Estimates Of Nutrient Content Research Articles

Early detection of N, P, K deficiency in Choy Sum using hyperspectral imaging-based spatial spectral feature mining

Leafy vegetables are widely consumed around the world for their rich nutritional qualities. To ensure a reliable and cost-effective supply of leafy vegetables in the future, advancements in their production are essential. Deficiencies of nitrogen (N), phosphorus (P), and potassium (K) impair growth of leafy vegetables and the ensuing visual symptoms make the plants unmarketable. We studied the use of non-contact large area hyperspectral imaging (HSI) for early detection of N, P and K deficiencies in the leafy vegetable, Choy Sum, before the appearance of visual symptoms. The wide spectral data of 500–900 nm extracted from the plants were subjected to advanced feature mining, facilitating the creation of novel spectral indices tailored to each vital nutrient by leveraging the Pearson’s correlations of 0.85 for N, 0.64 for P, and 0.68 for K with gold standard elemental concentration data. Early detection of deficiencies and timely replenishment of macronutrient(s) can prevent the development of obvious symptoms and thus maintain the visual quality of Choy Sum. These newly created spectral indices hold the potential to provide non-destructive estimation of nutrient content in plants, offering a promising avenue for future advancements in precision agriculture and resource-efficient crop management.

Nutrient Composition of Nine Festival Dishes Obtained from A Recipe Survey Among Lower to Middle-Income Households in Selected Urban Areas

Background and Objectives: Estimation of nutrient content of a recipe by a recipe calculation method may be cost‐effective and alternative to the chemical analysis for a range of applications, laboratory analysis of standardized recipes is considered a more authentic and accurate source of recipe nutrient data. Food composition data (FCD) regarding festival foods and dishes is very scanty in local and global food composition tables (FCT). The present study, thus, conducted a recipe survey on the ingredient profile of festival recipes consumed by lower to middle-income households in Bangladesh followed by proximate analysis of standard recipes obtained by collapsing similar recipes. Unanalyzed nutrients were obtained by data compilation from local and global food composition databanks (FCDB). Methodology: A cross-sectional recipe survey was conducted sequentially on festival foods and dishes among purposefully selected housewives residing at different areas of Dinajpur district and Dhaka city. The study instrument was a structured pre-tested questionnaire. Nine homemade festival recipes out of 40 recipes cited viz., plain polao, beef bhuna, fish fry, chicken bhuna, payesh, beef biriyani, chinese vegetable, chicken roast and baked fish were selected and standardized according to the method established in our laboratory. The selected recipes were prepared and analyzed for proximate composition along with an estimation of the cooking yield factor. Content of moisture, ash, protein, total fat, total dietary fiber (TDF), available carbohydrates, and calorie values of nine selected dishes were determined using standard operating procedures (SOP) established on the basis of AOAC methods. Precision and accuracy of analytical values were checked as part of Quality Assurance Program (QAP). Data compilation of missing values for vitamins and minerals was conducted according to FAO data compilation toolkit. Result: Moisture content ranges from 30.42-70.75%, protein content ranges from 2.60-35.70%, fat content ranges from 2.61-22.53%, ash content ranges from 0.6241-8.8%, TDF ranges from 0.58-8.49%, carbohydrates ranges from 0.65-45.7% and energy content ranges 101.5-368.4 kcal/100g fresh sample. The cooking yield ranges from 40.96-96.82%, which is highest in plain polao and lowest in fish fry. Conclusion: The data generated from this study can be used as reliable data which will enrich the national food composition table. Bioresearch Commu. 10(2): 1516-1523, 2024 (July)

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App.

