Homogeneity of meats prepared for analysis with a commercial food processor: collaborative study

The food processor was evaluated as an alternative to the food chopper and bowl cutter for preparing meat samples for analysis in AOAC procedure 24.001. Samples of 6 meat types--cooked sausage, pork sausage, canned ham, hamburger, water-added ham, and smoked ham--were distributed to 9 laboratories for preparation using a food processor. The resulting 54 samples were sent to a USDA-accredited laboratory for analysis in triplicate for moisture, protein, and fat. Standard deviations and their 95% confidence intervals calculated for the analytical results were compared with USDA Performance Standards. With few exceptions, the upper limits were lower than the Performance Standards and for the exceptions, the intervals included the Performance Standards. By these criteria, the food processor is as effective in preparing homogeneous samples as the preparation procedures used to set the Performance Standards. Collaborators found the processor faster to use and easier to clean than the food chopper. Use of the food processor has received interim approval as an alternative to the food chopper or bowl cutter in AOAC procedure 24.001 for preparing meat samples for analysis.     

Precision parameters of methods of analysis required for nutrition labeling. Part I. Major nutrients

Major components of foods and feeds are fat, protein, and carbohydrates. Fat and protein are determined by direct measurements that are interpreted as the quantity of the constituent. Carbohydrates are usually calculated by difference. For this calculation, values for moisture/solids, ash, and "fiber" are also needed. The readily available collaborative studies for the determination of these major components are reviewed in an attempt to assign precision parameters to validated methods of analysis. When a number of studies for the same analyte, in the same food, by the same method are available, it is seen that the precision parameters among laboratories (standard deviations, SR; relative standard deviations, RSDR) and the ISO maximum tolerable difference functions (repeatability value, r; reproducibility value, R) are not characterized by any conventional distribution. The precision data are best summarized as a median or average parameter and the interval containing the centermost 90% of reported values. Typically, the precision of methods of analysis can be expressed as a function of concentration only, independent of analyte, matrix, and method. The average RSDR value from each collaborative data set can then be used as the numerator in a ratio containing, as the denominator, the value calculated from the Horwitz equation: RSDR = 2 exp (1 - 0.5 log C) where C is the concentration as a decimal fraction. A series of ratios consistently above 1, and especially above 2, probably indicates that a method is unacceptable with respect to precision. By this criterion, only the protein (Kjeldahl) determination is unqualifiedly acceptable with a 90% interval for RSDR of 1 to 3% at C values above about 0.01 (1 g/100 g). Fat, moisture/solids, and ash are acceptable down to limiting concentrations in the region of 1 to 5 g/100 g, if a test portion large enough to provide at least 50 mg of weighable residue or volatiles is specified. Measurements of individual carbohydrates and fiber-related analytes have unexpectedly poor precisions among laboratories. The variability, although high, may still be suitable for nutrition labeling. Reliability of analyses for the control of labeling of the primary nutrients must be achieved through quality assurance programs that require strict adherence to the directions of empirical methods and the use of suitable reference materials for absolute methods.     

Automated methods for determination of fat and moisture in meat and poultry products: collaborative study

A collaborative study was conducted to compare automated methods for rapid determination of fat and moisture in meat and poultry products with the official AOAC solvent extraction and forced-air oven methods, respectively. Fourteen products were tested, with fat and moisture contents ranging from 2 to 43% and 44 to 74%, respectively. Eight of the collaborating laboratories analyzed the products by using a moisture/fat analyzer; 4 laboratories used the AOAC methods. Standard deviations for within-laboratory repeatability, between-laboratory reproducibility, and bias for each product indicated that the rapid methods were acceptable. The moisture/fat analyzer methods have been adopted official first action for fat and moisture analyses in meat and poultry products.     

