Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds

Solvent Extraction of PDMS Tubing as a New Method for the Capture of Volatile Organic Compounds from Headspace

Polydimethylsiloxane (PDMS) tubing is increasingly being used to collect volatile organic compounds (VOCs) from static biological headspace. However, analysis of VOCs collected using PDMS tubing often deploys thermal desorption, where samples are considered as 'one-offs' and cannot be used in multiple experiments. In this study, we developed a static headspace VOC collection method using PDMS tubing which is solvent-based, meaning that VOC extracts can be used multiple times and can be linked to biological activity. Using a synthetic blend containing a range of known semiochemicals (allyl isothiocyanate, (Z)-3-hexen-1-ol, 1-octen-3-one, nonanal, (E)-anethol, (S)-bornyl acetate, (E)-caryophyllene and pentadecane) with differing chemical and physicochemical properties, VOCs were collected in static headspace by exposure to PDMS tubing with differing doses, sampling times and lengths. In a second experiment, VOCs from oranges were collected using PDMS sampling of static headspace versus dynamic headspace collection. VOCs were eluted with diethyl ether and analysed using gas chromatography - flame ionization detector (GC-FID) and coupled GC - mass spectrometry. GC-FID analysis of collected samples showed that longer PDMS tubes captured significantly greater quantities of compounds than shorter tubes, and that sampling duration significantly altered the recovery of all tested compounds. Moreover, greater quantities of compounds were recovered from closed compared to open systems. Finally, analysis of orange headspace VOCs showed no qualitative differences in VOCs recovered compared to dynamic headspace collections, although quantities sampled using PDMS tubing were lower. In summary, extraction of PDMS tubing with diethyl ether solvent captures VOCs from the headspace of synthetic blends and biological samples, and the resulting extracts can be used for multiple experiments linking VOC content to biological activity.

Characterization of lipid composition and nutritional quality of yak ghee at different altitudes: A quantitative lipidomic analysis

Efficient and comprehensive analysis of lipid profiles in yak ghee samples collected from different elevations is crucial for optimal utilization of these resources. Unfortunately, such research is relatively rare. Yak ghee collected from three locations at different altitudes (S2: 2986 m; S5: 3671 m; S6: 4508 m) were analyzed by quantitative lipidomic. Our analysis identified a total of 176 lipids, and 147 s lipid of them were upregulated and 29 lipids were downregulated. These lipids have the potential to serve as biomarkers for distinguishing yak ghee from different altitudes. Notably, S2 exhibited higher levels of fatty acids (21:1) and branched fatty acid esters of hydroxy fatty acids (14:0/18:0), while S5 showed increased levels of phosphatidylserine (O-20:0/19:1) and glycerophosphoric acid (19:0/22:1). S6 displayed higher levels of triacylglycerol (17:0/20:5/22:3), ceramide alpha-hydroxy fatty acid-sphingosine (d17:3/34:2), and acyl glucosylceramides (16:0-18:0-18:1). Yak ghee exhibited a high content of neutralizing glycerophospholipids and various functional lipids, including sphingolipids and 21 newly discovered functional lipids. Our findings provide insights into quantitative changes in yak ghee lipids during different altitudes, development of yak ghee products, and screening of potential biomarkers.

Metal(loid)s Profile of Four Traditional Ethiopian Teff Brands: Geographic Origin Discrimination

Among the most renowned Ethiopian food crops, teff (Eragrostis tef (Zucc.)Trotter) is the most nutritious and gluten-free cereal. Because of the increase in demand for teff, it is necessary to establish geographic origin authentication of traditional teff brands based on multi-element fingerprint. For this purpose, a total of 60 teff samples were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Accuracy of the laboratory procedure was verified by the analysis of rice flour standard reference material (NIST SRM 1568b). In this context, four traditional teff brands (Ada'a, Ginchi, Gojam and Tulu Bolo) were analytically characterized using multi-element fingerprint and further treated statistically using linear discriminant analysis (LDA). Due to obvious extrinsic Fe, Al and V contamination, these elements were excluded from the discriminant model. Five elements (Cu, Mo, Se, Sr, and Zn) significantly contributed to discriminate the geographical origin of white teff. On the other hand, Mn, Mo, Se and Sr were used as discriminant variables for brown teff. LDA revealed 90 and 100% correct classifications for white and brown teff, respectively. Overall, multi-element fingerprint coupled with LDA can be considered a suitable tool for geographic origin discrimination of traditional teff brands.