BackgroundTechnological advancements have enabled nutrient estimation by smartphone apps such as goFOOD. This is an artificial intelligence–based smartphone system, which uses food images or video captured by the user as input and then translates these into estimates of nutrient content. The quality of the data is highly dependent on the images the user records. This can lead to a major loss of data and impaired quality. Instead of removing these data from the study, in-depth analysis is needed to explore common mistakes and to use them for further improvement of automated apps for nutrition assessment.ObjectiveThe aim of this study is to analyze common mistakes made by participants using the goFOOD Lite app, a version of goFOOD, which was designed for food-logging, but without providing results to the users, to improve both the instructions provided and the automated functionalities of the app.MethodsThe 48 study participants were given face-to-face instructions for goFOOD Lite and were asked to record 2 pictures (1 recording) before and 2 pictures (1 recording) after the daily consumption of each food or beverage, using a reference card as a fiducial marker. All pictures that were discarded for processing due to mistakes were analyzed to record the main mistakes made by users.ResultsOf the 468 recordings of nonpackaged food items captured by the app, 60 (12.8%) had to be discarded due to errors in the capturing procedure. The principal problems were as follows: wrong fiducial marker or improper marker use (19 recordings), plate issues such as a noncompatible or nonvisible plate (8 recordings), a combination of various issues (17 recordings), and other reasons such as obstacles (hand) in front of the camera or matching recording pairs (16 recordings).ConclusionsNo other study has focused on the principal problems in the use of automatic apps for assessing nutritional intake. This study shows that it is important to provide study participants with detailed instructions if high-quality data are to be obtained. Future developments could focus on making it easier to recognize food on various plates from its color or shape and on exploring alternatives to using fiducial markers. It is also essential for future studies to understand the training needed by the participants as well as to enhance the app’s user-friendliness and to develop automatic image checks based on participant feedback.

Food Folio by Columbia Center for Eating Disorders: A Freely Available Food Image Database.

Food images are useful stimuli for the study of cognitive processes as well as eating behavior. To enhance rigor and reproducibility in task-based research, it is advantageous to have stimulus sets that are publicly available and well characterized. Food Folio by Columbia Center for Eating Disorders is a publicly available set of 138 images of Western food items. The set was developed for the study of eating disorders, particularly for use in tasks that capture eating behavior characteristic of these illnesses. It contains foods that are typically eaten, as well as those typically avoided, by individuals with eating disorders. Each image has now been rated across 17 different attributes by a large general United States population sample via Amazon’s Mechanical Turk (n = 1054). Ratings included subjective attributes (e.g., tastiness, healthiness, and favorable texture) as well as estimates of nutrient content (e.g., fat and carbohydrate). Each participant rated a subset of stimulus set food items (46 foods) on all 17 dimensions. Additional description of the image set is provided in terms of physical image information and accurate nutritional information. Correlations between subjective ratings were calculated and an exploratory factor analysis and exploratory cluster analysis completed. Outcomes of the factor analysis suggested foods may be described along three latent factors of healthiness, tastiness, and umami taste; the cluster analysis highlighted five distinct clusters of foods varying on these same dimensions. Descriptive outcomes indicated that the stimulus set includes a range of foods that vary along multiple dimensions and thus is likely to be useful in addressing various research questions surrounding eating behavior and cognition in healthy populations, as well as in those with eating disorders. The provision of comprehensive descriptive information allows for stimulus selection that is optimized for a given research question and promotes strong inference.

225-OR: The Impact of Visualizing Macronutrient Content via Food-Log App "Calomeal" on Glycemic Control and QoL in People with Type 2 Diabetes

