Full Spectral Model Research Articles

Detection of Adulteration of Ziziphi Spinosae Semen Based on Near Infrared Hyperspectral Imaging

Based on the near-infrared hyperspectral imaging technology (NIR-HSI) (950-1700 nm), a rapid identification method was proposed for Ziziphi Spinosae Semen (Suanzaoren, SZR) and its three kinds of counterfeits, i.e. Ziziphus mauritiana lam (Lizaoren, LZR), Hovenia dulcis Thunb. (Zhijuzi, ZJZ) and Lens culinaris (Bingdou, BD). According to the proportion of 2:1, by randomly dividing the sample set, 480 samples are taken as the training set and 240 samples are taken as the test set. Five preprocessing methods were used to process the extracted raw spectra from region of interest, and the optimal preprocessing method was selected. The full spectral models were established by using the Grey Wolf Optimizer (GWO-SVM), partial least square discrimination analysis (PLS-DA) and soft independent modeling class analog (SIMCA) algorithms. The best classification results of the full spectrum-based PLS-DA, GWO-SVM and SIMCA models were 0.95, 0.99 and 0.97, respectively. Selecting characteristic wavelength by combining spectral data with Competitive adaptive reweighted sampling (CARS) and Successful projects algorithm (SPA) algorithms. The comparison results showed that the recognition rate of SPA-GWO-SVM and SPA-SIMCA were 0.97. The optimal model was SPA-NON-SIMCA. Finally, according to prediction results of the optimal model, the samples were marked with different colours to obtain the visualization map of SZR with different fake products.

Spitzer/IRS Full Spectral Modeling to Characterize Mineralogical Properties of Silicate Dust in Heavily Obscured AGNs

Mid-infrared silicate dust bands observed in heavily obscured active galactic nuclei (AGNs) include information on the mineralogical properties of silicate dust. We aim to investigate the mineralogical picture of the circumnuclear region of heavily obscured AGNs to reveal obscured AGN activities through the picture. In our previous study, we investigated the properties of silicate dust in heavily obscured AGNs, focusing on the mineralogical composition and the crystallinity with Spitzer/IRS 5.3–12 μm spectra. In this study, we model the full-range Spitzer/IRS 5–30 μm spectra of 98 heavily obscured AGNs using a one-dimensional radiative transfer calculation with four dust species in order to evaluate wider ranges of the properties of silicate dust more reliably. Comparing fitting results between four dust models with different sizes and porosities, 95 out of the 98 galaxies prefer a porous silicate dust model without micron-sized large grains. The pyroxene mass fraction and the crystallinity are overall consistent with—but significantly different from—the previous results for the individual galaxies. The pyroxene-poor composition, small dust size, and high porosity are similar to newly formed dust around mass-loss stars as seen in our Galaxy, which presumably originates from the recent circumnuclear starburst activity. The high crystallinity on average suggests dust processing induced by AGN activities.

The MASSIVE Survey. XVI. The Stellar Initial Mass Function in the Center of MASSIVE Early-type Galaxies

The stellar initial mass function (IMF) is a fundamental property in the measurement of stellar masses and galaxy star formation histories. In this work, we focus on the most massive galaxies in the nearby universe . We obtain high-quality Magellan/LDSS-3 long-slit spectroscopy with a wide wavelength coverage of 0.4–1.01 μm for 41 early-type galaxies (ETGs) in the MASSIVE survey and derive high signal-to-noise spectra within an aperture of R e/8. Using detailed stellar synthesis models, we constrain the elemental abundances and stellar IMF of each galaxy through full spectral modeling. All the ETGs in our sample have an IMF that is steeper than a Milky Way (Kroupa) IMF. The best-fit IMF mismatch parameter, α IMF = (M/L)/(M/L)MW, ranges from 1.1 to 3.1, with an average of 〈α IMF〉 = 1.84, suggesting that on average, the IMF is more bottom heavy than Salpeter. Comparing the estimated stellar masses with the dynamical masses, we find that most galaxies have stellar masses that are smaller than their dynamical masses within the 1σ uncertainty. We complement our sample with lower-mass galaxies from the literature and confirm that is positively correlated with , , and . From the combined sample, we show that the IMF in the centers of more massive ETGs is more bottom heavy. In addition, we find that is positively correlated with both [Mg/Fe] and the estimated total metallicity [Z/H]. We find suggestive evidence that the effective stellar surface density ΣKroupa might be responsible for the variation of α IMF. We conclude that σ, [Mg/Fe], and [Z/H] are the primary drivers of the global stellar IMF variation.

