Electronic nose and isotope ratio mass spectrometry in combination with chemometrics for the characterization of the geographical origin of Italian sweet cherries

1H NMR profiling and chemometric analysis for ripening and production characterization of Grana Padano cheese

Grana Padano (GP) cheese is a renowned PDO Italian cheese whose nutritional characteristics and market price are influenced by the ripening stage. In this work, it was demonstrated that the combined use of untargeted 1H NMR profiling and chemometric analysis can be used as a powerful tool to quantitatively characterize GP ripening and production, focusing on both aqueous and lipid fractions. An initial exploratory analysis revealed substantial variations in the aqueous fraction attributable to aging time, year and season of production. Multivariate analysis was adopted to show these differences, mainly attributable to amino acids. In contrast, the lipid fraction analysis highlighted the impact of production season on fatty acid unsaturation, influenced by feed variations. As regards the production process, this study focuses on the variations induced by bactofugation. In this respect, the aqueous fraction was found to be extensively influenced by this centrifugation step, affecting compounds crucial to organoleptic characteristics.

The Application of High-Resolution Melting Analysis to trnL (UAA) Intron Allowed a Qualitative Identification of Apple Juice Adulterations

Food authenticity plays a pivotal role in the modern age since an increased consumers awareness has led them to pay more attention to food commodities. For this reason, it is important to have reliable and fast techniques able to detect possible adulterations in food, which affect qualitative and economic value. Therefore, the aim of this study was to detect possible adulterations in apple juice from others fruit species (i.e., pear, peach, and kiwi) combining DNA barcoding approach, using trnL (UAA) intron, with high resolution melting analysis (HRMA). A preliminary phylogenetic analysis, using sequences retrieved by the GenBank, confirmed the discriminatory power of trnL (UAA) intron among the four fruit species examined. Moreover, the sequencing of the trnL (UAA) fragments obtained from apple, pear, peach, and kiwi, demonstrated the suitability of an inner shorter sequence, P6 loop, to differentiate the considered species. The HRMA coupled with trnL (UAA) intron allowed discrimination among the four fruits but provided incomplete results for juices. Whereas the HRMA targeting the P6 loop amplicons confirmed the suitability of the technique to qualitatively distinguish fruit juices composed by the combination of apple/pear and apple/peach. However, the impossibility of discriminating apple/kiwi juices from the pure kiwi sample highlighted limitations, most likely related to the DNA extraction process. This hypothesis was further confirmed by analyzing DNA blends obtained by combining nucleic acids extracted from pure matrixes (i.e., apple and kiwi fruits). In this specific case, the application of HRMA allowed both qualitative and quantitative assessment of the samples.

Agronomic, Physicochemical, Aromatic and Sensory Characterization of Four Sweet Cherry Accessions of the Campania Region

Sweet cherries (Prunus avium L.) are greatly appreciated fruits worldwide due to their taste, color, nutritional value, and beneficial health effects. The characterization of autochthonous germplasm allows to identify genotypes that possess superior characteristics compared to standard cultivars. In this work, four accessions of sweet cherry from the Campania region (Limoncella, Mulegnana Riccia, Mulegnana Nera and Montenero) were investigated for their morpho-physiological, qualitative, aromatic, and sensorial traits in comparison with two standard cultivars (Ferrovia and Lapins). A high variability in the pomological traits resulted among the samples. Montenero showed comparable fruit weight and titratable acidity to Ferrovia and Lapins, respectively. The highest total soluble solid content was detected in Mulegnana Riccia. A considerable variability in the skin and pulp color of the cherries was observed, varying from yellow-red in Limoncella to a dark red color in Montenero. Mulegnana Nera showed the highest content of polyphenols, flavonoids, anthocyanins, and ascorbic acid compared to the standard cultivars. Volatile organic compounds profile analysis identified 34 volatile compounds, 12 of which were observed at different concentrations in all the sweet cherry genotypes while the others were genotype-dependent. Conservation and cultivation of autochthonous accessions with suitable nutritional and morpho-physiologic characteristics promotes our agrobiodiversity knowledge and allows to better plan future breeding programs.

Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning

The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes.

Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis

Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of ⁸⁸Sr/⁸⁶Sr, ⁸⁴Sr/⁸⁶Sr, ⁸⁷Sr/⁸⁶Sr and ⁶³Cu/⁶⁵Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.

Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography-mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology.

