A novel method for classification of wine based on organic acids

Correlation analysis between quality characteristics and rhizosphere microorganisms of different wine grape varieties during their ripening phase

Wine grapes are the raw material used in wine brewing. The soil microenvironment is regulated by plant rhizosphere microorganisms, which can have a direct or indirect impact on plant growth and development. The population distribution of rhizosphere soil and endophytic microorganisms of Cabernet Sauvignon, Merlot, and Pinot Noir was investigated in this study utilizing high-throughput sequencing technology in relation to the characteristics of wine quality during the ripening phase. The results showed that the community composition of dominant fungi and bacteria in the rhizospheric soil of the three wine grapes varieties was similar at the phylum level. The microbial richness of Cabernet Sauvignon rhizosphere soil was higher than that of Merlot and Pinot Noir, and the bacterial community structure of various wine grape rhizosphere soil varied at the genus level. There were more differential microorganisms in rhizosphere soil than endophytic microorganisms. At the phylum level, malic acid correlated favorably with Mortierellomycota, while flavonol in the fruit peel and flesh of wine grapes correlated favorably with Aphelidiomyceta and Calcarisporiellomycota in rhizosphere soil fungi; The fruit peel's malic acid showed a negative correlation with the soil bacterial community's verrucomicrobiota, while the fruit flesh's succinic and oxalate acids showed a favorable correlation. Proanthocyanidin in wine grape fruit flesh positively correlated with several fungal genera in rhizosphere soil at the genus level, including Hydnocystis, Schizothecium. Additionally, there was more negative correlation than positive correlation between wine grape quality and soil bacterial community. Several endophytic fungal communities showed good correlations with the proanthocyanidin in wine grapefruit flesh. The fruit peel's ascorbic acid, phenolics, and tannins showed a favorable correlation with rhizosphere endophytic bacteria that were highly abundant at the genus level. However, some endophytic bacteria negatively correlated with malic acid in the fruit flesh. This study provides new ideas and theoretical support for improving the quality of grapes for winemaking.

Intelligent Screening of Prostate Cancer Individuals Using an Enzyme-Assisted Multicolor Visualization Platform

Rapid and intelligent identification of prostate cancer (PCa) is critical for early diagnosis. Herein, a convenient, reliable, and intelligent strategy is proposed to screen PCa individuals through indirectly quantifying sarcosine (Sar), an early indicator of PCa, in clinical urine samples. Success is achieved by integrating sarcosine oxidase (SOX) as a specific recognition unit; nanozyme-assisted multicolor intelligent visualization platform as a signal reporter. With the Fe-MOFs and peroxidase, the synergetic action of SOX and response gold nanorods (Au NRs) is controlled etched to exhibit a multicolored signal. The sensor exhibits excellent linearity with Sar within 1-60 × 10-6 m, boasting a remarkable detection limit of 0.12 × 10-6 m. The RGB value of the display color can be directly extracted using a mobile phone camera. PCa diagnosis can be swiftly made (within 15 min) and directly by identifying two RGB colors (R < 175 or B > 135). The enzyme-assisted multicolor intelligent visualization platform is adept at detecting minute differences in Sar concentration in urine samples between PCa patients and healthy individuals. The concept of enzyme-assisted multicolor sensing can be further expanded by modifying the type of immobilized enzymes, providing a valuable guideline for the rational design of multiple probes to measure specific biomarkers in biological samples.

The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants

Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, the rapid expansion of global food trade has made people's pursuit of high-quality food more urgent. However, traditional methods of food analysis have certain limitations, mainly manifested in the high degree of reliance on personal subjective judgment for assessing food quality. In this context, the emergence of artificial intelligence and biosensors has provided new possibilities for the evaluation of food quality. This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models to highlight the effectiveness and comprehensiveness of artificial intelligence and biosensors in food quality evaluation. The potential prospects and challenges of this method in the field of food safety are comprehensively discussed, aiming to provide valuable references for future research and practice.

Preliminary characterization of chemical and sensory attributes for grapes and wines of different cultivars from the Weibei Plateau region in China

Chemical and sensory attributes play a vital role in evaluating the quality of grapes and wines. This study compared basic physicochemical parameters, organic acids, phenolic compounds, and aroma profiles of grapes and wines of six cultivars using chemometrics. The results showed that the reducing sugar contents of Beibinghong, Gongniang, and Granoir grapes were significantly higher than those of others cultivars, whereas their juice yields were significantly lower. The phenolic compound contents in Moldova, Beibinghong, and Gongniang grape skins and wines were higher than those in others cultivars. The organic acid contents in Beibinghong grape and Dunkelfelder wine were highest. Beibinghong and Gongniang grapes and wines showed richer aldehyde and ester concentrations. Beibinghong wine obtained the highest sensory scores. Ethyl decanoate, coumaric acid, and methyl dodecanoate were characteristic variables distinguishing wine cultivars, exhibiting important contributions to their sensory characteristics. These findings were useful for viticulturists and winemakers to select grape varieties.

