Effective strategy for distinguishing raw and vinegar Schisandrae Chinensis Fructus based on electronic eye and electronic tongue combined with chemometrics.

INTRODUCTION: Schisandrae Chinensis Fructus (SCF), a traditional Chinese medicine, has been used in treating virtual injury and strain since ancient times. The Chinese Pharmacopoeia reveals that SCF includes raw (RSCF) and vinegar-processed (VSCF) decoction pieces. OBJECTIVE: This study developed an effective method combining the electronic eye (e-eye), electronic tongue (e-tongue), and chemometrics to discriminate RSCF and VSCF from the perspective of chemical composition, color, and taste. MATERIAL AND METHODS: First, RSCF were collected and processed into VSCF, and their color parameters, e-tongue sensory properties, high-performance liquid chromatography (HPLC) and ultra-HPLC (UPLC) characteristic fingerprints, and nominal ingredients were determined. Multivariate statistical analyses, including principal component, linear discriminant, similarity, and partial least squares discriminant analyses, were conducted. RESULTS: HPLC and UPLC fingerprints were established, demonstrating a > 0.900 similarity. The content determination indicated increased schisantherin A, schisantherin B, and schisandrin A contents in VSCF. The e-eye data demonstrated a > 1.5 total color difference before and after processing ΔE*ab, indicating the significantly changed sample color and appearance before and after processing. The e-tongue technology was used to quantitatively characterize the taste of RSCF and VSCF. The t-test revealed significantly reduced sourness, aftertaste-bitter, and aftertaste-astringent values of SCF after vinegar processing. Principal component and partial least squares discriminant analyses indicated that e-eye and e-tongue realize the rapid RSCF and VSCF identification. CONCLUSION: The proposed comprehensive strategy of electronic eye and electronic tongue combined with chemometrics demonstrated satisfactory results with high efficiency, accuracy, and reliability. This can be developed into a novel and accurate method for discriminating RSCF and VSCF.

The influence of processing methods on polyphenol profiling of tea leaves from the same large-leaf cultivar (Camellia sinensis var. assamica cv. Yunkang-10): nontargeted/targeted polyphenomics and electronic sensory analysis.

Tea polyphenols transform under processing methods, but a systematic study on their changes in the same large-leaf tea cultivar is lacking. Here, Camellia sinensis var. assamica cv. Yunkang-10 leaves underwent six processing methods and were assessed using optimized nontargeted (UHPLC-Q-Exactive Orbitrap-MS) and targeted (UHPLC-QqQ-MS) polyphenomics, along with molecular networking analysis. 903 and 52 polyphenolic compounds (catechins, flavones and flavonols, and phenolic acids) were respectively relatively and absolutely quantified for the first time. Dark and black teas, with the lowest polyphenol content, differed from the other four tea types, although variations existed among these four teas. However, some flavonol and flavone aglycones (e.g. kaempferol, apigenin), as well as some phenolic acids (e.g. ellagic acid, gallic acid), exhibited higher levels in dark and black teas. Correlations between polyphenolic composition and electronic sensory characteristics were observed using E-tongue and E-eye. This study enriches understanding of polyphenol profiles in Chinese teas post diverse processing.

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus.

BACKGROUND: Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness. RESULTS: The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices. CONCLUSION: The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

Quality difference analysis of raw and vinegar-processed products of Qingpi based on color and component correlation.

The dried young fruit of Citrus reticulata Blanco, known as Qingpi, is commonly used in clinic both with its raw and vinegar-processed products. However, the distinctions in quality between these two products remain unclear, and the methods for identification are considerably intricate. In this study, an electronic eye technique was applied to assess the overall color of Qingpi products before and after processing. The luminosity (L*) and yellow-blue (b*) values of Qingpi decreased after vinegar processing, while red-green (a*) values increased. The discriminant function models based on color parameters were established to effectively classify the two products. The chemical compositions of different Qingpi products were characterized using ultra-high performance liquid chromatography fingerprint technology, and 10 distinct components were considered as potential chemical markers. The correlation analysis revealed a significant relationship between chromatic values and chemical components. In conclusion, the results of this study suggested that chromaticity can be effectively considered as a valuable instrument for the prediction of component content in both raw and vinegar-processed Qingpi products. This study will provide new ideas and methods for identification and quality evaluation of Qingpi processed products, as well as provide a reference for standardizing traditional Chinese medicine processing techniques.

Nanomaterial-Based Artificial Vision Systems: From Bioinspired Electronic Eyes to In-Sensor Processing Devices.

