An Analysis of pH and Sugar Content of Commonly Prescribed Pediatric Liquid Medications: The Current Indian Scenario.

OBJECTIVES: Oral liquid medications are frequently prescribed to children because they are easier to swallow than other dosage forms. These pediatric liquid medications (PLMs) have sugars added to them for better compliance or as preservatives. Children with chronic illnesses may frequently consume these medications. The presence of sugars and their frequent exposure presents a high risk of dental caries in these children. Additionally, the critical pH can be reached if acids below a pH of 5.5 contact the tooth, causing enamel demineralization. Hence, there was a need to study the sugar content and pH of these medications. METHODS: Pediatricians and pharmacists in Vadodara city, Gujarat, India, were given a short questionnaire to assess the most prescribed and sold PLMs for analgesics, antibiotics, antiepileptics, multivitamins, and antitussives in the Indian pharmaceutical market. The sugar content and pH of the 15 most prescribed PLMs were assessed with ultraviolet/visible (UV/VIS) spectrophotometry and digital pH meter, respectively. Descriptive statistics were used to analyze the data. RESULTS: Only 1 of the 15 most sold/prescribed medicines did not contain sugar. Among the remaining PLMs, the sugar concentration ranged from 6.1% to 78.7%. The pH of the PLM ranged from 3.6 to 7.3. CONCLUSION: Sugar was present in 93.3% of the 15 analyzed PLMs and the pH was lower than the critical pH in 80% of them. Medications with high sugar content and low pH can cause caries development. Sugar-free PLMs are preferred alternatives.

Assessment of waterlogging-induced changes in enzymatic antioxidants and carbohydrate metabolism in peanuts genotypes.

Soil flooding, manifesting as submergence or waterlogging stress, significantly impacts plant species composition and agricultural productivity, particularly in regions with low rainfall. This study investigates the biochemical responses of two peanut (Arachis hypogaea L.) genotypes, DH-86 and GJG-32, under waterlogging stress. The experiment involved in-vivo pot trials where peanut plants were subjected to continuous waterlogging for 12 days at the flowering stage. Biochemical analyses of leaves conducted and revealed significant alterations in enzyme activities and metabolite concentrations. Key findings include variations in superoxide dismutase (SOD), catalase (CAT), guaiacol peroxidase (GPOD), α-amylase, invertase, acid phosphomonoesterase activities, and changes in starch, proline, reducing sugars, and chlorophyll content. SOD, CAT, and GPOD activities exhibited differential responses between genotypes, highlighting DH-86's quicker recovery post-waterlogging. Notably, DH-86 demonstrated higher resilience, reflected in its rapid normalization of biochemical parameters, while GJG-32 showed prolonged stress effects. These findings underscore the importance of antioxidative enzyme systems in mitigating oxidative damage induced by waterlogging. This study enhances our understanding of the biochemical adaptations of peanut genotypes to waterlogging stress, offering valuable insights for breeding programs focused on improving flood tolerance in crops.

[Microbial production of food compounds with carbon dioxide and derived low-carbon molecules as substrates].

The construction and optimization of microbial cell factories are crucial steps and key technologies in achieving green biomanufacturing. As concern has been aroused regarding the excessive carbon dioxide (CO2) emissions and food security, a new and promising research field, microbial conversion of CO2 into food compounds, has emerged. The research in this field not only holds significant implications for achieving the carbon peaking and carbon neutrality goals but also plays a role in maintaining food security. This paper provides a comprehensive review and outlook of the research on utilizing CO2 and its derived low-carbon chemicals for the production of food compounds, focusing on the production of glucose, sugar derivatives, and single-cell proteins and the development of artificial CO2 fixation pathways.

The relationship between dietary sugar consumption and anxiety disorders: A systematic review.

