Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – A review

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products

The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords "authentication" AND "food", "authentication," AND "beverage", from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion

This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.

Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible-Near-Infrared Spectroscopy and Image-Processing Technologies

During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible-near-infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea.

Progress in Multisensory Synergistic Salt Reduction

Excessive salt intake, primarily from sodium chloride prevalent in modern food processing, poses a significant public health risk associated with hypertension, cardiovascular diseases and stroke. Researchers worldwide are exploring approaches to reduce salt consumption without compromising food flavor. One promising method is to enhance salty taste perception using multisensory synergies, leveraging gustatory, olfactory, auditory, visual, tactile and trigeminal senses to decrease salt intake while preserving food taste. This review provides a comprehensive overview of salt usage in foods, mechanisms of salty taste perception and evaluation methods for saltiness. Various strategies for reducing salt consumption while maintaining food flavor are examined, with existing salt reduction methods' advantages and limitations being critically analyzed. A particular emphasis is placed on exploring the mechanisms and potential of multisensory synergy in salt reduction. Taste interactions, olfactory cues, auditory stimulation, visual appearance and tactile sensations in enhancing saltiness perception are discussed, offering insights into developing nutritious, appealing low-sodium foods. Furthermore, challenges in current research are highlighted, and future directions for effective salt reduction strategies to promote public health are proposed. This review aims to establish a scientific foundation for creating healthier, flavorful low-sodium food options that meet consumer preferences and wellness needs.

Microbial communities, functional, and flavor differences among three different-colored high-temperature Daqu: A comprehensive metagenomic, physicochemical, and electronic sensory analysis

High-temperature Daqu (HTD) is the starter for producing sauce-flavor Baijiu, with different-colored Daqu (white, yellow, and black) reflecting variations in fermentation chamber conditions, chemical reactions, and associated microbiota. Understanding the relationship between Daqu characteristics and flavor/taste is challenging yet vital for improving Baijiu fermentation. This study utilized metagenomic sequencing, physicochemical analysis, and electronic sensory evaluation to compare three different-colored HTD and their roles in fermentation. Fungi and bacteria dominated the HTD-associated microbiota, with fungi increasing as the fermentation temperature rose. The major fungal genera were Aspergillus (40.17%) and Kroppenstedtia (21.16%), with Aspergillus chevalieri (25.65%) and Kroppenstedtia eburnean (21.07%) as prevalent species. Microbial communities, functionality, and physicochemical properties, particularly taste and flavor, were color-specific in HTD. Interestingly, the microbial communities in different-colored HTDs demonstrated robust functional complementarity. White Daqu exhibited non-significantly higher α-diversity compared to the other two Daqu. It played a crucial role in breaking down substrates such as starch, proteins, hyaluronic acid, and glucan, contributing to flavor precursor synthesis. Yellow Daqu, which experienced intermediate temperature and humidity, demonstrated good esterification capacity and a milder taste profile. Black Daqu efficiently broke down raw materials, especially complex polysaccharides, but had inferior flavor and taste. Notably, large within-group variations in physicochemical quality and microbial composition were observed, highlighting limitations in color-based HTD quality assessment. Water content in HTD was associated with Daqu flavor, implicating its crucial role. This study revealed the complementary roles of the three HTD types in sauce-flavor Baijiu fermentation, providing valuable insights for product enhancement.

Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry

Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

Effect of Different Cooking Methods on the Aroma and Taste of Chicken Broth

A single combi oven, known for its versatility, is an excellent choice for a variety of chicken soup preparations. However, the impact of universal steam ovens on the flavor quality of chicken soup remains unclear. This study aimed to explore the impact of different cooking methods on the aroma and taste of chicken soup. Three cooking methods with various stewing times were compared: ceramic pot (CP), electric pressure cooker (EPC), and combi oven (CO). Analyses were conducted using electron-nose, electron-tongue, gas chromatography-ion mobility spectrometry (GC-IMS), automatic amino acid analysis, and chemometric methods. A total of 14 amino acids, including significant umami contributors, were identified. The taste components of CP and CO chicken soups were relatively similar. In total, 39 volatile aroma compounds, predominantly aldehydes, ketones, and alcohols, were identified. Aldehydes were the most abundant compounds, and 23 key aroma compounds were identified. Pearson's correlation analyses revealed distinct correlations between various amino acids (e.g., glutamic acid and serine) and specific volatile compounds. The aroma compounds from the CP and CO samples showed similarities. The results of this study provide a reference for the application of one-touch cooking of chicken soup in versatile steam ovens.

