Infrared Spectroscopy as a Tool for Assessing Fat Quality

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1 2002 Poultry Science Association, Inc. Infrared Spectroscopy as a Tool for Assessing Fat Quality T. A. van Kempen*,1 and S. McComas *Department of Animal Science, North Carolina State University, Raleigh, NC 27695; and Novus International, Inc., St. Charles, MO Primary Audience: Nutritionists, Quality Control Personnel SUMMARY Feed-grade fats are an important energy source in animal diets. However, their quality can vary greatly, affecting their value. A convenient method for routine quality control of such fats is infrared spectroscopy. By using field samples with known composition, excellent calibrations for free fatty acids and iodine value were obtained, especially using mid-infrared spectroscopy. For moisture, unsaponifiables, and energy, the quality of the calibration was limited by the quality of the reference method; with a better reference method, acceptable calibrations can be developed as well. No acceptable calibration was obtained for oxidative stability as measured with the peroxide method and the active oxygen method. In conclusion, infrared spectroscopy offers a rapid method for limited quality control of fat samples. Key words: Ethoxyquin, fat quality, Fourier transform infrared spectroscopy, infrared, nearinfrared spectroscopy, Santoquin 2002 J. Appl. Poult. Res. 11: DESCRIPTION OF PROBLEM Fats are common ingredients in animal diets. They are a low cost source of energy that aid in increasing the energy density of the diet and improving pellet quality. These fats are derived from a variety of sources: rendered animal fat, restaurant grease, and vegetable oils and are often blended to generate products with characteristics that are considered desirable by the feed industry. As reviewed by Calabotta and Shermer [1], the quality of these fat sources can have a profound impact on the quality of feed into which they are blended. Good quality fat can improve the palatability of a feed, but poor quality fat can greatly reduce palatability. Also, fat oxidation destroys pigments, vitamins, and essential fatty acids, leading to vitamin deficiency symptoms such as encephalomalacia and steatites. Oxidized fats may also be incorporated in animal products negatively affecting their quality, including flavor, shelf life, and processing ease. Given that consumers are increasingly healthconscious, such effects are undesirable. Assessing the quality of fats is thus of prime importance to the feed industry. Although a myriad of methods exist, many of them are costly or time-consuming, limiting their practicality for the feed industry. A tool that is very practical for quality control is infrared spectroscopy [2]. 1 To whom correspondence should be addressed: t_vankempen@ncsu.edu.

2 192 With this method, infrared light absorption of a sample is measured. This light absorption is a function of the organic composition of the sample and allows for measurement or prediction of composition. However, the suitability of infrared spectroscopy for quality parameters in feedgrade fat has received limited attention. The objective of this research was to evaluate the quality of feed-grade fats and to determine if nearand mid-infrared spectroscopy could be used as a method for assessing the quality of fat in the feed industry. MATERIALS AND METHODS Samples Forty-two fat samples were obtained (of which one was deleted as an outlier; see results). These samples were field samples of feed-grade fats as collected by three feed companies from 17 different suppliers across the US. Samples varied in presentation from liquid to solid at room temperature and in color from light amber to black. Chemical Analyses of the Samples For the chemical analyses of free fatty acid content, initial peroxide value, iodine value, unsaponifiables, and insolubles, two aliquots were taken from each sample and sent to a commercial laboratory where a blind duplicate analysis was obtained. These data were used to determine reproducibility of the assay. Peroxide values for the active oxygen method at 4 and 20 h (referred to as active oxygen at 4 and 20 h) and ethoxyquin (Santoquin) were assayed only once. The assay errors for these parameters were reported by the assay laboratory. Gross energy was determined at North Carolina State University using a bomb calorimeter [3]. Mean values, coefficients of variation (CV), and minima and maxima for the different parameters as obtained in this sample set are provided in Table 1. For a description of the different parameters, see Stauffer [4]. JAPR: Research Report Initial Peroxide Value and Active Oxygen Research by Dong et al. [5] suggested