Comparison of Babcock and ether extraction methods for determination of fat content of milk: collaborative study

Eleven collaborating laboratories analyzed 18 blind duplicate pairs of raw milk samples for fat by the Babcock method and by a modified Mojonnier ether extraction method in 7 round robins conducted over a 14 month period. Ten laboratories used the Babcock method and 10 used the modified Mojonnier method. Fat content of samples ranged from approximately 2.7 to 5.6%. Mean test value of samples analyzed was approximately 3.9% fat. Average standard deviations of within-laboratory repeatability (Sr) of the Babcock and ether extraction methods were 0.029 and 0.015% fat, respectively. Average standard deviations of between-laboratory reproducibility (SR) of the Babcock and ether extraction methods were 0.039 and 0.020% fat, respectively. Average repeatability relative standard deviations (RSDr) for the Babcock and ether extraction methods were 0.742 and 0.396%; average reproducibility relative standard deviations (RSDR) were 1.014 and 0.512%, respectively. Mean repeatability values (r) and reproducibility values (R) were 0.081 and 0.111% for Babcock and 0.044 and 0.056% for ether extraction, respectively. The ether extraction method demonstrated consistently better within- and between-laboratory agreement. The overall mean test value for the Babcock method was significantly higher (0.021% fat) than that for ether extraction. The difference between Babcock and ether extraction fat test results was different for different farms. In addition, the mean difference between percent fat determined by the Babcock and ether extraction methods was different in different months. There was no correlation in the difference between Babcock and ether extraction methods with the absolute level of fat in the samples in the range of 2.7 to 5.6% fat. The modifications of the AOAC Babcock method and the modified Mojonnier ether extraction method have been approved interim official first action.     

Determination of pentachlorophenol in animal tissues: a Canadian perspective

Analytical methods for pentachlorophenol (PCP) residues in edible animal tissue have been reviewed, with particular reference to gas chromatographic methods of analysis. Results of analyses demonstrate that significant residues of PCP can persist for several weeks in animals exposed to contaminated bedding. National surveys in Canada have found that the incidence of PCP residues in pork in excess of 0.1 ppm was reduced from 32% of survey samples in 1981-1982 to 6.6% of samples tested in 1987-1988. An interlaboratory sample exchange among Canadian laboratories demonstrated that the PCP analytical method currently used by Agriculture Canada could be successfully transferred to other laboratories. An exchange of samples between regulatory laboratories of Agriculture Canada and the Food Safety and Inspection Service of the U.S. Department of Agriculture (USDA) demonstrated equivalency of results for the 2 methods currently used in the respective laboratories, with relative standard deviations for analytical results ranging from 4.4 to 22.2%.     

Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study

Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.     

Composition of different bean varieties (Phaseolus vulgaris) of Northwestern Argentina (Region NOA): cultivation zone influence.

There have been prior investigations concerning the environmental effects and especially soil conditions upon the proximate analysis and mineral content of grains. However, the studies are not complete and have not involved beans grown in the northwestern regions of Argentina. For this reason, this study was initiated to determine the concentration of protein, moisture, ash, fat, and minerals of various bean samples grown in northwestern Argentina. Six varieties of beans were taken from seven different regions. AOAC standard methods were used for chemical analysis. The elements analyzed for all bean samples show that moisture varies from 12 to 14%, proteins from 18 to 22%, fat from 0.7 to 1.20%, copper from 0.8 to 1.20 mg/100 g, iron from 9 to 18 mg/100 g, zinc from 2.5 to 4 mg/100 g, and phosphorus from 295 to 542 mg/100 g. No arsenic was detected in the bean samples. Different analyzed bean varieties were significantly different for proximate composition and mineral content, and each variety from different regions of northwestern Argentina present significant differences.     

土壤水分、有机质和总氮含量的近红外光谱分析研究

本文首次采用了近红外光谱分析法对我国黄土区土壤水分,有机质和总氮含量进行主人分析。结果表明,该法与实验室化学分析法之间相关性高,误差小,５２份样品定标结果：水分,有机质和总氮的复合相关系数分别为０．９７４,０．９３８和０．９４２;标准误差分别为１．０８,０．２３和０．０４２。７４份样品的检验结果;相关系数分别为０．９６９,０．９２１和０．９２８;估测标准误差分别为１．１４,０．２８和０．０４６。三     

Diagnostic data evaluation. Part IV. Evaluation of a check sample series to determine analyst performance

Data from a check sample series were subjected to diagnostic data evaluation. The chemical analyses that were performed were moisture, protein, and fat in meat products. Four performance outliers were identified (1 protein, 1 moisture, and 2 fat values). With the performance outliers excluded from the statistical evaluation, the analyst standard deviation ranged from 0.088 to 0.218 for protein, 0.145 to 0.393 for moisture, and 0.271 to 0.378 for fat. No bias was present. All laboratories were able to meet the established standards. With the limited number of samples it was not possible to attribute any reasons for the outliers.     