Chemometric approach for an application of Atlantic salmons (Oncorhynchus keta) by-product for potential food sources

This study identified the aroma profile of salmon by-product for high utilization of by-products, including hydrolysates of head, frame, and skin were treated with reducing sugars and thermal processing. Electronic nose (E-nose) and gas chromatography-mass spectrometry (GC-MS) coupled with gas chromatography-olfactometry (GC-O) were used to analyzed the aroma profile. A total of 140 and 90 volatile compounds were detected through E-nose and GC-MS respectively, and the main volatile compounds were aldehydes. A total of 23 odor active compounds were recognized using GC-O, and 3-methyl-butanal, heptanal, benzaldehyde, octanal, furfural, and methoxy-phenyl-oxime were identified as the aroma of salmon. Using multivariate analysis, the pattern between the pretreated samples and aroma profiles was confirmed, and there were clear separations among the samples. The results of this study provide the aroma profile of salmon by-products and are expected salmon by-products to be used as a potential food source.

Advances and challenges in conventional and modern techniques for halal food authentication: A review

Food is one of the most necessary needs since human civilization. For Muslims, it is mandatory to consume halal food. From a halal authentication perspective, adulteration of food products is an emerging challenge worldwide. The demand for halal food consumption has resulted in an ever-increasing need for halal product validity. In the market, there are several food products in which actual ingredients and their source are not mentioned on the label and cannot be observed by the naked eye. Commonly nonhalal items include pig derivatives like lard, pork, and gelatin derivatives, dead meats, alcohol, blood, and prohibited animals. Purposely, various conventional and modern methods offer precise approaches to ensure the halalness and wholesomeness of food products. Conventional methods are physiochemical (dielectric) and electrophoresis. At the same time, modern techniques include high-pressure liquid chromatography (HPLC), gas chromatography (GC), electronic nose (E-Nose), polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), differential scanning calorimetry (DSC), nuclear magnetic resonance (NMR), near-infrared (NIR) spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. This review intends to give an extensive and updated overview of conventional and modern analytical methods for ensuring food halal authenticity.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Application of Data Fusion in Traditional Chinese Medicine: A Review

Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

The Application of Artificial Intelligence and Big Data in the Food Industry

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Vegetable and fruit freshness detection based on deep features and principal component analysis

Vegetable and fruit freshness detecting can ensure that consumers get vegetables and fruits with good taste and rich nutrition, improve the health level of diet, and ensure that the agricultural and food industries provide high-quality products to meet consumer needs and increase sales and market share. At present, the freshness detection of vegetables and fruits mainly relies on manual observation and judgment, which has the problems of subjectivity and low accuracy, and it is difficult to meet the needs of large-scale, high-efficiency, and rapid detection. Although some studies have shown that large-scale detection of vegetable and fruit freshness can be carried out based on artificially extracted features, there is still the problem of poor adaptability of artificially extracted features, which leads to low efficiency of freshness detection. To solve this problem, this paper proposes a novel method for detecting the freshness of vegetables and fruits more objectively, accurately and efficiently using deep features extracted by pre-trained deep learning models of different architectures. First, resized images of vegetables and fruits are fed into a pre-trained deep learning model for deep feature extraction. Then, the deep features are fused and the fused deep features are dimensionally reduced to a representative low-dimensional feature space by principal component analysis. Finally, vegetable and fruit freshness are detected by three machine learning methods. The experimental results show that combining the deep features extracted by the three architecture pre-trained deep learning models GoogLeNet, DenseNet-201 and ResNeXt-101 combined with PCA dimensionality reduction processing has achieved the highest accuracy rate of 96.98% for vegetable and fruit freshness detection. This research concluded that the proposed method is promising to improve the efficiency of freshness detection of vegetables and fruits.