that the initial peroxide value is difficult to calibrate; thus, fat samples (1 ml) were mixed with 40 µl of a 40% (wt/wt) triphenyl phosphate/chloroform solution and were rescanned on a midinfrared spectrometer [6]. Triphenyl phosphate reacts stoichiometrically with hydroperoxides to form triphenyl phosphine oxide, which can be monitored with near- and mid-infrared. Sample Preparation As several of the samples were solid at room temperature, which hindered analysis by the available setup, all samples were first preheated to 65 C in a thermal block. At this temperature, all samples were liquid. Near-Infrared Analysis For the analysis in the near-infrared region, samples were assayed with a near-infrared spectrometer equipped with a detector for transmission [7]. Samples were scanned in a heated transmission cell for liquids with a 1-mm path length. Scans (32 per sample) were collected from 400 to 2,500 nm at a resolution of 2 nm (see Figure 1 for example spectra). Mid-Infrared Analysis For the analysis in the mid-infrared region, samples were assayed by a mid-infrared spectrophotometer [6]. Samples were presented on a horizontal, attenuated, total reflectance (HATR) device, fitted with a 40 zinc selenide trough window [8]. Scans (80 per sample) were obtained from 4,000 cm 1 to 650 cm 1 with a resolution of 2 cm 1 (see Figure 1 for example spectra). Wave numbers (cm 1 ), the customary unit for mid-infrared as they are proportional to energy, can be converted to wavelengths in nanometers by dividing 10,000,000 by the wave number. The above wave number range thus corresponds to 2,500 to 15,385 nm. Data Analysis Relationships between quality parameters as observed in this sample set were investigated by a correlation analysis and analysis of variance with SPSS software [9], whereas sample statistics were calculated (after deletion of outliers, see results) with Excel software [10]. Infrared calibrations (mathematical formulas that can predict the sample composition based on spectral information) were developed by using the Unscrambler. For these calibrations, near-infrared data were first smoothed and then

3 VAN KEMPEN AND MCCOMAS: INFRARED AND FAT QUALITY 193 TABLE 1. Composition of the fat samples as assayed Item Average CV (%) Minimum Maximum Initial peroxide value (meq/kg) Free fatty acids (%) Moisture (%) Insolubles (%) Iodine value (g I 2 /100 g) A Unsaponifiables (%) Gross energy (kcal/g) Ethoxyquin B (ppm) Active oxygen, 4h(mEq/kg) Active oxygen, 20 h (meq/kg) A Grams of iodine (I 2 ) that react with the double bonds in 100 g of fat. B Only 13 samples yielded detectable ethoxyquin values (>20 ppm); data are for these 13 samples. derivatized using the second derivative, both using the Savitzky-Golay routine [11], similar to a 2,5,5,1 data pretreatment [12] in ISI software [13]. Mid-infrared data were reduced to a resolution of 4 cm 1 (to speed data analyses). No attempts were made to further optimize the calibrations with additional mathematical pretreatments of the data. Calibrations were developed with partial least-squares regression and full cross-validation, in which one sample is deleted from the database, a calibration is developed using the remaining samples, and the deleted sample is predicted with this calibration. This procedure is repeated such that all samples in the dataset are predicted once by using calibrations based on all remaining samples. This method thus allows for the determination of an accurate prediction error [11]. RESULTS AND DISCUSSION Correlation Analysis for Quality Parameters Reasonably strong correlations (r 0.60) were observed among free fatty acids and moisture, iodine values, and unsaponifiables (Table 2). Active oxygen at 4 and 20 h were positively correlated (r = 0.58, P < 0.05). The initial peroxide value was positively correlated with active oxygen at 4 h (but not with active oxygen at 20 h) and was negatively correlated with unsaponifiables, iodine value, and ethoxyquin (P < 0.05), but these correlations were not strong (r < 0.60). Effect of Ethoxyquin on Sample Composition and Stability The presence of detectable amounts of ethoxyquin (>20 ppm) in the samples had a profound effect on sample composition. Figure 2 shows the results of a statistical analysis of the data after separating the samples into two discrete groups: those with and those without measurable amounts of ethoxyquin (because only 13 samples had measurable levels of ethoxyquin, no further stratification was applied). Samples without detectable ethoxyquin had a statistically lower free fatty acid content and iodine value but a poorer oxidative stability as measured by the active oxygen method and initial peroxide value (P < 0.05). Repeatability of