Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion

Data Fusion of Acoustic and Optical Emission from Laser-Induced Plasma for In Situ Measurement of Rare Earth Elements in Molten LiCl-KCl

Molten salts have a significant potential for use as next-generation nuclear reactor coolants and in pyroprocessing for the recycling of used nuclear fuel. However, the molten salt composition needs to be known at all times, and high temperatures and intense ionizing radiation pose challenges for the monitoring instrumentation. Although the technique of laser-induced breakdown spectroscopy (LIBS) has been studied for in situ measurements of molten salts, trials to improve its monitoring accuracy using chemometrics are lacking. In this study, a data fusion technique using the LIBS optical and laser-induced acoustic (LIA) signals was investigated to enhance the measurement accuracy for molten salt monitoring. Prediction models were constructed using the partial least-squares method, and the variable importance in projection scores was analyzed to evaluate the effect of incorporating the LIA signal into the analysis. This study investigates rare earth elements Eu, Er, and Pr found not only in nuclear but also in other settings such as laser and magnetic materials. The analysis of LIBS data without data fusion resulted in a root-mean-square error of prediction (RMSEP) of 0.0774-0.0913 wt %, whereas the prediction model using data fusion led to approximately 18-40% enhanced RMSEP (0.0461-0.0679 wt %). The results suggest that fusing the LIBS data with the simultaneously recorded LIA data can improve the monitoring accuracy of rare earth element composition in molten salts.

Elucidating the effect of different processing methods on the sensory quality of chestnuts based on multi-scale molecular sensory science

Chestnuts are known for their unique flavor and nutritional value. However, the flavor changes in chestnuts after processing remain unclear. Multi-intelligent sensory technologies and headspace solid-phase microextraction-arrow gas chromatography-mass spectrometry (HS-SPME-Arrow-GC-MS) combined with multivariate statistical analysis were applied to evaluate the effect of packaging and heat sterilization procedures on the sensory quality of chestnuts. The results showed that the significant variations (p < 0.05) between the different chestnut processing methods were revealed via the electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue). The packaging had a more significant influence on the sensory quality of the chestnuts than heat sterilization procedures. HS-SPME-Arrow-GC-MS identified 83 volatile compounds. The processed chestnuts exhibited higher aldehyde, ester, and alkene concentrations, while N2 packaging was more favorable to flavor elicitation and retention. Therefore, combining intelligent sensory techniques with GC-MS can rapidly determine the chestnut quality and guide industrial production.

Fusion-based strategy of CSA and mobile NIR for the quantification of free fatty acid in wheat varieties coupled with chemometrics

The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R_C and R_P), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R_C = 0.8793, RMSECV = 7.91 mg/100 g, R_P = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour.

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine

Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R² values above 0.7 and fifteen with R² values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product's sensory characteristics from raw material spectral attributes.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

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.

Development of a novel green tea quality roadmap and the complex sensory-associated characteristics exploration using rapid near-infrared spectroscopy technology

Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.

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.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Influence of Cooking Conditions on Nutritional Properties and Sensory Characteristics Interpreted by E-Senses: Case-Study on Selected Vegetables

This study investigates the effects of three cooking methods (boiling, steaming and microwaving) on the nutritional and physical properties of cauliflowers, carrots and sweet potatoes; e-senses were applied to interpret the sensory characteristics according to physico-chemical aspects. The nutritional quality was evaluated by determining the concentrations of key components and the antioxidant activity; e-sense data, combined with texture parameters, were processed by a principal component analysis. The cooking method and time significantly influenced the quality of the three products. Boiling, which detrimentally affected ascorbic acid, total phenolic concentration and antioxidant activity, enhanced carotene accessibility. Steaming produced losses in ascorbic acid, increasing total phenolics and carotenoids. Microwaving resulted in minor changes in ascorbic acid concentrations, preserved carotenoids and increased total phenolics. The nutritional quality was better preserved or enhanced using shorter cooking times. The elaboration of the data collected by the e-senses showed a clear evolution according to the cooking method and time. The results helped to determine the cooking method that best preserves the nutritional properties of the vegetables, highlighting the applicability of rapid instrumental methods to interpret the evolution of sensory characteristics.