Evaluation of the Chemosensoric Properties of Commercially Available Dog Foods Using Electronic Sensors and GC-MS/O Analysis

Pet owners think of their animals as part of their family, which further promotes the growth of the pet food market, encouraging pet owners to select nutritious, palatable, and high-quality foods for pets. Therefore, the evaluation of taste and volatile compounds in pet foods is essential to improve palatability. In this study, the sensory characteristics of taste and odor compounds in 10 commercially available dry dog foods were investigated using electronic tongue (E-tongue), electronic nose (E-nose), gas chromatography-mass spectrometry (GC-MS), and gas chromatography-olfactometry (GC-O). Dry dog foods were separated based on the sensory properties of taste and volatile compounds through the multivariate analysis of integrated results of the E-tongue and E-nose. A total of 67 odor active compounds were detected through GC-MS and GC-O, and octanal, nonanal, 2-pentyl furan, heptanal, and benzaldehyde were identified as key odor compounds which may have positive effects on food intake. The multivariate analysis was used to classify samples based on key odor compounds. Volatile compounds responsible for aroma properties of samples were evaluated using GC-O and multivariate analysis in this present study for the first time. These results are expected to provide fundamental data for sensory evaluation in producing new dog foods with improved palatability.

Characterization of stewed beef by sensory evaluation and multiple intelligent sensory technologies combined with chemometrics methods

Though stewed beef is favored by consumers, the impact of the domestic high-pressure stewing method on beef has received little attention. This study characterized the beef cooked under varied pressures in the household pressure cooker by analytical instruments, sensory evaluation, and multiple intelligent sensory technologies. Totally, 165 volatile compounds were identified by SPME-GC-TOF-MS, with more detected in samples with higher pressure. The glutamic acid contributed significantly to the umami taste of beef (TAV > 1.25). The meaty and fatty odor, hardness, chewiness, and sweet taste contributed to the overall liking of stewed beef (P < 0.05). The multiple-target BPNN model based on fused data from multiple intelligent sensory technologies could simultaneously predict sensory perception intensities with a satisfying performance (R² > 0.9340), but could not efficiently predict subjective overall liking scores. The study guides the domestic cooking of beef stew and quantitative sensory prediction based on multiple intelligent sensory techniques.

A Multimodal Sensing CMOS Imager Based on Dual-Focus Imaging

Advanced machine intelligence is empowered not only by the ever-increasing computational capability for information processing but also by sensors for collecting multimodal information from complex environments. However, simply assembling different sensors can result in bulky systems and complex data processing. Herein, it is shown that a complementary metal-oxide-semiconductor (CMOS) imager can be transformed into a compact multimodal sensing platform through dual-focus imaging. By combining lens-based and lensless imaging, visual information, chemicals, temperature, and humidity can be detected with the same chip and output as a single image. As a proof of concept, the sensor is equipped on a micro-vehicle, and multimodal environmental sensing and mapping is demonstrated. A multimodal endoscope is also developed, and simultaneous imaging and chemical profiling along a porcine digestive tract is achieved. The multimodal CMOS imager is compact, versatile, and extensible and can be widely applied in microrobots, in vivo medical apparatuses, and other microdevices.

Characterization of the Pure Black Tea Wine Fermentation Process by Electronic Nose and Tongue-Based Techniques with Nutritional Characteristics

Wine is an alcoholic beverage, consisting of several compounds in various ranges of concentrations. Wine quality is usually assessed by a sensory panel of trained personnel. Electronic tongues (e-tongues) and electronic noses (e-noses) have been established in recent years to assess the quality of beverages and foods. Response surface and electronic analysis tools were used to examine the quality of black tea wine. The results indicated the optimum initial sugar level (25 °Brix), yeast addition (0.5%), and fermentation temperature (25 °C) for Golden Peony black tea wine. The black tea wine produced under these conditions with 14.0% vol alcohol has as an orange-red color, full wine and tea flavor, and mild and mellow taste. The sourness of the wine was most affected by fermentation factors-yeast addition, fermentation temperature, and initial sugar level. Alcohols, aldehydes, ketones, and alkanes contributed to most of the volatile components under the influence of yeast addition and fermentation temperature. In contrast, nitrogen oxides, aromatics, and organic sulfides contributed under the influence of the initial sugar level. This study provided a facilitated strategy for obtaining the optimum black tea wine fermentation process through electronic nose and tongue-based techniques. The analysis of wines requires new technologies able to detect various different compounds simultaneously, providing worldwide information about the sample instead of information about specific compounds.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