A Characteristic Interval Modeling Method for Simultaneous Detection of Multiple Metal Ions.

Aiming at the problems of low accuracy and large prediction errors caused by the serious overlap of multi-metal spectral signals in zinc smelting industrial wastewater, a characteristic interval modeling method is proposed. First, according to the absorption spectra of mixed solution, the characteristic intervals of copper and nickel are preliminarily screened by using different partition lengths. Second, take the smallest root mean squares error of cross validation and the largest correlation coefficient as the evaluation indicators, compare the full-spectral model and each local model, and select the optimal feature sub-intervals of copper and nickel. Last, the partial least squares method is used to model the combined wavelengths of the optimal sub-intervals to realize the simultaneous detection of copper and nickel. The linear determination ranges are 0.3–3.0 mg/L for copper and nickel. the correlation coefficients of copper and nickel are 0.9974 and 0.9966, respectively. The results show that the method reduces the complexity of the wavelength variable screening process, improves the accuracy of the model, and lays the foundation for the accurate analysis of polymetallic ions in zinc smelting industrial wastewater.

Knowledge Gaps in the Cometary Spectra of Oxygen-bearing Molecular Cations

Abstract Molecular cations are present in various astronomical environments, most notably in cometary atmospheres and tails where sunlight produces exceptionally bright near-UV to visible transitions. Such cations typically have longer-wavelength and brighter electronic emission than their corresponding neutrals. A robust understanding of their near-UV to visible properties would allow these cations to be used as tools for probing the local plasma environments or as tracers of neutral gas in cometary environments. However, full spectral models are not possible for characterization of small, oxygen-containing molecular cations given the body of molecular data currently available. The five simplest such species (H2O+, CO2 +, CO+, OH+, and O 2 + ) are well characterized in some spectral regions but are lacking robust reference data in others. Such knowledge gaps hinder fully quantitative models of cometary spectra, specifically hindering accurate estimates of physical-chemical processes originating with the most common molecules in comets. Herein, the existing spectral data are collected for these molecules, and the places where future work is needed are highlighted, specifically where the lack of such data would greatly enhance the understanding of cometary evolution.

Raman spectroscopy combined with support vector regression and variable selection method for accurately predicting salmon fillets storage time

The storage time of salmon is one of the critical indexes to evaluate salmon quality, while the traditional methods are costly, destructive and time-consuming. This study highlights the effectiveness of Raman spectroscopy combined with support vector regression (SVR) and variable selection method in the prediction of salmon storage time. The spectra of salmon fillets during storage at −18 ℃, 4 ℃ and 20 ℃ were collected by the portable Raman spectrometer. To decrease the model complexity and improve the model prediction accuracy, three spectral variable selection algorithms were investigated for model optimizing, including competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and hybrid method (CARS-SPA). Comparing the full spectral models with the models established based on the variables selected by above variable selection methods, the results showed that CARS-SPA coupled with SVR (CARS-SPA-SVR) produced the optimal prediction performance. The determination coefficient and root mean square error of prediction set (R2p and RMSEP) at −18 ℃, 4 ℃ and 20 ℃ can reach 0.9986 and 0.1606, 0.9988 and 0.1485, 0.9995 and 0.0371, respectively. Therefore, this study have proved that Raman spectroscopy coupled with CARS-SPA-SVR model is an effective strategy for rapidly, nondestructively and accurately predicting the storage time of salmon fillets stored at different temperatures.

Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy.

Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near‐infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near‐infrared transmittance spectra of apples in the wavelength range of 590–1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.

