Rapid identification of peanut oil adulteration by near infrared spectroscopy and chemometrics.

Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near-infrared spectroscopy to non-invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high-quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near-infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R2) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition.

[Characterization of carbon and oxygen isotopes of plant on climate and plant physiology at the southeastern margin of Qinghai-Tibet Plateau, China]. / é’è—é«˜åŽŸä¸œå—ç¼˜æ¤ç‰©ç¢³æ°§åŒä½ç´ å¯¹æ°”å€™åŠæ¤ç‰©ç”Ÿç†çš„è¡¨å¾.

To investigate the correlation between carbon and oxygen isotope compositions of plant cellulose and climatic factors as well as plant physiological indices on the southeastern margin of the Qinghai-Tibet Plateau, we examined plant species in eight sampling sites with similar latitudes and different longitudes in this region. Through the characteristics of δ13C and δ18O values, fractionation values (Δ13C and Δ18O) in leaf cellulose, we discussed water use efficiency (WUE) and the environmental factors, the variation of carbon and oxygen isotopes in the southeastern margin of the Qinghai-Tibet Plateau with elevation and longitude, and revealed the indication degrees of isotopic signals to different environments and vegetation physiology. By using the semi-quantitative model of carbon and oxygen dual isotopes, we investigated the physiological adaptation mechanisms of plants to varying environmental conditions. The results demonstrated that both Δ13C and Δ18O of cellulose decreased with increasing elevation and longitude, and Δ13C was more influenced by longitude, while Δ18O was more susceptible to elevation variation. Additionally, Δ13C and Δ18O were significantly and positively correlated with temperature (TEM), precipitation (PRE), potential evapotranspiration (PET), and relative humidity (RH). PRE was the dominant meteorological factor driving the variation of Δ13C, while RH was the dominant meteorological factor influencing Δ18O variation. In contrast to Δ13C, WUE showed a stronger correlation with elevation than with longitude, which increased as elevation and longitude increased. According to the carbon-oxygen model, plant stomatal conductance (gs) and photosynthetic capacity (Amax) decreased with increasing precipitation and relative humidity, while the values increased with increasing elevation and longitude. The combined analysis of carbon and oxygen isotopes of organic matters would yield additional environmental and gas exchange information for studies on climate tracing and vegetation physiology studies on the southeastern margin of the Qinghai-Tibet Plateau.

New insight into the functional group mechanism and structure-activity relationship of the complexation between DOM and Cr(III) in landfill leachate.

Widespread landfills represent a significant source of groundwater contamination. Due to the unique and diverse nature of dissolved organic matter (DOM) in landfill leachate, the interaction between DOM and heavy metals, along with its quantitative evaluation, remains unknown. Consequently, we collected ten samples from various landfill types to serve as representatives for a comprehensive investigation of the mechanism involving functional groups and Cr(III) through the establishment of a quantitative structure-activity relationship (QSAR). We employed ESI FT-ICR MS, (MW) 2D-COS, and DFT calculations for this purpose. Our findings indicate that DOM from landfill leachate contains a higher proportion of CHON molecules on intensity compared to those from natural sources. The maximum complexation capacity was determined by the proportion of proteins (69%), normalized carbon average oxidation state (16%), double bond equivalence (8%), and the number of oxygen atoms (7%) in landfill leachate DOM. Besides, N-containing groups such as N = O and C-N in landfill leachate DOM with lower humification, can exhibit stronger affinities than COOH, ArOH, CO, and polysaccharide C-O groups, which are typically identified as dominant sites in natural DOM. A QSAR model incorporating four parameters demonstrated an impressive accuracy rate of 98.8%, underscoring its reliability in predicting the complexation potential of different landfill leachate DOM with Cr(III).

Equations describing semi-confluent cell growth (I) Analytical approximations.

A set of differential equations with analytical solutions are presented that can quantitatively account for variable degrees of contact inhibition on cell growth in two- and three-dimensional cultures. The developed equations can be used for comparative purposes when assessing contribution of higher-order effects, such as culture geometry and nutrient depletion, on mean cell growth rate. These equations also offer experimentalists the opportunity to characterize cell culture experiments using a single reductive parameter.

Research on highly sensitive quantitative detection of aflatoxin B2 solution based on THz metamaterial enhancement.

