaluation for the external defects present in it. Convolutional Neural Networks is the method used in image processing as this is more advantageous since it can learn special characteristics from images and by establish CNN for image classification where analyst have to gather the dataset. The requirement for doing this project is to have a computer and a python program for use the driver code. Image processing method is used for estimating quality of the particular food product efficiently in these days. This review paper examines current developments in methods for assessing food quality through image processing. The use of CNN (Convolutional Neural Networks) to identify characteristics of vegetables and its diseases through several built-in python libraries

Artificial Intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: the challenge of quantitative volatilomics in food quality assessment

Effective investigation of food volatilome by comprehensive two-dimensional gas chromatography with parallel detection by mass spectrometry and flame ionization detector (GC×GC–MS/FID) gives access to valuable information related to industrial quality. However, without accurate quantitative data, results transferability over time and across laboratories is prevented. The study applies quantitative volatilomics by multiple headspace solid phase microextraction (MHS-SPME) to a large selection of hazelnut samples (Corylus avellana L. n = 207) representing the top-quality selection of interest for the confectionery industry. By untargeted and targeted fingerprinting, performant classification models validate the role of chemical patterns strongly correlated to quality parameters (i.e., botanical/geographical origin, post-harvest practices, storage time and conditions). By quantification of marker analytes, Artificial Intelligence (AI) tools are derived: the augmented smelling based on sensomics with blueprint related to key-aroma compounds and spoilage odorant; decision-makers for rancidity level and storage quality; origin tracers. By reliable quantification AI can be applied with confidence and could be the driver for industrial strategies.

Assessment of food quality and safety among Nigerian school children

Abstract Nutrition is of vital importance in the physical, emotional, and intellectual development of children, for retarded academic performance is not the only risk of lack of good food; various health risks that may deform the body system or result in sickness or death are also, not unconnected to consumption of foods devoid of quality and safety. This article aims to assess the food quality and safety among Nigerian school children. A review of related online free-full articles written in the English language published from 2018 to 2022 was done. A consequent title and abstract review of articles published on food quality and safety, and over 1056 published studies identified, they were visualized, profiled, cleansed, prepared, analyzed, and summarized, of which 36 most relevant articles met the inclusion criteria and were utilized for the review. This study found that providing high-quality and safe foods to children is key to achieving sustainable development goals by 2030, and it is in this quest that tremendous educational and health benefits. We also found that the foods served or sold to children in schools are sometimes of low quality and unsafe to satisfy learners’ esthetic or nutritional needs, which have grave consequences for their physical growth, health status, and academic development. In conclusion, there is a need to train and monitor food handlers on food quality and safety in schools in Nigeria.

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network.

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

Keeping treasure safe: Eurasian red squirrels cache valuable food far from the food source with low canopy cover

Abstract Scatter-hoarding animals decide their food hoarding location by assessing food quality and pilfering risk. Previous studies have proposed two non-mutually exclusive hypotheses; the optimal density model (ODM) and the habitat structure hypothesis (HSH). The ODM proposes that animals utilize low cache density to protect their valuable caches by transporting food far from food sources. The HSH proposes that animals utilize predation risk to protect their valuable caches by hoarding food in open areas. Here, we investigated the hoarding behaviours in Eurasian red squirrels Sciurus vulgaris by experimentally providing high-quality (walnuts) and low-quality (acorns) food, to examine if they follow ODM or HSH. Compared to acorns, squirrels hoarded walnuts in places further from the area where food was provisioned and in areas with low canopy cover. These results agree with both ODM and HSH, providing the first evidence that hoarding behaviour in Eurasian red squirrels is shaped by multiple factors.

Trends in Molecularly Imprinted Polymers (MIPs)-Based Plasmonic Sensors

In recent years, plasmonic sensors have been used in various fields ranging from environmental monitoring, pharmaceutical analysis, medical diagnosis, and food quality assessment to forensics. A significant amount of information on plasmonic sensors and their applications already exists and there is a continuing development of reliable, selective, sensitive, and low-cost sensors. Combining molecularly imprinting technology with plasmonic sensors is an increasingly timely and important challenge to obtain portable, easy-to-use, particularly selective devices helpful in detecting analytes at the trace level. This review proposes an overview of the applications of molecularly imprinted plasmonic chemosensors and biosensors, critically discussing the performances, pros, and cons of the more recently developed devices.

Raman on ramen: Nutritional analysis and brand identification

AbstractPrecise and nondestructive assessment of food quality and its nutritional composition can be used to personalize nutrition, which, in turn, will improve well‐being and help to prevent chronic diseases for millions of people around the world. Currently used methods for elucidation of nutritional composition of food are destructive, time, and labor consuming. In this study, we investigate the potential of Raman spectroscopy (RS), an emerging analytical technique, in a noninvasive, nondestructive, and a chemical‐free analysis of the nutritional composition of ramen, one of the most popular foods in the world. We found that RS could be used to quantify the amount of carbohydrates, gluten, and lipids in ramen. In coupled to chemometrics, RS enables ~100% accurate differentiation between gluten‐rich and gluten‐free ramen. Furthermore, using RS, one can identify the brand and the country of origin of ramen. These findings suggest that Raman‐based sensors can be used in daily life for quality control of consumed food in both restaurants and grocery stores.

