Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

Probabilistic topic modelling in food spoilage analysis: A case study with Atlantic salmon (Salmo salar)

Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling - more specifically, Latent Dirichlet Allocation (LDA) - in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo salar) at 4 °C under different gaseous atmospheres (% CO₂/O₂/N₂): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N₂ (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis.

Fish Quality Index Method: Principles, weaknesses, validation, and alternatives-A review

Fish is a high nutritional value matrix of which production and consumption have been increasing in the last years. Advancements in the efficient evaluation of freshness are essential to optimize the quality assessment, to improve consumer safety, and to reduce raw material losses. Therefore, it is necessary to use rapid, nondestructive, and objective methodologies to evaluate the quality of this matrix. Quality Index Method (QIM) is a tool applied to indicate fish freshness through a sensory evaluation performed by a group of assessors. However, the use of QIM as an official method for quality assessment is limited by the protocol, sampling size, specificities of the species, storage conditions, and assessor's experience, which make this method subjective. Also, QIM may present divergences regarding the development of microorganisms and chemical analysis. In this way, novel quality evaluation methods such as electronic noses, electronic tongues, machine vision system, and colorimetric sensors have been proposed, and novel technologies such as proteomics and mitochondrial analysis have been developed. In this review, the weaknesses of QIM were exposed, and novel methodologies for quality evaluation were presented. The consolidation of these novel methodologies and their use as methods of quality assessment are an alternative to sensory methods, and their understanding enables a more effective fish quality control.

Electroanalysis from the past to the twenty-first century: challenges and perspectives

A personal mini-review is presented on the history of electroanalysis and on their present achievements and future challenges. The manuscript is written from the subjective view of two generations of electroanalytical chemists that have witnessed for many years the evolution of this discipline.

Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis

Gas sensors are critical components when adhering to health safety and environmental policies in various manufacturing industries, such as the petroleum and oil industry; scent and makeup production; food and beverage manufacturing; chemical engineering; pollution monitoring. In recent times, gas sensors have been introduced to medical diagnostics, bioprocesses, and plant disease diagnosis processes. There could be an adverse impact on human health due to the mixture of various gases (e.g., acetone (A), ethanol (E), propane (P)) that vent out from industrial areas. Therefore, it is important to accurately detect and differentiate such gases. Towards this goal, this paper presents a novel electronic nose (e-nose) detection method to classify various explosive gases. To detect explosive gases, metal oxide semiconductor (MOS) sensors are used as reliable tools to detect such volatile gases. The data received from MOS sensors are processed through a multivariate analysis technique to classify different categories of gases. Multivariate analysis was done using three variants—differential, relative, and fractional analyses—in principal components analysis (PCA). The MOS sensors also have three different designs: loading design, notch design, and Bi design. The proposed MOS sensor-based e-nose accurately detects and classifies three different gases, which indicates the reliability and practicality of the developed system. The developed system enables discrimination of these gases from the mixture. Based on the results from the proposed system, authorities can take preventive measures to deal with these gases to avoid their potential adverse impacts on employee health.

A Review on Electrochemical Sensors and Biosensors Used in Phenylalanine Electroanalysis

Phenylalanine is an amino acid found in breast milk and in many foods, being an essential nutrient. This amino acid is very important for the human body because it is transformed into tyrosine and, subsequently, into catecholamine neurotransmitters. However, there are individuals who were born with a genetic disorder called phenylketonuria. The accumulation of phenylalanine and of some metabolites in the body is dangerous and may cause convulsions, brain damage and mental retardation. Determining the concentration of phenylalanine in different biologic fluids is very important because it can provide information about the health status of the individuals envisaged. Since such determinations may be made by using electrochemical sensors and biosensors, numerous researchers have developed such sensors for phenylalanine detection and different sensitive materials were used in order to improve the selectivity, sensitivity and detection limit. The present review aims at presenting the design and performance of some electrochemical bio (sensors) traditionally used for phenylalanine detection as reported in a series of relevant scientific papers published in the last decade.

A Filter Supported Surface-Enhanced Raman Scattering "Nose" for Point-of-Care Monitoring of Gaseous Metabolites of Bacteria

This work designs a convenient method for fabrication of surface-enhanced Raman scattering (SERS) devices by loading gold nanostars (AuNSs) on a flat filter support with vacuum filtration. The dense accumulation of AuNSs results in a strong sensitization to SERS signal and shows sensitive response to gaseous metabolites of bacteria, which produces a SERS "nose" for rapid point-of-care monitoring of these metabolites. The "nose" shows good reproducibility and stability and can be used for SERS quantitation of a gaseous target with Raman signal. The impressive performance of the proposed SERS "nose" for detecting gaseous metabolites of common foodborne bacteria like Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa from inoculated samples demonstrates its much higher sensitivity than that of human sense and application in distinguishing spoiled food at an early stage and real-time tracing of food spoilage degree. The strong point-of-care testing ability of the designed SERS "nose" and the miniaturization of whole equipment extend greatly the analytical application of SERS technology in food safety and public health.