Food such as cereal crops, oil crops and dairy products are very easy to produce highly toxic and carcinogenic aflatoxins during inappropriate storage. Therefore, it is of great significance to achieve rapid, non-destructive and highly sensitive detection of aflatoxin. A terahertz metamaterial sensor with "X" compound double-peak structure is designed based on electromagnetic theory to realize highly sensitive detection of aflatoxin B2 solution. It is found that the amplitude of the transmission peak of the terahertz transmission spectrum of aflatoxin B2 (AFB2) solution around 1.2 THz and 2.0 THz gradually decreased with the increase of the concentration of aflatoxin B2 solution, and the frequency of the transmission peak gradually shifted to high frequency with the increase of the concentration of aflatoxin B2 solution, hence a full concentration model was established. And a strategy of first classifying concentration intervals and then building a grouped quantitative model was proposed. The Limit of Detection (LOD) of the interval sub-model of low and medium concentration of aflatoxin B2 solution has been greatly improved with the LOD of the optimal grouping model was 7.28 × 10-11 mg/ml, 4.19 × 10-9 mg/ml and 1.22 × 10-7 mg/ml, respectively. This research verifies the feasibility of terahertz metamaterial sensor based on "X" composite double-peak structure combined with THz-TDS technology for highly sensitive detection of aflatoxin B2 solution. And it provides a new rapid, non-destructive and highly sensitive detection of aflatoxin in food.

A diazotrophy-ammoniotrophy dual growth model for the sulfate reducing bacterium Desulfovibrio vulgaris var. Hildenborough.

Sulfate reducing bacteria (SRB) comprise one of the few prokaryotic groups in which biological nitrogen fixation (BNF) is common. Recent studies have highlighted SRB roles in N cycling, particularly in oligotrophic coastal and benthic environments where they could contribute significantly to N input. Most studies of SRB have focused on sulfur cycling and SRB growth models have primarily aimed at understanding the effects of electron sources, with N usually provided as fixed-N (nitrate, ammonium). Mechanistic links between SRB nitrogen-fixing metabolism and growth are not well understood, particularly in environments where fixed-N fluctuates. Here, we investigate diazotrophic growth of the model sulfate reducer Desulfovibrio vulgaris var. Hildenborough under anaerobic heterotrophic conditions and contrasting N availabilities using a simple cellular model with dual ammoniotrophic and diazotrophic modes. The model was calibrated using batch culture experiments with varying initial ammonium concentrations (0-3000 µM) and acetylene reduction assays of BNF activity. The model confirmed the preferential usage of ammonium over BNF for growth and successfully reproduces experimental data, with notably clear bi-phasic growth curves showing an initial ammoniotrophic phase followed by onset of BNF. Our model enables quantification of the energetic cost of each N acquisition strategy and indicates the existence of a BNF-specific limiting phenomenon, not directly linked to micronutrient (Mo, Fe, Ni) concentration, by-products (hydrogen, hydrogen sulfide), or fundamental model metabolic parameters (death rate, electron acceptor stoichiometry). By providing quantitative predictions of environment and metabolism, this study contributes to a better understanding of anaerobic heterotrophic diazotrophs in environments with fluctuating N conditions.

Modeling Behavioral Persistence with Resurgence as Choice in Context (RaC2): A Tutorial.

Resurgence as Choice in Context (RaC2) is a quantitative model for evaluating the reemergence of a previously extinguished response when alternative reinforcement is worsened. Rooted in the matching law, RaC2 proposes that allocation between target and alternative responding is based on changes in the relative value of each response option over time, accounting for periods with and without alternative reinforcement. Given that practitioners and applied researchers may have limited experience with constructing quantitative models, we provide a step-by-step task analysis for building RaC2 using Microsoft Excel 2013. We also provide a few basic learning activities to help readers better understand RaC2 itself, the variables that affect the model's predictions, and the clinical implications of those predictions. Supplementary Information: The online version contains supplementary material available at 10.1007/s40617-023-00796-y.

Rapid determination of chemical components and antioxidant activity of the fruit of Crataegus pinnatifida Bunge by NIRS and chemometrics.