High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here, we discuss recent progress in artificial vision systems with high-performance imaging and data processing capabilities enabled by distinctive electrical, optical, and mechanical characteristics of nanomaterials surpassing the limitations of traditional silicon technologies. In particular, we focus on nanomaterial-based electronic eyes and in-sensor processing devices inspired by biological eyes and animal visual recognition systems, respectively. We provide perspectives on key nanomaterials, device components, and their functionalities, as well as explain the remaining challenges and future prospects of the artificial vision systems.

Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation.

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category "extra", Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative.

Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy.

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

Rapid quality evaluation of four kinds of Polygoni Multiflori Radix Praeparata by electronic eye combined with chemometrics.

INTRODUCTION: Polygonum multiflorum Thunb., a widely used herbal medicine, has trouble with the hepatic adverse effect. Processing is an effective method to increase potency and reduce the adverse effects of herbal medicines. Polygoni Multiflori Radix Praeparata (PMRP), the decoction pieces processed from raw material, is widely consumed in clinical practice in many countries. The quality control of PMRP has attracted more and more attention worldwide. OBJECTIVE: A simple and rapid quality evaluation method using an electronic eye (E-eye) combined with chemometrics was proposed for controlling the quality of PMRP. MATERIALS AND METHODS: The semi-quantitative and quantitative data of 105 major components in 128 batches of PMRP samples obtained by three different analysis instruments were fused to investigate the correlation with the dynamic exterior colour determined by E-eye. The correlation between exterior colour and chemical fusion dataset was investigated by orthogonal partial least squares discriminant analysis (OPLS-DA) and partial least squares regression (PLSR). According to the results of correlation analysis, the color parameters of high-quality PMRP was set. RESULTS: Correlation studies by chemometrics revealed that the exterior colour depth was significantly correlated with 32 components [variable importance in the projection (VIP) > 1.0, p < 0.05]. The colour parameter of E * ab located in the range of 46.69-51.66 can be used easily, rapidly, and in an environment-friendly way to determine whether the PMRP sample has reached sufficient processing time with good quality. CONCLUSION: This study adds some scientific information to our understanding of traditional medicine while contributing an alternative method for assessing the quality of other decoction pieces.

Instrumental and Sensory Analyses of Salami from Autochthonous and Conventional Pig Breeds.

Typical salami produced from Mora Romagnola (MR), an autochthonous pig breed extensively farmed within a geographically confined Italian area, are food products of commercial interest. This investigation aimed to highlight elements for the recognition and authentication of such typical salami. Five MR salami were analyzed using a sensory and instrumental approach, and the results were compared with those from eight salami made from a conventional pig breed. The sensory profiles were defined through attributes such as seasoning, pepper, garlic, fermented etc.; no differences in the products ascribable to the pig breed were highlighted. By analyzing volatile compounds, 33 molecules were detected; significant differences were found among samples, probably related to processing methods. Color differences between MR and conventional salami were identified by an electronic eye. According to instrumental texture analysis, large variability among the MR samples was detected, probably related to different types of salami (recipe, casing, size, ripening). Correlations were found among the sensory results, volatile compounds, and textural properties of the samples. Most differences do not appear to be specifically related to MR breed; only red color intensity evaluated by an electronic eye showed a correlation with breed, i.e., MR salami. This study highlights the large variability among the salami analyzed.

A Novel Method for Quality Evaluation of Gardeniae fructus Praeparatus during Heat Processing Based on Sensory Characteristics and Chemical Compositions.

The intrinsic chemical components and sensory characteristics of Gardeniae fructus Praeparatus (GFP) directly reflect its quality and subsequently, affect its clinical curative effect. However, there is little research on the correlation between the appearance traits and chemical compositions of GFP during heat processing. In this study, the major components of five typical processed decoction pieces of GFP were determined. With the deepening of processing, the contents of geniposidic acid and 5-HMF gradually increased, while the contents of deacetyl-asperulosidic acid methyl ester, gardenoside, and two pigments declined. Moreover, the electronic eye, electronic tongue, and electronic nose were applied to quantify GFP's sensory properties. It was found that the chroma values showed a downward trend during the processing of GFP. The results of odor showed that ammonia, alkenes, hydrogen, and aromatic compounds were the material base for aroma characteristics. Complex bitterness in GF was more obvious than that in other GFP processed products. Furthermore, one mathematical model was established to evaluate the correlation between the sensory characteristics and chemical composition of GFP during five different stages. A cluster analysis and neural network analysis contributed to recognizing the processing stage of GFP. This study provided an alternative method for the exterior and interior correlation-based quality evaluation of herbs.

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes.

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the "smell", "taste", and "color" of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review's purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review.

