- Email: [email protected]
Determination of Body Fat and Moisture in Dwarf Hens with Near-Infrared Reflectance Spectroscopy
Determination of Body Fat and Moisture in Dwarf Hens with Near-Infrared Reflectance Spectroscopy J. A. RENDEN 1 , S. S. OATES 1 , and R. B. REED 2 Alabama Agricultural Experiment Station3, Auburn, University, Alabama 36849 (Received for publication October 21, 1985)
1986 Poultry Science 65:1539-1541 INTRODUCTION
Carcass compostion, particularly fat, is an important criterion in genetic (Hocking et al., 1985), nutritional (Morrison and Leeson, 1978), and physiological (Bornstein et al, 1984) studies of poultry. Standard methods for determination of total body fat and moisture are time consuming, and rapid procedures would be very desirable. Near-infrared (NIR) reflectance (R) spectrophotometric techniques have been developed to measure oil and moisture content in grains, forages, and meats such as beef, pork, and lamb (reviewed by Lanza, 1983). The objective of this study was to determine the accuracy of NIR techniques for measuring total fat and moisture in chicken carcasses. MATERIALS AND METHODS
Sexually mature hens from dwarf Single Comb White Leghorn stocks were maintained in individual cages and received feed (16% protein and 2816 kcal/kg metabolizable energy) and water ad libitum. Ninety-nine birds were randomly selected for percent fat and moisture 1
Dept. Poultry Science. Dept. of Research Data Analysis. 'Alabama Agricultural Experiment Station No. 12-85910. 4 Hobart Manufacturing Co., Troy, OH. s Dickey-John Corp., Auburn, IL. 6 Ivan Sorvall, Inc., Norwalk, CT. 2
carcass evaluation. The birds were killed by cervical dislocation, scalded (63 C), and defeathered. Carcasses including viscera, were individually stored in airtight containers at —15 C for no more than 5 months. The frozen carcasses were cut into smaller sections with a Hobart 1 band saw and ground sequentially through plate openings of 6.4 and 3.2 mm (Hobart 4 Model 4046). Two representative samples (100 g each)/bird were placed in individual airtight containers at —15 C for later analysis. The grinder was steam cleaned between carcasses. One sample from each bird was analyzed in duplicate for percent fat and moisture (wetweight basis) according to standard Association of Official Analytical Chemists techniques (1980). The other sample from each bird was prepared for scanning in a NIR R spectrophotometer (Dickey-John 5 Instalab Model 810) by the following procedures. Frozen samples were allowed to thaw at room temperature (22.2 C) and were homogenized in a Sorvall6 Omni-Mixer. The homogenized samples were then refrigerated in airtight containers at 1.1 C for approximately 18 hr. Five to 10 g of the refrigerated sample were placed into a DickeyJohn open sample cup, and readings (expressed as log 1/R) were taken in the NIR spectrophotometer at 10 wavelengths (1.445, 1.680, 1.715, 1.940, 2.100, 2.139, 2.180, 2.230, and 2.310 /um) within 10 sec. The cup was cleaned and dried, and a duplicate sample was measured.
1539
Downloaded from http://ps.oxfordjournals.org/ at Kungl Tekniska Hogskolan Biblioteket on July 19, 2015
ABSTRACT The accuracy of near-infrared (NIR) reflectance (R) spectroscopy for predicting whole-carcass percent fat and moisture in mature dwarf hens was established. Fat and moisture contents of ground samples from 99 hens were determined by standard fat extraction and oven-drying procedures. Homogenized samples from the same birds were also scanned in a NIR spectrophotometer (1.445 to 2.310 Mm), and the log 1/R was recorded. Calibration equations were generated with 49 randomly selected samples, and the coefficients of determination between chemical values for fat and moisture and reflectance readings were .92 and .94, respectively. The caliration equations were then used to predict fat and moisture values in the remaining 50 samples. Coefficients of determination between the predicted fat and moisture values and actual chemical values were .96 and .95, respectively. Near-infrared reflectance spectroscopy was found to be a rapid and effective method for determining percent body fat and moisture. (Key words: near-infrared spectroscopy, body composition, chickens)
RENDEN ET AL.