Prediction of Triticale Grain Quality Properties,Based on Both Chemical and Indirectly Measured Reference Methods,Using Near‐Infrared Spectroscopy

The increasing demand for triticale as food, feed, and fuel has resulted in the availability of cultivars with different grain quality characteristics. Analyses of triticale composition can ensure that the most appropriate cultivars are obtained and used for the most suitable applications. Near‐infrared (NIR) spectroscopy is often used for rapid measurements during quality control and has consequently been investigated as a method for the measurement of protein, moisture, and ash contents, as well as kernel hardness (particle size index [PSI]) and sodium dodecyl sulfate (SDS) sedimentation from both whole grain and ground triticale samples. NIR spectroscopy prediction models calculated using ground samples were generally superior to whole grain models. Protein content was the most effectively modeled quality property; the best ground grain calibration had a ratio of the standard error of test set validation to the standard deviation of the reference data of the test set (RPD_test) of 4.81, standard error of prediction (SEP) of 0.52% (w/w), and r² of 0.95. Whole grain protein calibrations were less accurate, with optimum RPD_test of 3.54, SEP of 0.67% (w/w), and r² of 0.92. NIR spectroscopy calibrations based on direct chemical reference measurements (protein and moisture contents) were better than those based on indirect measurements (PSI, ash content, and SDS sedimentation). Calibrations based on indirect measurements would, however, still be useful to identify extreme samples.     

Enumeration of total bacteria and coliforms in milk by dry rehydratable film methods: collaborative study

Eleven laboratories participated in a collaborative study to compare the dry rehydratable film (Petrifilm SM and Petrifilm VRB) methods, respectively, to the standard plate count (SPC) and violet red bile agar (VRBA) standard methods for estimation of total bacteria and coliform counts in raw and homogenized pasteurized milk. Each laboratory analyzed 16 samples (8 different samples in blind duplicate) for total count by both the SPC and Petrifilm SM methods. A second set of 16 samples was analyzed by the VRBA and Petrifilm VRB methods. The repeatability standard deviations (the square root of the between-replicates variance) of the SPC, Petrifilm SM, VRBA, and Petrifilm VRB methods were 0.05104, 0.0444, 0.14606, and 0.13806, respectively; the reproducibility standard deviations were 0.7197, 0.06380, 0.15326, and 0.13806, respectively. The difference between the mean log10 SPC and the mean log10 Petrifilm SM results was 0.027. For the VRBA and Petrifilm VRB methods, the mean log10 difference was 0.013. These results generally indicate the suitability of the dry rehydratable film methods as alternatives to the SPC and VRBA methods for milk samples. The methods have been adopted official first action.     

Performance specifications for infrared milk analysis

Maximum limits of 0.02% are recommended for precision of IR analysis for fat, protein, and lactose, and 0.04% for total solids. Recommended maximum systematic errors are 0.06% for fat, protein, and lactose, and 0.12% for total solids, when instrument results are compared with results of specified AOAC methods. Recommended maximum mean differences between instrument and standard results are based on 95% confidence limits for the mean of 8 samples; precision and systematic errors are calculated as the standard deviation of difference between duplicates for 8 samples, and systematic errors are calculated as the standard deviation of difference between instrument and reference results for 8 samples. Automated sections for milk of Official Methods of Analysis were rewritten to eliminate references to individual instruments, to eliminate possible differences between calibration procedures for different instruments, and to stress important instrument operational characteristics which can have significant effects on instrument performance.     