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

[Quality analysis of Citri Trifoliatae Fructus in different harvest time by HPLC combined with electronic eye technology].

As a local variety of medicinal material, Citri Trifoliatae Fructus is widely used in many places, whereas its harvest time remains unclear. Therefore, studying its harvest time can make more reasonable use of this medicinal material. In this study, we determined the flavonoids content and compared the color of Citri Trifoliatae Fructus harvested in different time, aiming to guide the harvest of this medicinal material. The fresh fruits of Citrus trifoliata were collected from Xinxiang city, Henan province, graded according to the diameter range, and then dried. The contents of isonaringin, naringen, and poncirin in Citri Trifoliatae Fructus were determined by HPLC, and the color values of the samples were detected by electronic eye. The correlation analysis of the obtained data was carried out to explore the relationships of color and diameter with quality. The results showed that the contents of isonaringin, naringen, and poncirin varied significantly in different harvest time, within the ranges of 0.21-1.20, 2.21-11.59, and 3.73-23.16 mg·g~(-1), respectively. With the delay of harvest time, Citri Trifoliatae Fructus showed the color changing from green to yellow, gradually increased diameter, and gradually decreased contents of isonaringin, naringen, and poncirin. The contents of isonaringin, naringen, and poncirin were negatively correlated with the degree of red and green(a~*) and positively correlated with the degree of yellow and blue(b~*). The contents of naringen and poncirin had significantly negative correlations with the diameter. This study indicates that the quality of Citri Trifoliatae Fructus can be judged by its diameter and skin color, which provides a theoretical basis for the rational harvest of this medicinal material.

Electronic Eye Based on RGB Analysis for the Identification of Tequilas.

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image's pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers' performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen's kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

Microfluidic-based fluorescent electronic eye with CdTe/CdS core-shell quantum dots for trace detection of cadmium ions.

Cadmium ions (Cd2+) greatly threat human health and the environment due to its extremely severe toxicity. Therefore, it is of paramount importance to establish a sensitive and portable platform for monitoring Cd2+ on site. In this work, a novel microfluidic-based fluorescent electronic eye (E-eye) combined with tetrasodium iminodisuccinate (IDS)-etched CdTe/CdS quantum dots (QDs) was developed for portable and sensitive detection of trace Cd2+ in water environment. The fluorescent E-eye consists of a microfluidic chip for miniaturized flow analysis, an ultraviolet light excitation module for fluorescent excitation, an optical lens for device miniaturization and a smartphone for portable photographing and analysis. The IDS was added in the CdTe/CdS QDs to cause fluorescence quenching due to the chemical etching. Subsequently added Cd2+ will be recognized by etched QDs, thus inducing the fluorescence changes that can be directly captured by the E-eye for quantitative detection of Cd2+. With the optimization of all parameters including pH, reaction time and the concentration of IDS, the proposed platform could detect Cd2+ with a low detection limit of 0.26 µg/L in the range of 1-250 µg/L. It is worth noting that the performance of the developed fluorescent E-eye is quite comparable to a commercial microplate reader with a detailed comparison in linearity, sensitivity and detection limit. In summary, the proposed microfluidic-based fluorescent E-eye provides a promising platform for portable and high sensitive detection of trace cadmium in water environment.

[A rapid identification of authenticity and specifications of Chinese medicine Fritillariae Cirrhosae Bulbus based on E-eye technology].

The quality of traditional Chinese medicine tablets is correlated with clinical efficacy and drug safety, and plays a great role in promoting the development of traditional Chinese medicine. However, the existing traditional artificial identification and modern instrument detection in terms of accuracy and timeliness have both advantages and disadvantages. Therefore, how to quickly and accurately identify the quality of traditional Chinese medicine tablets has become a high-profile issue. The purpose of this paper is to explore the feasibility of the application of electronic eye technology in the study of rapid identification of traditional Chinese medicine quality. A total of 80 batches of samples were collected and tested by Fritillariae Cirrhosae Bulbus for traditional empirical identification(M_1) and modern pharmacopeia(M_2). The optical data was collected from electronic eyes, and the chemical metrology was used to establish suitable discrimination models(M_3). Four authenticity and commodity specification models, namely identification analysis(DA), minimum bidirectional support vector machine(LS-SVM), partial minimum two-multiplier analysis(PLS-DA), main component analysis identification analysis(PCA-DA), were established, respectively. The accuracies of the authenticity identification models were 82.5%, 90.0%, 96.2% and 93.8%, while the accuracies of the commodity specification identification models were 89.3%, 96.0%, 90.7% and 97.3%, respectively. The models were well judged, the authenticity identification was based on the final identification model of PLS-DA, and the commodity specification was based on the final identification model of PCA-DA. There was no significant difference between its accuracy and M_1, and the time of determination was much shorter than M_2(P<0.01). Therefore, electronic-eye technology could be used for the rapid identification of the quality of Fritillariae Cirrhosae Bulbus.

