The prediction of food additives in the fruit juice based on electronic nose with chemometrics

Characterization of volatile profile from different coriander (Coriandrum sativum L.) varieties via HS-SPME/GC-MS combined with E-nose analyzed by chemometrics

Headspace-solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) and electronic nose (E-nose) technologies were implemented to characterize the volatile profile of aerial part from 40 coriander varieties. A total of 207 volatile compounds were identified and quantified, including aldehydes, alcohols, terpenes, hydrocarbons, esters, ketones, acids, furans, phenols and others. E-nose results showed that W5S and W2W were representative sensors responding to coriander odor. Among all varieties, the number (21-30 species) and content (449.94-1050.55 μg/g) of aldehydes were the highest, and the most abundant analytes were (Z)-9-hexadecenal or (E)-2-tetratecenal, which accounted for approximately one-third of the total content. In addition, 37 components were determined the characteristic constituents with odor activity values (OAVs) ≥ 1, mainly presenting citrusy, fatty, soapy and floral smells. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) could effectively distinguish different varieties. This study provided a crucial theoretical basis for flavor evaluation and quality improvement of coriander germplasm resources.

Flexible Wearable Chemoresistive Ethylene Gas-Monitoring Device Utilizing Pd/Ti3C2Tx Nanocomposites for In Situ Nondestructive Monitoring of Kiwifruit Ripeness

Kiwifruit, renowned for its antioxidant properties and nutritional richness, faces challenges in maintaining quality during transportation, often leading to suboptimal products reaching the market. To address this issue, a wireless transmission flexible ethylene monitoring device (WFEMD) was developed. This device comprises a flexible ethylene gas sensor and a signal transmission processing unit integrated with electronic components, enabling real-time monitoring capabilities. In this study, the catalytic activity of Pd and Pd/Ti heterojunctions was leveraged to enhance the ethylene gas sensing. The impact of Ti3C2Tx modified with varying masses of Pd nanoparticles on ethylene gas response levels was investigated. The signal transmission processing unit, fabricated by using the laser direct-writing method, was optimized to collect signals from the flexible ethylene gas sensor, convert them into corresponding ethylene concentrations, and transmit data via an antenna. By introducing a random forest (RF) classification algorithm, a remarkable 97.5% accuracy in predicting kiwifruit ripeness grades was achieved. The algorithm facilitated precise classification by collecting key parameters such as ethylene and CO2 during transportation. The WFEMD enables real-time acquisition of kiwifruit ethylene gas information, which is transmitted wirelessly for data visualization and traceability via mobile terminals. This empowers managers with timely insights into ethylene emissions and ripeness predictions, facilitating informed decision-making processes.

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Volatilome and flavor analyses based on e-nose combined with HS-GC-MS provide new insights into ploidy germplasm diversity in Platostoma palustre

Platostoma palustre (Mesona chinensis Benth or Hsian-tsao, also known as "Xiancao" in China), is an edible and medicinal plant native to India, Myanmar, and Indo-China. It is the main ingredient in the popular desserts Hsian-tsao tea, herbal jelly, and sweet herbal jelly soup. P. palustre is found abundantly in nutrient-rich substances and possesses unique aroma compounds. Variations in the contents of volatile compounds among different germplasms significantly affect the quality and flavor of P. palustre, causing contradiction in demand. This study investigates the variation in the volatile compound profiles of distinct ploidy germplasms of P. palustre by utilising headspace gas chromatography-mass spectrometry (HS-GC-MS) and an electronic nose (e-nose). The results showed significant differences in the aroma characteristics of stem and leaf samples in diverse P. palustre germplasms. A total of sixty-seven volatile compounds have been identified and divided into ten classes. Six volatile compounds (caryophyllene, α-bisabolol, benzaldehyde, β-selinene, β-elemene and acetic acid) were screened as potential marker volatile compounds to discriminate stems and leaves of P. palustre. In this study, leaves of P. palustre showed one odor pattern and stems showed two odor patterns under the influence of α-bisabolol, acetic acid, and butyrolactone. In addition, a correlation analysis was conducted on the main volatile compounds identified by HS-GC-MS and e-nose. This analysis provided additional insight into the variations among samples resulting from diverse germplasms. The present study provides a valuable volatilome, and flavor, and quality evaluation for P. palustre, as well as new insights and scientific basis for the development and use of P. palustre germplasm resources.