OBJECTIVE: To establish a method for quality evaluation of the fruit of Crataegus pinnatifida Bunge, also known as Shanzha, by near-infrared spectroscopy combined with chemometrics. METHOD: Seventy-two batches of Shanzha samples were collected, and the content of total components (flavonoids, phenols and organic acids), monomer components (chlorogenic acid, hyperoside and isoquercitrin), as well as the antioxidant activity of 60% ethanol extract were determined by usual methods. Then, all measured values were correlated with the near infrared spectra of Shanzha, and the partial least squares regression models were established. As to improve the model performance, various methods for spectra pretreatment and wavelength selection were investigated. RESULTS: After optimization, the models obtained the coefficients of determination in both calibration and prediction >0.9, and the residual prediction deviations >3, indicating that the models had good prediction abilities. CONCLUSION: The present method can serve as an alternative to the methods for comprehensive and rapid quality evaluation of Shanzha.

NIR quantitative model trans-scale calibration from small scale to pilot scale via directed DOSC-SBC algorithm.

In order to solve the problem of inapplicability of NIR quantitative models due to the large difference between the modeling samples and the samples to be tested, Directed DOSC-SBC（DDOSC-SBC）algorithm is proposed in this paper based on Direct Orthogonal Signal Correction combined with Slope/Bias Correction (DOSC-SBC) algorithm. To obtain the suitable spectral matrix transfer parameters for the test set during DDOSC spectral preprocessing, several representative test samples in the test set were selected, then the spectral systematic errors between the modeling set and the test set were corrected with the SBC method in order to realize the trans-scale prediction of the NIR quantitative model. NIR data and the critical quality attributes（CQAs）were detected in the small scale and pilot scale pharmaceutical process of the fluidized bed granulation of dextrin and water extraction of honeysuckle. After the small scale model was calibrated via the directed DOSC-SBC algorithm which was guided by representative pilot scale samples, the small scale model was able to predict the pilot scale test samples more accurately. The NIR quantitative model trans-scale calibration from small scale to pilot scale was also successfully realized with a RPD value higher than 3.5 and RSEP value lower than 10%. DDOSC-SBC algorithm is a successful model trans-scale calibrated method that can be applied to NIR real-time monitoring of CQAs in the preparation process of Chinese herbal medicine.

Modeling the elemental stoichiometry and silicon accumulation in diatoms.

Diatoms are important microorganisms involved in global primary production, nutrient cycling, and carbon sequestration. A unique feature of diatoms is their silica frustules, which impact sinking speed, defense against predators and viruses, and growth cycling. Thus, frustules are inherently linked to their role in ecosystems and biogeochemical cycles. However, constraints on cellular silicon levels remain unclear and few existing models resolve diatom elemental stoichiometry to specifically include variable silicon levels. Here, we use a coarse-grained model of the diatom, Thalassiosira pseudonana, compared with laboratory results to illustrate the relationship of silicon uptake with elemental stoichiometry of other nutrients. The model-data comparison suggests the balance between growth rate and silicon uptake constrains the amount of cellular silicon. Additionally, it expresses relationships between silicon, nitrogen, phosphorus, and carbon to changing growth rates in nitrogen-limited and phosphorus-limited regimes. First, our model-data comparison suggests Si uptake hits a maximum cellular quota at low growth rates and below this maximum there is independent Si uptake. In each nutrient regime, Si:N, Si:P, and Si:C decrease exponentially with growth rate when Si is below the maximum limit. This is explained by independent Si uptake and increased loss of Si to new cells. These results provide predictions of diatom stoichiometry and allocation, which can be used in ecosystem models to differentiate phytoplankton types to better represent diatoms' contribution to global biogeochemical cycles and ecosystems.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy.

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R 2) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g-1, R 2 of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

Discrimination and chemical composition quantitative model of Raw Moutan Cortex and Moutan Cortex Carbon based on electronic nose and machine learning.