Analysis of Water Coupling in Inelastic Neutron Spectra of Uranyl Fluoride

Inelastic neutron scattering (INS) is uniquely sensitive to hydrogen due to its comparatively large thermal neutron scattering cross-section (82 b). Consequently, the inclusion of water in real samples presents significant challenges to INS data analysis due directly to the scattering strength of hydrogen. Here, we investigate uranyl fluoride (UO2F2) with inelastic neutron scattering. UO2F2 is the hydrolysis product of uranium hexafluoride (UF6), and is a hygroscopic, uranyl-ion containing particulate. Raman spectral signatures are commonly used for inferential understanding of the chemical environment for the uranyl ion in UO2F2, but no direct measurement of the influence of absorbed water molecules on the overall lattice dynamics has been performed until now. To deconvolute the influence of waters on the observed INS spectra, we use density functional theory with full spectral modeling to separate lattice motion from water coupling. In particular, we present a careful and novel analysis of the Q-dependent Debye–Waller factor, allowing us to separate spectral contributions by mass, which reveals preferential water coupling to the uranyl stretching vibrations. Coupled with the detailed partial phonon densities of states calculated via DFT, we infer the probable adsorption locations of interlayer waters. We explain that a common spectral feature in Raman spectra of uranyl fluoride originates from the interaction of water molecules with the uranyl ion based on this analysis. The Debye–Waller analysis is applicable to all INS spectra and could be used to identify light element contributions in other systems.

Chemometric determination of time series moisture in both potato and sweet potato tubers during hot air and microwave drying using near/mid-infrared (NIR/MIR) hyperspectral techniques

Near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques in tandem with chemometric analyses were employed for developing multispectral real-time systems allowing the food industry to monitor moisture content (MC) in tubers including various potato and sweet potato products during drying. Multivariate models were established by partial least-squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and back propagation artificial neural network (BPANN) using full spectral ranges of 10372–6105 cm−1 (Spectral Set I), 3996–600 cm−1 (Spectral Set II), and 1700–900 cm−1 (Spectral Set III). The LWPLSR from Spectral Set I and BPANN from Spectral Set II and III, obtained the highest accuracies for tuber MC prediction. Then, both regression coefficient (RC) and successive projection algorithm (SPA) were respectively used for the selection of feature wavelengths in Spectral Set I, II and III. Instead of choosing many groups of characteristic variables for different varieties of potatoes and sweet potatoes, one set of feature variables for all tubers was selected from each spectral region for the convenience of industrial application. Eventually, six sets of feature wavelengths chosen from Spectral Set I, II and III were used to optimize models. The simplified SPA-LWPLSR from Spectral Set II and SPA-BPANN from Spectral Set III acquired good model performances for the tuber MC prediction, with determination coefficients in prediction (R2P) of 0.950 and 0.904, respectively. The RC-BPANN model from Spectral Set I achieved the highest R2P of 0.965. Such accuracies were comparable to that of full spectral models. The results reveal that hyperspectral techniques have great potential in the food industry for real-time measurement of tuber MC.

Detection of Adulteration in Camellia Oil Using Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) combined with chemometrics analysis was used in this study to qualitatively and quantitatively determine the adulterated Camellia oil. A binary model was constructed for determining both the authenticity and the number of adulterated contents. NIRS combined with support vector machine classification was used to establish a full spectral model and a selected spectral model via competitive adaptive heavy-weighted sampling and backward interval partial least squares. Notably, both of them were proved to be suitable for determining the authenticity of Camellia oil. NIRS combined with support vector machine regression may be used to predict the amount of adulterated content in Camellia oil because of the high model correlation coefficient (R was higher than 99%, and the maximum mean square error was 0.0605).