Simultaneous Analysis of Organic Acids, Glycerol and Phenolic Acids in Wines Using Gas Chromatography-Mass Spectrometry

Fermented beverages, particularly wines, exhibit variable concentrations of organic and phenolic acids, posing challenges in their accurate determination. Traditionally, enzymatic methods or chromatographic analyses, mainly high-performance liquid chromatography (HPLC), have been employed to quantify these compounds individually in the grape must or wine. However, chromatographic analyses face limitations due to the high sugar content in the grape must. Meanwhile, phenolic acids, found in higher quantities in red wines than in white wines, are typically analyzed using HPLC. This study presents a novel method for the quantification of organic acids (OAs), glycerol, and phenolic acids in grape musts and wines. The approach involves liquid-liquid extraction with ethyl acetate, followed by sample derivatization and analysis using gas chromatography-mass spectrometry (GC-MS) in selected ion monitoring (SIM) detection mode. The results indicated successful detection and quantification of all analyzed compounds without the need for sample dilution. However, our results showed that the method of adding external standards was more suitable for quantifying wine compounds, owing to the matrix effect. Furthermore, this method is promising for quantifying other metabolites present in wines, depending on their extractability with ethyl acetate. Fermented beverages, particularly wines, exhibit variable concentrations of organic and phenolic acids, posing challenges in their accurate determination. Traditionally, enzymatic methods or chromatographic analyses, mainly high-performance liquid chromatography (HPLC), have been employed to quantify these compounds individually in the grape must or wine. The approach of this proposed method involves (i) methoximation of wine compounds in a basic medium, (ii) acidification with HCl, (iii) liquid-liquid extraction with ethyl acetate, and (iv) silyl derivatization to analyze samples with gas chromatography-mass spectrometry (GC-MS) in ion monitoring detection mode (SIM). The results indicated successful detection and quantification of all analyzed compounds without the need for sample dilution. However, our results showed that the method of adding external standards was more suitable for quantifying wine compounds, owing to the matrix effect. Furthermore, this method is promising for quantifying other metabolites present in wines, depending on their extractability with ethyl acetate. In other words, the proposed method may be suitable for profiling (targeted) or fingerprinting (untargeted) strategies to quantify wine metabolites or to classify wines according to the type of winemaking process, grape, or fermentation.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Recent Advances in Electrochemical Enzyme-Based Biosensors for Food and Beverage Analysis

Food analysis and control are crucial aspects in food research and production in order to ensure quality and safety of food products. Electrochemical biosensors based on enzymes as the bioreceptors are emerging as promising tools for food analysis because of their high selectivity and sensitivity, short analysis time, and high-cost effectiveness in comparison to conventional methods. This review provides the readers with an overview of various electrochemical enzyme-based biosensors in food analysis, focusing on enzymes used for different applications in the analysis of sugars, alcohols, amino acids and amines, and organic acids, as well as mycotoxins and chemical contaminants. In addition, strategies to improve the performance of enzyme-based biosensors that have been reported over the last five years will be discussed. The challenges and future outlooks for the food sector are also presented.

Aggregation Interface and Rigid Spots Sustain the Stable Framework of a Thermophilic N-Demethylase

Enzymes from thermophilic microorganisms usually show high thermostability, which is of great potential in industrial application; to understand the structural logic of these enzymes is helpful for the construction of robust biocatalysts. In this study, based on the crystal structure of an N-demethylase─TrSOX─with outstanding thermostability from Thermomicrobium roseum, substitutions were introduced on the aggregation interface and rigid spots to reduce the aggregation ratio and the rigidity. Four substitutions on the aggregation interface─V162S, M308S, F170S, and V306S─considerably reduced the thermostability and slightly enhanced the catalytic efficiency. In addition, the thermostable framework was considerably disrupted in several multiple P → G substitutions in several local motifs (P129G/P134G, P237G/P259G, and P259G/P276G). These structural fluctuations were in good accordance with whole-structure or partial root-mean-square deviation, radius of gyration H-bonds, and solvent-accessible surface area values in molecular dynamics simulation. Furthermore, these key spots were introduced into an unstable homolog from Bacillus sp., resulting in a dramatical increase in the half-life at 60 °C from <10 to 1440 min. These results could help understand the natural stable framework of thermophilic enzymes, which could be references for the construction of robust enzymes in industrial applications.