Combination of total solids determined by oven drying and fat determined by Mojonnier extraction for measurement of solids-not-fat content of raw milk: collaborative study

Each of 9 laboratories analyzed 9 pairs of blind duplicate raw milk samples for fat, using the Mojonnier ether extraction method (16.E13-16.E17), and for total solids, using a new direct forced air oven method. Solids-not-fat was determined by subtracting percent fat from percent total solids. The solids-not-fat content of the samples tested in the collaborative study ranged from 8.48 to 9.36%. The average repeatability standard deviation (sr) and the average reproducibility standard deviation (SR) for the solids-not-fat method were 0.019 and 0.041, respectively. Average repeatability (RSDr) and reproducibility (RSDR) relative standard deviations were 0.218 and 0.466%, respectively. The mean repeatability value (r) was 0.055; the mean reproducibility value (R) was 0.117. The difference between milk total solids determined by direct forced air oven drying and milk fat determined by Mojonnier ether extraction has been approved for interim official first action for determination of solids-not-fat content of milk.     

Disposable biosensor test for organophosphate and carbamate insecticides in milk

A highly sensitive and rapid biosensor test based on disposable screen-printed thick-film electrodes was developed, which is suitable for monitoring organophosphate and carbamate residues in foods of animal origin with increased fat contents such as milk. The wild-type enzyme was combined with three engineered variants of Nippostrongylus brasiliensis acetylcholinesterase (NbAChE), to obtain enhanced sensitivity. The sample pretreatment could be reduced to a minimum. There was no extraction or fat removal necessary. With the biosensor test paraoxon concentrations down to 1 microg/L could be detected in milk. The detection limit for carbaryl was 20 microg/L. Recovery rates for paraoxon and carbaryl in milk samples lay between 89 and 107%. Ten milk samples from local markets were tested both with the biosensor test and with standard chromatographic multiresidue methods. Two milk samples caused AChE inhibition rates of >50%. Accordingly, 4 microg/L tebufenpyrad, 4 microg/L tetraconazole, and 2 microg/L bifenthrin were detected in one of these milk samples. The other milk sample contained 2 microg/L tebufenpyrad.     

Glucose oxidase-mediated gelation: a simple test to detect glucose in food products

This paper reports a simple, rapid, and sugar-selective method to induce gelation from glucose-containing samples. This method employs glucose oxidase (GOx) to selectively "recognize" and oxidize glucose to generate gluconic acid, which acts to solubilize calcium carbonate and release calcium ions. The release of calcium ions triggers gelation of the calcium-responsive polysaccharide alginate to form a calcium-alginate hydrogel. Rheological measurements confirm that gel formation is triggered by glucose but not fructose or sucrose (consistent with GOx's selectivity). Vial inversion tests demonstrate that gel formation can be readily observed without the need for instrumentation. Proof-of-concept studies demonstrate that this gel-forming method can detect glucose in food/beverage products sweetened with glucose or high-fructose corn syrups. These results indicate that the enzyme-induced gelation of alginate may provide a simple means to test for sweeteners using components that are safe for use on-site or in the home.     

An adaptive analysis of covariance using tree-structured regression

In this article, we propose an adaptive procedure for testing for the effect of a factor of interest in the presence of one or more confounding variables in observational studies. It is especially relevant for applications where the factor of interest has a suspected causal relationship with a response. This procedure is not tied to linear modeling or normal distribution theory, and it offers a valuable alternative to traditional methods. It is suitable for applications where a factor of interest is categorical and the response is continuous. Confounding variables may be continuous or categorical. The method is comprised of two basic steps that are performed in sequence. First, confounding variables alone (i.e., without the factor of interest) are used to group observations into subsets. These subsets have the property that, when restricted to a subset, there is little or no remaining variation in the response that is attributable to the confounding variables. We then test for the factor of interest within each subset of observations. We propose to implement the first step using a technique that is generally referred to as tree-structured regression. We use a non parametric permutation procedure to carry out the second step. The proposed method is illustrated through an analysis of a U. S. Department of Agriculture (USDA) Forest Service data set and an air pollution data set.     