Rapid Determination of Unsaturated Fatty Acids in Vegetable Oil by Raman Spectroscopy and Chemometrics

ABSTRACTThe accuracy for the determination of total monounsaturated and polyunsaturated fatty acids in vegetable oil by Raman spectroscopy was enhanced by spectral wavelength selection with competitive adaptive reweighted sampling to obtain partial least squares models based on a small subset of wavelengths. Wavelengths of 26 and 31 were selected from the 842 original points for the total monounsaturated and polyunsaturated fatty acids, respectively. Compared to full spectral models and four frequently-used variable selection methods, the competitive adaptive reweighted sampling achieved better results. The external correlation coefficients for total monounsaturated fatty acids and polyunsaturated fatty acids were increased to 0.9555 and 0.9963, and root mean square errors of prediction were reduced to 0.0207 and 0.0054. The results indicate that a portable Raman spectrometer using competitive adaptive reweighted sampling with partial least squares may be a promising approach for the determination of unsaturated fatty acids in vegetable oil.

Micro-Electro-Mechanical Systems/Near-Infrared Validation of Different Sampling Modes and Sample Sets Coupled with Multiple Models

The aim of the present study was to demonstrate the reliability of micro-electro-mechanical systems/near-infrared technology by investigating analytical models of two modes of sampling (integrating sphere and fiber optic probe modes) and different sample sets. Baicalin in Yinhuang tablets was used as an example, and the experimental procedure included the optimization of spectral pretreatments, selection of wavelength regions using interval partial least squares, moving window partial least squares, and validation of the method using an accuracy profile. The results demonstrated that models that use the integrating sphere mode are better than those that use fiber optic probe modes. Spectra that use fiber optic probe modes tend to be more susceptible to interference information because the intensity of the incident light on a fiber optic probe mode is significantly weaker than that on an integrating sphere mode. According to the test set validation result of the method parameters, such as accuracy, precision, risk, and linearity, the selection of variables was found to make no significant difference to the performance of the full spectral model. The performance of the models whose sample sets ranged widely in concentration (i.e., 1-4 %) was found to be better than that of models whose samples had relatively narrow ranges (i.e., 1-2 %). The establishment and validation of this method can be used to clarify the analytical guideline in Chinese herbal medicine about two sampling modes and different sample sets in the micro-electro-mechanical systems/near-infrared technique.

Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning

The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1,734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation. A region of 15 pixel x 15 pixel was selected as region of interest (ROI) and the average spectral information of ROI was obtained as sample spectral information. Multiplicative scatter correction was selected as pretreatment method to reduce the noise of spectrum. The performance of four classification algorithms including Ada-boost algorithm, extreme learning machine (ELM), random forest (RF) and support vector machine (SVM) were examined in this study. In order to simplify the input variables, 10 effective wavelengths (EMS) including 1,002, 1,005, 1,015, 1,019, 1,022, 1,103, 1,106, 1,167, 1,237 and 1,409 nm were selected by analysis of variable load distribution in PLS model. The reflectance of effective wavelengths was taken as the input variables to build effective wavelengths based models. The results indicated that the classification accuracy of the four models based on full-spectral were over 90%, the optimal models were extreme learning machine and random forest, and the classification accuracy achieved 100%. The classification accuracy of effective wavelengths based models declined slightly but the input variables compressed greatly, the efficiency of data processing was improved, and the classification accuracy of EW-ELM model achieved 100%. ELM performed well both in full-spectral model and in effective wavelength based model in this study, it was proven to be a useful tool for spectral analysis. So rapid and nondestructive recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning is feasible, and it provides a new method for on line batch variety recognition of Chinese cabbage seeds.

IMPROVING SENSITIVITY TO WEAK PULSATIONS WITH PHOTON PROBABILITY WEIGHTING

All gamma-ray telescopes suffer from source confusion due to their inability to focus incident high-energy radiation, and the resulting background contamination can obscure the periodic emission from faint pulsars. In the context of the Fermi Large Area Telescope, we outline enhanced statistical tests for pulsation in which each photon is weighted by its probability to have originated from the candidate pulsar. The probabilities are calculated using the instrument response function and a full spectral model, enabling powerful background rejection. With Monte Carlo methods, we demonstrate that the new tests increase the sensitivity to pulsars by more than 50% under a wide range of conditions. This improvement may appreciably increase the completeness of the sample of radio-loud gamma-ray pulsars. Finally, we derive the asymptotic null distribution for the H-test, expanding its domain of validity to arbitrarily complex light curves.