Raw Moutan Cortex (RMC) is a traditional medicinal material commonly used in China. Moutan Cortex Carbon (MCC) is a processed product of RMC by stir-frying. As raw and processed products of the same Chinese herb pieces, they have different effects. RMC has the effects of clearing heat and cooling blood, promoting blood circulation and removing blood stasis, but MCC has the contrary effect of cooling blood and hemostasis. Therefore, it is necessary to distinguish them effectively. The traditional quality evaluation method of RMC and MCC still adopts character identification, and mainly relies on the working experience and sensory judgment of employees with experience. This will lead to strong subjectivity and poor repeatability. And the final evaluation result may cause inevitable errors and the processed products with different processing degrees in actual production, which affects the clinical efficacy. In this study, the electronic nose technology was introduced to objectively digitize the odor of RMC and MCC. And the discrimination model of RMC and MCC was constructed in order to establish a rapid, objective and stable quality evaluation method of RMC and MCC. According to the correlation analysis, the experiment found the content of gallic acid, 5-hydroxymethylfurfural (5-HMF), paeoniflorin and paeonol determined by high performance liquid chromatography (HPLC) had a certain correlation with their odor characteristics. Thus, partial least squares regression (PLSR) and support vector machine regression (SVR) were compared and established the chemical composition quantitative model. Results showed that the quantitative data of RMC and MCC odor could be used to predict the contents of the chemical components. It can be used for quality control of RCM and MCC.

Research on Quantitative Model of Brand Recognition Based on Sentiment Analysis of Big Data.

This paper takes laptops as an example to carry out research on quantitative model of brand recognition based on sentiment analysis of big data. The basic idea is to use web crawler technology to obtain the most authentic and direct information of different laptop brands from first-line consumers from public spaces such as buyer reviews of major e-commerce platforms, including review time, text reviews, satisfaction ratings and relevant user information, etc., and then analyzes consumers' sentimental tendencies and recognition status of the product brands. This study extracted a total of 437,815 user reviews of laptops from e-commerce platforms from January 1, 2019 to December 31, 2021, and performed data preprocessing on the obtained review data, followed by sentiment dictionary construction, attribute expansion, text quantification and algorithm evaluation. This paper analyzed the information receiving and processing hierarchy of the quantitative model of brand recognition, discussed the interactive relationship between brand recognition and consumer sentiment, discussed the brand recognition bias, style and demand in the context of big data, and performed the sentiment statistics and dimension analysis in the quantitative model of brand recognition. The study results show that the quantitative model of brand recognition based on sentiment analysis of big data can transform and map the keywords in text to word vectors in the high-dimensional semantic space by performing unsupervised machine learning on the text based on artificial neural network computer bionic metaphors; the model can accumulate each brand-related buyer review in the corresponding brand recognition dimension, so as to obtain the value of each product in each dimension of brand recognition; finally, the model will add the values of each dimension of brand recognition, that is, obtain the relevant value of the sum of each brand recognition. The results of this paper may provide a reference for further research on the quantitative model of brand recognition based on sentiment analysis of big data.

Choosing a future from a murky past: A generalization-based model of behavior.

Remembering the past appears critical in allowing organisms to detect order in an environment, and hence to behave in accordance with likely future events. Yet the shortcomings of remembering and perceiving typically mean that the remembered past differs from the actual past, and hence that behavior does not perfectly track the structure of the environment. Here, we outline how the process of generalization might be used to understand differences between what an organism does, and the structure of the past and potential structure of the environment. We explore how different sources of generalization - both from within the same stimulus situation, and from different stimulus situations - might be modeled quantitatively, and how predictions made by this modeling approach are supported by research. Finally, we discuss how generalization from multiple stimulus situations, longer-term experience, and from stimulus situations in the past that are not identical to the stimulus situation in the present, might contribute to our understanding of how an organism's experience translates into behavior.

[Integrated smart hyperspectral imaging and CARS-based characteristic band selection for rapid determination of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix].

In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.

Co3O4@TiO2@Y2O3 nanocomposites for a highly sensitive CO gas sensor and quantitative analysis.

In order to predict the early failure of organic insulator, Co3O4@TiO2@Y2O3 nanocomposites was prepared and characterized (XRD, SEM, EDS, FTIR, UV-vis-NIR, XPS) to detect decomposition CO gas. A simple experimental platform was built to verify the excellent adsorption, stability, selectivity and repeatability of the composite. Then, the mechanism of adsorption enhancement was analyzed by heterojunction. Aiming at 170 sets of gas sensing data sets, Successive Projections Algorithm (SPA) was used to extract data features, and grey wolf optimization vector machine regression (GWO-SVR) model was established to predict carbon monoxide concentration. The correlation coefficient (RP), root mean square error (RMSEP) and calculation time of prediction set were 99.3025%, 0.0418 and 1.47 s, respectively. Therefore, the combination of the superior properties of a composite sensitive material and the small sample quantitative prediction model is a promising method for gas sensors in the future.