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems

The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

Geographical origin discriminatory analysis of onions: Chemometrics methods applied to ICP-OES and ICP-MS analysis

Geographical origin is an important determinant of agricultural product quality and safety. Herein, inductively coupled plasma (ICP) analysis was applied to determine the inorganic elemental content of onions and identify their geographical origin (Korean or Chinese). Chemometric, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to the ICP results. OPLS-DA distinguished each group, and 17 elements with variable importance in projection (VIP) values of ≥ 1 were selected. The receiver operating characteristic (ROC) curve had an area under the curve (AUC) of 1, indicating excellent discriminatory power. Differences in elemental content between groups were visually observed in a heatmap, and the country of origin was determined with 100% accuracy using canonical discriminant analysis (CDA). This method accurately distinguishes between Korean and Chinese onions and is expected to be beneficial for identifying agricultural products.

Multivariate Profiling of Metabolites and Volatile Organic Compounds in Citrus depressa Hayata Fruits from Kagoshima, Okinawa, and Taiwan

Citrus depressa Hayata is a small-fruit citrus species; it is indigenous to Kagoshima, Okinawa, and Taiwan. The metabolites and volatile organic compounds (VOCs) that affect the flavor of its fruits have not been investigated based on geographical origin. In the present study, we investigated the metabolite and VOC profiles of 18 C. depressa cultivation lines from these regions. Multivariate analysis revealed differences in the metabolites of C. depressa based on its cultivation origins; variations in sugar, sugar alcohol, and amino acid contents were also observed. Fruits from Kagoshima and Okinawa had higher galactinol, trehalose, xylose, glucose, and sucrose intensities than fruits from Taiwan (log2-fold change; 2.65-3.44, 1.68-2.13, 1.37-2.01, 1.33-1.57, and 1.07-1.43, respectively), whereas the Taiwanese lines contained higher leucine, isoleucine, serine, and alanine. In contrast to the Taiwanese Nantou line, other cultivation lines had comparable total VOC contents, and the VOCs of all lines were dominated by limonene, γ-terpinene, and p-cymene. Accordingly, the highest VOC intensities were recorded in the Nantou line, which was followed by Shikunin sweet (Kagoshima) and Taoyuan (Taiwan) (log10 normalize concentration; 5.11, 3.08, and 3.01, respectively). Moreover, multivariate analysis plots elucidated the difference in the VOCs of Ishikunibu (Okinawa), Shikunin sweet, and Taoyuan and between those of most Kagoshima and Okinawa cultivation lines. These results suggest that both the cultivation line and origin influence the metabolites and VOCs of C. depressa, thus possibly affecting its flavor quality; the data provide a valuable insight for utilizing C. depressa of different cultivation lines and origins to produce foods and beverages.

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.

Mass spectrometry-based electronic nose to authenticate 100% Italian durum wheat pasta and characterization of volatile compounds

Headspace solid-phase microextraction (HS-SPME) coupled with mass spectrometry-based electronic nose (MS-eNose), in combination with multivariate statistical analysis was used as untargeted method for the rapid authentication of 100% Italian durum wheat pasta. Among the tested classification models, i.e. PCA-LDA, PLS-DA and SVMc, SVMc provided the highest accuracy results in both calibration (90%) and validation (92%) processes. Potential markers discriminating pasta samples were identified by HS-SPME/GC-MS analysis. Specifically, the content of a pattern of 8 out of 59 volatile organic compounds (VOCs) was significantly different between samples of 100% Italian durum wheat pasta and pasta produced with durum wheat of different origins, most of which were related to different lipidic oxidation in the two classes of pasta. The proposed MS-eNose method is a rapid and reliable tool to be used for authenticating Italian pasta useful to promote its typicity and preserving consumers from fraudulent practices.

