Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits

A dual-functional needle-based VOC sensing platform for rapid vegetable phenotypic classification

Volatile organic compounds (VOCs) are common constituents of fruits, vegetables, and crops, and are closely associated with their quality attributes, such as firmness, sugar level, ripeness, translucency, and pungency levels. While VOCs are vital for assessing vegetable quality and phenotypic classification, traditional detection methods, such as Gas Chromatography-Mass Spectrometry (GC-MS) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) are limited by expensive equipment, complex sample preparation, and slow turnaround time. Additionally, the transient nature of VOCs complicates their detection using these methods. Here, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device. The needle sampling device helped release specific VOCs from the studied vegetables that usually require mechanic stimulation, while maintaining the vegetable viability. On the other hand, the colorimetric sensor array was optimized for sulfur compound-based VOCs with a limit of detection (LOD) in the 1-25 ppm range, and classified fourteen different vegetable VOCs, including sulfoxides, sulfides, mercaptans, thiophenes, and aldehydes. By combining principal components analysis (PCA) analysis, the integrated sensor platform proficiently discriminated between four vegetable subtypes originating from two major categories within 2 min of testing time. Additionally, the sensor demonstrates the capability to distinguish between different types of tested fruits and vegetables, including garlic, green pepper, and nectarine. This rapid and minimally invasive sensing technology holds great promise for conducting field-based vegetable quality monitoring.

Novel Insights into the Effects of Different Cooking Methods on Salted Egg Yolks: Physicochemical and Sensory Analysis

In this study, the flavor characteristics and physicochemical properties of salted egg yolk (SEY) under different cooking methods (steaming/baking/microwaving) were investigated. The microwave-treated SEY exhibited the highest levels of salt content, cooking loss, lightness, and b* value, as well as the highest content of flavor amino acids. A total of 31, 27, and 29 volatile compounds were detected after steaming, baking, and microwave treatments, respectively, covering 10 chemical families. The partial least squares discriminant analysis confirmed that 21 compounds, including octanol, pyrazine, 2-pentyl-furan, and 1-octen-3-ol, were the key volatile compounds affecting the classification of SEY aroma. The electronic nose revealed a sharp distinction in the overall flavor profile of SEY with varying heat treatments. However, no dramatic differences were observed in terms of fatty acid composition. Microwave treatment was identified as presenting a promising approach for enhancing the aroma profile of SEY. These findings contribute novel insights into flavor evaluation and the development of egg products as ingredients for thermal processing.

Comparison of the volatile organic compounds in Citrus reticulata 'Chachi' peel with different drying methods using E-nose, GC-IMS and HS-SPME-GC-MS

Introduction

Citrus reticulata 'Chachi' peel (CRCP), which is named "Guangchenpi" in China, is a geographical indication product with unique flavor properties. CRCP has been used for centuries as a traditional genuine herb because of its excellent therapeutic effects. In addition, owing to its unique odor and high nutrition, it is widely used in various food preparations. Volatile organic compounds (VOCs) are regarded as an important quality marker for CRCP and are highly susceptible to effects in the drying process due to their thermal instability.

Methods

In the current study, the main VOCs in CRCP were processed using different drying methods, including sun-drying, hot air drying, and vacuum-freeze drying. The VOCs were identified by the electronic nose (E-nose), gas chromatography-ion mobility spectrometry (GC-IMS), and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS).

Results

The results showed that the CRCP dried by vacuum-freeze exhibited the highest VOCs contents and retained the richest compounds compared to those dried by other methods, which indicated that vacuum-freeze drying is the most suitable for CRCP production. Furthermore, the chemometrics analysis revealed that the primary differential metabolites of the samples generated using different drying methods were terpenes and esters.

Discussion

Overall, our study would help better understand the VOCs present in CRCP with different drying methods. The outcomes of the current study would guide the drying and processing of CRCP, which is beneficial for large-scale storage and industrial production of CRCP.

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.

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China

Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory.