the Laboratory Assays The quality of a calibration is mathematically limited to the quality of the reference method. Thus, if the reference method has a measurement error of, e.g., 1, the infrared calibration will have a prediction error that is at least 1. A general principle is that prediction errors 1.5 to 2 times the error of the reference method are excellent. Similarly, the r 2 of the reference method is always higher than the r 2 of the calibration. However, as the r 2 is a quadratic parameter (1-prediction error 2 /standard error 2 ), a reasonable r 2 for the reference method, e.g., 0.80, limits the r 2 for the calibration from 0.55 to 0.20 for a prediction error of 1.5 to 2 times that of the reference method, thus yielding a mediocre-to-unacceptable calibration. The measurement errors obtained or estimated are listed in Table 3. For initial peroxide values, free fatty acids, iodine values, ethoxyquin, and active oxygen method, the repeatability of the laboratory method was excellent, relative to the range of values observed for those parameters. This repeatability means that the ref-

4 194 JAPR: Research Report FIGURE 1. Near- and mid-infrared spectra of two extreme samples and regression coefficients for each region for predicting free fatty acids and iodine value. Top: Infrared spectra (left: mid-infrared, right: near-infrared) of a fat sample with very high free fatty acid content and a high iodine value (Sample 2) and a fat sample with a very low free fatty acid content and a low iodine value (Sample 6). Center: regression coefficients for free fatty acids (left: mid-infrared, right: near-infrared). Lower: regression coefficients for iodine value (left: mid-infrared, right: near-infrared). erence method introduced little relevant error in the measurements. For moisture, insolubles, unsaponifiables, and energy, the repeatability of the lab method was such that it prevented development of a good calibration. Outliers With mid-infrared, several samples yielded totally absorbing peaks. These peaks occurred around 1,460, 1,750, 2,850, and 2,920 cm 1. Before developing calibrations, these regions were deleted; otherwise, these samples were marked as outliers. This problem could be easily prevented through the use of a crystal with a slightly smaller angle or a shorter crystal, as this reduces the effective path length. One sample failed to yield light transmission in the near-infrared spectrum. In mid-infrared, the sample could be analyzed, but it was marked as an outlier for virtually every parameter evalu-

5 VAN KEMPEN AND MCCOMAS: INFRARED AND FAT QUALITY 195 TABLE 2. Pearson correlation coefficients among the quality parameters as observed for 41 fat samples Initial Free Active Active peroxide fatty Iodine oxygen, oxygen, Gross Item value Moisture Insolubles Unsaponifiables acids value 4 h 20 h Ethoxyquin energy Initial peroxide value ** 0.31* 0.34** 0.50** 0.27* 0.41** 0.18 Moisture ** 0.67** 0.48** 0.33** * Insolubles Unsaponifiables 0.40** 0.42** ** 0.28* ** ** Free fatty acids 0.31* 0.67** ** ** 0.26* 0.42** 0.33** 0.52** Iodine value 0.34** 0.48** * 0.60** Active oxygen, 4 h 0.50** 0.33** * ** 0.30* 0.35 * Active oxygen, 20 h 0.27* ** 0.42** ** ** Ethoxyquin 0.41** ** * Gross energy * ** 0.52** * 0.52** *P < **P < ated. This sample was, therefore, deleted prior to the final analysis. This sample had the highest analyzed value for unsaponifiables, moisture, insolubles, and initial peroxide value, suggesting it was restaurant grease of extremely poor quality. Initial Peroxide Value Calibration The initial peroxide value was measured with excellent repeatability (Table 3). For nearand mid-infrared region, however, the calibrations obtained were unacceptable, as less than 10% of the variation in initial peroxide value was explained. Sedman et al. [14] reported a successful calibration for hydroperoxides by using mid-infrared; however, they pointed out that it was critical that the experimental conditions including temperature were tightly controlled. Sedman et al. also monitored the progress of lipid oxidation of fat samples placed on an HATR crystal heated to 65 C and noticed spectral changes due to peroxidation over time, suggesting that the assay conditions themselves promoted peroxidation. These findings suggest that the stability of the hydroperoxides and the sample under the assay conditions are responsible for the inability to calibrate for these compounds under the test conditions used in this experiment. Dong et al. [5] were able to develop a nearinfrared calibration for initial peroxide value but concluded that this calibration was not suitable for practical use. Therefore, they developed a procedure that relied on addition of