A Targeted Metabolomics Approach to Study Secondary Metabolites and Antioxidant Activity in 'Kinnow Mandarin' during Advanced Fruit Maturity

In this study, we investigated the impact of harvest maturity stages and contrasting growing climates on secondary metabolites in Kinnow mandarin. Fruit samples were harvested at six harvest maturity stages (M1−M6) from two distinct growing locations falling under subtropical−arid (STA) and subtropical−humid (STH) climates. A high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) technique was employed to identify and quantify secondary metabolites in the fruit juice. A total of 31 polyphenolics and 4 limonoids, with significant differences (p < 0.05) in their concentration, were determined. With advancing maturity, phenolic acids and antioxidant activity were found to increase, whereas flavonoids and limonoids decreased in concentration. There was a transient increase in the concentration of some polyphenolics such as hesperidin, naringin, narirutin, naringenin, neoeriocitrin, rutin, nobiletin and tangeretin, and limonoid aglycones such as limonin and nomilin at mid-maturity stage (M3) which coincided with prevailing low temperature and frost events at growing locations. A higher concentration of limonin and polyphenolics was observed for fruit grown under STH climates in comparison to those grown under STA climates. The data indicate that fruit metabolism during advanced stages of maturation under distinct climatic conditions is fundamental to the flavor, nutrition and processing quality of Kinnow mandarin. This information can help in understanding the optimum maturity stage and preferable climate to source fruits with maximum functional compounds, less bitterness and high consumer acceptability.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Environmentally-driven metabolite and lipid variations correspond to altered bioactivities of black wolfberry fruit

Black wolfberry is a commonly cultivated woody plant in China, and is rich in nutrients that are beneficial for human. To characterize the endogenous metabolite differences among black wolfberry fruits grown in different geographical regions, mass spectrometry-based metabolomic and lipidomic analyses were performed in black wolfberry grown in nine locations throughout five provinces in China, from which 204 primary and specialized metabolites, and 267 lipids were identified in their fruits. Three samples from Alxa Left Banner, Jinta, and Minqin showed dramatically altered metabolite profiles, displaying higher levels of phenolic acids, soluble sugars and flavonoids, but lower levels of tricarboxylic acid cycle intermediates and aromatic amino acids. Moreover, the lipid profile of the Alxa Left Banner sample was strikingly distinct from all other samples, with high levels of monogalactosyl diacylglycerol and sulfoquinovosyl diacylglycerol, which are positively correlated with their anti-inflammatory capacities. These findings thus prompt for further studies on black wolfberry fruit for their health benefits.

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.

Volatile Composition Variability of Arnica montana Wild Populations of Trentino-Alto Adige, Italy, Determined by Headspace-Solid Phase Microextraction

Arnica montana is a plant distributed in most of Europe, including the Alpine arc and Apennines in Italy, and traditionally used worldwide for medicinal properties. Twelve natural populations of the species from Trentino-Alto Adige, Italy, were characterized using Headspace-Solid Phase Microextraction analysis for their volatile profile. Fifty-one compounds were detected in flower heads, the most abundant being (E)-Caryophyllene (23.4 %), 2,2,4,6,6-Pentamethylheptane (8.3 %), α- trans-Bergamotene (7.2 %), Germacrene D (5.7 %), and Hexanal (5.3 %). A multivariate analysis performed on the ten most abundant compounds grouped these investigated accessions into five main clusters. Three clusters, comprising together five accessions, were linked to the geographical origin of two collection sites. This work is a complete characterization of volatiles of the species by SPME analysis reported to date. Furthermore, results suggest that the species' volatile profile can be linked to the geographical origin of the natural populations and, therefore, represent a tool for evaluating biodiversity within the species.