Determination and Characterization of Phenolic Compounds from Australia-Grown Sweet Cherries (Prunus avium L.) and Their Potential Antioxidant Properties

Sweet cherries (Prunus avium L.) are popular fruits around the world with a high nutritional value and abundant phenolic compounds. Phenolic compounds of cherries contribute to positive health benefits. This study aimed at determining the phenolic content and antioxidant activities in four Australian-grown sweet cherry cultivars, including Bing, Ron's, Merchant, and Lapins, as well as the identification of individual phenolic compounds with liquid chromatography-electrospray ionization-quantum time-of-flight-mass spectrometry (LC-ESI-QTOF-MS²). Lapins exhibits the highest total phenolic content (TPC) value (1.73 ± 0.90 mg gallic acid equivalents (GAE)/g) while Ron's exhibits the highest total flavonoid content (TFC) value (0.51 ± 0.02 mg QE/g). In 2,2'-azinobis-(3-ethylbenzo-thiazoline-6-sulfonic acid) (ABTS), reducing power assay (RPA), and total antioxidant content (TAC) assays, Merchant exhibited the highest values (0.51 ± 0.07, 1.74 ± 0.04, and 2.79 ± 0.09 mg AAE/g, respectively) and almost showed the highest antioxidant activity. Ron's presented the highest value (1.21 ± 0.09 mg EDTA/g) in ferrous ion-chelating activity (FICA) assay and exhibits the strongest metal chelating ability. The correlation between phenolic contents and antioxidant assays was observed. In the LC-ESI-QTOF-MS² analysis, a total of 43 phenolic compounds has been detected in four sweet cherry cultivars, including 11 phenolic acids, 25 flavonoids, 5 other phenolic compounds, 1 lignan, and 1 stilbene. Venn graph showed that Lapins has the greatest number of unique compounds. Our study shows the presence of phenolic acids and provides information to be utilized as an ingredient in food, pharmaceutical, and nutraceutical industries.

An artificial class modelling approach to identify the most largely diffused cultivars of sweet cherry (Prunus avium L.) in Italy

The nutritional and commercial value of the sweet cherry provides it a great economic importance in Italy. The aim of this study was to characterize 35 sweet cherry cultivars and one of sour cherry, by analyzing values of different pomological and nutraceutical traits, identifying cultivars with antioxidant activity and total anthocyanins content closest to those present in literature for Ferrovia (largely diffused in Italy). To this goal, a multivariate metric index through the Soft Independent Modeling of Class Analogy analyzing an artificial dataset and testing a real one, two hierarchical clustering and a principal component analysis, were performed. The multivariate analyses result simultaneously investigated all the variables highlighting cvs. Sylvia, Graffione nero Col di Mosso, Ferrovia, Mora della Punta, Bianchetta Nuchis and Sandra to be more similar to literature data of Ferrovia. This matrix index was a useful tool, to select the most commercial promising varieties.

Geographical origin of Chinese apples based on multiple element analysis

BACKGROUND

Apples from different regions of China show different qualities and internal characteristics, and appeal to different customers. However, these aspects have not been studied in depth. We characterized the profiles of 14 elements in 317 apple samples collected from five regions of China. Principal component analysis (PCA), linear discriminant analysis (LDA), and back-propagation artificial neural networks analysis (BP-ANN) were used to build models for apple authentication.

RESULTS

Fourteen elements were successfully identified in apple samples by performing graphite furnace atomic absorption spectrometry (GFAAS) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) analyses. Comparative analysis showed significantly different element profiles in samples from different regions. The first five principal components obtained by PCA accounted for 71.8% of the total variance. The LDA obtained 70.0% classification rates. The BP-ANN obtained 82.7% classification rates.

CONCLUSION

This study indicated the possibility that apples could be authenticated based on differences in their element profiles, and provided a basis for further geographical origin studies. © 2019 Society of Chemical Industry.

Physicochemical, Spectroscopic and Chromatographic Analyses in Combination with Chemometrics for the Discrimination of Four Sweet Cherry Cultivars Grown in Northern Greece

A total of 56 sweet cherry samples belonging to four cultivars (Ferrovia, Canada Giant, Lapins, and Germersdorfer) grown in northern Greece were characterized and differentiated according to botanical origin. For the above purpose, the following parameters were determined: conventional quality parameters (titratable acidity (TA), pH, total soluble solids (TSS), total phenolic content (TPC), mechanical properties and sensory evaluation, sugars by High Performance Liquid Chromatography (HPLC), volatile compounds by GC/MS, and minerals by ICP-OES. Statistical treatment of the data was carried out using Multivariate Analysis of Variance (MANOVA) and Linear Discriminant Analysis (LDA). The results showed that the combination of volatile compounds and conventional quality parameters provided a correct classification rate of 84.1%, the combination of minerals and conventional quality parameters 86.4%, and the combination of minerals, conventional quality parameters and sugars provided the highest correct classification rate of 88.6%. When the above four cherry cultivars were combined with previously studied Kordia, Regina, Skeena and Mpakirtzeika cultivars, collected from the same regions during the same seasons, the respective values for the differentiation of all eight cultivars were: 85.5% for the combination of conventional quality parameters, volatiles and minerals; and 91.3% for the combination of conventional quality parameters, volatiles, minerals, and sugars.