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system's performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

Colorimetric sensor array combined with chemometric methods for the assessment of aroma produced during the drying of tencha

The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.

GC-MS, GC-IMS, and E-Nose Analysis of Volatile Aroma Compounds in Wet-Marinated Fermented Golden Pomfret Prepared Using Different Cooking Methods

The cooking method is extremely important for the production of low-salt, wet-marinated, fermented golden pomfret because it strongly influences its flavor components and organoleptic quality. There are also significant differences in flavor preferences in different populations. The present study analyzed differences in the aroma characteristics of wet-marinated fermented golden pomfret after boiling, steaming, microwaving, air-frying, and baking using a combination of an electronic nose, GC-IMS, and SPME-GC-MS. Electronic nose PCA showed that the flavors of the boiled (A), steamed (B), and microwaved (C) treatment groups were similar, and the flavors of the baking (D) and air-frying (E) groups were similar. A total of 72 flavor compounds were detected in the GC-IMS analysis, and the comparative analysis of the cooked wet-marinated and fermented golden pomfret yielded a greater abundance of flavor compounds. SPME-GC-MS analysis detected 108 flavor compounds, and the results were similar for baking and air-frying. Twelve key flavor substances, including hexanal, isovaleraldehyde, and (E)-2-dodecenal, were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and VIP analysis. These results showed that the cooking method could be a key factor in the flavor distribution of wet-marinated fermented golden pomfret, and consumers can choose the appropriate cooking method accordingly. The results can provide theoretical guidance for the more effective processing of fish products and the development of subsequent food products.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination

This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential oils (EOs), various discrimination approaches were explored to ensure quality, safety, and authenticity, thereby establishing consumer confidence. Through the utilization of physical-chemical and instrumental analyses, the purity of EOs was evaluated via qualitative and quantitative assessments, enabling the identification of constituents or compounds within the oils. Consequently, a diverse array of techniques has been documented, encompassing organoleptic, physical, chemical, and instrumental methodologies, such as spectroscopic and chromatographic methods. Electronic noses (e-noses) exhibit significant potential for identifying cinnamon adulteration, presenting a rapid, non-destructive, and cost-effective approach. Leveraging their capability to detect and analyze volatile organic compound (VOC) profiles, e-noses can contribute to ensuring authenticity and quality in the food and fragrance industries. Continued research and development efforts in this domain will assuredly augment the capacities of this promising avenue, which is the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in conjunction with spectroscopic data to combat cinnamon adulteration.

Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin". For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation-extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder

Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its counterfeit. Electronic sensory technologies are the inheritance and development of traditional empirical identification. Considering that each drug has its own specific odor and taste characteristics, electronic tongue (E-tongue), electronic nose (E-nose) and GC-MS were used to evaluate the aroma and taste of BBP and its common counterfeit. Two active components of BBP, namely tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) were measured and linked with the electronic sensory data. The results showed that bitterness was the main flavor of TUDCA in BBP, saltiness and umami were the main flavor of TCDCA. The volatiles detected by E-nose and GC-MS were mainly aldehydes, ketones, alcohols, hydrocarbons, carboxylic acids, heterocyclic, lipids, and amines, mainly earthy, musty, coffee, bitter almond, burnt, pungent odor descriptions. Four different machine learning algorithms (backpropagation neural network, support vector machine, K-nearest neighbor, and random forest) were used to identify BBP and its counterfeit, and the regression performance of these four algorithms was also evaluated. For qualitative identification, the algorithm of random forest has shown the best performance, with 100% accuracy, precision, recall and F1-score. Also, the random forest algorithm has the best R² and the lowest RMSE in terms of quantitative prediction.