Optimization of HS-SPME for GC-MS Analysis and Its Application in Characterization of Volatile Compounds in Sweet Potato

Volatile compounds are the main chemical species determining the characteristic aroma of food. A procedure based on headspace solid-phase microextraction (HP-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) was developed to investigate the volatile compounds of sweet potato. The experimental conditions (fiber coating, incubation temperature and time, extraction time) were optimized for the extraction of volatile compounds from sweet potato. The samples incubated at 80 °C for 30 min and extracted at 80 °C by the fiber with a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coating for 30 min gave the most effective extraction of the analytes. The optimized method was applied to study the volatile profile of four sweet potato cultivars (Anna, Jieshu95-16, Ayamursaki, and Shuangzai) with different aroma. In total, 68 compounds were identified and the dominants were aldehydes, followed by alcohols, ketones, and terpenes. Significant differences were observed among the volatile profile of four cultivars. Furthermore, each cultivar was characterized by different compounds with typical flavor. The results substantiated that the optimized HS-SPME GC-MS method could provide an efficient and convenient approach to study the flavor characteristics of sweet potato. This is the basis for studying the key aroma-active compounds and selecting odor-rich accessions, which will help in the targeted improvement of sweet potato flavor in breeding.

Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis

BACKGROUND

The edible oil storage period is one of the important indicators for evaluating the intrinsic quality of edible oil. The present study aimed to develop a portable electronic nose device for the qualitative identification of the edible oil storage period. First, four metal oxide semiconductor gas sensors, comprising TGS2600, TGS2611, TGS2620 and MQ138, were selected to prepare a sensor array to assemble a portable electronic nose device. Second, the homemade portable electronic nose device was used to obtain the odor change information of edible oil samples during different storage periods, and the sensor features were extracted. Finally, three pattern recognition methods, comprising linear discriminant analysis (LDA), K-nearest neighbors (KNN) and support vector machines (SVM), were compared to establish a qualitative identification model of the edible oil storage period. The input features and related parameters of the model were optimized by a five-fold cross-validation during the process of model establishment.

RESULTS

The research results showed that the recognition performance of the non-linear SVM model was significantly better than that of the linear LDA and KNN models, especially in terms of generalization performance, which had a correct recognition rate of 100% when predicting independent samples in the prediction set.

CONCLUSION

The overall results demonstrate that it is feasible to apply the homemade portable electronic nose device with the help of the appropriate pattern recognition methods to achieve the fast and efficient identification of the edible oil storage period, which provides an effective analysis tool for the quality detection of the edible oil storage. © 2020 Society of Chemical Industry.

Comparative metabolic study of Citrus sinensis leaves cultivars based on GC-MS and their cytotoxic activity

Citrus sinensis L. Osbeck, (family Rutaceae), known as sweet orange, is a fruit-bearing shrub that widely cultivated all over the world due to its nutritive value, nutraceutical attributes and economic importance. In the present study, a comparative metabolic profile study of the essential oils of the leaves of nine cultivars of Citrus sinensis cultivated in Egypt was carried out based on GC-MS analysis coupled to chemometrics. A total of 47 compounds were identified, where monoterpenes hydrocarbons (61.39 %-78.26 %) represented the main recognized class in the essential oils. Sabinene (8.25 %-28.81 %), 2-carene (11.25 %-16.72 %) and cis-β-ocimene (10.22 %-13.93 %) were the major components identified in different cultivars. Chemometric analyses comprising Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were experienced for their ability to discriminate between closely related cultivars. Both PCA score plot and HCA dendrogram could successfully segregate different cultivars based on their metabolic profiles. The MTT assay was used to evaluate the cytotoxicity of essential oils of Citrus sinensis leaves cultivars on different cell lines; MCF-7, HepG-2, HeLa, were all essential oils showed moderate cytotoxic activity. The MCF-7 cell lines were the most resistant cells with IC50 range from 243 to 343 μg/mL, while HeLa cells were the most sensitive cell lines IC50 range from 203 to 283 μg/mL. This may be attributed to the synergistic effects of different chemical components.