High-sugar intake is a risk factor for chronic diseases such as cardiovascular disease and type 2 diabetes, but less is known about its role in anxiety disorders. This systematic review aimed to systematically synthesise and assess the existing evidence regarding the association between dietary sugars intake and anxiety disorders. Following PRISMA guidelines, a systematic search of PubMed, MEDLINE, Embase, APA PsycArticles and APA PsycINFO was conducted up to 19th August 2022. Study quality was assessed by the Newcastle-Ottawa scale (NOS) and the Cochrane risk of bias tool. Eleven studies (10 cross-sectional and 1 randomised controlled trial [RCT]) were included. Seven cross-sectional studies had very good quality or good quality, and the quality of the RCT was at low risk of bias. These studies examined sugar-sweetened beverages (n = 7), sugar-sweetened foods (n = 4) and/or added sugar (n = 5). The findings suggest a possible positive relationship of added sugar consumption with anxiety disorders, with age as a potential moderator in such association. No conclusions can be drawn on the associations between sugar-sweetened beverages, sugar-sweetened foods consumption and anxiety disorders. Due to the included studies being mostly cross-sectional, the conclusions drawn from the existing evidence should be interpreted with caution. The longitudinal design is warranted to investigate any causal relationship and the potential mechanisms underlying these heterogeneous results. The potential difference in effect at different ages observed in this review should be further examined.

The frequency and chemical phenotype of neighboring plants determine the effects of intraspecific plant diversity.

Associational effects, whereby plants influence the biotic interactions of their neighbors, are an important component of plant-insect interactions. Plant chemistry has been hypothesized to mediate these interactions. The role of chemistry in associational effects, however, has been unclear in part because the diversity of plant chemistry makes it difficult to tease apart the importance and roles of particular classes of compounds. We examined the chemical ecology of associational effects using backcross-bred plants of the Solanum pennellii introgression lines. We used eight genotypes from the introgression line system to establish 14 unique neighborhood treatments that maximized differences in acyl sugars, proteinase inhibitor, and terpene chemical diversity. We found that the chemical traits of the neighboring plant, rather than simply the number of introgression lines within a neighborhood, influenced insect abundance on focal plants. Furthermore, within-chemical class diversity had contrasting effects on herbivore and predator abundances, and depended on the frequency of neighboring plant chemotypes. Notably, we found insect mobility-flying versus crawling-played a key role in insect response to phytochemistry. We highlight that the frequency and chemical phenotype of plant neighbors underlie associational effects and suggest this may be an important mechanism in maintaining intraspecific phytochemical variation within plant populations.

Deep convolutional neural network-based 3D fluorescence sensor array for sugar identification in serum based on the oxidase-mimicking property of CuO nanoparticles.

Developing sensor arrays capturing comprehensive fluorescence (FL) spectra from a single probe is crucial for understanding sugar structures with very high similarity in biofluids. Therefore, the analysis of highly similar sugar' structures in biofluids based on the entire FL of a single nanozyme probe needs more concern, which makes the development of novel alternative approaches highly wanted for biomedical and other applications. Herein, a well-designed deep learning model with intrinsic information of 3D FL of CuO nanoparticles (NPs)' oxidase-like activity was developed to classify and predict the concentration of a group of sugars with very similar chemical structures in different media. The findings presented that the overall accuracy of the developed model in classifying the nine selected sugars was (99-100 %), which prompted us to transfer the developed model to predict the concentration of the selected sugars at a concentration range of (1-100 µM). The transferred model also gave excellent results (R2 = 97-100 %). Therefore, the model was extended to other more complex applications, namely the identification of mixtures of sugars in serum and the detection of polysaccharides in different media such as serum and lake water. Notably, LOD for fructose was determined at 4.23 nM, marking a 120-fold decrease compared to previous studies. Our developed model was also compared with other deep learning-based models, and the results have demonstrated remarkable progress. Moreover, the identification of other possible coexisting interference substances in lake water samples was considered. This work marks a significant advancement, opening avenues for the widespread application of sensor arrays integrating nanozymes and deep learning techniques in biomedical and other diverse fields.

The Effect of Allulose on the Attenuation of Glucose Release from Rice in a Static In Vitro Digestion Model.

