Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model

Review of Applications of Cyclodextrins as Taste-Masking Excipients for Pharmaceutical Purposes

It is widely recognized that many active pharmaceutical ingredients (APIs) have a disagreeable taste that affects patient acceptability, particularly in children. Consequently, developing dosage forms with a masked taste has attracted a lot of interest. The application of cyclodextrins as pharmaceutical excipients is highly appreciated and well established, including their roles as drug delivery systems, solubilizers and absorption promoters, agents that improve drug stability, or even APIs. The first work describing the application of the taste-masking properties of CDs as pharmaceutical excipients was published in 2001. Since then, numerous studies have shown that these cyclic oligosaccharides can be effectively used for such purposes. Therefore, the aim of this review is to provide insight into studies in this area. To achieve this aim, a systematic evaluation was conducted, which resulted in the selection of 67 works representing both successful and unsuccessful works describing the application of CDs as taste-masking excipients. Particular attention has been given to the methods of evaluation of the taste-masking properties and the factors affecting the outcomes, such as the choice of the proper cyclodextrin or guest-host molar ratio. The conclusions of this review reveal that the application of CDs is not straightforward; nevertheless, this solution can be an effective, safe, and inexpensive method of taste masking for pharmaceutical purposes.

Evaluating and forecasting the associated main flavor components in Baijiu (Chinese distilled spirits) with alcohol metabolism and hangover symptoms through mice acute withdrawal model

Over the past few decades, more alcohol-problem concerns focused on reducing the risk of hangover caused by the alcoholic beverages over-consumption. Chinese distilled spirits (Baijiu) is one of the most favorite alcoholic beverages. The intention of this study is to explore the associations of main flavor components in Baijiu and hangover symptoms using mice acute alcohol withdrawal model. The behaviors of each mouse were assessed by open-field tests using separate groups of mice with the treatment of sauce-aroma Baijiu, light-aroma Baijiu, strong-aroma Baijiu, pure alcohol, and distilled water, respectively. The behavioral data including total move distance and immobile time were used as indicators for the evaluation of the liquor intoxicating effects. Alcohol and acetaldehyde concentrations in mice plasma and the neurotransmitter contents of GABA and Glu in mice cerebellum were detected afterward. The results showed that the mice with the treatment of Baijiu samples displayed unusual exciting behaviors including increased alcohol metabolization with alleviating drunken and hangover symptoms, compared with that of pure alcohol control groups after 2-4 h. Moreover, the sauce-aroma Baijiu treatment group showed lessening intoxicated symptoms than those of light-aroma Baijiu and strong-aroma Baijiu. In addition, there were significant differences between Baijiu and pure alcohol treatment groups at the inhibitory neurotransmitter GABAergic levels and its receptor GABA-AR1 activating levels in the mice neuron cells. Furthermore, the Principal Component Analysis (PCA) analysis inferred that the flavor compounds acetic acid, ethyl acetate, ethyl lactate, and 1-propanol in the sauce-aroma Baijiu were played the major roles in the drunk behaviors that caused by the hangover. While, the acetic acid in the sauce-aroma Baijiu was speculated as a major flavor component to accelerate the alcohol metabolism and retard hangover symptoms.

No ambiguity: Chemosensory-based ayurvedic classification of medicinal plants can be fingerprinted using E-tongue coupled with multivariate statistical analysis

Background: Ayurveda, the indigenous medical system of India, has chemosensory property (rasa) as one of its major pharmacological metric. Medicinal plants have been classified in Ayurveda under six rasas/tastes—sweet, sour, saline, pungent, bitter and astringent. This study has explored for the first time, the use of Electronic tongue for studies of rasa-based classification of medicinal plants.

Methods: Seventy-eight medicinal plants, belonging to five taste categories (sweet, sour, pungent, bitter, astringent) were studied along with the reference taste standards (citric acid, hydrochloric acid, caffeine, quinine, L-alanine, glycine, β-glucose, sucrose, D-galactose, cellobiose, arabinose, maltose, mannose, lactose, xylose). The studies were carried out with the potentiometry-based Electronic tongue and the data was analysed using Principle Component Analysis, Discriminant Function Analysis, Taste Discrimination Analysis and Soft Independent Modeling of Class Analogy.

Results: Chemosensory similarities were observed between taste standards and the plant samples–citric acid with sour group plants, sweet category plants with sucrose, glycine, β-glucose and D-galactose. The multivariate analyses could discriminate the sweet and sour, sweet and bitter, sweet and pungent, sour and pungent plant groups. Chemosensory category of plant (classified as unknown) could also be identified.

Conclusion: This preliminary study has indicated the possibility of fingerprinting the chemosensory-based ayurvedic classification of medicinal plants using E-tongue coupled with multivariate statistical analysis.