Ultrasound-assisted thawing of frozen white yak meat: Effects on thawing rate, meat quality, nutrients, and microstructure

The objective of this study was to assess the effects of ultrasound-assisted thawing (UAT) on the quality of longissimus dorsi muscles from white yak meat (WYM). Ultrasonic power levels of 0, 200, 400, and 600 W (frequency of 20 kHz) were used to assist thawing. The thawing rate, meat quality, nutrient substances, volatile compounds, and microstructure of the WYM were determined. The results showed that ultrasonic thawing treatment reduced thawing times by 30.95-64.28% compared to control. The meat quality results revealed that the thawing loss, cooking loss, L* and b* values, and pH values decreased significantly while the a* value and cutting force increased significantly (P < 0.05) at the lower 400 W power level compared with the control. In addition, the free amino acid (FAA), mineral, and vitamin (especially water-soluble vitamins) contents were significantly (P < 0.05) increased with the ultrasound treatment. UAT significantly (P < 0.05) increased the content of volatile compounds, an effect that was highest in the UAT-400 W group. Partial least squares discrimination analysis (PLS-DA) showed that 2,4-heptadienal was critical in distinguishing the UAT groups from the control. When the ultrasonic power was lower than 400 W, the muscle cell area was significantly (P < 0.05) increased but decreased when higher power was used. Therefore, UAT improves the thawing efficiency and quality of frozen WYM, particularly at a power level of 400 W, and these findings have potential applications in the meat industry.

Application of electronic nose and GC–MS for detection of strawberries with vibrational damage

Objectives

This study evaluated the potential of using electronic nose (e-nose) technology to non-destructively detect strawberry fruits with vibrational damage based on their volatile substances (VOCs).

Materials and methods

Four groups of strawberries with different durations of vibrations (0, 0.5, 1, and 2 h) were prepared, and their e-nose signals were collected at 0, 1, 2, and 3 days after vibration treatment.

Results

The results showed that when the samples from all four sampling days during storage were used for modelling, both the levels of vibrational damage and the day after the damage happened were accurately predicted. The best models had residual prediction deviation values of 2.984 and 5.478. The discrimination models for damaged strawberries also obtained good classification results, with an average correct answer rate of calibration and prediction of 99.24%. When the samples from each sampling day or vibration time were used for modelling, better results were obtained, but these models were not suitable for an actual situation. The gas chromatography–mass spectrophotometry results showed that the VOCs of the strawberries varied after experiencing vibrations, which was the basis for e-nose detection.

Limitations

The changes in VOCs released by other forces should be studied in the future.

Conclusions

The above results showed the potential use of e-nose technology to detect strawberries that have suffered vibrational damage.

Effect of red thyme oil (Thymus vulgaris L.) vapours on fungal decay, quality parameters and shelf-life of oranges during cold storage

This work has been aimed at studying the effect of red thyme oil (RTO, Thymus vulgaris L.) on the shelf-life and Penicillium decay of oranges during cold storage. RTO vapours significantly reduced (P ≤ 0.05) the percentage of infected wounds, the external growth area and the production of spores in inoculated orange fruit stored for 12 days at 7 °C in a polypropylene film selected for its appropriate permeability. Among the RTO compounds, p-cymene and thymol were the most abundant in packed boxes at the end of cold storage. The RTO vapours did not affect the main quality parameters of the oranges, or the taste and odour of the juice. The results have shown that an active packaging, using RTO vapours, could be employed, by the citrus industry, to extend the shelf-life of oranges for fresh market use and juice processing.

Geographical Origin Authentication of Agri-Food Products: Α Review

This study is a systematic literature review of geographical origin authentication by elemental analytical techniques. Authentication and certification of geographic origin of agri-food products is a useful tool toward the protection of the quality for products. The aim of this work was to map the current state of research in the area of agricultural products and food, identifying emerging fields to the geographical origin of products. The article is divided in three parts. The first part of the article deals with the analytical techniques applied in the food authentication. Special mention is made to elemental analysis and multiple isotope ratio. The second section focuses on statistically published data concerning published research for geographical origin authentication for the period 2015-2019. Specific results are presented inter alia: number of articles according to the type of product, articles according to the type of the analytical techniques, and others. The third part contains characteristic results from articles that were published in the period 2015-2019, on certification of geographical origin on specific agricultural products.