Allulose is a rare sugar that provides <10% of the energy but 70% of the sweetness of sucrose. Allulose has been shown to attenuate glycemic responses to carbohydrate-containing foods in vivo. This study aimed to determine the optimal allulose dose for minimizing in vitro glucose release from rice compared to a rice control and fructose. A triphasic static in vitro digestion method was used to evaluate the in vitro digestion of a rice control compared to the co-digestion of rice with allulose (10 g, 20 g, and 40 g) and fructose (40 g). In vitro glucose release was affected by treatment (p < 0.001), time (p < 0.001), and treatment-by-time interaction (p = 0.002). Allulose (40 g) resulted in a reduction in in vitro glucose release from rice alone and rice digested with allulose (10 g), allulose (20 g), and fructose. The incremental area under the curve (iAUC) for in vitro glucose release was lower after allulose (40 g) (p = 0.005) compared to rice control and allulose (10 g) but did not differ from allulose (20 g) or fructose. This study demonstrates that allulose reduces glucose release from carbohydrates, particularly at higher doses, underscoring its potential as a food ingredient with functional benefits.

Valorization of Algal Biomass to Produce Microbial Polyhydroxyalkanoates: Recent Updates, Challenges, and Perspectives.

Biopolymers are highly desirable alternatives to petrochemical-based plastics owing to their biodegradable nature. The production of bioplastics, such as polyhydroxyalkanoates (PHAs), has been widely reported using various bacterial cultures with substrates ranging from pure to biowaste-derived sugars. However, large-scale production and economic feasibility are major limiting factors. Now, using algal biomass for PHA production offers a potential solution to these challenges with a significant environmental benefit. Algae, with their unique ability to utilize carbon dioxide as a greenhouse gas (GHG) and wastewater as feed for growth, can produce value-added products in the process and, thereby, play a crucial role in promoting environmental sustainability. The sugar recovery efficiency from algal biomass is highly variable depending on pretreatment procedures due to inherent compositional variability among their cell walls. Additionally, the yields, composition, and properties of synthesized PHA vary significantly among various microbial PHA producers from algal-derived sugars. Therefore, the microalgal biomass pretreatments and synthesis of PHA copolymers still require considerable investigation to develop an efficient commercial-scale process. This review provides an overview of the microbial potential for PHA production from algal biomass and discusses strategies to enhance PHA production and its properties, focusing on managing GHGs and promoting a sustainable future.

Identifying the Leading Sources of Saturated Fat and Added Sugar in U.S. Adults.

The 2020-2025 Dietary Guidelines for Americans recommend limiting intakes of saturated fat and added sugars (SF/AS) to <10% total energy. Data-driven approaches to identify sources of SF/AS are needed to meet these goals. We propose using a population-based approach to identify the leading food and beverage sources of SF/AS consumed by US adults. Foods and beverages reported as consumed were assessed from two, 24 h dietary recalls (24HRDR) from 36,378 adults aged 19 years and older from the 2005-2018 National Health and Nutrition Examination Survey. Intakes of SF/AS were aggregated across both 24HRDR to identify What We Eat in America food categories accounting for ≥90% of SF/AS, respectively, by the total population and within population subgroups. Data were weighted to estimate a nationally representative sample. Ninety-five discrete food categories accounted for ≥90% of the total SF/AS intakes for >88% of the representative sample of U.S. adults. The top sources of SF were cheese, pizza, ice cream, and eggs. The leading sources of AS were soft drinks, tea, fruit drinks, and cakes and pies. This analysis reflects a parsimonious approach to reliably identify foods and beverages that contribute to SF/AS intakes in U.S. adults.

Is too much sugar bitter? The impacts of sugars on health.