A Machine Learning Method for the Quality Detection of Base Liquor and Commercial Liquor Using Multidimensional Signals from an Electronic Nose

Chinese liquor is a world-famous beverage with a long history. Base liquor, a product of liquor brewing, significantly affects the flavor and quality of commercial liquor. In this study, a machine learning method consisting of a deep residual network (ResNet)18 backbone with a light gradient boosting machine (LightGBM) classifier (ResNet-GBM) is proposed for the quality identification of base liquor and commercial liquor using multidimensional signals from an electronic nose (E-Nose). Ablation experiments are conducted to analyze the contribution of the framework’s components. Five evaluation metrics (accuracy, sensitivity, precision, F1 score, and kappa score) are used to verify the performance of the proposed method, and six other frameworks (support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), multidimensional scaling-support vector machine (MDS-SVM), and back-propagation neural network (BPNN)) on three datasets (base liquor, commercial liquor, and mixed base and commercial liquor datasets). The experimental results demonstrate that the proposed ResNet-GBM model achieves the best performance for identifying base liquor and commercial liquors with different qualities. The proposed framework has the highest F1 score for the identification of commercial liquor in the mixed dataset due to the contribution of similar microconstituents from the base liquor. The proposed method can be used for the quality control of Chinese liquor and promotes the practical application of E-nose devices.

Volatile Organic Compound Detection by Graphene Field-Effect Transistors Functionalized with Fly Olfactory Receptor Mimetic Peptides

An olfactory receptor mimetic peptide-modified graphene field-effect transistor (gFET) is a promising solution to overcome the principal challenge of low specificity graphene-based sensors for volatile organic compound (VOC) sensing. Herein, peptides mimicking a fruit fly olfactory receptor, OR19a, were designed by a high-throughput analysis method that combines a peptide array and gas chromatography for the sensitive and selective gFET detection of the signature citrus VOC, limonene. The peptide probe was bifunctionalized via linkage of a graphene-binding peptide to facilitate one-step self-assembly on the sensor surface. The limonene-specific peptide probe successfully achieved highly sensitive and selective detection of limonene by gFET, with a detection range of 8-1000 pM, while achieving facile sensor functionalization. Taken together, our target-specific peptide selection and functionalization strategy of a gFET sensor demonstrates advancement of a precise VOC detection system.

A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition

The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and humidity changes), which lead to a serious decline in gas recognition accuracy or even invalidation. To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be retrained quickly with a new gas at a low accuracy cost. Compared with gas recognition approaches such as support vector machine (SVM), k-nearest neighbor (KNN), principal component analysis (PCA) +SVM, PCA+KNN, and artificial neural network (ANN), our network achieves the highest accuracy of 98.75% in five-fold cross-validation for identifying nine types of gases, each with five different concentrations. In particular, the proposed network has a 5.09% higher accuracy than that of other gas recognition algorithms, which validates its robustness and effectiveness for real-life fire scenarios.

Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics

As goat milk has a higher economic value compared to cow milk, the phenomenon of adulterating goat milk with cow milk appears in the market. In this study, the potential of Raman spectroscopy along with chemometrics was investigated for the authentication and quantitation of liquid goat milk adulterated with cow milk. First, the results of principal component analysis (PCA) showed that there were differences between the Raman spectra of cow and goat milk, which made quantitative experiments possible. For quantification, three different brands of cow milk and goat milk were selected randomly and adulterated goat milk with cow milk at the proportion of 5-95%. 342 samples were used for the construction of the partial least squares regression (PLSR) model with 80% for the calibration set and 20% for the prediction set. The PLSR model showed excellent performance in quantifying the level of adulteration, for the prediction set, with a coefficient of determination (R²) of 0.9781, root mean square error (RMSE) of 3.82%, and a ratio of prediction to deviation (RPD) of 6.8. The results demonstrated the potential of Raman spectroscopy as a rapid, low cost and non-destructive analytical tool for detecting adulteration in goat milk.