Untargeted and Targeted Discrimination of Honey Collected by Apis cerana and Apis mellifera Based on Volatiles Using HS-GC-IMS and HS-SPME-GC-MS

Fraudulent acts regarding honey authenticity that use Apis mellifera honey as a substitute for Apis cerana honey have garnered considerable concern in China and triggered a trust crisis from consumers. In this study, untargeted metabolomics analysis was carried out based on volatile fractions in honey from A. cerana and A. mellifera using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). Honey from A. cerana and A. mellifera was discriminated by HS-GC-IMS profiling, principal component analysis, and orthogonal partial least-squares discrimination analysis. Tentative markers were identified from p-values and the variable importance in projection analysis and confirmed using the retention index, mass fragments, and reference standards by gas chromatography-mass spectrometry (GC-MS). A targeted method was established using the headspace solid phase coupled with microextraction GC-MS (HS-SPME-GC-MS) to quantitate the markers. The results demonstrated that the developed untargeted and targeted metabolomics approach performed well when discriminating honey from A. cerana and A. mellifera.

Multistage Signals Based on Cyclic Chemiluminescence for Decoding Complex Samples

Identification of complex samples presents a difficult challenge for modern analytical techniques, and the differentiation among closely similar mixtures often remains indeterminate. In this article, we designed a simplified cyclic chemiluminescence (CCL) system that is able to measure multistage signals in a single sample injection. The system was used to investigate the CCL reactions of the binary, ternary, and multicomponent mixtures. Results showed that each mixture has a unique exponential decay equation (EDE) with a constant decay coefficient (k-value) to describe the change law of its multistage signals. Further studies found that different brands of liquor, beer, toner, and baby powder have different k-values, and the same brand of the commodities between different batches have the same k-values, which allows facile identification of these complex samples. We then used different catalysts to design digital codes of the k-value for further improving the identifying ability of CCL. Moreover, the multistage signals are like fingerprints and could be used for linear discriminate analysis, which provides another complementary approach for identification of complex samples. Finally, we demonstrated that CCL shows potential applications in certification and quality assurance according to the change of the k-values of the sample. This work demonstrates that excellent discrimination ability of CCL for the identification of complex samples and provides a promising technology for quality assurance.

Perilla frutescens Britton: A Comprehensive Study on Flavor/Taste and Chemical Properties During the Roasting Process

This study investigated changes of volatile compounds, sniffing test-assisted sensory properties, taste associated-constituent and free amino acid compositions, taste description by electronic-tongue, and chemical characteristics in Perilla frutescens Britton var. acuta Kudo after roasting at 150 °C for 0–8 min. A total of 142 volatile compounds were identified, among which methyl benzoate and limonene were predominant, regardless of roasting time, and these were also detected as the major compounds in the sniffing test by GC-olfactometry. For constituent amino acids analyzed by the acid hydrolysis method using hydrochloric acid (HCl), the concentration of glutamic acid, aspartic acid, and leucine showed an increase pattern with increased roasting time, which results in umami taste, sour taste, and bitter taste, respectively. For free amino acids, valine and hydroxylysine eliciting bitter and bitter and sweet tastes, respectively, also tend to increase by roasting. The pattern of amino acid concentration by roasting was readily matched to the taste description by electronic-tongue but that of sweetness and sourness by electronic-tongue did not coincide with the amino acid composition. For the chemical properties, total phenolic content, antioxidative capacity, and browning intensity tend to increase with roasting but decreased by 8 min. The results of this study provide fundamental information on perilla in both the food industry and cooking environment for the sake of increasing the utilization of perilla as a food source and ingredient.