A novel strategy for discriminating different cultivation and screening odor and taste flavor compounds in Xinhui tangerine peel using E-nose, E-tongue, and chemometrics

A rapid strategy for discriminating Quanzhi (QZ) and Bozhi (BZ) of different cultivation of Xinhui tangerine peel was established by combining electronic nose, electronic tongue and chemometrics, which can be used as tool in the market to identify food fraud. 30 volatiles and 34 low molecular weight compounds of characteristic fingerprints of Xinhui tangerine peel of 108 samples were identified using GC-MS and UHPLC-Q-TOF-MS. Key compounds of BZ and QZ were screened and further compared by chemometrics. We discriminated odor and taste of BZ and QZ using electronic nose and electronic tongue, respectively. Our studies showed that β-myrcene, limonene, β-trans-Ocimene, γ-terpinene and terpinolene, etc, were screened the chief volatile flavor compounds by Spearman's rank correlation. Hydroxymethyl furfural, hesperitin, nobiletin and tangeretin, etc, were screened the key taste flavor compounds based gray relational analysis and partial least squares regression. Our study provides further insight for quality evaluation of Xinhui tangerine peel.

Determination of Bioactive Components in Mandarin Fruits: A Review

During the last decade, there has been a continuous rise in the consumption of fresh easy-to-peel mandarins. However, the majority of the knowledge comes from other citrus fruit, like orange, while there are relatively few studies about mandarins and no comprehensive research on literature data about them. One of the most important steps in the analytical process is sample preparation. Its value is evident in analyzing the samples with complex matrices, such as in mandarin fruit. In addition, mandarin contains hundreds to thousands of various compounds and metabolites, some of them present in extremely low concentrations, that interfere with the detection of one another. Hence, mandarin samples are commonly pretreated by extraction to facilitate analysis of bioactive compounds, improve accuracy and quantification levels. There is an abundance of extraction techniques available, depending on the group of compounds of interest. Finally, modern analytical techniques, have been applied to cope with numerous bioactive compounds in mandarins. Considering all the above, this review aims to (i) list the most valuable procedures of sample preparation, (ii) highlight the most important techniques for extraction of bioactive compounds from mandarin fruit, and (iii) summarize current trends in the identification and determination of bioactive compounds in mandarin.

The State of Lifelong Learning in Service Robots:

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

Sensory Quality Evaluation of Korla Pear from Different Orchards and Analysis of Their Primary and Volatile Metabolites

Metabolites play vital roles in shaping the quality of fresh fruit. In this study, Korla pear fruit harvested from twelve orchards in South Xinjiang, China, were ranked in sensory quality by fuzzy logic sensory evaluation for two consecutive seasons. Then, gas chromatography-mass spectrometry (GC-MS) was applied to determine the primary metabolites and volatile compounds. Sensory evaluation results showed that the panelists were more concerned about ‘mouth feel’ and ‘aroma’ than about ‘fruit size’, ‘fruit shape’ and ‘peel color’. In total, 20 primary metabolites and 100 volatiles were detected in the pear fruit. Hexanal, (E)-2-hexenal, nonanal, d-limonene, (Z)-3-hexen-1-yl acetate and hexyl acetate were identified as the major volatile compounds. Correlation analysis revealed that l-(+)-tartaric acid, hexanoic acid, trans-limonene oxide and 2,2,4-trimethyl-1,3-pentanediol diisobutyrate were negatively correlated with sensory scores. Furthermore, OPLS-DA results indicated that the fruit from three orchards with lower ranks in quality could be distinguished from other samples based on the contents of l-(+)-tartaric acid and other eight metabolites, which were all associated with ‘mouth feel’ and ‘aroma’. This study reveals the metabolites that might be closely associated with the sensory quality attributes of Korla pear, which may provide some clues for promoting the fruit quality in actual production.

Characterization and Analysis of Okoume and Aiele Essential Oils from Gabon by GC-MS, Electronic Nose, and Their Antibacterial Activity Assessment

Essential oil resins of Aucoumea klaineana (Okoume) and Canarium schweinfurthii (Aiele) species, of the Burseraceae family, were studied to investigate their bioactive constituents and their antibacterial activities. Aiele resin had a higher yield (6.86%) of essential oil than Okoume (3.62%). Twenty-one compounds for Okoume and eighteen for Aiele essential oil were identified using a gas chromatography-mass spectrometry (Gp-C-MS) technique. The main compounds identified in Okoume essential oil were benzenemethanol, α, α,4-trimethyl (28.85%), (+)-3-carene (3,7,7-trimethyl bicyclo[4.1.0]hept-3-ene) (17.93%), D-Limonene ((4R)-1-methyl-4-prop-1-en-2-ylcyclohexene) (19.36%). With regard to the Aiele essential oil, we identified (1R,4S)-1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.64%), and 1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.83%). Two strains of bacteria, Escherichia coli and Staphylococcus aureus, were used in antibacterial tests. S. aureus was found to be more sensitive to Okoume and Aiele essential oils, with a high inhibition zone ranging from 20 to 16 mm. In comparison, the inhibition zone ranged from 6 to 12 mm for E. coli. An electronic nose (e-nose) combined with pattern analysis methods such as principal component analysis (PCA), discriminant function analysis (DFA), and hierarchical cluster analysis (HCA) were used to discriminate the essential oil samples. In summary, the e-nose and GC-MS allowed the identification of bioactive compounds in the essential oil samples, which have a strong antimicrobial activity, with satisfactory results.