STRATEGIES IN USING NUMERICAL WAVE MODELS IN OCEAN/COASTAL APPLICATIONS

The objective of the present work is to illustrate the performances of the numerical wave models in ocean and coastal environment. Third generation wave models are considered nowadays the most appropriate for such task. These are full spectral models based on the integration on the wave energy (or alternatively wave action) balance equation. In order to cover more aspects related with the modelling process hindcast, nowcast and forecast schemes are discussed and illustrated along six case studies. The major model used was SWAN (acronym for Simulating Waves Nearshore) which is a very flexible model that can be applied in a wide range of coastal applications being effective from high resolution coastal areas up to quasi oceanic scales. In both hindcasts and forecasts the wave forcing was provided by generation models (WAM and WW3), while in nowcast schemes buoy data were used. Various coastal environments that are rather different from the point of view of the bathymetric features and of the characteristics of the environmental matrix were considered. These are the Portuguese continental nearshore with higher resolution sub domains, Madeira Archipelago, the nearshore of Sardinia Island in the Mediterranean Sea and the Black Sea. A general conclusion of this work would be that, despite some limitations, the wave models provide an effective framework in predicting wave conditions in ocean and coastal environment.

Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of ‘Fuji’ apple based on BiPLS and FiPLS models

The feasibility that used the efficient selection of wavelength regions in FT-NIR for a rapid and conclusive determination of fruit inner qualities such as soluble solids content (SSC) of apples was investigated. An apples NIRS acquisition device was developed in this study. With this device, the apple was rolling while collecting the NIR spectroscopy. Graphically oriented local multivariate calibration modeling procedures such as interval partial least-squares (iPLS), backward interval partial least-squares (BiPLS), and forward interval partial least-squares (FiPLS) were applied to select the efficient spectral regions that provides the lowest prediction error, in comparison to the full-spectral model. Among 40 intervals, the optimal combinations of 10 spectral intervals were chosen by FiPLS to obtain a satisfactory result, while those of 5 by BiPLS for the simplicity. The intervals chosen by BiPLS are not the same as those by FiPLS, due to the different algorithm of the two methods. In the determinations, a root mean square error of prediction (RMSEP) of 0.732 was obtained after interval selection.

Comparison of the nonlocal transport characteristics of a series of one-dimensional oceanic boundary layer models

Seven one-dimensional oceanic boundary layer models are investigated to assess the possible nonlocal transport characteristics of mass and heat in the upper ocean. The dynamical models have been chosen from the diffusion and bulk types currently in use plus two modifications of the transilient type that have been used extensively for atmospheric work by Stull. The models are forced using wind speed and insolation conditions recorded during a 9-day oceanographic cruise near Bermuda in March 1993 during the decline of the spring bloom. The attenuation of sunlight in the upper ocean is calculated using a full spectral model for downwelling irradiance. The vertical heat transport characteristics are reported and compared. A series of spectral diagnostic tests (Green's function analysis, process spectra, and overall mixing lengths) reveal significant differences in the vertical transport characteristics of the models that are not observed in commonly used diagnostics such as sea surface temperature or mixed layer depth. Age spectra (or modal time since last surface contact) are calculated from Green's functions and reveal how a water mass can be cut off from the surface within a short time period. The large differences in the vertical mixing characteristics of the upper ocean boundary layer models have potential implications on the vertical distributions of short-lived chemical tracers and phytoplankton.