triphenyl phosphine to the fat sample prior to scanning the sample. Triphenyl phosphine reacts with hydroperoxides to form triphenyl phosphine oxide, which interacts strongly with infrared light, both in the mid- and near-infrared region. This procedure was tested with the available samples as well, but no suitable calibration was obtained, which might have occurred because the temperature of the sample was not as accurately controlled as prescribed by Dong et al. [5]. More likely, however, the peroxide content of the sample might have changed due to repeated heating during shipping to the point that the peroxide content had changed, in line with the observations of Sedman et al. [14]. Dong et al. [5] measured the initial peroxide value at the time of the sample assay, whereas in the current experiment samples were assayed, then shipped at room tem-

6 196 JAPR: Research Report FIGURE 2. Difference in sample characteristics for samples with detectable levels of ethoxyquin (n = 13) and samples without detectable levels of ethoxyquin (n = 28). Data are expressed relative to that observed in samples with measurable levels of ethoxyquin and are graphed using a log scale. Parameters marked with an asterisk were significantly different between groups (P < 0.05). AOM4 = active oxygen measured at 4 h; AOM20 = active oxygen measured at 20 h; FA = fatty acids; Unsapon. = unsaponifiables. perature, analyzed in mid- and near-infrared (on separate days), and then measured for peroxides with the procedure of Dong et al. [5]. Free Fatty Acids Calibration The repeatability of the free fatty acid assay was excellent (r 2 of 1.00), and an excellent midinfrared calibration was obtained for free fatty acids, explaining 99% of the variation. In nearinfrared, 89% of the variation was explained, which is very good. The near-infrared prediction error was nevertheless three times greater than the mid-infrared prediction error (Table 3). As free fatty acids were present in concentrations easily detectable by both methods (in the percentage range), the reason for the improved calibration in mid-infrared is likely that the free carboxy group of the free fatty acid is more easily recognized in mid-infrared (Figure 1). Moisture Calibration Moisture was measured with an error of 0.06% and an r 2 of The prediction errors obtained with near- and mid-infrared are roughly twice that (0.10 and 0.12%, respectively), suggesting that the quality of the calibrations was limited by the quality of the reference method. Nevertheless, these calibrations should be considered mediocre as only 65 and 55% of the variation were explained, respectively. For developing good quality infrared calibrations, a more repeatable method for measuring moisture is thus required. Van der Voort et al. [15] illustrated that moisture can be determined with good accuracy by infrared; they worked with mayonnaise samples and actually had a larger prediction error then what was obtained in the current study (0.30%). However, as the moisture content in mayonnaise is much higher than in feed-grade fats (which makes the measurement error less relevant), this calibration was able to explain a large portion of the variation in moisture content between samples. Insolubles Calibration The measurement error for the insolubles assay was 0.09% and the r 2 was The prediction errors for near- and mid-infrared measurements were 0.40 and 0.37 and the r 2 were 0.01 and 0.15, respectively. These data suggest that calibrating for insolubles is not practical for midor near-infrared. A reason for this inability to calibrate may be linked to the inhomogeneous nature of insolu-

7 VAN KEMPEN AND MCCOMAS: INFRARED AND FAT QUALITY 197 TABLE 3. Measurement error and calibration statistics for fat samples analyzed chemically and by using near- and mid-infrared spectroscopy Reference method Mid-infrared calibration Near-infrared calibration Parameter n A r 2 SEM B r 2 SEP C Parameters D SEP/SEM r 2 SEP Parameters SEP/SEM Mid/near E Initial peroxide value (meq/kg) Free fatty acids (%) Moisture (%) Insolubles (%) Iodine value (g I 2 /100 g) F Unsaponifiables (%) Gross energy (kcal/g) Ethoxyquin (ppm) G 8.86 G Active oxygen, 4 h (meq/kg) G 3.3 G Active oxygen, 20 h (meq/kg) G 11.0 G A Number of samples used for developing the calibration after removal of outliers. B Root mean squared error of measurement. C Root mean squared error of prediction. D Parameters included in the calibration. E Ratio of SEP obtained for the mid-infrared calibration divided by SEP obtained for the near-infrared calibration. F Grams of iodine (I2 ) that react with the double bonds in 100 g of fat. G Measurement error as reported by the assay laboratory and resulting r 2 value.