Background: The estimation of nutrient content is difficult, but important for self-management of people with type 2 diabetes mellitus (T2DM). In Japan, food-log app has not been penetrated as a treatment tool yet. That could be because Japanese food is diverse and consists of various ingredients. In this study, we investigated the efficacy of app “Calomeal” that enables the patients to search for what they eat among the database of 26300 kinds of foods and to visualize the nutrients using smartphone. Method: We recruited 20 people with T2DM in Juntendo University. They used this app for 3 months on the regular treatment. The blood and urine exams were performed monthly. At the beginning and the end of the study, the estimation of food was also assessed by the dietitian and the questionnaires about QOL (DTSQ/PAID) were performed. Results: 2 were dropped out. 18 participants were 52.3±9.2 y.o., male/female 12/8, BMI 25.8±4.7 kg/m2, and HbA1c 8.1±0.4%. At the end of study, significant reduction was observed in HbA1c and BMI (7.92±0.4 to 7.75±0.5%[p=0.0410], 25.69±5.0 to 25.10±4.8 kg/m2 [p=0.0057]). Reduction of HbA1c and BMI were both correlated to the reduction of carbohydrate (r=0.5018 [p=0.0338], r =0.5542 [p=0.0170]). High correlation was also observed between the reduction of carbohydrate and calorie (r=0.8167 [p<0.0001]). QOL scores showed no-statistically significant improvement. Improvement of PAID score was strongly correlated to how close they could adjust calorie intake to their appropriate amount (r=0.5964 [p=0.0090]). Intriguingly, in some patients, this strong association could be due to not the reduction but the increase of food intake, because the baseline calorie intake of those were lower than the proper amount. Conclusion: Visualizing nutrients via Calomeal improved glycemic control and BMI by reducing over intake. It is of note that in some Japanese people, the adjusting nutrient intake by raising the calorie may contribute to their QOL. Disclosure A. Tsunemi: None. J. Sato: Research Support; Self; Sanofi. Speaker’s Bureau; Self; Daiichi Sankyo, Medtronic, Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. M. Enomoto: None. Y. Iwagaki: None. S. Sugimoto: None. Y. Someya: None. M. Kiya: None. E. Matsuhashi: None. Y. Wakabayashi: None. T. Funayama: None. T. Uchida: None. T. Miyatsuka: None. K. Azuma: None. T. Shimizu: None. H. Satoh: None. A. Kanazawa: Speaker’s Bureau; Self; Novartis Pharma K.K., Sanofi, Takeda Pharmaceutical Company Limited. H. Watada: Advisory Panel; Self; Abbott, Ajinomoto, Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Fuji Film, Janssen Pharmaceuticals, Inc., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi-Aventis, Takeda Pharmaceutical Company Limited, Terumo Medical Corporation. Research Support; Self; Astellas Pharma Inc., Bayer Yakuhin, Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharma K.K., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Pfizer Japan Inc., Sanofi-Aventis, Sanwa Kagaku Kenkyusho, Shionogi & Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited, Yakult. Speaker’s Bureau; Self; Astellas Pharma Inc., AstraZeneca, Bayer Yakuhin, Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo, Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Hakko Kirin Co., Ltd., Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Sanofi-Aventis, Sanwa Kagaku Kenkyusho, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited.

Silvicultural Implications in Hibrid of Eucalyptus urophylla S.T. Blake × Eucalyptus grandis Hill ex Maiden Stand

The objective of this work was to evaluate the silvicultural implications in a Eucalyptus urophylla S.T. Blake × Eucalyptus grandis Hill ex Maiden (Eucalyptus urograndis) stand 4.5 years-old, located in Alegrete - RS. The trees were fractionated in the following components: root, wood, bark, branches and leaves for later estimation of nutrient content and stock. An inventory was made for the dendrometric characterization of the stand. The nutrient concentration and stock in each component were evaluated. The nutrient removal was estimated considering three biomass harvesting scenarios: wood with bark + canopy, wood with bark and wood only. The nutritional balance and the number of rotations (4.5 years) of production were determined, considering the nutrient stock in the soil, the inputs through precipitation + mineral fertilization and the outputs from the biomass harvest. The risk of yield limitation among the nutrients considering the harvest of biomass showed the following pattern: Wood with bark + canopy: Sulfur ~ Potassium > Nitrogen > Calcium > Magnesium; Wood with bark: Sulfur > Potassium > Calcium > Nitrogen > Magnesium; Wood: Sulfur > Potassium > Nitrogen > Magnesium > Calcium. Phosphorus showed a tendency of nutritional sustainability in the three harvest scenarios evaluated.