Protein Content of Bulk Wheat from Near‐Infrared Reflectance of Individual Kernels

Protein content of wheat by near‐infrared (NIR) reflectance of bulk samples is routinely practiced. New instrumentation that permits automated NIR analysis of individual kernels is now available, with the potential for rapid NIR‐based determinations of color, disease, and protein content, all on a single kernel (sk) basis. In the event that the protein content of the bulk sample is needed rather than that of the individual kernels, the present study examines the feasibility of estimating bulk sample protein from sk spectral readings. On the basis of 318 wheat samples of 10 kernels per sample, encompassing five U.S. wheat classes, the study demonstrates that with as few as 300 kernels bulk sample protein content may be estimated by sk NIR reflectance spectra at an accuracy equivalent to conventional bulk kernel NIR instrumentation.     

Determination of acid-detergent fiber and crude protein in forages by near-infrared reflectance spectroscopy: collaborative study

A collaborative study was conducted to determine the standard error of difference among laboratories for near-infrared reflectance spectroscopic (NIRS) determination of acid-detergent fiber (ADF) and crude protein in forages. The 6 participating laboratories were members of the USDA/ARS National Near-Infrared Reflectance Spectroscopy Forage Research Project. The NIRS calibration equations were developed in the Associate Referee's laboratory for crude protein and ADF and were transferred to the instrument in each of the other collaborating laboratories. The calibration set included over 650 diverse forage samples with crude protein and ADF calibration data; the validation set included 94 samples of bermudagrass. Among-laboratory reproducibility for the NIRS method, calculated as the relative standard deviation for reproducibility (RSDR), was 1.14% for ADF and 0.42% for crude protein. The variance component for among-laboratory variation (coefficient of variation) was 2.54% for ADF and 2.89% for crude protein. These results confirm that it is possible to calibrate, validate, and transfer (NIRS) equations and data among laboratories for the accurate determination of ADF and crude protein, and thereby demonstrate that NIRS can be used as a standard method for the analysis of forages. The method has been adopted official first action.     

Qualitative and quantitative PCR methods for detection of three lines of genetically modified potatoes

Qualitative and quantitative polymerase chain reaction (PCR) methods have been developed for the detection of genetically modified (GM) potatoes. The combination of specific primers for amplification of the promoter region of Cry3A gene, potato leafroll virus replicase gene, and potato virus Y coat protein gene allows to identify each line of NewLeaf, NewLeaf Y, and NewLeaf Plus GM potatoes. Multiplex PCR method was also established for the simple and rapid detection of the three lines of GM potato in a mixture sample. For further quantitative detection, the realtime PCR method has been developed. This method features the use of a standard plasmid as a reference molecule. Standard plasmid contains both a specific region of the transgene Cry3A and an endogenous UDP-glucose pyrophosphorylase gene of the potato. The test samples containing 0.5, 1, 3, and 5% GM potatoes were quantified by this method. At the 3.0% level of each line of GM potato, the relative standard deviations ranged from 6.0 to 19.6%. This result shows that the above PCR methods are applicable to detect GM potatoes quantitatively as well as qualitatively.     

Comparison of two methods for determination of total solids content of milk: collaborative study

Ten laboratories analyzed 9 pairs of blind duplicate raw milk samples for total solids. A direct forced air oven method (4 h at 100 degrees C) and a modification of the AOAC predry method (16.032) were used. Preliminary evaluation of the modified AOAC method indicated that blank determinations were necessary. Total solids content ranged from 12.0 to 14.6%. Average repeatability standard deviations (Sr) of the direct forced air oven and modified AOAC methods were 0.019 and 0.017, respectively. Average reproducibility standard deviations (SR) of the direct forced air oven and the modified AOAC methods were 0.042 and 0.047, respectively. Average repeatability relative standard deviations (RSDr) for the direct forced air oven and the modified AOAC methods were 0.149 and 0.136%, respectively; average reproducibility relative standard deviations (RSDR) were 0.327 and 0.370%, respectively. Mean repeatability values (r) and reproducibility values (R) were 0.054 and 0.118 for the direct forced air oven method and 0.049 and 0.133 for the modified AOAC method, respectively. The mean test result of the direct forced air oven method (12.7293%) was comparable to that for the modified AOAC method (12.7273%). The modification of AOAC method 16.032 and the direct forced air oven method have been approved interim official first action.