8 198 bles in fat samples. In the mid-infrared, only 1 µl of sample is truly scanned (the material in close proximity to the crystal), a volume that may be too small to obtain a representative sample of insolubles. Although a substantially larger quantity of sample is scanned in near-infrared, the near-infrared calibration was not more accurate than the mid-infrared calibration. Another factor may be that samples were assayed in the transmission mode; insolubles can be expected to scatter light independent of wavelength rather than absorb it in a wavelength-dependent matter. Iodine Value Calibration Iodine values were measured with excellent repeatability (measurement error of 0.60, r 2 of 1.00). The near- and mid-infrared calibrations were also excellent, with 92 and 98% of the variation explained, respectively. Interestingly, in mid-infrared, seven parameters were included in the calibration versus one for near-infrared, suggesting that unrelated changes in spectra were used to calculate the iodine value in midinfrared. Indeed, Figure 1 shows that trans and cis unsaturated bonds gave rise to different changes in mid-infrared spectra, thus requiring different parameters for detection (an official AOAC [16] method for measuring trans fatty acids is based on mid-infrared spectroscopy). In near-infrared, trans double bonds do not absorb infrared light [17], thus a simpler, less robust calibration is obtained with near-infrared. Unsaponifiables Calibration The sample set contained three samples that were considered outliers in near- and mid-infrared, and these samples were excluded from the calculations. The measurement error for unsaponifiables was substantial: 0.12 (%), resulting in an r 2 value of The prediction error obtained with near- and mid-infrared was only 1.4 times the measurement error, indicating that, as was the case for moisture, the quality of the calibration is limited by the ability to measure unsaponifiables. The actual calibrations had r 2 values of 0.71 and 0.68, of the variation, respectively. JAPR: Research Report Energy Calibration Variation in gross energy content between samples was low; the coefficient of variation was only 0.84%. Although the accuracy of the measurement was relatively good (0.036 kcal/ g, or 0.4%), this error limits the maximum obtainable r 2 to 0.79, as the variation is low. For mid-infrared, a prediction error of was obtained, and for near-infrared it was kcal/ g. These prediction errors are 1.6 and 1.7 times the error of the reference method, which is commendable. Nevertheless, the r 2 values obtained were only 0.41 and 0.37 for mid- and nearinfrared, respectively. These data suggest that an infrared calibration for energy is feasible, but that a better reference is required if a large portion of the variation in energy content in fat samples needs to be explained. Given that this variation is limited, the need for an improved method for estimating energy can be questioned. Ethoxyquin Calibration Ethoxyquin was only detected in 13 samples, and this number of samples is inadequate for developing a robust calibration. With near-infrared, no calibration for ethoxyquin could be obtained, in line with the rule of thumb that nearinfrared can only detect compounds present in the range of grams per kilogram; ethoxyquin averaged 0.1 g/kg and was, thus, 10-fold lower than is detectable. With mid-infrared, a poor quality calibration was obtained, which explained only 30% of the variation and that had a prediction error of 86 ppm. Although this r 2 is not acceptable for a calibration, these data should be considered encouraging, as they suggest that in mid-infrared ethoxyquin can be detected at 0.1 g/kg, but additional samples with a known ethoxyquin value are needed to establish the true potential for mid-infrared to predict ethoxyquin. Active Oxygen Method Calibration Although this method per se is not highly repeatable [18], given the large range in this data set the variation explained in this data set was still very good. Near- and mid-infrared calibrations for active oxygen at 4 h were, nevertheless, unacceptable (r 2 of 0.12 and 0.24, respectively),

9 VAN KEMPEN AND MCCOMAS: INFRARED AND FAT QUALITY 199 FIGURE 3. Predicted versus measured active oxygen (AOM) at 20 h using a calibration developed with all samples. These data suggest that two groups of samples exist: those that can be predicted with reasonable accuracy (those following the trend line; note that this line is biased because of the samples that can not be predicted) and those that had a good oxidative stability (low measured oxidative stability) but were predicted otherwise. whereas mid-infrared yielded a mediocre calibration for active oxygen at 20 h with an r 2 value of only Mixing triphenyl phosphine with the sample before scanning did not improve this calibration. Upon careful examination of the calibration results for active oxygen at 20 h in mid-infrared, it became apparent that two populations existed: those that had a very low active oxygen measurement, but that were predicted erroneously to have high or negative active oxygen, and those that were predicted with reasonable accuracy (see Figure 3). Removal of samples that were erroneously predicted yielded a calibration based on 32 samples with an r 2 of 0.82 and a prediction error of 54, if the scans were obtained after mixing the samples with triphenyl phosphine. For scans obtained on samples without triphenyl phosphine, the r 2 was 0.51 and the prediction error was 88, indicating that the triphenyl phosphine aided in recognizing oxidative stability. The results for active oxygen at 4 h had a similar split in the data set (data not shown). Calibrations could be developed with an r 2 of 0.62 for scans obtained with triphenyl phosphine and an r 2 of 0.47 for scans