Preparation of Colorimetric Sensor Array System to Evaluate the Effects of Alginate Edible Coating on Boiled-Dried Anchovy.

The colorimetric sensor array (CSA) is a simple, rapid, and cost-effective system widely used in food science to assess food quality by identifying undesirable volatile organic compounds. As a prospective alternative to conventional techniques such as total volatile basic nitrogen, peroxide value, and thiobarbituric acid reactive substance analysis, the CSA system has garnered significant attention. This study evaluated the quality of edible-coated food products using both conventional and CSA methods in order to demonstrate that the CSA approach is a feasible alternative to conventional methods. Boiled-dried anchovies (BDA) were selected as the model food product, and the sample's quality was assessed as a function of storage temperature and incubation period using conventional techniques and the CSA system. The surface of BDA was coated with an edible alginate film to form the surface-modified food product. The conventional methods revealed that an increase in storage temperature and incubation time accelerated the lipid oxidation process, with the uncoated BDA undergoing lipid oxidation at a faster rate than the coated BDA. Utilizing multivariate statistical analysis, the CSA approach essentially yielded the same results. In addition, the partial least square regression technique revealed a strong correlation between the CSA system and conventional methods, indicating that the CSA system may be a feasible alternative to existing methods for evaluating the quality of food products with surface modifications.

A label-free fluorescent biosensor based on specific aptamer-templated silver nanoclusters for the detection of tetracycline

Tetracycline (TET) is a broad-spectrum antibiotic commonly used in the treatment of animals. TET residues in food inevitably threaten human health. High-performance analytical techniques for TET detection are required in food quality assessment. The objective of this study was to establish a label-free fluorescent biosensor for TET detection using specific aptamer-templated silver nanoclusters (AgNCs). An aptamer with a high specific binding ability to TET was used to synthesize a novel DNA-templated AgNCs (DNA-AgNCs). When TET is present, the aptamer’s conformation switched from an antiparallel G-quadruplex to a hairpin structure, altering the connection between AgNCs and the aptamer. Following the transformation of AgNCs into large sized silver nanoparticles (AgNPs), a fluorescence decrease was detected. When used to detect TET in milk, the proposed biosensor displayed high sensitivity and selectivity, with a limit of detection of 11.46 ng/mL, a linear range of 20 ng/mL−10 g/mL, and good recoveries of 97.7–114.6% under optimized conditions. These results demonstrate that the proposed biosensor was successfully used to determine TET quantitatively in food samples, suggesting that our method provides an efficient and novel reference for detecting antibiotics in food while expanding the application of DNA-AgNCs in related fields.

Estimation of Sugar Content in Wine Grapes via In Situ VNIR-SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques.

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR-SWIR spectrum (350-2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR-SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.

Nanostructured metal oxide heterojunctions for chemiresistive gas sensors

Metal oxides have been extensively used in environmental monitoring, breath analysis, and food quality assessment. This review summarizes the effects of nanoscale heterojunctions on enhancing the gas-sensing performance of metal oxide devices.

Food quality 4.0: From traditional approaches to digitalized automated analysis

Food quality has recently received considerable attention from governments, researchers, and consumers due to the increasing demand for healthier and more nutritious food products. Traditionally, food quality is determined using a range of destructive and time-consuming approaches with modest analytical performance, underscoring the urgent need to develop novel analytical techniques. The Fourth Industrial Revolution (called Industry 4.0) is progressing exponentially, driven by the advent of a range of digital technologies and other innovative technological advances. “Food Quality 4.0” is a new concept referring to the use of Industry 4.0 technologies in food analysis to achieve rapid, reliable, and objective assessment of food quality. In this review, we will first discuss the fundamentals and principles of Food Industry 4.0 technologies and their connections with the Food Quality 4.0 concept. Then, the most common techniques used to determine food quality will briefly be reviewed before highlighting the advancements made in analytical techniques to assess food quality in the era of Industry 4.0. Food Quality 4.0 is characterized by growing digitalization and automation of food analysis using the most advanced technologies in the food industry. Key aspects of Food Quality 4.0, including, among others, non-destructive fingerprinting techniques, omics technologies and bioinformatics tools, Artificial Intelligence and Big Data, have great potential to revolutionize food quality. Although most of these technologies are still under development, it is anticipated that future research will overcome current limitations for large-scale applications. • Consumer interest in food quality call for advanced and reliable analytical methods. • Industry 4.0 has offered numerous opportunities in many fields, including food analysis. • Food Quality 4.0 concept - determination of food quality using Industry 4.0 technologies. • AI, Big Data, and smart sensors are important enablers of Food Quality 4.0 • Innovations, digitalization, and automation experienced a massive boost.