This paper reviews the associations between sugars consumption and non-communicable diseases. Systematic reviews demonstrate associations between sugars intake and dental caries, weight gain, type 2 diabetes and cardiovascular diseases. Children consuming more sugar-sweetened beverages (SSBs) are 1.55 times more likely to be overweight. In adults, higher consumption of SSBs is associated with a 27% higher relative risk of developing type 2 diabetes. In adults, greater free sugar consumption was positively associated with total CVD (HR 1.07; 95% CI: 1.03-1.10), ischaemic heart disease (HR 1.06; 95%CI: 1.02,1.10), and stroke (HR 1.10, 95% CI: 1.04, 1.17). Those consuming sugars higher than the recommended level of 10% of total energy are more likely to develop dental caries; 42 out of 50 studies involving children and 5 out of 5 in adults reported at least one positive association between sugars and caries. Reduction in sugars consumption requires a myriad of interventions to reduce supply and demand at national and global levels, fiscal policies, alongside high-quality research and promoting environments to reduce the burden of NCDs.

Canopy and understory nitrogen additions differently affect soil microbial residual carbon in a temperate forest.

Atmospheric nitrogen (N) deposition in forests can affect soil microbial growth and turnover directly through increasing N availability and indirectly through altering plant-derived carbon (C) availability for microbes. This impacts microbial residues (i.e., amino sugars), a major component of soil organic carbon (SOC). Previous studies in forests have so far focused on the impact of understory N addition on microbes and microbial residues, but the effect of N deposition through plant canopy, the major pathway of N deposition in nature, has not been explicitly explored. In this study, we investigated whether and how the quantities (25 and 50 kg N ha-1 year-1) and modes (canopy and understory) of N addition affect soil microbial residues in a temperate broadleaf forest under 10-year N additions. Our results showed that N addition enhanced the concentrations of soil amino sugars and microbial residual C (MRC) but not their relative contributions to SOC, and this effect on amino sugars and MRC was closely related to the quantities and modes of N addition. In the topsoil, high-N addition significantly increased the concentrations of amino sugars and MRC, regardless of the N addition mode. In the subsoil, only canopy N addition positively affected amino sugars and MRC, implying that the indirect pathway via plants plays a more important role. Neither canopy nor understory N addition significantly affected soil microbial biomass (as represented by phospholipid fatty acids), community composition and activity, suggesting that enhanced microbial residues under N deposition likely stem from increased microbial turnover. These findings indicate that understory N addition may underestimate the impact of N deposition on microbial residues and SOC, highlighting that the processes of canopy N uptake and plant-derived C availability to microbes should be taken into consideration when predicting the impact of N deposition on the C sequestration in temperate forests.

Determination of Carbohydrate Composition in Lentils Using Near-Infrared Spectroscopy.

Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a significant influence on the techno-functional properties of lentil-derived products. In this study, the use of near-infrared spectroscopy (NIRS) to predict the concentration of total carbohydrate, fibre, starch, total sugars, fructose, sucrose and raffinose was investigated. For this purpose, six different cultivars of macrosperm (n = 37) and microsperm (n = 43) lentils have been analysed, the samples were recorded whole and ground and the suitability of both recording methods were compared. Different spectral and mathematical pre-treatments were evaluated before developing the calibration models using the Modified Partial Least Squares regression method, with a cross-validation and an external validation. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the total sugars and fructose, sucrose, and raffinose. The recording of ground samples allowed for obtaining better models for the calibration of starch content (R > 0.8), total sugars and sucrose (R > 0.93), and raffinose (R > 0.91). The results obtained confirm that there is sufficient information in the NIRS spectral region for the development of predictive models for the quantification of the carbohydrate content in lentils.

Application of Immobilized ß-Glucosidase from Candida boidinii in the Hydrolysis of Delignified Sugarcane Bagasse.

Candida boidinii is a methylotrophic yeast with wide geographical distribution. In the present study, the microorganism was isolated from the Bahian semiarid and the enzymatic extract containing ß-glucosidase was obtained through submerged fermentation. Response surface methodology was employed to optimize of fermentation medium. The higher production of ß-glucosidase was obtained after 71 h of fermentation in an optimized medium composed of 3.35% glucose, 1.78% yeast extract and 1% peptone. The optimum pH and temperature of enzymatic activity were 6.8 (citrate-phosphate buffer) and 71.7 °C, respectively. Salts tested (10 mM) CaCl2, Na2SO4 and ZnSO4 promotes the increase of 91%, 45% and 80% of activity, respectively. The enzyme retained 64% ± 2.3 of its initial activity after 1 h heating at 90 °C. The production of reducing sugars was 95.94% after 24 h of hydrolysis and, with the addition of metal ions, this value increased more than 2 times. Among the supports analyzed for immobilization, chitosan showed higher residual activity during reuse. The immobilized enzyme showed higher activity at 60 °C with pH 6 and preserved almost 100% of the initial activity after 30 min at 70 °C.

