Detection of rancid defect in virgin olive oil by the electronic nose

A SMART methodology for assessment of hexanal in potato crisps using electronic nose technology: sensor screening by scalar machine learning classifier method

There is a pertinent need to develop a rapid and accurate methodology for the detection of the onset and the progression of rancidity in the most popular savory product worldwide, viz. fried potato crisps for food safety and health concerns. Rancidity in the fried crisps-one set prepared using C18:2-lean deodorized virgin coconut oil under modified deep frying conditions (140 °C, 5 min),-and another set deep fried (170 °C, 3 min) in C18:2-rich oil (simulating commercial frying conditions) was determined by 'rancidity indices' generated (using Mahalanobis distance) from the data obtained by MO-based electronic nose analysis of hexanal (in Likens-Nickerson extract of volatiles from potato crisps), the most prominent rancidity marker, using screened sensors calibrated with standard hexanal, and classified using support vector machine. This also allowed unambiguous discrimination of the two sets of potato fries. The correlation of hexanal contents with the said indices yielded robust regression models which could accurately predict rancidity status of the crisps, forgoing GC-FID analysis of rancidity marker in the same. The 'SMART' models developed would allow rapid-cum-accurate detection of the onset and progression of rancidity in fried potato crisps on an industrial scale, forgoing the need to conduct biochemical analyses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-023-05831-y.

Evaluation of Red Wine Acidification Using an E-Nose System with Venturi Tool Sampling

The quality of wine is checked both during the production process and upon consumption. Therefore, manual wine-tasting work is still valuable. Due to the nature of wine, many volatile components are released, and it is therefore difficult to determine which elements need to be controlled. Acetic acid is one of the substances found in wine and is a crucial substance for wine quality. Gas sensor systems may be a potential alternative for manual wine tasting. In this work, we have developed a TGS2620 gas sensor module to analyze acetic acid levels in red wine. The gas sensor module was refined according to the Venturi effect along with signal slope analysis, providing promising results. The example included in this paper demonstrates that there is a direct relationship between the slope of the MOS gas sensor response and the acetic acid concentration. This relationship is useful to evaluate the ethanol oxidation in acetic acid in red wine during its production process.

Variations in volatile flavour compounds in Crataegi fructus roasting revealed by E-nose and HS-GC-MS

Introduction

Crataegi fructus (CF) is an edible and medicinal functional food used worldwide that enhances digestion if consumed in the roasted form. The odour of CF, as a measure of processing degree during roasting, significantly changes. However, the changes remain unclear, but are worth exploring.

Methods

Herein, the variations in volatile flavour compounds due to CF roasting were investigated using an electronic nose (E-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS).

Results

A total of 54 components were identified by GC-MS. Aldehydes, ketones, esters, and furans showed the most significant changes. The Maillard reaction, Strecker degradation, and fatty acid oxidation and degradation are the main reactions that occur during roasting. The results of grey relational analysis (GRA) showed that 25 volatile compounds were closely related to odour (r > 0.9). Finally, 9 volatile components [relative odour activity value, (ROAV) ≥ 1] were confirmed as key substances causing odour changes.

Discussion

This study not only achieves the objectification of odour evaluation during food processing, but also verifies the applicability and similarity of the E-nose and HS-GC-MS.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Chemical QR Code: A simple and disposable paper-based optoelectronic nose for the identification of olive oil odor

Olive oil is an appreciated food product with high nutritional value, besides being an essential component in many culture diets. In this study, we present for the first time the application of a simple and non-invasive paper-based optoelectronic nose designed in a QR code configuration to evaluate the odor of olive oils. The chemical QR code was fabricated by the addition of 12 dyes, which provided high dimensional data resulting from the interaction between the volatile compounds and the colorimetric array. The color changes were employed to build differential maps with a unique fingerprint (i) to discriminate between olive oil and other edible oil samples; (ii) to quantify nonanaldehyde as an oxidation marker; and (iii) to identify oxidized oils through principal component analysis (PCA) and hierarchical component analysis (HCA). By developing suitable mobile apps, we anticipate the employment of the chemical QR code for portable, low-cost, and in-situ evaluation of food product quality.