Classification and Antioxidant Activity Evaluation of Edible Oils by Using Nanomaterial-Based Electrochemical Sensors

The classification of olive oils and the authentication of their biological or geographic origin are important issues for public health and for the olive oil market and related industries. The development of techniques for olive oil classification that are fast, easy to use, and suitable for online, in situ and remote operation is of high interest. In this study, the possibility of discriminating and classifying vegetable oils according to different criteria related to biological or geographical origin was assessed using cyclic voltammograms (CVs) as input data, obtained with electrochemical sensors based on carbonaceous nanomaterials and gold nanoparticles. In this context, 44 vegetable oil samples of different categories were analyzed and the capacity of the sensor array coupled with multivariate analysis was evaluated. The characteristics highlighted in voltammograms are related to the redox properties of the electroactive compounds, mainly phenolics, existing in the oils. Moreover, the antioxidant activity of the oils' hydrophilic fraction was also estimated by conventional spectrophotometric methods (1,1-diphenyl-2-picrylhydrazyl (DPPH) and galvinoxyl) and correlated with the voltammetric responses of the sensors. The percentage of DPPH and galvinoxyl inhibition was accurately predicted from the voltammetric data, with a correlation coefficients greater than 0.97 both in calibration and in validation. The results indicate that this method allows for a clear discrimination of oils from different biological or geographic origins.

Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.

A Comparison between the Egg Yolk Flavor of Indigenous 2 Breeds and Commercial Laying Hens Based on Sensory Evaluation, Artificial Sensors, and GC-MS

The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (n = 135), Xueyu White (n = 135), and Xinyang Blue (n = 135)) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milky flavor, moisture, and compactness scores (p < 0.05) of egg yolk from Xueyu White and Xinyang Blue were significantly higher than that of Hy-Line Brown. The aroma preference scores of Xinyang Blue (p < 0.05) were significantly higher, compared to Hy-Line Brown and Xueyu White. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher foe egg yolks of Hy-Line Brown (p < 0.05), compared to that of Xueyu White and Xinyang Blue. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of Xueyu White (p < 0.05), compared to that of Hy-Line Brown and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (p < 0.05) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.

Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares–Regression (PLS-R) and support vector machine–regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error—RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Data fusion of electronic noses and electronic tongues aids in botanical origin identification on imbalanced Codonopsis Radix samples

Codonopsis Radix (CR) is an edible food and traditional Chinese herb medicine in China. Various varieties of Codonopsis Radix have different tastes. To make the flavor of processed food stable, two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample. The optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods. A comparison were performed on the models trained on the data collected by electronic nose and electronic tongue. The results showed that the model trained on the fused dataset outperformed the models trained separately on the electronic nose data and electronic tongue data. The two preprocessing approaches could improve the prediction performance of all classification methods. Classification and Regression Tree approach performed better than Partial Least Square Discriminant Analysis and Linear Discriminant Analysis in terms of accuracy. But Classification and Regression Tree tends to assign the samples of minority class to the majority class. Meanwhile, Partial Least Square Discriminant Analysis keeps a good balance between the identification requirements of all the two groups of samples. Taking all the results above, the model built using the Partial Least Square Discriminant Analysis method on the fused data after z-score was used to identify the botanical origin of Codonopsis Radix.