E-Senses, Panel Tests and Wearable Sensors: A Teamwork for Food Quality Assessment and Prediction of Consumer’s Choices

At present, food quality is of utmost importance, not only to comply with commercial regulations, but also to meet the expectations of consumers; this aspect includes sensory features capable of triggering emotions through the citizen’s perception. To date, key parameters for food quality assessment have been sought through analytical methods alone or in combination with a panel test, but the evaluation of panelists’ reactions via psychophysiological markers is now becoming increasingly popular. As such, the present review investigates recent applications of traditional and novel methods to the specific field. These include electronic senses (e-nose, e-tongue, and e-eye), sensory analysis, and wearables for emotion recognition. Given the advantages and limitations highlighted throughout the review for each approach (both traditional and innovative ones), it was possible to conclude that a synergy between traditional and innovative approaches could be the best way to optimally manage the trade-off between the accuracy of the information and feasibility of the investigation. This evidence could help in better planning future investigations in the field of food sciences, providing more reliable, objective, and unbiased results, but it also has important implications in the field of neuromarketing related to edible compounds.

Electronic Sensor Technologies in Monitoring Quality of Tea: A Review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea.

Method of carrier frequency arrangement for suppressing the adjacent channel interference caused by camera nonlinearity during LED-multispectral imaging

Using a camera and frequency-division modulation to obtain LED-multispectral images has the advantages of fast speed and low cost. However, when the carrier frequency is not arranged properly, the nonlinearity of the camera causes harmonic interference, which affects the signal quality of adjacent channels. This paper proposes a scheme for carrier frequency arrangement according to the high frequency, which is four times as high as the adjacent low frequency; that is f_n+1=4f_n,(n=1,2,3,…). The experimental result shows that the scheme can suppress the interference among carrier channels. In addition, the high-speed, phase-locked algorithm is used to demodulate the images, saving about 97.73% of the time compared to the traditional demodulation algorithm. The method not only realizes the fast acquisition of LED-multispectral images, but also improves the demodulation quality and speed of the images. It also provides a reference for a camera to obtain high-quality, LED-multispectral images.

A novel method for selecting the set optimal wavelength combination in multi-spectral transmission image

In the process of detecting heterogeneity in breast tissue based on multi-spectral transmission imaging, the detection accuracy will be affected due to the high redundancy degree of information between bands. In order to select the reasonable wavelength combination, this paper uses various nonlinear transformations to convert the multi-spectral images into spectral data for the first time, so as to select the set optimal wavelength combination based on the successive projections algorithm (SPA). Firstly, we design the collection experiment of 4-wavelength multi-spectral image. And then, K-SVD dictionary learning method, texture extraction method and gray correlation analysis method are used to obtain the feature spectral information. Finally, the set optimal wavelength combination is selected based on SPA. The experimental results show that random forest (RF) classification model and Faster-RCNN recognition models effectively verify that the combination of wavelengths 1,2,4 selected has the highest accuracy in the heterogeneous detection. In conclusion, this paper uses modulation-frame accumulation technique to improve the quality of multi-spectral transmission images. And based on the RF and Faster-RCNN models, the effectiveness of SPA-based optimal wavelength combination method proposed is verified, which will provide a new idea of feature wavelength selection for screening early breast masses through multi-spectral transmission imaging.

Apple quality identification and classification by image processing based on convolutional neural networks

This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on convolutional neural networks (CNN) which aimed at accurate and fast grading of apple quality. Specific, complex, and useful image characteristics for detection and classification were captured by the proposed model. Compared with existing methods, the proposed model could better learn high-order features of two adjacent layers that were not in the same channel but were very related. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. The overall accuracy of the proposed model tested using an independent 300 apple dataset was 95.33%. The results showed that the training accuracy, overall test accuracy and training time of the proposed model were better than Google Inception v3 model and traditional imaging process method based on histogram of oriented gradient (HOG), gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The proposed model has great potential in Apple’s quality detection and classification.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

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.

Higher precision integer operations instead of floating-point operations in computers or microprocessors

The rounding errors of floating-point operations are inevitable in computers or microprocessors, and this issue will make the calculation results inaccurate, unreliable, or even completely incorrect. For this purpose, this paper proposes to replace floating-point operations with integer operations to improve the operation precision. The key lies in not only controlling the variable type as the integer to avoid the automatic conversion of intermediate operation results into floating-point numbers but also converting floating-point operations in the operation process into integer operations using some numerical calculation methods. Lock-in amplifier is one of the most widely used instruments in the field of weak signal detection. This paper only takes the digital lock-in amplifier (DLIA) as an example for detailed analysis and proposes a DLIA based on integer calculation. The experimental results show that replacing floating-point operations with integer operations can obtain higher operation precision without "wasting" memory, and the improvement will be more significant as the calculation amount increases. The research will help to further improve the calculation accuracy of digital signal processing and other scientific computations in computers or microprocessors.

Challenges in using electronic tongue to study rasa of plants: I. Finding the right tool for the right job

There is growing interest in understanding how ayurveda, the indigenous medical system of India, uses plants for therapeutic purpose. The aim of this two parts article is to explore how the analytical technique of Electronic tongue (E-tongue) can be used for studying rasa, one of the major ayurvedic parameter in the study of medicinal and nutritional plants. Although E-tongue is widely used in pharmaceutical, food and beverage industries for objective evaluation of taste, its use in plants from an ayurvedic perspective is novel. This first of the two parts article introduces the technique of E-tongue, positioning it in context for the following part. The latter discusses crucial control experiments required prior using E-tongue for studies on medicinal plants from an ayurvedic standpoint.