Metabolomics for Evaluating Flavor-Associated Metabolites in Plant-Based Products

Plant-based diets (PBDs) are associated with environmental benefits, human health promotion and animal welfare. There is a worldwide shift towards PBDs, evident from the increased global demand for fresh plant-based products (PBPs). Such shifts in dietary preferences accompanied by evolving food palates, create opportunities to leverage technological advancements and strict quality controls in developing PBPs that can drive consumer acceptance. Flavor, color and texture are important sensory attributes of a food product and, have the largest influence on consumer appeal and acceptance. Among these, flavor is considered the most dominating quality attribute that significantly affects overall eating experience. Current state-of-art technologies rely on physicochemical estimations and sensory-based tests to assess flavor-related attributes in fresh PBPs. However, these methodologies often do not provide any indication about the metabolic features associated with unique flavor profiles and, consequently, can be used in a limited way to define the quality attributes of PBPs. To this end, a systematic understanding of metabolites that contribute to the flavor profiles of PBPs is warranted to complement the existing methodologies. This review will discuss the use of metabolomics for evaluating flavor-associated metabolites in fresh PBPs at post-harvest stage, alongside its applications for quality assessment and grading. We will summarize the current research in this area, discuss technical challenges and considerations pertaining to sampling and analytical techniques, as well as s provide future perspectives and directions for government organizations, industries and other stakeholders associated with the quality assessment of fresh PBPs.

The Ancient Neapolitan Sweet Lime and the Calabrian Lemoncetta Locrese Belong to the Same Citrus Species

"Neapolitan limmo" is an ancient and rare sweet Mediterranean lime, now almost extinct but used until a few decades ago for the production of a fragrant liqueur called the "four citrus fruits". The objective of this work was to compare, through the use of chemical (flavonoids, volatile organic compounds, and chiral compounds) and molecular (DNA fingerprint based on RAPD-PCR) markers, the residual population of Neapolitan limmo with other populations of sweet limes, identified in Calabria and known as "lemoncetta Locrese". We report for the first time specific botanical characteristics of the two fruits and unequivocally show that the ancient sweet Mediterranean limes Neapolitan limmo and lemoncetta Locrese are synonyms of the same Citrus species. Owing to the biodiversity conserved in their places of origin, it will now be possible to recover, enhance and implement the use of this ancient sweet lime for agro-industrial purposes.

Tracing internal quality and aroma of a red-fleshed kiwifruit during ripening by means of GC-MS and E-nose

'Hongyang' kiwifruit is a new breed of red-fleshed cultivar that has become broadly popular with consumers in recent years. In this study, the internal quality and aroma of this kiwifruit during ripening were investigated by means of gas chromatography-mass spectrometry (GC-MS) and electronic nose (E-nose). Results showed that the green note aldehydes declined, the main fruity esters increased, and the terpenes had no obvious changes during ripening. Correlations between quality indices, volatile compounds, and E-nose data were analyzed by ANOVA partial least squares regression (APLSR), and the results showed that firmness and titratable acidity (TA) had highly positive correlations with (E)-2-hexenal and hexanal, while soluble solids content (SSC) and SSC/TA ratio had positive correlations with ester compounds. The E-nose sensors of S7, S10, S8, S6, S9, and S2 were positively correlated with ester compounds, S1, S3, and S5 were mainly correlated with hexanal, and S4 was correlated with terpene compounds. Partial least squares regression (PLSR) and support vector machine (SVM) were employed to predict the quality indices by E-nose data, and SVM presented a better performance in predicting firmness, SSC, TA, and SSC/TA ratio (R ² > 0.98 in the training set and R ² > 0.94 in the testing set). This study demonstrated that the E-nose technique could be used as an alternative to trace the flavor quality of kiwifruit during ripening.