Digital Taste in Mulsemedia Augmented Reality: Perspective on Developments and Challenges

Digitalization of human taste has been on the back burners of multi-sensory media until the beginning of the decade, with audio, video, and haptic input/output(I/O) taking over as the major sensory mechanisms. This article reviews the consolidated literature on augmented reality (AR) in the modulation and stimulation of the sensation of taste in humans using low-amplitude electrical signals. Describing multiple factors that combine to produce a single taste, various techniques to stimulate/modulate taste artificially are described. The article explores techniques from prominent research pools with an inclination towards taste modulation. The goal is to seamlessly integrate gustatory augmentation into the commercial market. It highlights core benefits and limitations and proposes feasible extensions to the already established technological architecture for taste stimulation and modulation, namely, from the Internet of Things, artificial intelligence, and machine learning. Past research on taste has had a more software-oriented approach, with a few trends getting exceptions presented as taste modulation hardware. Using modern technological extensions, the medium of taste has the potential to merge with audio and video data streams as a viable multichannel medium for the transfer of sensory information.

Gas Recognition in E-Nose System: A Review

Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.

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.

Use of mineral element profiling coupled with chemometric analysis to distinguish Zanthoxylum bungeanum cultivars and health risks of potentially toxic elements in pericarps

BACKGROUND

Zanthoxylum bungeanum pericarps (ZBP) are commonly used as food additives and traditional herbal medicines. Several mineral elements are known to have important physiological functions in organisms, whereas others are reported to have toxic effects. We determined levels of macro elements (Mg, S and Ca), essential trace elements (B, Mn, Fe, Cu, Zn, Se and Mo) and toxic elements (Ni, Al, Cr, As, Cd, Hg and Pb) in the pericarps of 19 Z. bungeanum cultivars. Hazard index values and incremental lifetime cancer risks were calculated to express health risks associated with pericarp consumption. Moreover, several chemometric analyses based on the mineral elements were used to distinguish Z. bungeanum cultivars.

RESULTS

The concentrations of 17 determined elements in the pericarps were ranked: Ca > Mg > S > Fe > Al > Mn > Zn > B > Cu > Ni > Pb > Cr > Mo > As > Cd > Hg > Se. The elements Zn, Cr and As had the highest variations in their concentrations. Cu, Mn, Se, Zn, Al, As, Cd, Cr, Hg, Ni and Pb posed some non-cancer risks, while As and Cd posed cancer risks. Mn, Fe, Zn, and Al were chosen as critical element markers for assessing ZBP using chemometric analyses.

CONCLUSION

Chemometric analyses could highlight mineral concentration differentiation among the 19 cultivars. The Z. bungeanum cultivar Z12 (from Wudu, Gansu) is best for producing ZBP, and cultivar Z18 (Guanling, Guizhou) can be a reference to classify and evaluate ZBP quality. The results provide valuable information for evaluating the potential safety risks of ZBP and contribute to inter-cultivar discrimination. © 2021 Society of Chemical Industry.

Analysis of volatile organic compounds and metabolites of three cultivars of asparagus (Asparagus officinalis L.) using E-nose, GC-IMS, and LC-MS/MS

Asparagus (A. officinalis L.) is a perennial herb of the genus Asparagus that is rich in nutrients. This study aimed to distinguish three cultivars of asparagus (Paladin, Grace, and Jinggang red) based on their volatile organic compounds (VOCs) and metabolic profiles. VOCs in the three cultivars were separated and identified using electronic nose (E-nose) and gas chromatography–ion mobility spectrometry (GC–IMS). Differences in metabolites among the three cultivars were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). E-nose and GC-IMS showed that the VOCs in asparagus differed significantly among the three groups. E-nose result showed that purple asparagus (Jinggang red) was connected to a stronger earthy odor; green asparagus (Paladin and Grace) were shown characteristic dill flavor. Moreover, 50 VOCs were detected by using GC–IMS. Ketones and alcohols were most abundant in Paladin; methyl benzoate and dimethyl sulfide were most abundance in Grace; aldehydes and acids were most abundance in Jinggang red. Moreover, 130 and 71 different metabolites were detected in the positive and negative modes among three cultivars, such as quercetin and rutin. Functional analysis revealed that these metabolites were involved in beta-alanine metabolism and ATP-binding cassette (ABC) transporters. In summary, E-nose combined with GC-IMS and LC-MS/MS methods has good application prospects in evaluating and identifying VOCs and metabolites of different cultivars of asparagus. The identified VOCs and metabolites can provide guidelines for the development of functional asparagus products.