Nanotechnologies in Food Science: Applications, Recent Trends, and Future Perspectives

Nanotechnology is a key advanced technology enabling contribution, development, and sustainable impact on food, medicine, and agriculture sectors. Nanomaterials have potential to lead qualitative and quantitative production of healthier, safer, and high-quality functional foods which are perishable or semi-perishable in nature. Nanotechnologies are superior than conventional food processing technologies with increased shelf life of food products, preventing contamination, and production of enhanced food quality. This comprehensive review on nanotechnologies for functional food development describes the current trends and future perspectives of advanced nanomaterials in food sector considering processing, packaging, security, and storage. Applications of nanotechnologies enhance the food bioavailability, taste, texture, and consistency, achieved through modification of particle size, possible cluster formation, and surface charge of food nanomaterials. In addition, the nanodelivery-mediated nutraceuticals, synergistic action of nanomaterials in food protection, and the application of nanosensors in smart food packaging for monitoring the quality of the stored foods and the common methods employed for assessing the impact of nanomaterials in biological systems are also discussed.

Assessment of Spoilage Bacterial Communities in Food Wrap and Modified Atmospheres-Packed Minced Pork Meat Samples by 16S rDNA Metagenetic Analysis

Although several studies have focused on the dynamics of bacterial food community, little is known about the variability of batch production and microbial changes that occur during storage. The aim of the study was to characterize the microbial spoilage community of minced pork meat samples, among different food production and storage, using both 16S rRNA gene sequencing and classical microbiology. Three batches of samples were obtained from four local Belgian facilities (A–D) and stored until shelf life under food wrap (FW) and modified atmosphere packaging (MAP, CO₂ 30%/O₂ 70%), at constant and dynamic temperature. Analysis of 288 samples were performed by 16S rRNA gene sequencing in combination with counts of psychrotrophic and lactic acid bacteria at 22°C. At the first day of storage, different psychrotrophic counts were observed between the four food companies (Kruskal-Wallist test, p-value < 0.05). Results shown that lowest microbial counts were observed at the first day for industries D and A (4.2 ± 0.4 and 5.6 ± 0.1 log CFU/g, respectively), whereas industries B and C showed the highest results (7.5 ± 0.4 and 7.2 ± 0.4 log CFU/g). At the end of the shelf life, psychrotrophic counts for all food companies was over 7.0 log CFU/g. With metagenetics, 48 OTUs were assigned. At the first day, the genus Photobacterium (86.7 and 19.9% for food industries A and C, respectively) and Pseudomonas (38.7 and 25.7% for food companies B and D, respectively) were dominant. During the storage, a total of 12 dominant genera (>5% in relative abundance) were identified in MAP and 7 in FW. Pseudomonas was more present in FW and this genus was potentially replaced by Brochothrix in MAP (two-sided Welch’s t-test, p-value < 0.05). Also, a high Bray-Curtis dissimilarity in genus relative abundance was observed between food companies and batches. Although the bacteria consistently dominated the microbiota in our samples are known, results indicated that bacterial diversity needs to be addressed on the level of food companies, batches variation and food storage conditions. Present data illustrate that the combined approach provides complementary results on microbial dynamics in minced pork meat samples, considering batches and packaging variations.

Tetranitrile-anthracene as a probe for fluorescence detection of viscosity in fluid drinks via aggregation-induced emission

An AIE-based fluorescent probe was developed for monitoring the viscosity change during the spoilage process of fluid drinks.

Bulk and Surface Acoustic Wave Sensor Arrays for Multi-Analyte Detection: A Review

Bulk acoustic wave (BAW) and surface acoustic wave (SAW) sensor devices have successfully been used in a wide variety of gas sensing, liquid sensing, and biosensing applications. Devices include BAW sensors using thickness shear modes and SAW sensors using Rayleigh waves or horizontally polarized shear waves (HPSWs). Analyte specificity and selectivity of the sensors are determined by the sensor coatings. If a group of analytes is to be detected or if only selective coatings (i.e., coatings responding to more than one analyte) are available, the use of multi-sensor arrays is advantageous, as the evaluation of the resulting signal patterns allows qualitative and quantitative characterization of the sample. Virtual sensor arrays utilize only one sensor but combine it with enhanced signal evaluation methods or preceding sample separation, which results in similar results as obtained with multi-sensor arrays. Both array types have shown to be promising with regard to system integration and low costs. This review discusses principles and design considerations for acoustic multi-sensor and virtual sensor arrays and outlines the use of these arrays in multi-analyte detection applications, focusing mainly on developments of the past decade.

Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.

Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure

The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression

The development of quality monitoring systems for perishable food products like seafood requires extensive data collection under specified packaging and storage conditions, followed by advanced data analysis and interpretation. Even though the benefits of using volatile organic compounds as food quality indices have been recognized, few studies have focused on real-time quantification of the seafood volatilome and subsequent systematic identification of the most important spoilage indicators. In this study, spoilage of Atlantic salmon (Salmo salar) stored under modified atmospheres (% CO₂/O₂/N₂) and air was characterized by performing multivariate statistical analysis and augmented ordinal regression modelling for data collected by microbiological, chemical and sensory analyses. Out of 25 compounds quantified by selected-ion flow-tube mass spectrometry, ethanol, dimethyl sulfide and hydrogen sulfide were found characteristic under anaerobic conditions (0/0/100 and 60/0/40), whereas spoilage under air was primarily associated with the production of alcohols and ketones. Under high-O₂ MAP (60/40/0), only 3-methylbutanal fulfilled the identification criteria. Overall, this manuscript presents a systematic and widely applicable methodology for the identification of most potential seafood spoilage indicators within the context of intelligent packaging technology development. In particular, parallel application of statistics and modelling was found highly beneficial for the performance of the quality characterization process and for the practical applicability of the obtained results in food quality monitoring.

Critical review of electronic nose and tongue instruments prospects in pharmaceutical analysis

Electronic nose (enose, EN) and electronic tongue (etongue, ET) have been designed to simulate human senses of smell and taste in the best possible way. The signals acquired from a sensor array, combined with suitable data analysis system, are the basis for holistic analysis of samples. The efficiency of these instruments, regarding classification, discrimination, detection, monitoring and analytics of samples in different types of matrices, is utilized in many fields of science and industry, offering numerous practical applications. Popularity of both types of devices significantly increased during the last decade, mainly due to improvement of their sensitivity and selectivity. The electronic senses have been employed in pharmaceutical sciences for, among others, formulation development and quality assurance. This paper contains a review of some particular applications of EN and ET based instruments in pharmaceutical industry. In addition, development prospects and a critical summary of the state of art in the field were also surveyed.

Use of Electronic Noses for Diagnosis of Digestive and Respiratory Diseases through the Breath

The increased occurrence of chronic diseases related to lifestyle or environmental conditions may have a detrimental effect on long-term health if not diagnosed and controlled in time. For this reason, it is important to develop new noninvasive early diagnosis equipment that allows improvement of the current diagnostic methods. This, in turn, has led to an exponential development of technology applied to the medical sector, such as the electronic nose. In addition, the appearance of this type of technology has allowed the possibility of studying diseases from another point of view, such as through breath analysis. This paper presents a bibliographic review of past and recent studies, selecting those investigations in which a patient population was studied with electronic nose technology, in order to identify potential applications of this technology in the detection of respiratory and digestive diseases through the analysis of volatile organic compounds present in the breath.

Comparison of electronic sensing techniques for screening dried shrimps irradiated using three types of approved radiation with standard analytical methods

Rapid analytical methods for screening irradiated foods are required to comply with the approved standards for international trade. Dried shrimps irradiated at 1-7 kGy with gamma rays, electron beam (E-beam), and X-rays were screened with an electronic nose (E-nose) and electronic tongue (E-tongue). The data were compared with those from European standard methods (photostimulated luminescence, PSL) and direct epifluorescent filter technique/aerobic plate count, DEFT/APC). All irradiated shrimp samples were clearly discriminated from the non-irradiated control based on PSL photon count measurements and DEFT/APC microbial enumeration. The volatile patterns and taste attributes of the irradiated (>1 kGy from three sources) and control samples could be distinguished by the E-nose and E-tongue analyses through principal component analysis. Verification through electron spin resonance and thermoluminescence analyses validated screening results. The results indicate that E-sensing techniques showed potential for the rapid screening of irradiated foods like dried shrimps.

Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses

A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH₄ and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH₄ and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.

Analysis of volatile compounds in fresh sturgeon with different preservation methods using electronic nose and gas chromatography/mass spectrometry

Contamination of microorganisms causes a rapid deterioration in the quality of fresh sturgeon meat, which results in the shortening of the shelf-life and increase in the health risk. In this paper, two preservation treatments based on microbial control were considered. During the chilling storage (0–6 days) period, the sensory analysis and the volatile compound (VOC) evaluation were performed by electronic nose and SPME-GC/MS. Results showed that washing with acidic oxidized electrolyzed water and the addition of ε-PL influences the sensitive VOCs of the fresh sturgeon by inhibiting the spoilage of microbes or introducing the chemical agents like free chlorine and reactive oxygen species. Furthermore, GC/MS analysis detected more than 40 kinds of VOCs, mainly aldehydes and ketones, in the fresh sturgeon during the chilling storage period. The relative content of heptanal, nonanal, and acetophenone increased linearly with the storage time in all the groups, where R² of all the groups was larger than 0.9. However, the content of hexanal and octanal decreased simultaneously. This indicated that the present work discovered the potential biomarkers acting as indicators for rapidly evaluating the quality of sturgeon products.

Contamination of microorganisms caused rapidly quality deterioration of fresh sturgeon meat. During the chilling storage, the sensory analysis and volatile compounds evaluation were well performed by electronic nose and SPME-GC/MS.