obtained on samples without triphenyl phosphine. Only 25 samples were used for this calibration, however. The reason a calibration could be developed for one portion of the samples but not for another is not clear. A logical explanation could have been that the samples that did not fit the calibration contained an anti-oxidant, but this explanation could not be confirmed based on the measured ethoxyquin data. Another plausible explanation may be that the samples that were erroneously predicted were much lower in metals such as copper and iron. These metals catalyze peroxidation, and their absence might have protected the sample [19]; however, metals are not detected in mid-infrared and metal information is thus not included in a calibration [20]. What is known about these samples is that they had extremes in iodine values. The samples that were predicted to have a negative active oxygen at 20 h had a very high iodine value (unprocessed vegetable oil), and those that were incorrectly predicted to have a high active oxygen at 20 h had a very low iodine value (tallow or hydrogenated fats). A better understanding of the relationship between iodine value and oxidative sta-

10 200 bility and more samples in those categories may well result in a usable calibration for all types of fat samples. Near- versus Mid-Infrared Near-infrared is commonly used in the feed industry, predominantly because it is easy to use with solid samples. Near-infrared spectroscopy only observes C-H, N-H, and O-H overtones. These overtones result in broad peaks that are difficult to quantify directly [20]. In Figure 1, two near-infrared spectra are provided. One from a sample (2) with a very high iodine value (114.3 g I 2 /100 g) and free fatty acid content (65.6%) and one from a sample (6) with a very low iodine value (47 g I 2 /100 g) and free fatty acid content (1.4%). A major difference in absorption is observed in the visible region of the spectrum (400 to 700 nm), indicative of a difference in color between the two samples. Besides that, some shifts in the spectra can be observed, noticeably between 1,800 and 2,300 nm and above 2,400 nm. Note that this near-infrared spectrometer uses separate detectors for 400 to 1,100 nm and for 1,100 to 2,500 nm, and at the transition point a sudden shift in absorption is observed due to differences in detector sensitivity. In Figure 1, the regression coefficients obtained for free fatty acids and iodine values are graphed. However, these regression coefficients are hard to link to functional absorption bands or to major changes in absorption (note that derivatized spectra were used for developing the calibration, which further clouds interpretation). Nearinfrared data are thus typically analyzed with advanced statistical procedures because they are not suited for direct interpretation. Mid-infrared is commonly used in the food and chemical industry for analysis of fat samples. In the mid-infrared range, all organic bonds interact with infrared light, and primary absorption bands are observed. In Figure 1, two mid-infrared spectra are provided from the same samples as displayed in near-infrared. Differences in absorption with midinfrared can be interpreted more easily, and peak identifications have been provided for several of the important peaks. For example, the largest peak is from the C=O bond of the carboxy group of the fatty acids. For Sample 2 (high free fatty acids), this peak is split in two and partially shifted JAPR: Research Report to the left relative to Sample 6 (almost devoid of free fatty acids). Apparently, the peak observed for Sample 6 corresponds to C=O as part of the ester linkage between fatty acids and glycerol, whereas the shift observed in Sample 2 is from the free carboxy group. Concomitantly, the peak identified as corresponding to free fatty acids gets a strong positive coefficient, but the peak corresponding to fatty acids incorporated in triglycerides gets a strong negative coefficient. Note that a portion of this peak was deleted prior to making calibrations, as these peaks were totally absorbing in some of the samples, which explains the long straight line between the positive and the negative coefficient. The iodine value of a sample is determined by double bonds that can have a cis or trans configuration. In mid-infrared, these bonds each have specific infrared absorption regions as indicated in Figure 1. This figure shows that Sample 6 contains a mixture of trans and cis double bonds (hydrogenated), whereas Sample 2 contains predominantly cis double bonds. The regions identified as corresponding to these double bonds also had positive regression coefficients in the calibration for predicting iodine values, indicating that the mid-infrared calibration recognized the regions corresponding to double bonds. Few published reports have compared midto near-infrared. Reeves [21] found that nearand mid-infrared calibrations performed comparably for parameters that were easy to predict (high r 2 value), but for difficult to predict parameters, the variation explained was numerically higher with the mid-infrared region, suggesting that mid-infrared had an advantage. Our data show that mid-infrared yielded a noticeably more accurate calibration for free fatty acids. The prediction error was three times less than for near-infrared, even though a portion of the spectrum had been deleted prior to analysis, thus reducing the amount of information available. For iodine value, the prediction error was almost two times smaller. For ethoxyquin, the data suggest that possibly mid-infrared could be used for developing calibrations, although the quality of such a calibration cannot be predicted from the available data. For all other parameters, the prediction errors for near- and mid-infrared were close.