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning

Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.

Image learning to accurately identify complex mixture components

The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniques have the ability to cope with this problem in a data-driven way. Herein, we report a 2D-spectral imaging method to collect matter information from mixture components, and the obtained feature images can be easily provided to deep convolutional neural networks (CNNs) for establishing a spectral network. The results demonstrated that a single CNN trained end-to-end from the proposed images can directly accomplish synchronous measurement of multi-component samples using only raw pixels as inputs. Our strategy has some innate advantages, such as fast data acquisition, low cost, and simple chemical treatment, suggesting that it can be extensively applied in many fields, including environmental science, biology, medicine, and chemistry.

Quality control of fresh strawberries by a random forest model

BACKGROUND

Strawberry quality is one of the most important factors that guarantees consistent commercialization of the fruit and ensures the consumer's satisfaction. This work makes innovative use of random forest (RF) to predict sensory measures of strawberries using physical and physical-chemical variables. Furthermore, it also employs these same physical and physical-chemical variables to classify strawberries in the classes "satisfied" or "not satisfied" and "would pay more" or "wouldn't pay more. The RF-based model predicts the acceptance, expectation, ideal of sweetness, ideal of acidity, and the ideal of succulence based on the physical and physical-chemical data. Then, the predicted parameters are used as input for the RF-based classification model.

RESULTS

The RF achieved a coefficient of determination R²  > 0.72 and a root-mean-squared error (RMSE) smaller than 0.17 for the prediction task, which indicates that one can estimate the sensory measures of strawberries using physical and physical-chemical data. Furthermore, the RF was able to classify 87.95% of the strawberry samples correctly into the classes 'satisfied' and 'not satisfied' and 78.99% in the classes 'would pay more' or 'would not pay more'. A two-step RF model, which employed both physical and physical-chemical data to classify strawberry samples regarding the consumer's response also correctly classified 100% and 90.32% of the samples with respect to consumers' satisfaction and their willingness to pay more, respectively.

CONCLUSION

The results indicate that the developed models can be used in the quality control of strawberries, supporting the establishment of quality standards that consider the consumer's response. The proposed methodology can be extended to control the sensory quality of other fruits. © 2021 Society of Chemical Industry.

Early identification of Aspergillus spp. contamination in milled rice by E-nose combined with chemometrics

BACKGROUND

Rice grains can be contaminated easily by certain fungi during storage and in the market chain, thus generating a risk for humans. Most classical methods for identifying and rectifying this problem are complex and time-consuming for manufacturers and consumers. However, E-nose technology provides analytical information in a non-destructive and environmentally friendly manner. Two-feature fusion data combined with chemometrics were employed for the determination of Aspergillus spp. contamination in milled rice.

RESULTS

Linear discriminant analysis (LDA) indicated that the efficiency of fusion signals ('80^th s values' and 'area values') outperformed that of independent E-nose signals. Linear discriminant analysis showed clear discrimination of fungal species in stored milled rice for four groups on day 2, and the discrimination accuracy reached 92.86% by using an extreme learning machine (ELM). Gas chromatography-mass spectrometry (GC-MS) analysis showed that the volatile compounds had close relationships with fungal species in rice. The quantification results of colony counts in milled rice showed that the monitoring models based on ELM and the genetic algorithm optimized support vector machine (GA-SVM) (R²  = 0.924-0.983) achieved better performances than those based on partial least squares regression (PLSR) (R²  = 0.877-0.913). The ability of the E-nose to monitor fungal infection at an early stage would help to prevent contaminated rice grains from entering the food chains.