Volatile Compounds in Citrus Essential Oils: A Comprehensive Review

The essential oil fraction obtained from the rind of Citrus spp. is rich in chemical compounds of interest for the food and perfume industries, and therefore has been extensively studied during the last decades. In this manuscript, we provide a comprehensive review of the volatile composition of this oil fraction and rind extracts for the 10 most studied Citrus species: C. sinensis (sweet orange), C. reticulata (mandarin), C. paradisi (grapefruit), C. grandis (pummelo), C. limon (lemon), C. medica (citron), C. aurantifolia (lime), C. aurantium (bitter orange), C. bergamia (bergamot orange), and C. junos (yuzu). Forty-nine volatile organic compounds have been reported in all 10 species, most of them terpenoid (90%), although about half of the volatile compounds identified in Citrus peel are non-terpenoid. Over 400 volatiles of different chemical nature have been exclusively described in only one of these species and some of them could be useful as species biomarkers. A hierarchical cluster analysis based on volatile composition arranges these Citrus species in three clusters which essentially mirrors those obtained with genetic information. The first cluster is comprised by C. reticulata, C. grandis, C. sinensis, C. paradisi and C. aurantium, and is mainly characterized by the presence of a larger abundance of non-terpenoid ester and aldehyde compounds than in the other species reviewed. The second cluster is comprised by C. junos, C. medica, C. aurantifolia, and C. bergamia, and is characterized by the prevalence of mono- and sesquiterpene hydrocarbons. Finally, C. limon shows a particular volatile profile with some sulfur monoterpenoids and non-terpenoid esters and aldehydes as part of its main differential peculiarities. A systematic description of the rind volatile composition in each of the species is provided together with a general comparison with those in leaves and blossoms. Additionally, the most widely used techniques for the extraction and analysis of volatile Citrus compounds are also described.

Cultivar differentiation of Citri Reticulatae Pericarpium by a combination of hierarchical three-step filtering metabolomics analysis, DNA barcoding and electronic nose

The traditional Chinese medicine Citri Reticulatae Pericarpium (CRP) was mainly originated from the dried pericarp of Citrus reticulata 'Chachi' (Crc), Citrus reticulata 'Dahongpao' (Crd), Citrus reticulata 'Unshiu' (Cru) and Citrus reticulata 'Tangerina' (Crt) in China. Since these four cultivars have great similarities in morphology, reliable methods to differentiate CRP cultivars have rarely been reported. To discriminate the differences of these CRP cultivars, herein an efficient and reliable method by combining metabolomics, DNA barcoding and electronic nose was first established. The hierarchical three-step filtering metabolomics analysis based on liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) indicated that 9 species-specific chemical markers including 6 flavanone glycosides and 3 polymethoxyflavones could be considered as marker metabolites for discrimination of the geoherb Crc from other cultivars. A total of 19 single nucleotide polymorphism (SNP) sites were found in nuclear internal transcribed spacer 2 (ITS2) of CRP, and three stable SNP sites (33, 128 and 174) in the ITS2 region can distinguish the four CRP cultivars. The electronic nose coupled with chemometrics could also be used to effectively distinguish Crc from other CRP cultivars. Therefore, our results indicated that the integrated method will be an effective strategy for discrimination of similar herbal medicines.

Machine Learning in Human Olfactory Research

The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and recognition processes, 2) pattern recognition in olfactory phenotypes, 3) the development of complex disease biomarkers including olfactory features, 4) odor prediction from physico-chemical properties of volatile molecules, and 5) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.

Heating and cysteine effect on physicochemical and flavor properties of soybean peptide Maillard reaction products

Maillard reaction products (MRPs) were obtained by using the xylose and soybean peptide system through a 2 h heating at pH of 7.6. Cysteine addition and thermal treatment at 80, 100, 120 and 140 °C were investigated via E-nose and E-tongue, free amino acids (FAA) and GC-MS analyses of MRPs. Afterwards, the combined effects were performed using the partial least square regression (PLSR). Results suggested that MRPs without cysteine addition (XSs) had stronger browning intensity, and the cysteine would be beneficial to the pH reduction with heating temperature increasing. PLSR analysis revealed that MRPs with cysteine addition heated at 140 °C (XSC-140) showed the lowest bitterness, and XS-100 had the highest umami and saltiness. Both bitter and umami FAA increased with the addition of cysteine, and more furans and nitrogen-containing compounds formed in the XSs brought caramel-like flavor, while XSCs exhibited meat-like flavor attributed to sulphides generation.

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO2, CH4, NH3, and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.