11 VAN KEMPEN AND MCCOMAS: INFRARED AND FAT QUALITY 201 CONCLUSIONS AND APPLICATIONS 1. Fat samples can be analyzed with minimal sample preparation in a near- or mid-infrared spectrometer. 2. Highly accurate infrared calibrations for free fatty acids and iodine value could be developed with near- and mid-infrared. 3. Calibrations for moisture, unsaponifiables, and energy are feasible as well, but these calibrations may be less effective as the accuracy of the reference methods (relative to variation observed) limits their accuracy. 4. Calibrations for ethoxyquin (Santoquin) may be possible in mid-infrared, but additional research is needed to establish the quality of such a calibration. 5. Mid-infrared proved slightly more powerful than near-infrared for analyzing fat samples. 6. Insolubles and initial peroxide values were not satisfactorily predicted with infrared spectroscopy. 7. Peroxide values for the active oxygen method at 4 and 20 h could be predicted with reasonable accuracy in a subset of the samples mixed with triphenyl phosphine. 1. Calabotta, D. F., and W. D. Shermer Controlling feed oxidation can be rewarding. Feedstuffs 57(Nov. 25):24, van Kempen, T. A Infrared technology in animal production. World s Poult. Sci. J. 57: a. Model C5000; IKA, Wilmington, NC. 4. Stauffer, C. E Fats and oils. American Association of Cereal Chemists, Inc., St. Paul, MN. 5. Dong, J., K. Ma, F. R. van de Voort, and A. A. Ismail Stoichiometric determination of hydroperoxides in oils by Fourier transform near-infrared spectroscopy. J. Assoc. Off. Anal. Chem. Int. 80: Magna 760 mid-infrared spectrometer; Nicolet, Madison, WI. 7. NIRSystems model 6500 near-infrared spectrometer; Foss NIRSystems, Silver Springs, MD. 8. Spectratech, Shelton, CT. 9. SPSS Inc., Chicago, IL. 10. Microsoft Corp., Redmond, WA. 11. Esbensen, K. H., S. Schönkopf, and T. Midtgaard Multivariate analysis in practice. Wennbergs Trykkeri AS, Trondheim, Norway. 12. van Kempen, T. A., and J.-C. Bodin Near-infrared reflectance spectroscopy (NIRS) appears to be superior to nitrogenbased regression as a rapid tool in predicting the poultry digestible amino acid content of commonly used feedstuffs. Anim. Feed Sci. Technol. 76: Infrasoft International, Port Matilda, PA. 14. Sedman, J., F. R. van de Voort, and A. A. Ismail Attenuated total reflectance spectroscopy: Principles and applications REFERENCES AND NOTES in infrared analysis of food. Pages in Spectral Methods in Food Analysis. M.M. Mossoba, ed. Marcel Dekker, New York. 15. Van de Voort, F. R., J. Sedman, and A. A. Ismail A rapid FTIR quality-control method for determining fat and moisture in high-fat products. Food Chem. 48: AOAC Official Methods of Analysis. 15th ed. Assoc. Off. Anal. Chem., Arlington, VA. 17. Cast, J Infrared spectroscopy of lipids. Pages in Developments in Oils and Fats. R.J. Hamilton, ed. Chapman and Hall, Glasgow, UK. 18. Shermer, W. D., and A. F. Giesen What do fat tests tell you? Feed Manage. (May): Berger, K. G., and R. J. Hamilton Lipids and oxygen: is rancidity avoidable in practice? Pages in Developments in Oils and Fats. R.J. Hamilton, ed. Chapman and Hall, Glasgow, UK. 20. Osborne, B. G., T. Fearn, and P. H. Hindle Practical NIR spectroscopy: With applications in food and beverage analysis. 2nd ed. Longman Singapore Publishers, Singapore. 21. Reeves, J. B., III Concatenation of near- and mid infrared spectra to improve calibrations for determining forage composition. J. Agric. Food Chem. 45: Acknowledgments We express our appreciation to the industry nutritionists and quality control personnel that collected the samples for this study, Fred McClure and Don Stanfield for allowing us to use their nearinfrared spectrometer, Novus International for funding the analysis of these samples, and Stephanie Wolford and Natalie Nash for scanning the samples.