CONCLUSIONS

The results indicated that an E-nose coupled with ELM or GA-SVM algorithm could be a useful tool for the rapid detection of fungal infection in milled rice, to prevent contaminated rice from entering the food chain. © 2021 Society of Chemical Industry.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Flavor of rapeseed oil: An overview of odorants, analytical techniques, and impact of treatment

As one of the three major vegetable oils in the world, rapeseed oil is appreciated for its high nutritional value and characteristic flavor. Flavor is an essential attribute, determining rapeseed oil quality and consumer acceptance. The present manuscript provides a systematic literature review of recent advances and knowledge on the flavor of rapeseed oil, which focuses on aroma-active as well as off-flavor compounds, flavor analysis techniques (i.e., extraction, qualitative, quantitative, sensory, and chemometric methods), and effects of treatments (storage, dehulling, roasting, microwave, flavoring with herbs, refining, and oil heating) on flavor from sensory and molecular perspectives. One hundred thirty-seven odorants found in rapeseed oil from literature are listed and possible formation pathways of some key aroma-active compounds are also proposed. Future flavor analysis techniques will evolve toward time-saving, portability, real-time monitoring, and visualization, which aims to obtain a "complete" flavor profile of rapeseed oil. The changes of volatile compounds in rapeseed oil under different treatments are summarized in this view. Studies to elucidate the influence of different treatments on the formation of aroma-active compounds are needed to get a deeper understanding of factors leading to the variations of rapeseed oil flavor.

Recent Developments in Smart Food Packaging Focused on Biobased and Biodegradable Polymers

Food packaging has a crucial function in the modern food industry. New food packaging technologies seek to meet consumers and industrial's demands. Changes related to food production, sale practices and consumers' lifestyles, along with environmental awareness and the advance in new areas of knowledge (such as nanotechnology or biotechnology), act as driving forces to develop smart packages that can extend food shelf-life, keeping and supervising their innocuousness and quality and also taking care of the environment. This review describes the main concepts and types of active and intelligent food packaging, focusing on recent progress and new trends using biodegradable and biobased polymers. Numerous studies show the great possibilities of these materials. Future research needs to focus on some important aspects such as possibilities to scale-up, costs, regulatory aspects, and consumers' acceptance, to make these systems commercially viable.

Tea quality evaluation by applying E-nose combined with chemometrics methods

Tea is one of the most popular beverage with distinct flavor consumed worldwide. It is of significance to establish evaluation method for tea quality controlling. In this work, electronic nose (E-nose) was applied to assess tea quality grades by detecting the volatile components of tea leaves and tea infusion samples. The "35th s value", "70th s value" and "average differential value" were extracted as features from E-nose responding signals. Three data reduction methods including principle component analysis (PCA), multi-dimensional scaling (MDS) and linear discriminant analysis (LDA) were introduced to improve the efficiency of E-nose analysis. Logistic regression (LR) and support vector machine (SVM) were applied to set up qualitative classification models. The results indicated that LDA outperformed original data, PCA and MDS in both LR and SVM models. SVM had an advantage over LR in developing classification models. The classification accuracy of SVM based on the data processed by LDA for tea infusion samples was 100%. Quantitative analysis was conducted to predict the contents of volatile compounds in tea samples based on E-nose signals. The prediction results of SVM based on the data processed by LDA for linalool (training set: R2 = 0.9523; testing set: R2 = 0.9343), nonanal (training set: R2 = 0.9617; testing set: R2 = 0.8980) and geraniol (training set: R2 = 0.9576; testing set: R2 = 0.9315) were satisfactory. The research manifested the feasibility of E-nose for qualitatively and quantitatively analyzing tea quality grades.