Trajectories of Sugar-Sweetened Beverage Intake in Early Life: Evidence from a Birth Cohort Study.

Infancy and early childhood are periods of dietary transition. Early exposure to specific foods and the establishment of dietary habits during this period can shape long-term food preferences and have lasting effects on health. This study aimed to examine the longitudinal trajectories of sugar-sweetened beverage (SSB) intake in Australian children from birth to age 3 years and identify early-life and socioeconomic factors influencing those trajectories. Mother-infant dyads (n = 934) from the Healthy Smiles Healthy Kids birth cohort study were interviewed on their weekly frequency of SSB intake at 4-month, 8-month, 1-year, 2-year, and 3-year age points. Group-based trajectory modelling analysis was performed to identify trajectories for SSB intake among Australian children. A multivariable logistic regression was performed to identify the maternal and child-related predictors of resulting trajectories. The intake of SSBs showed two distinct quadratic trajectories (high and low) with age. While the two trajectories remained distinctive throughout, the SSB consumption for both groups consistently increased between 4 months and 2 years of age and subsequently stabilised. Compared to low SSB consumers (75%), the high SSB consumers (25%) were significantly more likely to be living in households with three or more children (relative risk (RR): 1.59, 95%CI: 1.02-2.48), had low maternal education (left school < year 12-RR: 1.75, 95%CI: 1.09-2.81; completed year 12-RR: 1.57, 95%CI: 1.02-2.81), and resided in highly/the most socioeconomically disadvantaged areas (highly disadvantaged-RR: 1.89, 95%CI: 1.13-3.18; most disadvantaged-RR: 2.06, 95%CI: 1.25-3.38). Children's SSB intake patterns are established early in life as they transition from infancy to preschool age, and the trajectories of intake established during early childhood are strongly influenced by socioeconomic factors. Hence, interventions targeted to limit SSB intake and improve nutrition amongst children should occur in early life.

Comprehensive Analysis of Biomass from Chlorella sorokiniana Cultivated with Industrial Flue Gas as the Carbon Source.

Chlorella sorokiniana, isolated from a pond adjacent to a cement plant, was cultured using flue gas collected directly from kiln emissions using 20 L and 25000 L photobioreactors. Lipids, proteins, and polysaccharides were analyzed to understand their overall composition for potential applications. The lipid content ranged from 17.97% to 21.54% of the dry biomass, with carotenoid concentrations between 8.4 and 9.2 mg/g. Lutein accounted for 55% of the total carotenoids. LC/MS analysis led to the identification of 71 intact triacylglycerols, 8 lysophosphatidylcholines, 10 phosphatidylcholines, 9 monogalactosyldiacylglycerols, 12 digalactosyldiacylglycerols, and 1 sulfoquinovosyl diacylglycerol. Palmitic acid, oleic acid, linoleic acid, and α-linolenic acid were the main fatty acids. Polyunsaturated fatty acid covers ≥ 56% of total fatty acids. Protein isolates and polysaccharides were also extracted. Protein purity was determined to be ≥75% by amino acid analysis, with all essential amino acids present. Monomer analysis of polysaccharides suggested that they are composed of mainly D-(+)-mannose, D-(+)-galactose, and D-(+)-glucose. The results demonstrate that there is no adverse effect on the metabolite profile of C. sorokiniana biomass cultured using flue gas as the primary carbon source, revealing the possibility of utilizing such algal biomass in industrial applications such as animal feed, sources of cosmeceuticals, and as biofuel.

Foliar Spraying with ZnSO4 or ZnO of Vitis vinifera cv. Syrah Increases the Synthesis of Photoassimilates and Favors Winemaking.