A fast, low-cost, sensitive, selective, and non-laborious method based on functionalized magnetic nanoparticles, magnetic solid-phase extraction, and fluorescent carbon dots for the fluorimetric determination of copper in wines without prior sample treatment

A new fluorimetric method for copper(II) determination in wines was developed combining functionalized magnetic nanoparticles (FMNP) and fluorescent carbon dots (FCD). To produce FMNP, Fe₃O₄ was coated with Al₂O₃ forming Fe₃O₄@Al₂O₃ core-shell magnetic nanoparticles and functionalized with PAN and SDS. FCD was synthesized from pineapple juice through hydrothermal carbonization. For copper determination, aliquots of wine, the FMNP dispersion, and Britton-Robinson buffer (pH = 4.0) were mixed under stirring to allow the adsorption of copper by FMNP. Cu-FMNP complex was attracted by a niobium magnet and, after discarding the non-magnetic material, the copper(II) ions were eluted with an FCD dispersion before fluorescence quenching measurements. The proposed method presented a linear range from 0.020 to 0.100 mg L^-1 (r² = 0.9953), RSD (intraday) < 3.0%, and recovery rates from 96 to 105 %. FMNP and FCD properties permitted extraction/preconcentration/determination of copper within 1 min with an enrichment factor of nine and without prior sample treatment.

Identification and characterization of volatile compounds in Lentinula edodes during vacuum freeze-drying

In this study, modified headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) and electronic nose (E-nose) were utilized to investigate the dynamic aroma changes of Lentinula edodes (L. edodes) at different stages of vacuum freeze drying (VFD). The extraction efficiency of volatile compounds from vacuum freeze-dried L. edodes was improved by optimizing five parameters of the HS-SPME. A total of 50 volatiles were identified in L. edodes from different VFD stages by GC-MS. Alcohols, aldehydes, and volatile sulfur-containing compounds (VSCs) were the main flavor constituents of fresh L. edodes, frozen L. edodes, and secondary dried L. edodes. Aldehydes, ketones, and VSCs were the main aroma groups in L. edodes after primary drying. There were 20 volatiles as key odorants with the odor activity values greater than 1, in which esters appeared only before secondary drying of L. edodes. These findings could contribute to a comprehensive insight into the formation mechanism of flavor in the VFD process of L. edodes. PRACTICAL APPLICATIONS: Lentinula edodes is the second most widely cultivated edible fungus worldwide. It is considered a valuable health food not just because of its abundance of nutrients but also because of its delicious taste. This study investigated the regularity regarding the changes of volatile compounds in L. edodes during vacuum freeze drying. The results of the present study offer valuable knowledge for the formation mechanism of volatile substances in the drying process of L. edodes, which can be beneficial to promote the development and utilization of flavor substances in L. edodes.

Progress in pretreatment and analysis of organic Acids: An update since 2010

Organic acids, as an important component of food, have great influence on the flavor, texture, freshness of food. By lowering the pH of food to bacteriostatic acidity, organic acids are also used as additives and preservatives. Because organic acids are crucial to predict and evaluate food maturity, production and quality control, the rapid and sensitive determination methods of organic acids are necessary. This review aims to summarize and update the progress of the determination of organic acids in food samples. Pretreatment methods include simple steps (e.g., "dilute and shoot," protein precipitation, filtration, and centrifugation) and advanced microextraction methods (e.g., hollow fiber liquid phase microextraction, stir bar sorptive extraction and dispersive micro-solid phase extraction). Advances in novel materials (nanomaterial), solvents (ionic liquids and supercritical fluids) and hybrid methods are clearly displayed in detail. Continuous progress which has been made in electrochemical method, two-dimensional chromatography, high resolution mass is thoroughly illustrated.

Addressing the Selectivity of Enzyme Biosensors: Solutions and Perspectives

Enzymatic biosensors enjoy commercial success and are the subject of continued research efforts to widen their range of practical application. For these biosensors to reach their full potential, their selectivity challenges need to be addressed by comprehensive, solid approaches. This review discusses the status of enzymatic biosensors in achieving accurate and selective measurements via direct biocatalytic and inhibition-based detection, with a focus on electrochemical enzyme biosensors. Examples of practical solutions for tackling the activity and selectivity problems and preventing interferences from co-existing electroactive compounds in the samples are provided such as the use of permselective membranes, sentinel sensors and coupled multi-enzyme systems. The effect of activators, inhibitors or enzymatic substrates are also addressed by coupled enzymatic reactions and multi-sensor arrays combined with data interpretation via chemometrics. In addition to these more traditional approaches, the review discusses some ingenious recent approaches, detailing also on possible solutions involving the use of nanomaterials to ensuring the biosensors' selectivity. Overall, the examples presented illustrate the various tools available when developing enzyme biosensors for new applications and stress the necessity to more comprehensively investigate their selectivity and validate the biosensors versus standard analytical methods.