Safety monitoring method of moving target in underground coal mine based on computer vision processing

Coal is one of the main energy sources in China. The country attaches great importance to the development of coal mining industry, and coal production is on the rise. At the same time, mine safety accidents are becoming more and more frequent, and the country is paying more and more attention to mine safety accidents. The underground environment of coal mine is complex, noisy and uneven, and there will be problems such as occlusion and high false detection rate during video monitoring. In order to ensure the safety of underground personnel, moving target detection and tracking based on video monitoring information is of great significance for coal mine safety production. The purpose of this paper is to study how to analyze and study the monitoring of moving targets in coal mines based on computer vision processing, and describe the image processing methods. This paper puts forward the problem of target monitoring, which is based on image processing, and then elaborates on the concept of image enhancement and related algorithms. From the average gradient, the algorithm in this paper is 56.60% higher than the histogram equalization algorithm, and 68.26% higher than the dark primary color prior dehazing algorithm. and designs and analyzes cases of image enhancement in coal mines. The experimental results show that the information entropy of the algorithm in this paper is 31.10% higher than that of the dark primary color prior dehazing algorithm, and 18.72% higher than that of the histogram equalization algorithm. It can be seen that the algorithm in this paper can achieve better enhancement effect.

Flavor Profile Analysis of Instant and Traditional Lanzhou Beef Bouillons Using HS-SPME-GC/MS, Electronic Nose and Electronic Tongue

The volatile profiles and taste properties of Lanzhou beef bouillons prepared with traditional (A1−A8) and modern (B1, B2) processing methods were evaluated. A total of 133 volatiles were identified: olefins, aldehydes and alcohols from spices in traditional bouillons were significantly higher (p < 0.05) than those in instant bouillons. The characteristic volatile substances in traditional beef bouillons were eucalyptol, linalool, 2-decanone, β-caryophyllene and geraniol; instant bouillons lacked 2-decanone and β-caryophyllene, and the contents of the other three substances were low. PCA (principal component analysis) and CA (clustering analysis) showed that the instant bouillons have a similar volatile profile to traditional bouillons, and the results of E-nose and sensory evaluation also supported this conclusion. The E-tongue showed that the taste profiles of instant bouillons were significantly different from those of traditional bouillons, mainly due to lack of umami; however, sensory evaluation revealed that taste differences were not perceptible.

Improved Model for Starch Prediction in Potato by the Fusion of Near-Infrared Spectral and Textural Data

In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a partial least squares regression (PLSR) prediction model was then established. The results revealed that low-level data fusion could not improve accuracy in predicting starch content. Therefore, to improve prediction accuracy, key variables were selected from the spectral and textural data through competitive adaptive reweighted sampling (CARS) and correlation analysis, respectively, and mid-level data fusion was performed. With a residual predictive deviation (RPD) value > 2, the established PLSR model achieved satisfactory prediction accuracy. Therefore, this study demonstrated that appropriate data fusion can effectively improve the prediction accuracy for starch content and thus aid the sorting of potato starch content in the production line.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Research progress on the formation mechanism and detection technology of bread flavor

With a long history of fermentation technology and rich flavors, bread is widely consumed by people all around the world. The consumer market is huge and the demand is wide. However, the formation mechanism of bread baking flavor has not been completely defined. In order to improve the breadmaking process and the quality of bread, the main flavor substances produced in bread baking, the formation mechanism, and the detection technology of bread baking flavor are carefully summarized in this paper. The generation conditions and formation mechanism of flavor substances during the bread baking process are expounded, and the limitations of some current bread flavor detection technologies are proposed, which will provide theoretical basis for effectively regulating the generation of flavor substances in the bread baking process and making bread with good flavor and rich nutrition in the future.

E-Senses, Panel Tests and Wearable Sensors: A Teamwork for Food Quality Assessment and Prediction of Consumer’s Choices

At present, food quality is of utmost importance, not only to comply with commercial regulations, but also to meet the expectations of consumers; this aspect includes sensory features capable of triggering emotions through the citizen’s perception. To date, key parameters for food quality assessment have been sought through analytical methods alone or in combination with a panel test, but the evaluation of panelists’ reactions via psychophysiological markers is now becoming increasingly popular. As such, the present review investigates recent applications of traditional and novel methods to the specific field. These include electronic senses (e-nose, e-tongue, and e-eye), sensory analysis, and wearables for emotion recognition. Given the advantages and limitations highlighted throughout the review for each approach (both traditional and innovative ones), it was possible to conclude that a synergy between traditional and innovative approaches could be the best way to optimally manage the trade-off between the accuracy of the information and feasibility of the investigation. This evidence could help in better planning future investigations in the field of food sciences, providing more reliable, objective, and unbiased results, but it also has important implications in the field of neuromarketing related to edible compounds.

Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis

Magnolia officinalis Rehd. et Wils. and Magnolia officinalis Rehd. et Wils. var. biloba Rehd. et Wils, as the legal botanical origins of Magnoliae Officinalis Cortex, are almost impossible to distinguish according to their appearance traits with respect to medicinal bark. The application of AFLP molecular markers for differentiating the two origins has not yet been successful. In this study, a combination of e-nose measurements, e-tongue measurements, and chemical analyses coupled with multiple-source data fusion was used to differentiate the two origins. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were applied to compare the discrimination results. It was shown that the e-nose system presented a good discriminant ability with a low classification error for both LDA and QDA compared with e-tongue measurements and chemical analyses. In addition, the discriminating capacity of LDA for low-level fusion with original data, similar to a combined system, was superior or equal to that acquired individually with the three approaches. For mid-level fusion, the combination of different principals extracted by PCA and variables obtained on the basis of PLS-VIP exhibited an analogous discrimination ability for LDA (classification error 0.0%) and was significantly superior to QDA (classification error 1.67-3.33%). As a result, the combined e-nose, e-tongue, and chemical analysis approach proved to be a powerful tool for differentiating the two origins of Magnoliae Officinalis Cortex.

Recent technology for food and beverage quality assessment: a review

Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.

pH indicator-based sensor array in combination with hyperspectral imaging for intelligent evaluation of withering degree during processing of black tea

Withering is one of the most critical steps in the processing of black tea. The degree of withering affects the aroma quality of the finished tea. In this study, we used a pH indicator-based colorimetric sensor array in combination with hyperspectral imaging to intelligently evaluate the withering degree. After analyzing the difference between images taken before and after the reaction of pH indicators with withered leaves, six pH indicators were selected to build a sensor array. Then, the hyperspectral image of each pH indicator was obtained at wavelengths between 400 and 1000 nm. Nonlinear support vector machine (SVM) and least-squares (LS) SVM models were established to determine the degree of withering. Results revealed that the spectral information from single pH indicator failed to accurately evaluate the withering degree. The LS-SVM model achieved satisfactory discriminant results with the low-level data fusion of six pH indicators followed by principal component analysis for dimensionality reduction. The optimal model yielded accuracies of 93.75% and 90.00% for the calibration and prediction sets, respectively. The results indicated that colorimetric sensor array in combination with hyperspectral imaging can effectively determine the withering degree, thus providing a novel method for the intelligent processing of food and tea.

Overview of advanced technologies for volatile organic compounds measurement in food quality and safety

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling

The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94–96%; 92–97%, respectively) and white wines (96–97%; 90–97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.

Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.

Strategic Priorities of the Scientific Plan of the European Research Infrastructure METROFOOD-RI for Promoting Metrology in Food and Nutrition

The pan-European distributed Research Infrastructure for Promoting Metrology in Food and Nutrition (METROFOOD-RI) has evolved in the frame of the European Strategy Forum on Research Infrastructures (ESFRI) to promote high-quality metrology services across the food chain. The METROFOOD-RI comprises physical facilities and electronic facilities. The former includes Reference Material plants and analytical laboratories (the ‘Metro’ side) and also experimental fields/farms, processing/storage plants and kitchen-labs (the ‘Food’ side). The RI is currently prepared to apply for receiving the European Research Infrastructure Consortium (ERIC) legal status and is organised to fulfil the requirements for operation at the national, European Union (EU) and international level. In this view, the METROFOOD-RI partners have recently reviewed the scientific plan and elaborated strategic priorities on key thematic areas of research in the food and nutrition domain to which they have expertise to contribute to meet global societal challenges and face unexpected emergencies. The present review summarises the methodology and main outcomes of the research study that helped to identify the key thematic areas from a metrological standpoint, to articulate critical and emerging issues and demands and to structure how the integrated facilities of the RI can operate in the first five years of operation as ERIC.