333-OR: Assessing Mealtime Macronutrient Content: Patient Perceptions vs. Expert Analyses via a Novel Phone App

People with type 1 diabetes (T1D) estimate the carbohydrate (CHO) content of meals to enable accurate insulin dosing, yet protein and fat content of meals also influences post-prandial glycemia. As part of an observational study examining the impact of exercise and nutrient consumption on glycemia, we examined accuracy in estimating macronutrient content of free-living meals via a novel phone app. Participant estimates of nutrient content were compared to expert nutrition analyses performed via the Remote Food Photography Method (RFPM). We report results from 30 of 33 randomized with analyzable food photos. Participants were 15-65 years (32±14 years); 27% identified as female. Participants were asked to take photos before and after meals/snacks on up to 16 days over a 28-day period, enter CHO estimates and estimate if meals were low (<13%), typical (13-<18%), or high (≥18%) protein and if meals were low (<26%), medium (26-<32%), or high (≥32%) fat. The phone app plus RFPM captured 92±27% of estimated energy needs. Of 1,292 food photos analyzed, 429 contained < 25 g (small), 641 contained 25-75 g (medium), and 222 contained > 75 g (large) amounts of CHO. Participants estimated CHO in small or medium meals within 10 g of the expert analyses. They were less accurate estimating CHO for larger meals (-56 ± 46 g). Likewise, most correctly categorized low and typical protein (63%, 50%) as well as low and typical fat (62%, 67%) meals. Few correctly categorized meals high in protein (17%) or fat (16%). Participants’ estimation accuracy for larger meals did not differ by T1D duration. The phone app successfully collected individuals’ food intake and estimated macronutrient intake, but showed that participants consistently underestimated nutrient intake for large meals. Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop systems; development of tools to improve macronutrient estimation skills should be considered. Disclosure M.B. Gillingham: None. C.K. Martin: Advisory Panel; Self; EHE. Consultant; Self; ACAP Health, Florida Hospital, WW, Zafgen, Inc. Other Relationship; Self; Academy of Nutrition and Dietetics. S.R. Patton: None. Z. Li: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. M. Riddell: Advisory Panel; Self; Xeris Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Insulet Corporation, Medtronic MiniMed, Inc. Stock/Shareholder; Self; Zucara Therapeutics Inc. M.R. Rickels: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. M.A. Clements: Advisory Panel; Self; Glooko, Inc. Consultant; Self; Eli Lilly and Company. Speaker's Bureau; Self; Medtronic. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. F.J. Doyle: Consultant; Self; ModeAGC. Other Relationship; Self; Insulet Corporation. P. Calhoun: Stock/Shareholder; Self; Dexcom, Inc. R.L. Gal: None. R. Beck: Other Relationship; Self; Abbott Laboratories, Ascensia Diabetes Care, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Lilly Diabetes, Roche Diabetes Care, Tandem Diabetes Care. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Regularized Neural Networks Fusion and Genetic Algorithm Based On-Field Nitrogen Status Estimation of Wheat Plants

The estimation of nutrient content of plants is considerably important in agricultural practices, especially in enabling the application of precision farming. A plethora of methods has been used to estimate nitrogen amount in plants, including the utilization of computer vision. However, most of the image-based nitrogen estimation methods are conducted in controlled environments. These methods are not so practical, time consuming, and require many equipment. Therefore, there is a crucial need to develop a method to estimate nitrogen content of plants based on leaves images captured on field. It is a very challenging task since the intensity of sunlight is always changing and this leads to an inconsistent image capturing problem. In this paper, we develop a low-cost, simple, and accurate approach image-based nitrogen amount estimation. Plant images are captured directly under sunlight by using a conventional digital camera and are subject to a variation in lighting conditions. We propose a color constancy method using neural networks fusion and a genetic algorithm to normalize various plant images due to different sunlight intensities. A Macbeth color checker is utilized as the reference to normalize the color of the images. We also develop a combination of neural networks using a committee machine to estimate the nitrogen content in wheat leaves. Twelve statistical RGB color features are used as the input parameters for the nutrient estimation. The obtained result shows considerable better performance than the conventional gray-world and scale-by-max approaches, as well as linear model and single neural network methods. Finally, we show that our nutrient estimation approach is superior to the commonly used soil-plant analysis development meter based prediction.

Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging

The nutritional status of citrus leaves is very important to the determining of fertilization plans. The spectrum technique is a quick, un-injured method and is becoming widely used for plant nutrient estimation. The possibility and method of using spectrum technique to estimate the nutrient of citrus leaf was explored in this study. A total amount of 135 leaves from the mature spring shoots of navel orange trees (C. sinensis Osbeck, “Newhall”) were collected and randomly grouped into two sets of samples: 100 leaves for the calibration set and 35 leaves for the prediction set. The hyperspectral images were scanned upper and lower side of each leaf and then the total nitrogen (N) and phosphorus (P) contents of each leaf were measured. The raw spectra data were extracted to generate average spectra curves, preprocessed with five different methods, and was used to build N and P content prediction models. The performances of the five preprocessing methods, i.e., Savitzky-Golay smoothing (SGS), standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative (1-Der) and second-derivative (2-Der), were tested with linear partial least squares (PLS) models and nonlinear least squares-support vector machine (LS-SVM) models. The results showed that the SG-PLS and PLS were the best for the N predicting (Rp=0.9049, RMSEP=0.1041) and P (Rp=0.9235, RMSEP=0.0514) in citrus leaves, respectively; the hyperspectral image data from leaf’s upper side predicting better for the contents of N and P. The study suggested that the hyperspectral image data from the upper side of the citrus leaves are suitable for nondestructive estimation of nutrient content. Keywords: citrus leaves, nitrogen, phosphorus, prediction, hyperspectral imaging DOI: 10.3965/j.ijabe.20150802.1464

Partitioning stoichiometric components of epilithic biofilm using mixing models

Epilithic biofilms are an important and complex food resource in shallow aquatic ecosystems. Standard methods of sampling these biofilms typically yield a combination of algal cells, other organic material, and inorganic sediment. Physical methods of isolating algal fractions rely on differences in density among biofilm components, and clean separation is not always possible. Here, we explore the application of linear mixing models to the problem of estimating nutrient content of biofilm components. This method is based on the assumption that variation in bulk sample elemental composition within a sampling site is due to differences among samples in relative composition. By using standard methods to quantify organic content and algal biomass within biofilm samples, the mixing model produces estimates of nutrient content of each end member. To test this method, we analyzed the phosphorus (P) content of algal cells, non‐algal organic material, and inorganic sediment associated with biofilm samples that increase in P‐content 4‐fold across a natural P gradient in lowland streams in Costa Rica. Our analyses show that nearly all of the observed increase in biofilm P from high‐P streams was due to elevated P‐content of algal cells, rather than P sorbed to inorganic sediments or in Prich heterotrophic bacteria. Results from the mixing model were supported by results from density fractionation. This linear mixing model approach complements empirical methods of separating complex food resources and may lead to a better understanding of trophic interactions in aquatic food webs.

Estimation of nutrient content of woody plants using allometric relationships: quantifying the difference between concentration values from the literature and actuals

The objective of this study was to evaluate the reliability of allometric methods for assessing biomass and nutrient contents of woody species at the stand scale. Allometric relationships were built from 13 stands of a woody species of moderate height (European gorse: Ulex europaeus L.). In eight other stands, the above-ground biomass of the species was estimated using allometric relationships. Total nutrient contents (N, P, K, Ca, Mg) of these eight stands were also computed either with nutrient concentrations obtained from local stratifi ed samples or with values derived from the literature. The estimated above-ground biomass was consistent with the measured values obtained by complete harvest of the stand. The nutrient contents calculated using the local samples were also in agreement with measured values. Conversely, the use of nutrient concentrations values derived from the literature led to signifi cant errors which were up to 104 per cent in the estimation of nutrient contents. We conclude that allometric methods can give reliable and accurate estimates of above-ground biomass and nutrient content of woody species, provided that the values for nutrient concentrations are obtained from local samples and not from average values found in the literature.