A novel portable biosensor based on aptamer functionalized gold nanoparticles for adenosine detection.

Adenosine has received great attentions acting as a potential biomarker for monitoring lung cancer. Most of the reported studies for adenosine detection require large instruments and complicated procedures. Herein, a sensitive, rapid and in-situ colorimetric aptasensor was developed for adenosine detection. Moreover, a homemade biomimetic electronic-eye (E-eye) was established and utilized as a portable in-time detection equipment. The entire measurement can be completed within 20 min, including the combination of aptamer with adenosine or AuNPs and the detection of adenosine. Four different kinds of aptamer were compared and the results showed that the AuNPs-aptamer-biotin system was the most stable and with the widest detection range of 5.0 µM-60.0 µM and the lowest LOD of 0.17 µM. Moreover, the artificial urine samples were also tested with a linear range from 5.0 to 50.0 µM and a LOD of 0.48 µM. The results validated that the aptasensor together with the E-eye can be a promising platform for adenosine detection.

The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics.

Electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) combined with chemometrics methods were applied for qualitative identification and quantitative prediction of tea quality. Main chemical components, such as amino acids, catechins, polyphenols and caffeine were measured by traditional methods. Feature-level fusion strategy for the integration of the signals was introduced to integrate the E-nose, E-tongue and E-eye signals, aiming at improving the performances of identification and prediction models. Perfect results with an accuracy of 100% were obtained for qualitative identification of tea quality grades, based on fusion signals by support vector machine and random forest. Quantitative models were established for predicting the contents of the chemical components based on independent electronic signals and fusion signals by partial least squares regression, support vector machine and random forest. Random forest based on the fusion signals achieved the best performance in predicting the concentration of those chemical components.

Towards Infrared Electronic Eyes: Flexible Colloidal Quantum Dot Photovoltaic Detectors Enhanced by Resonant Cavity.

Electronic eye cameras are receiving increasing interest due to their unique advantages such as wide field of view, low aberrations, and simple imaging optics compared to conventional planar focal plane arrays. However, the spectral sensing ranges of most electronic eyes are confined to the visible, which is limited by the energy gaps of the sensing materials and by fabrication obstacles. Here, a potential route leading to infrared electronic eyes is demonstrated by exploring flexible colloidal quantum dot (CQD) photovoltaic detectors. Benefitting from their tunable optical response and the ease of fabrication as solution processable materials, mercury telluride (HgTe) CQD detectors with mechanical flexibility, wide spectral sensing range, fast response, and high detectivity are demonstrated. A strategy is provided to further enhance the light absorption in flexible detectors by integrating a Fabry-Perot resonant cavity. Integrated short-wave IR detectors on flexible substrates have peak D* of 7.5 × 1010 Jones at 2.2 µm at room temperature and promise the development of infrared electronic eyes with high-resolution imaging capability. Finally, infrared images are captured with the flexible CQD detectors at varying bending conditions, showing a practical approach to sensitive infrared electronic eyes beyond the visible range.

E-nose, e-tongue and e-eye for edible olive oil characterization and shelf life assessment: A powerful data fusion approach.

The aim of this work was to investigate the applicability of e-senses (electronic nose, electronic tongue and electronic eye) for the characterization of edible olive oils (extra virgin, olive and pomace) and for the assessment of extra virgin olive oil and olive oil quality decay during storage at different temperatures. In order to obtain a complete description of oil samples, physico-chemical analyses on quality and nutritional parameters were also performed. Data were processed by PCA and a targeted data processing flow-sheet has been applied to physico-chemical and e-senses dataset starting from data pre-processing introducing an innovative normalization method, called t0 centering. On e-senses data a powerful mid-level data fusion approach has been employed to extract relevant information from different analytical sources combining their individual contributions. On physico-chemical data, an alternative approach for grouping extra virgin olive oil and olive oil samples on the basis of their freshness was applied and two classes were identified: fresh and oxidized. A k-NN classification rule was developed to test the performance of e-senses to classify samples in the two classes of freshness and the average value of correctly classified samples was 94%. Results demonstrated that the combined application of e-senses and the innovative data processing strategy allows to characterize edible olive oils of different categories on the basis of their sensorial properties and also to follow the evolution during storage of extra-virgin olive oil and olive oil sensorial properties thus assessing the quality decay of oils.