Zinc enrichment of edible food products, through the soil and/or foliar application of fertilizers, is a strategy that can increase the contents of some nutrients, namely Zn. In this context, a workflow for agronomic enrichment with zinc was carried out on irrigated Vitis vinifera cv. Syrah, aiming to evaluate the mobilization of photoassimilates to the winegrapes and the consequences of this for winemaking. During three productive cycles, foliar applications were performed with ZnSO4 or ZnO, at concentrations ranging between 150 and 1350 g.ha-1. The normal vegetation index as well as some photosynthetic parameters indicated that the threshold of Zn toxicity was not reached; it is even worth noting that with ZnSO4, a significant increase in several cases was observed in net photosynthesis (Pn). At harvest, Zn biofortification reached a 1.2 to 2.3-fold increase with ZnSO4 and ZnO, respectively (being significant relative to the control, in two consecutive years, with ZnO at a concentration of 1350 g.ha-1). Total soluble sugars revealed higher values with grapes submitted to ZnSO4 and ZnO foliar applications, which can be advantageous for winemaking. It was concluded that foliar spraying was efficient with ZnO and ZnSO4, showing potential benefits for wine quality without evidencing negative impacts.

Mass Spectrometry Characterization of Honeydew Honey: A Critical Review.

Honeydew honey is produced by bees (Apis mellifera) foraging and collecting secretions produced by certain types of aphids on various parts of plants. In addition to exhibiting organoleptic characteristics that distinguish them from nectar honey, these honeys are known for their functional properties, such as strong antioxidant and anti-inflammatory activities. Despite their importance, they remain poorly characterized in comparison with flower honeys, as most studies on this subject are not only carried out on too few samples but also still focused on traditional chemical-physical parameters, such as specific rotation, major sugars, or melissopalynological information. Since mass spectrometry has consistently been a primary tool for the characterization and authentication of honeys, this review will focus on the application of these methods to the characterization of the minor fraction of honeydew honey. More specifically, this review will attempt to highlight what progress has been made so far in identifying markers of the authenticity of the botanical and/or geographical origin of honeydew honeys by mass spectrometry-based approaches. Furthermore, strategies devoted to the determination of contaminants and toxins in honeydew honeys will be addressed. Such analyses represent a valuable tool for establishing the level of food safety associated with these products. A critical analysis of the presented studies will identify their limitations and critical issues, thereby describing the current state of research on the topic.

Snowpack permanence shapes the growth and dynamic of non-structural carbohydrates in Juniperus communis in alpine tundra.

Climate warming is altering snowpack permanence in alpine tundra, modifying shrub growth and distribution. Plant acclimation to snowpack changes depends on the capability to guarantee growth and carbon storage, suggesting that the content of non-structural carbohydrates (NSC) in plant organs can be a key trait to depict the plant response under different snow regimes. To test this hypothesis, we designed a 3-years long manipulative experiment aimed at evaluating the effect of snow melt timing (i.e., early, control, and late) on NSC content in needles, bark and wood of Juniperus communis L. growing at high elevation in the Alps. Starch evidenced a general decrease from late spring to summer in control and early melting, while starch was low but stable in plants subjected to a late snow melt. Leaves, bark and wood have different level of soluble NSC changing during growing season: in bark, sugars content decreased significantly in late summer, while there was no seasonal effect in needles and wood. Soluble NSC and starch were differently related with the plant growth, when considering different tissues and snow treatment. In leaf and bark we observed a starch depletion in control and early melting plants, consistently to a higher growth (i.e., twig elongation), while in late snow melt, we did not find any significant relationship between growth and NSC concentration. Our findings confirmed that snowpack duration affects the onset of the growing season promoting a change in carbon allocation in plant organs and, between bark and wood in twigs. Finally, our results suggest that plants, at this elevation, could take advantage from an early snow melt caused by climate warming, most likely due to photosynthetic activity by maintaining the level of reserves and enhancing the carbon investment for growth.

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN's demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.