Nondestructive monitoring, kinetics and antimicrobial properties of ultrasound technology applied for surface decontamination of bacterial foodborne pathogen in pork

In this study, electronic nose (E-nose) and Hyperspectral Imaging (HSI) was employed for nondestructive monitoring of ultrasound efficiency (20KHZ) in the inactivation of Salmonella Typhimurium, and Escherichia coli in inoculated pork samples treated for 10, 20 and 30 min. Weibull, and Log-linear model fitted well (R2 ≥ 0.9) for both Salmonella Typhimurium, and Escherichia coli inactivation kinetics. The study also revealed that ultrasound has antimicrobial effects on the pathogens. For qualitative analysis, unsupervised (PCA) and supervised (LDA) chemometric algorithms were applied. PCA was used for successful sample clustering and LDA approach was used to construct statistical models for the classification of ultrasound treated and untreated samples. LDA showed classification accuracies of 99.26%,99.63%,99.70%, 99.43% for E-nose - S. Typhimurium, E-nose -E. coli, HSI - S. Typhimurium and HSI -E. coli respectively. PLSR quantitative models showed robust models for S. Typhimurium- (E-nose Rp2 = 0.9375, RMSEP = 0.2107 log CFU/g and RPD = 9.7240 and (HSI Rp2 = 0.9687 RMSEP = 0.1985 log CFU/g and RPD = 10.3217) and E. coli -(E-nose -Rp2 = 0.9531, RMSEP = 0.2057 log CFU/g and RPD = 9.9604) and (HIS- Rp2 = 0.9687, RMSEP = 0.2014 log CFU/g and RPD = 10.1731). This novel study shows the overall effectiveness of applying E-nose and HSI for in-situ and nondestructive detection, discrimination and quantification of bacterial foodborne pathogens during the application of food processing technologies like ultrasound for pathogen inactivation.

Fraud detection and quality assessment of olive oil using ultrasound

Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks.

A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

Integration of a low-cost electronic nose and a voltammetric electronic tongue for red wines identification

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi-sensor fusion technology based on a novel cost-effective electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) was proposed. The E-nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E-Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E-nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost-effective E-nose created and voltammetric E-tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Effect of particle size on the stability and flavor of cloudy apple juice

Different particle sizes in cloudy apple juice were obtained following filtration with different mesh sizes (100, 200, 300, and 400-mesh). The effects of cloud particle size on the stability, nutrient content, and volatile flavor of cloudy apple juice were evaluated. With increasing mesh number, particle size decreased (p < 0.05) and particle shape changed. Particle size had an effect on volatile flavor compounds, especially nitrogen oxides, alcohols, and aromatic compounds. The content of pectin and total phenol decreased with decreasing particle size, while the content of soluble protein was not affected. The reduction of cloud particle size increased absolute value of ζ-potential, cloud stability, and apparent viscosity and decreased turbidity and cloud values. Pearson correlation analysis showed that there was a strong correlation between particle size and quality indicators, except for soluble protein.

Discrimination of five brands of instant vermicelli seasonings by HS-SPME/GC-MS and electronic nose

The flavor profile of five brands of instant vermicelli seasonings were identified by headspace solid-phase micro-extraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS) and electronic nose (e-nose). GC-MS showed that the volatile compounds of instant vermicelli seasonings were significantly different. Alkenes, alcohols, aldehydes and ketones were the major volatile compounds in instant vermicelli seasonings. The seasonings could be classified based on differences in volatile compounds. The overall volatiles profiles were also analyzed by e-nose. E-nose determination and GC-MS statistical analysis had similar results. The volatile compounds showed good correlation with e-nose sensors according to partial least square regression models. Both methods had good potential application in evaluating flavor quality and differentiating among instant vermicelli seasonings.

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R² ≥ 0.9998).

Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation

Due to the difference of raw materials and brewing technology, the quality and flavours of vinegar are different. Different kinds of vinegar have different functions and effects. Therefore, it is important to classify the vinegar varieties correctly. This work presented a new fuzzy feature extraction algorithm, called fuzzy Foley-Sammon transformation (FFST), and designed the electronic nose (E-nose) system for classifying vinegar varieties successfully. Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. Then, K nearest neighbor (KNN) served as a classifier for the classification of vinegar varieties. The highest identification accuracy rate was 96.92% by using the FFST and KNN. Therefore, the E-nose system combined with the FFST was an effective method to identify Chinese vinegar varieties and this method has wide application prospects.

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.