Palaeoceanographic change in the northeastern South China Sea during the last 15000 years

The sedimentary succession of piston core RC26-16, dated by 14C accelerator mass spectrometry, provides a nearly continuous palaeoceanographic record of the northeastern South China Sea for the last 15000 yr. Planktic foraminiferal assemblages indicate that winter sea-surface temperatures (SSTs) rose from 18°C to about 24°C from the last glacial to the Holocene. A short-lived cooling of 1°C in winter temperature centred at about 11000 14C yr ago may reflect the Younger Dryas cooling event in this area. Summer SSTs have remained between 27°C and 29°C throughout the record. The temperature difference between summer and winter was about ca. 9°C during the last glacial, much higher than the Holocene value of ca. 5°C. During the late Holocene a short-lived cooling event occurred at about 4000 14C yr ago. Oxygen and carbon isotopic gradients between surface (0–50 m) and subsurface (50–100 m) waters were smaller during the last glacial than those in the Holocene. The fluctuation in the isotopic gradients are caused most likely by changes in upwelling intensity. Smaller gradients indicate stronger upwelling during the glacial winter monsoon. The fauna-derived estimates of nutrient content of the surface waters indicate that the upwelling induced higher fertility and biological productivity during the glacial. The winter monsoon became weaker during the Holocene. The carbonate compensation depth and foraminiferal lysocline were shallower during the Holocene, except for a short-lived deepening at about 5000 14C yr ago. A preservation peak of planktic foraminifera and calcium carbonate occurred between 13400 and 12000 14C yr ago, synchronous to the global preservation event of Termination I.

Palaeoceanographic change in the northeastern South China Sea during the last 15000 years

The sedimentary succession of piston core RC26-16, dated by 14 C accelerator mass spectrometry, provides a nearly continuous palaeoceanographic record of the northeastern South China Sea for the last 15 000 yr. Planktic foraminiferal assemblages indicate that winter sea-surface temperatures (SSTs) rose from 18°C to about 24°C from the last glacial to the Holocene. A short-lived cooling of 1°C in winter temperature centred at about 11 000 14 Cy r ago may reflect the Younger Dryas cooling event in this area. Summer SSTs have remained between 27°C and 29°C throughout the record. The temperature difference between summer and winter was about ca. 9°C during the last glacial, much higher than the Holocene value of ca. 5°C. During the late Holocene a short-lived cooling event occurred at about 4000 14 Cy r ago. Oxygen and carbon isotopic gradients between surface (0-50 m) and subsurface (50-100 m) waters were smaller during the last glacial than those in the Holocene. The fluctuation in the isotopic gradients are caused most likely by changes in upwelling intensity. Smaller gradients indicate stronger upwelling during the glacial winter monsoon. The fauna-derived estimates of nutrient content of the surface waters indicate that the upwelling induced higher fertility and biological productivity during the glacial. The winter monsoon became weaker during the Holocene. The carbonate compensation depth and foraminiferal lysocline were shallower during the Holocene, except for a short-lived deepening at about 5000 14 C yr ago. A preservation peak of planktic foraminifera and calcium carbonate occurred between 13400 and 12 000 14 C

Preparation of sampling plans for B 2 vitamers in fast food hamburgers

Fast food products are becoming a significant source of nutrients in the American diet. Frequently, investigators determine the nutrient content of a food from a limited local sampling, which can produce misleading results when used to provide estimates of nutrient content on a nationwide basis. It is the purpose of this paper to illustrate how a national sampling plan that will provide representative nutrient content values can be developed from the results of a limited local sampling. The riboflavin, flavin mononucleotide, flavin adenine dinucleotide, and total riboflavin contents of fast food hamburgers collected in Maryland are used as an illustration, and three distinct national sampling plans are developed, each of which will estimate the national B 2 vitamer contents of fast food hamburgers with ⩽10% variability.