Novel Nondestructive Biosensors for the Food Industry

An increasing number of foodborne outbreaks, growing consumer desire for healthier products, and surging numbers of food allergy cases necessitate strict handling and screening of foods at every step of the food supply chain. Current standard procedures for detecting food toxins, contaminants, allergens, and pathogens require costly analytical devices, skilled technicians, and long sample preparation times. These challenges can be overcome with the use of biosensors because they provide accurate, rapid, selective, qualitative, and quantitative detection of analytes. Their ease of use, low-cost production, portability, and nondestructive measurement techniques also enable on-site detection of analytes. For this reason, biosensors find many applications in food safety and quality assessments. The detection mechanisms of biosensors can be varied with the use of different transducers, such as optical, electrochemical, or mechanical. These options provide a more appropriate selection of the biosensors for the intended use. In this review, recent studies focusing on the fabrication of biosensors for food safety or food quality purposes are summarized. To differentiate the detection mechanisms, the review is divided into sections based on the transducer type used.

Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs

The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.

A Knowledge-Based System as a Sustainable Software Application for the Supervision and Intelligent Control of an Alcoholic Fermentation Process

One goal of specialists in food processing is to increase production efficiency in accordance with sustainability by optimising the consumption of raw food materials, water, and energy. One way to achieve this purpose is to develop new methods for process monitoring and control. In the winemaking industry, there is a lack of procedures regarding the common work based on knowledge acquisition and intelligent control. In the present article, we developed and tested a knowledge-based system for the alcoholic fermentation process of white winemaking while considering the main phases: the latent phase, exponential growth phase, and decay phase. The automatic control of the white wine’s alcoholic fermentation process was designed as a system on three levels. Level zero represents the measurement and adjustment loops of the bioreactor. At the first level of control, the three phases of the process are detected functions of the characteristics of the fermentation medium (the initial substrate concentration, the nitrogen assimilable content, and the initial concentration of biomass) and, thus, functions on the phase’s duration. The second level achieves the sequence supervision of the process (the operation sequence of a fermentation batch) and transforms the process into a continuous one. This control level ensures the quality of the process as well as its diagnosis. This software application can be extended to the industrial scale and can be improved by using further artificial intelligence techniques.

Effect of Saccharomyces cerevisiae, Torulaspora delbrueckii and malolactic fermentation on fermentation kinetics and sensory property of black raspberry wines

Alcoholic fermentation (AF) and malolactic fermentation (MLF) both have significant influence on the production of black raspberry wine. In this study, three microbes associated with AF and MLF including S. cerevisiae, T. delbrueckii and O. oeni were used to investigate their combined effect on basic compositional, volatile and sensory property of black raspberry wine, and four fermentation trials including single S. cerevisiae inoculation plus spontaneous MLF (BSU) and controlled MLF with O. oeni (BSO), sequential culture of T. delbrueckii and S. cerevisiae plus spontaneous MLF (BTSU) and controlled MLF (BTSO) were tested and compared. Fermentation results showed MLF in BSU, BSO and BTSO were successful, with respective period of 40, 25 and 23 days, whereas a stuck MLF occurred in BTSU. Volatile compounds were determined by HS-GC-IMS method, with a total of 45 aromas identified. BTSO was distinguished by a significant higher signal intensity of many fruity esters and a lower production of several alcohols and terpenes, which was in agreement with its perception result of strong 'fruity' and slight note of 'solvent' and 'herbaceous' during quantitative descriptive analysis. On the contrary, BSU was found to reinforce the synthesis of most detected volatiles, resulting in the enhancement of both beneficial and off-flavour compounds, therefore scoring lower in the 'global aroma' descriptor. Principal component analysis showed BSU and BSO were similar in the volatile composition, whereas BTSO was quite different. Overall, BTSO had greater potential to be used in the production of black raspberry wine.

A colorimetric sensor array based on natural pigments for the discrimination of saccharides

A colorimetric sensor array based on natural pigments was developed to discriminate between various saccharides. Anthocyanins, pH-sensitive natural pigments, were extracted from fruits and flowers and used as components of the sensor array. Variation in pH, due to the reaction between saccharides and boronic acids, caused obvious colour changes in the natural pigments. Only by observing the difference map with the naked eye could 11 common saccharides be divided into independent individuals. In conjunction with pattern recognition, the sensor array clearly differentiated between sugar and sugar alcohol with highly accuracy and allowed rapid quantification of different concentrations of maltitol and fructose. This sensor array for saccharides is expected to become a promising alternative tool for food monitoring. The link between anthocyanin and saccharide detection opened a new guiding direction for the application of anthocyanins in foods.

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

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.