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Organoleptic Analysis of Drinking Water Using an Electronic Tongue Based on Electrochemical Microsensors

The standards that establish water’s quality criteria for human consumption include organoleptic analysis. These analyses are performed by taste panels that are not available to all water supply companies with the required frequency. In this work, we propose the use of an electronic tongue to perform organoleptic tests in drinking water. The aim is to automate the whole process of these tests, making them more economical, simple, and accessible. The system is composed by an array of electrochemical microsensors and chemometric tools for multivariable processing to extract the useful chemical information. The array of sensors is composed of six Ion-Sensitive Field Effect Transistors (ISFET)-based sensors, one conductivity sensor, one redox potential sensor, and two amperometric electrodes, one gold microelectrode for chlorine detection, and one nanocomposite planar electrode for sensing electrochemical oxygen demand. A previous study addressed to classify water samples according to taste/smell descriptors (sweet, acidic, salty, bitter, medicinal, chlorinous, mouldy, and earthy) was performed. A second study comparing the results of two organoleptic tests (hedonic evaluation and ranking test) with the electronic tongue, using Partial Least Squares regression, was conducted. The results show that the proposed electronic tongue is capable of analyzing water samples according to their organoleptic characteristics, which can be used as an alternative method to the taste panel.

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.

Strategies to develop robust neural network models: Prediction of flash point as a case study

Artificial neural network (ANN) is one of the most widely used methods to develop accurate predictive models based on artificial intelligence and machine learning. In the present study, the important practical aspects of developing a reliable ANN model e.g. appropriate assignment of the number of neurons, number of hidden layers, transfer function, training algorithm, dataset division and initialization of the network are discussed. As a case study, predictability of the flash point for a dataset of 740 organic compounds using ANNs was investigated via a total number of 484220ANNs to allow covering a wide range of parameters affecting the performance of an ANN. Among all studied parameters, the number of neurons or layers was found to be the most important parameters to develop a reliable ANN with low overfitting risk. To evaluate appropriate number of neurons and layers, a value of equal or greater than 10 for the ratio of the training samples to the ANN constants was suggested as a rule of thumb. More ever, a strategy for evaluation of the authentic performance of ANNs and deciding about the reliability of an ANN model was proposed which is applicable to other models developed by supervised learning. Based on the introduced considerations, an ANN model was proposed for predicting the flash point of pure organic compounds. According to the results, the new model was found to produce the lowest error compared to other available models.

Prediction of parameters related to grape ripening by multivariate calibration of voltammetric signals acquired by an electronic tongue

An electronic tongue (ET) consisting of two voltammetric sensors, namely a poly-ethylendioxythiophene modified Pt electrode and a sonogel carbon electrode, has been developed aiming at monitoring grape ripening. To test the effectiveness of device and measurement procedures developed, samples of three varieties of grapes have been collected from veraison to harvest of the mature grape bunches. The derived musts have been then submitted to electrochemical investigation using Differential Pulse Voltammetry technique. At the same time, quantitative determination of specific analytical parameters for the evaluation of technological and phenolic maturity of each sample has been performed by means of conventional analytical techniques. After a preliminary inspection by principal component analysis, calibration models were calculated both by partial least squares (PLS) on the whole signals and by the interval partial least squares (iPLS) variable selection algorithm, in order to estimate physico-chemical parameters. Calibration models have been obtained both considering separately the signals of each sensor of the ET, and by proper fusion of the voltammetric data selected from the two sensors by iPLS. The latter procedure allowed us to check the possible complementarity of the information brought by the different electrodes. Good predictive models have been obtained for estimation of pH, total acidity, sugar content, and anthocyanins content. The application of the ET for fast evaluation of grape ripening and of most suitable harvesting time is proposed.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste.

Data fusion methodologies for food and beverage authentication and quality assessment - a review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.