Variations of volatile flavour compounds in finger citron (Citrus medica L. var. sarcodactylis) pickling process revealed by E-nose, HS-SPME-GC-MS and HS-GC-IMS

The pickled products of finger citron are famous in southern China for their unique taste and flavor. Although pickling process involves complex treatments including salting, desalting, sugaring, cooking and drying, extended shelf-life up to ten years after pickling can be achieved. In this study, the variations of volatile flavour components in the pickling process of finger citron were investigated by electronic nose (E-nose), headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). HS-SPME-GC-MS identified 85 substances, and HS-GC-IMS identified 81 substances, including terpenoids (21), aromatic hydrocarbons (11), alcohols (11), aldehydes (10), esters (7), phenols (6), acids (5), ethers (2), ketones (2), and other species (10). Linalool, limonene, (E)-3,7-dimethyl-1,3,6-octatriene, myrcene, 3-carene, β-pinene, α-pinene, terpinolene, 1-methyl-4-(1-methylethyl)-1,4-cyclohexadiene, α-terpinene, (S)-β-bisabolene, 1-isopropyl-2-methylbenzene and 1-methyl-4-(1-methylethenyl)-benzene were the stable substances at relatively high contents in finger citron at different pickling process. Salting and drying steps in the pickling process exerted greatest influence on the volatile components of finger citron. Salting promoted the generation of aldehydes, esters and acids, but led to the disappearance of alcohols, while drying promoted the generation of alcohols, phenols, aldehydes and acids at the expense of reduction in terpenoids. Our study revealed that the characteristic volatile compounds of finger citron pickled products was mainly formed by the biological reactions in the salting stage and thermal chemical transformations in the drying stage. This study also validated the suitability of E-nose combined with HS-SPME-GC-MS and HS-GC-IMS in tracking the changes of volatile components in finger citron during the pickling process.

Headspace Gas Chromatography Coupled to Mass Spectrometry and Ion Mobility Spectrometry: Classification of Virgin Olive Oils as a Study Case

Due to its multiple advantages, ion mobility spectrometry (IMS) is being considered as a complementary technique to mass spectrometry (MS). The goal of this work is to investigate and compare the capacity of IMS and MS in the classification of olive oil according to its quality. For this purpose, two analytical methods based on headspace gas chromatography (HS-GC) coupled with MS or with IMS have been optimized and characterized for the determination of volatile organic compounds from olive oil samples. Both detectors were compared in terms of sensitivity and selectivity, demonstrating that complementary data were obtained and both detectors have proven to be complementary. MS and IMS showed similar selectivity (10 out of 38 compounds were detected by HS-GC-IMS, whereas twelve compounds were detected by HS-GC-MS). However, IMS presented slightly better sensitivity (Limits of quantification (LOQ) ranged between 0.08 and 0.8 µg g⁻¹ for HS-GC-IMS, and between 0.2 and 2.1 µg g⁻¹ for HS-GC-MS). Finally, the potential of both detectors coupled with HS-GC for classification of olive oil samples depending on its quality was investigated. In this case, similar results were obtained when using both HS-GC-MS and HS-GC-IMS equipment (85.71 % of samples of the external validation set were classified correctly (validation rate)) and, although both techniques were shown to be complementary, data fusion did not improve validation results (80.95% validation rate).

Characterization of the Key Odorants in High-Quality Extra Virgin Olive Oils and Certified Off-Flavor Oils to Elucidate Aroma Compounds Causing a Rancid Off-Flavor

To identify the odorants responsible for a rancid off-flavor in olive oils, first, the key aroma compounds in a premium extra virgin olive oil (PreOO1) were characterized by the sensomics approach and were then compared to those present in a certified rancid off-flavor olive oil (RanOO1) obtained from the International Olive Council (IOC). By application of an aroma extract dilution analysis, 46 odorants were detected and subsequently identified in PreOO1 and 35 odorants in RanOO1, respectively. After quantitation by stable isotope dilution assays, calculation of odor activity values (OAVs; ratio of concentration to odor threshold) revealed only 5 odorants with OAVs > 10 in PreOO1, while 13 odorants showed OAVs > 100 in RanOO1, with (E,Z)-2,4-decadienal, hexanoic acid, octanal, hexanal, (E)-2-octenal, and (Z)-2-nonenal being among the most odor-active compounds. Thus, marker aroma compounds for this off-flavor type could be suggested. Additionally, based on the OAVs obtained, the overall aroma profiles of both oils were mimicked by aroma recombination experiments. As proof of concept, 16 marker odorants were quantitated in two additional extra virgin olive oils and in eight further olive oils eliciting a rancid off-flavor. Application of a principal component analysis (PCA) and a hierarchical cluster analysis successfully discriminated both categories of olive oils. In the 12 olive oils used, acetic acid showed the highest Pearson coefficient between the perceived intensity of the rancid defect and the odorant concentration. In particular, (E,Z)- and (E,E)-2,4-decadienal and (Z)-2-nonenal can be suggested as chemical markers for olive oil rancidity in combination with positive aroma markers, for example, acetaldehyde and (Z)-3-hexenal.

Silicon-Based Optoelectronic Tongue for Label-Free and Nonspecific Recognition of Vegetable Oils

A special electronic tongue system based on photoelectric measurements on Si-Si/SiN X sensitive structures is reported. The sensing approach is based on measuring of minority carrier lifetime in silicon-based substrates using microwave-detected photoconductance decay. This inexpensive and environmentally friendly combinatorial electronic sensing platform is able to create characteristic electronic fingerprints of liquids, detect, and recognize them. In particular, an application of the optoelectronic tongue for recognition of vegetable oils and their mixtures is described.