Conformal Prediction Based on Raman Spectra for the Classification of Chinese Liquors

This work extends the conventional back-propagation neural network (BPNN) to the classification of Chinese liquors of different flavors according to their Raman spectra. Conformal prediction is applied to assign reliable confidence measures for each classification and support an effective framework to make the machine learning on classification trustable. The BPNN can be used to predict the flavors of Chinese liquors according to their Raman spectra, and a classification rate of 88.96% can be achieved. In order to evaluate each classification, a non-conformity score is defined to generate a P-value for each classification. Moreover, the validity of conformal prediction in online mode is discussed. The number of cumulative errors in the conformal prediction is much less than that without conformal prediction. The relationship between the cumulative error and confidence levels shows that a high confidence level leads to low cumulative errors, but many cumulative errors will occur under a very high confidence level. The result implies that conformal prediction is a useful framework, which can employ classification satisfying a certain level of confidence. Meanwhile, the conformal prediction can improve our classification using a BPNN, when the number of data points is limited.

Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor

A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising.

Characterization of dried and freeze-dried sea fennel (Crithmum maritimum L.) samples with headspace gas-chromatography/mass spectrometry and evaluation of an electronic nose discrimination potential

Processed samples (air-dried @ 40 and @ 60 °C and freeze-dried) of sea fennel (Crithmum maritimum L.), an autochthonous spice with interesting market potential, were analyzed by headspace gas-chromatography/mass spectrometry and classification capabilities of an electronic nose in discriminating between samples with stepwise forward statistics were evaluated as well. Freeze-drying process was the most preservative in terms of limiting darkening without compromising appearance of the final product, providing weight loss of about 85% and water activity below the limit for mold growth issues. Headspace analysis of samples highlighted the presence of 35 volatiles grouped as terpene hydrocarbons, oxygenated terpenes, sesquiterpen hydrocarbons, phenyl propanoids, not-terpenic aldehydes and not-terpenic ketones. Correlations emerged between selected sensors and some detected volatile organic compounds. Stepwise linear discriminant analysis and simple K-nearest neighbors obtained a 100% overall correct classification rate in cross-validation of the electronic nose in classifying samples, whereas stepwise quadratic discriminant analysis and Naive-Bayes gave 93.3%. The sea fennel could be a new interesting spice to launch in the food market and the electronic nose showed the potential to be used in monitoring the industrial process aimed at extending its shelf-life.

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.

Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape

BACKGROUND

Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. ² H/¹ H, ¹³ C/¹² C, ¹⁵ N/¹⁴ N and ¹⁸ O/¹⁶ O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape.

RESULTS

In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ¹³ C and δ¹⁸ O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively.

CONCLUSION

The present findings suggest that stable isotopes (i.e. δ¹⁸ O, δ² H and δ¹³ C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry.

Assessment of the Indoor Odour Impact in a Naturally Ventilated Room

Indoor air quality influences people’s lives, potentially affecting their health and comfort. Nowadays, ventilation is the only technique commonly used for regulating indoor air quality. CO₂ is the reference species considered in order to calculate the air exchange rates of indoor environments. Indeed, regarding air quality, the presence of pleasant or unpleasant odours can strongly influence the environmental comfort. In this paper, a case study of indoor air quality monitoring is reported. The indoor field tests were conducted measuring both CO₂ concentration, using a photoacoustic multi-gas analyzer, and odour trends, using an electronic nose, in order to analyze and compare the information acquired. The indoor air monitoring campaign was run for a period of 20 working days into a university room. The work was focused on the determination of both CO₂ and odour emission factors (OEF) emitted by the human activity and on the evaluation of the odour impact in a naturally ventilated room. The results highlighted that an air monitoring and recycling system based only on CO₂ concentration and temperature measurements might be insufficient to ensure a good indoor air quality, whereas its performances could be improved by integrating the existing systems with an electronic nose for odour detection.

Drying of restructured chips made from the old stalks of Asparagus officinalis: impact of different drying methods

BACKGROUND

Old stalks of Asparagus officinalis, which account for one third of the total length of each spear, are always discarded as waste. To make full use of the resource, a kind of restructured Asparagus officinalis chip was made. The effects of pulse-spouted microwave-assisted vacuum drying (PSMVD), microwave-assisted vacuum drying (MVD) and vacuum drying (VD) on texture, color and other quality parameters of restructured chips were then studied to obtain high-quality dried chips.

RESULTS

Results indicated that the drying time was significantly affected by drying methods, and PSMVD had much better drying uniformity than MVD. The expansion ratio and crispness of chips increased with increasing microwave power and vacuum degree. The browning reaction of samples in VD was more serious, which was confirmed by the results of color test and electronic nose.

CONCLUSIONS

The PSMVD drying method showed much better drying uniformity than MVD. The dried chips obtained by PSMVD showed optimal quality and were more readily accepted by consumers. © 2015 Society of Chemical Industry.

The classification and prediction of green teas by electrochemical response data extraction and fusion approaches based on the combination of e-nose and e-tongue

Aroma and taste are the most important attributes that influence the pleasantness of tea infusion.