Lessons learned from full-spectral modeling of signatures of an estuary front

Previously, we have modeled radar signatures, involving large variations (/spl sim/10-15 dB) in radar cross section (RCS), that have been observed at strongly convergent ocean fronts and at an estuarine front. In each of these cases, we obtained quantitative agreement with measurement but only by including wave-breaking (WB) effects in an approximate manner. However, in each case, we used the composite scattering (CS) model at a frequency (9.4 GHz) where this model may be deficient. For this reason, questions remain concerning the importance of WB effects in these simulations. In the present study, we monitor the sensitivity of the simulations with respect to this CS approximation by comparing the results of this model with those from an alternative theory, based on simulations of RCS, derived from a common wave spectrum. The spectrum is calculated using a full-spectral treatment of wave-current interaction. The resulting simulations are used to model the radar signature of the buoyant plume associated with the efflux of fresh water from the Chesapeake Bay that was observed during the Chesapeake Outflow Plume Experiment 2 (COPE-II). In both cases, it is possible to simulate this signature, in quantitative agreement with experiment, but only by including WB effects. We find that the CS model predictions for the behavior of the signature do not agree with the comparable predictions from the remaining model. Additional simulations indicate the difference between the two models, which is rather large because of the large (60/spl deg/) angle of incidence, occurs because the CS model includes higher order terms that are not included in the Kirchoff Approximation.

Parameterization of a spectral solar irradiance model for the global ocean using multiple satellite sensors

Monthly and seasonal means of the spectral solar radiation at the sea surface is determined at global scale (1° × 1° grid) from satellite composite imagery. The transmittance functions of each atmospheric component have been modified from an existing clear‐sky radiative model to assimilate data sets from multiple satellite sensors, including the Sea‐viewing Wide Field‐of‐view Sensor (NASA). All satellite‐derived parameters and other climatologies were resampled at common scales in time and space, and a cloud correction scheme has been implemented by comparing two different methods based on ultraviolet reflectance from the Total Ozone Mapping Spectrometer (NASA) and cloud fraction from the Special Sensor Microwave Imager (NASA), respectively. The new “PARsat” model relies on a complete computation of the radiative transfer at each grid point, while being less dependent on some meteorological parameters that are often known at local scale only. Although PARsat is a full spectral model providing downwelling irradiance at daily scale and up, the model is applied for 1997 up to present, and only the results for 1998 are shown in this work as monthly global maps of surface photosynthetically active radiation (PAR). A comprehensive evaluation of the model is conducted through comparisons with monthly PAR values obtained globally with other well‐referenced models and with a large data set of in situ measurements. An overall uncertainty of 5.2% is obtained when the performance of the model is validated with in situ measurements collected from two array systems of moored buoys.

A predictive model for estimating rates of primary production in the subtropical North Pacific Ocean

Abstract A primary production model, specific to the subtropical North Pacific, was developed using productivity and chlorophyll data collected at Station ALOHA (22.75°N, 158.00°W) and measured phytoplankton physiological parameters. The production algorithm is a mechanistic, full spectral model that provides depth-dependent photosynthesis rates. Six years of phytoplankton pigment data were analyzed to derive seasonal cycles and to parameterize chlorophyll a (Chl; monovinyl and divinyl chlorophyll a) versus depth profiles. The Chl profiles fit a Gaussian-type distribution with depth over a constant background concentration equal to mixed-layer values. Maximum Chl concentrations were always found below the surface, and the parameters displayed distinct monthly and seasonal patterns. Photosynthesis versus irradiance experiments were conducted at a number of locations and seasons in the vicinity of Station ALOHA to calculate phytoplankton physiological parameters needed for the computation of primary production rates. Relationships were explored between environmental conditions and the physiological and profile parameters in order to develop algorithms based on remotely sensed sea-surface variables and time of year. These relationships were used to model primary production rates using data collected during the Hawaii Ocean Time-series (HOT) program. The performance of the model and model parameters were tested by comparing modeled results to those measured directly at Station ALOHA by trace metal-clean, in situ 14C incubation techniques. Monthly averaged chlorophyll profile parameters and a constant maximum quantum yield (0.026 mol C(mol quanta)−1) generates an annual production estimate of 168 g C m−2, which closely resembles that measured at Station ALOHA (181 g C m−2) during the same period. The measured and modeled primary production rates for the subtropical North Pacific are twice as high as values derived from Coastal Zone Color Scanner (CZCS) data (60–90 g C m−2) during the period 1978–1986. If the study area near Station ALOHA is representative of most oligotrophic waters, current global estimates of primary production rates have been significantly underestimated.