New Volatile Molecular Markers of Rancidity in Virgin Olive Oils under Nonaccelerated Oxidative Storage Conditions

Evolution of the volatile profile of two extra-virgin olive oils with very different fatty acid composition (monounsaturated fatty acid/polyunsaturated fatty acid ratio) stored in several nonaccelerated oxidative conditions was studied by a validated headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method. The role of C8 volatile compounds in oxidative processes was highlighted, and controversial aspects regarding the origin of some volatiles were clarified. Specific volatile markers for rancidity were proposed: sum of pentanal, hexanal, nonanal, E-2-heptenal, propanoic acid, and hexanoic acid for oils stored in the dark; sum of pentanal, heptanal, nonanal, decanal, E-2-heptenal, E-2-decenal, E,E-hepta-2,4-dienal, and E,E-deca-2,4-dienal, octane for oils stored under light exposure; sum of pentanal, nonanal, decanal, E-2-heptenal, E-2-decenal, E,E-hepta-2,4-dienal, nonan-1-ol, propanoic acid, octane, 6-methylhept-5-en-2-one, and oct-1-en-3-ol for oils stored under light exposure with oxygen in headspace. A simplified marker (sum of pentanal, nonanal and E-2-heptenal) suitable for all conditions was also proposed.

A modified mid-level data fusion approach on electronic nose and FT-NIR data for evaluating the effect of different storage conditions on rice germ shelf life

Evaluating the possibility of extending shelf life of rice germ (a by-product of rice milling process) by reducing water activity in combination with storage atmosphere packaging, without any heat treatment, is the aim of the present study. Samples at different water activities (0.55, 0.45 and 0.36) were packed in air, argon or under vacuum, and stored at 27 °C for 150 days. To the aim, a non-targeted approach was applied by means of an FT-NIR spectrometer in reflectance with a rotating sample holder and a portable electronic nose, equipped with 10 non-specific sensors. For understanding the impact of the factors under study on the rice germ shelf life, a modified mid-level data fusion approach was applied to enhance the information most correlated with time. Moreover, Principal Component Analysis was applied on fused data to follow samples evolution during storage and identify different clusters according to the storage conditions. The rice germ case study allowed to better understand the information captured by the non-specific sensors: a 2D correlation map was developed combining the e-nose data with the NIR spectral information, highlighting relationships among NIR absorption bands and classes of chemical compounds inducing e-nose responses. A data fusion approach highlighted the importance of water activity on rice germ storage, while no interesting differences were ascribable to storage atmosphere packaging systems. In terms of correlation, the sensors could be divided in two groups, negatively inter-correlated: sensors ascribable to aromatic compounds (WC) and correlated with the NIR band around 4800-4900 cm^-1 (N-H bending of primary amides, typical for peptides coming from protein hydrolysis); broad-range response sensors (WS), linked with the NIR band at 5128 cm^-1 (second overtone of CO stretching of esters).

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis

Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

The Compounds Responsible for the Sensory Profile in Monovarietal Virgin Olive Oils

Monovarietal virgin olive oils (VOOs) are very effective to study relationships among sensory attributes, the compounds responsible for flavour, and factors affecting them. The stimulation of the human sensory receptors by volatile and non-volatile compounds present in monovarietal virgin olive oils gives rise to the sensory attributes that describe their peculiar delicate and fragrant flavours. The formation of these compounds is briefly illustrated and the influence of the agronomic and technological factors that affect their concentrations in the oil is examined. The relationships between compounds responsible for the olive oil flavour and sensory attributes are discussed. Several approaches for the varietal differentiation of monovarietal virgin olive oils are also overviewed.

Volatile Compound Profiling by HS-SPME/GC-MS-FID of a Core Olive Cultivar Collection as a Tool for Aroma Improvement of Virgin Olive Oil

Virgin olive oil (VOO) is the only food product requiring official sensory analysis to be classified in commercial categories, in which the evaluation of the aroma plays a very important role. The selection of parents, with the aim of obtaining new cultivars with improved oil aroma, is of paramount importance in olive breeding programs. We have assessed the volatile fraction by headspace-solid-phase microextraction/gas chromatography-mass spectrometry-flame ionization detection (HS-SPME/GC-MS-FID) and the deduced aroma properties of VOO from a core set of olive cultivars (Core-36) which possesses most of the genetic diversity found in the World Olive Germplasm Collection (IFAPA Alameda del Obispo) located in Cordoba, Spain. The VOO volatile fractions of Core-36 cultivars display a high level of variability. It is mostly made of compounds produced from polyunsaturated fatty acids through the lipoxygenase pathway, which confirms to be a general characteristic of the olive species (Olea europaea L.). The main group of volatile compounds in the oils was six straight-chain carbon compounds derived from linolenic acid, some of them being the main contributors to the aroma of the olive oils according to their odor activity values (OAV). The high level of variability found for the volatile fraction of the oils from Core-36 and, therefore, for the aroma odor notes, suggest that this core set may be a very useful tool for the choice of optimal parents in olive breeding programs in order to raise new cultivars with improved VOO aroma.

Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis

An electronic nose (e-nose), having 18 metal oxide semiconductor (MOS) sensors, guided determination of frying disposal time of sunflower oil is reported. The ranking and screening of MOS sensors, specific for volatile organic compounds, was performed using fuzzy logic. A correlation was examined between rancidity indices of fried oil (total polar compounds (TPC), and triglyceride dimers-polymers (TGDP), among others) and e-nose based odor index. Fuzzy logic screened 6 MOS sensors (LY2/G, LY2/AA, LY2/GH, LY2/gCT1, T30/1, and P30/1) to deconvolute the rancid fried oils using hierarchical clustering on principal component space. A good relationship was noted between rancidity indices and odor index (R²>0.85). Based on maximum discard limits of rancidity indices (25% TPC and 10% TGDP), the frying disposal time of 15.2h (TPC) vs. 15.8h (e-nose) and 15.5h (TGDP) vs. 16.3h (e-nose) was determined. The demonstrated methodology holds a potential extension to different fried oils and products.

A novel method for qualitative analysis of edible oil oxidation using an electronic nose

An electronic nose (E-nose) was used for rapid assessment of the degree of oxidation in edible oils. Peroxide and acid values of edible oil samples were analyzed using data obtained by the American Oil Chemists' Society (AOCS) Official Method for reference. Qualitative discrimination between non-oxidized and oxidized oils was conducted using the E-nose technique developed in combination with cluster analysis (CA), principal component analysis (PCA), and linear discriminant analysis (LDA). The results from CA, PCA and LDA indicated that the E-nose technique could be used for differentiation of non-oxidized and oxidized oils. LDA produced slightly better results than CA and PCA. The proposed approach can be used as an alternative to AOCS Official Method as an innovative tool for rapid detection of edible oil oxidation.

Assessment of volatile compound profiles and the deduced sensory significance of virgin olive oils from the progeny of Picual×Arbequina cultivars

Volatile compounds are responsible for most of the sensory qualities of virgin olive oil and they are synthesized when enzymes and substrates come together as olive fruit is crushed during the industrial process to obtain the oil. Here we have studied the variability among the major volatile compounds in virgin olive oil prepared from the progeny of a cross of Picual and Arbequina olive cultivars (Olea europaea L.). The volatile compounds were isolated by SPME, and analyzed by HRGC-MS and HRGC-FID. Most of the volatile compounds found in the progeny's oil are produced by the enzymes in the so-called lipoxygenase pathway, and they may be clustered into different groups according to their chain length and polyunsaturated fatty acid origin (linoleic and linolenic acids). In addition, a group of compounds derived from amino acid metabolism and two terpenes also contributed significantly to the volatile fraction, some of which had significant odor values in most of the genotypes evaluated. The volatile compound content of the progeny was very varied, widely transgressing the progenitor levels, suggesting that in breeding programs it might be more effective to consider a larger number of individuals within the same cross than using different crosses with fewer individuals. Multivariate analysis allowed genotypes with particularly interesting volatile compositions to be identified and their flavor quality deduced.

Validation of SPME-GCMS method for the analysis of virgin olive oil volatiles responsible for sensory defects

Volatile compounds are responsible for the aroma of virgin olive oil and also for its quality. The high number and different nature of volatile compounds drive to the need of a reliable analytical method that allows their proper quantification to explain the standard method of panel test. Although there are some analytical solutions available, they have not been validated and the regulatory bodies are reluctant to adopt them since they can be subjected to unknown errors. In this regards, the European Union has encouraged the validation of these analytical tools through the research program Horizon2020, which involves gaining knowledge from the analytical properties of the chemical methods for sensory assessment. This work is focused on the analytical validation of the methodology used to determine the actual concentration of volatiles in virgin olive oils when applying SPME-GCMS. The validation process includes the calibration curves for 29 volatile compounds responsible for the most common sensory perceptions in virgin olive oils, the determination of their working ranges with linear response, the detection and quantification limits, the sensitivity, the accuracy estimated as trueness and precision and the selectivity. Sixty-seven percent of the compounds presented a relative standard deviation in repeatability lower than 10%, and this percentage rises to 95% in lampante virgin olive oils. The accuracy was established in 97% of the studied volatile compounds. Finally, an empirical example of the ability of the method to discriminate virgin olive oils of different categories (extra virgin, virgin, ordinary and lampante) by the quantification of their volatiles is provided.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.