Rapid quality evaluation of Plantaginis Semen by near infrared spectroscopy combined with chemometrics.

OBJECTIVE: To establish a rapid and comprehensive method for the quality evaluation of Plantaginis Semen (PS) by using near-infrared spectroscopy combined with chemometrics to determine the content of geniposidic acid (GEA), verbascoside (VE), isoverbascoside (IVE) and total flavonoids (TF) in PS, as well as the antioxidant activity (AOA). METHODS: The content of GEA, VE and IVE in PS were determined by HPLC, the content of TF in PS was determined by UV-Vis spectrophotometry, and the AOA of PS was characterized by the DPPH, ABTS and FRAP, respectively. Then, the measured values of each item were used as reference values and were correlated with the near infrared spectra of PS. Seven quantitative models were established by the partial least squares regression. A variety of spectral preprocessing, such as standard normal variation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), derivative and their combination methods, were investigated. In addition, genetic algorithm (GA), particle swarm optimization (PSO), as well as competitive adaptive reweighted sampling (CARS) were also compared. All samples were divided into a calibration set and a prediction set at the ratio of 3:1 by the descending order of reference values. The coefficients of determination (R2), root mean square error (RMSE) and residual predictive deviation (RPD) were calculated to evaluate model performance. RESULTS: After optimization, the performance of each model was greatly improved, where the R2 for calibration and prediction were both greater than 0.8, the RPD were both greater than 2. Such satisfactory results indicated that the present models had good prediction accuracy. CONCLUSION: Quantitative models based on near infrared spectroscopy were herein established, which proved to be able to quickly and accurately determine the content of GEA, VE and IVE and TF, as well as AOA in PS, and which might provide a new method for rapid and comprehensive quality evaluation of PS.

Integrated smart hyperspectral imaging and CARS-based characteristic band selection for rapid determination of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix / 中国中药杂志

In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.

Quantifying Cyanothece growth under DIC limitation.

The photoautotrophic, unicellular N2-fixer, Cyanothece, is a model organism that has been widely used to study photosynthesis regulation, the structure of photosystems, and the temporal segregation of carbon (C) and nitrogen (N) fixation in light and dark phases of the diel cycle. Here, we present a simple quantitative model and experimental data that together, suggest external dissolved inorganic carbon (DIC) concentration as a major limiting factor for Cyanothece growth, due to its high C-storage requirement. Using experimental data from a parallel laboratory study as a basis, we show that after the onset of the light period, DIC was rapidly consumed by photosynthesis, leading to a sharp drop in the rate of photosynthesis and C accumulation. In N2-fixing cultures, high rates of photosynthesis in the morning enabled rapid conversion of DIC to intracellular C storage, hastening DIC consumption to levels that limited further uptake. The N2-fixing condition allows only a small fraction of fixed C for cellular growth since a large fraction was reserved in storage to fuel night-time N2 fixation. Our model provides a framework for resolving DIC limitation in aquatic ecosystem simulations, where DIC as a growth-limiting factor has rarely been considered, and importantly emphasizes the effect of intracellular C allocation on growth rate that varies depending on the growth environment.

Modeled temperature dependencies of macromolecular allocation and elemental stoichiometry in phytoplankton.

Warming oceans may affect how phytoplankton allocate nutrients to essential cellular processes. Despite the potential impact of such processes on future biogeochemical cycles, questions remain about how temperature affects macromolecular allocation and elemental stoichiometry within phytoplankton cells. Here, we present a macromolecular model of phytoplankton and the effect of increasing temperature on the intracellular allocation of nutrients at a constant growth rate. When temperature increases under nitrogen (N) and phosphorus (P) co-limitation, the model shows less investment in phosphorus-rich RNA molecules relative to nitrogen-rich proteins, leading to a more severe decrease in cellular P:C than N:C causing increased cellular N:P values. Under P limitation, the model shows a similar pattern, but when excess P is available under N limitation, we predict lowered N:P due to the effect of luxury uptake of P. We reflected our model result on the surface ocean showing similar latitudinal patterns in N:P and P:C to observation and other model predictions, suggesting a considerable impact of temperature on constraining the elemental stoichiometry in the ocean.