1540
TABLE 1. Percent body fat and moisture of dwarf hens determined by standard chemical analysis Fat
Moisture
Group
X1
SD
N
r'
X
SD
N
Calibration Prediction
17.17 16.17
4.58 4.40
49 50
.995 .998
62.78 63.92
4.09 4.03
49 50
1
i
r
.996 .998
X = Mean, SD = standard deviation, N = sample size, r' = repeatability.
with the SAS Varcomp procedure. Validations (R 2 , SE, and CV) of the calibration equations were performed by the Reg procedure of SAS within the prediction group by regressing chemically determined fat and moisture values on fat and moisture estimates predicted by the calibration equations.
RESULTS AND DISCUSSION
Percent body fat and moisture levels of dwarf hens in the calibration and prediction groups are shown in Table 1. Repeatability values were greater than .99 for both composition parameters and for both the calibration and prediction groups. Mean percent fat and moisture were very similar for the two groups. Five wavelengths (1.445, 1.680, 1.722, 2.110, and 2.139 M m ) w e r e used to generate a
TABLE 2. Partial regression coefficient estimates and standard errors of estimates (SE) of calibration equations for percent fat and moisture Moisture
Fat X1
Estimate
SE
1.445 1.680 1.715 1.722 1.940 2.100 2.139 Intercept
127 273
.038** .034***
410
r\-t H * * *
276 306 23 898
074*** .068 4.371
Estimate
SE
(%)
'Wavelengths (Mm) at which reflectance was measured. *P<.05. **P<.01. ***P<.001.
.165 .099 -.129 -.205 -.075 .127 55.147
.033*** .041* .052* .035*** .014*** .033*** 3.721
Downloaded from http://ps.oxfordjournals.org/ at Kungl Tekniska Hogskolan Biblioteket on July 19, 2015
The 99 samples were randomly assigned to either a calibration (N = 49) or prediction group (N = 50). Samples in the calibration group were used to generate multiple-linearregression (calibration) equations for predicting percent fat and moisture. Samples in the prediction group were used to verify the calibration equations. Optimum wavelengths and calibration equations were selected by regressing chemically determined percent fat or moisture values on log 1/R readings with the stepwise regression procedure of SAS (SAS, 1982). Partial regression coefficient estimates, standard error of the estimates (SE), coefficients of determination (R 2 ), and coefficients of variation (CV) for the calibrated equations were obtained with the General Linear Models procedure of SAS. Variance components for repeatability estimates (r ) were calculated
FAT AND MOISTURE IN DWARF HENS
1541
TABLE 3. Coefficients of determination (R'1), standard errors of the estimate (SE), and coefficients of variation (CV) for calibration and prediction equations1 Fat
Moisture
Group
R2
SE
CV
Calibration Prediction
.92 .96
1.29 .92
7.52 5.69
R2
SE
CV
.94 .95
1.01 .95
1.61 1.48
(%)
1
All R 2 values were significant at P<,0001.
The calibration equations accurately predicted percent fat and moisture values in the prediction group. Regression of the predicted composition values on the actual composition values exhibited high coefficients of determination for fat and moisture (.96 and .95, respectively) and low standard errors and coefficients of variation for both composition variables (Table 3). High coefficients of determination and small standard errors for predicted and actual chemical fat and moisture values have been found in ground beef, pork, and lamb (Bjarno, 1981; Kruggell et al, 1981; Lanza, 1983) and for body composition in mice (Eisenef «/., 1984). The results of this study demonstrate that NIR spectroscopy is an effective alternative to standard chemical analyses of whole body fat and moisture content in chickens. Expensive and hazardous chemicals and procedural difficulties associated with fat extraction are eliminated with the NIR technique. It is important, however, to note that the calibration equations are dependent upon sample compositions, wavelengths, and instrument type. Both
temperature and particle size of the sample can influence the accuracy of the calibration equations (Whetsel, 1968). The predictive value of NIR spectroscopy on homogenized meat is greater than on ground meat (Kruggell et al., 1981). Near-infrared analysis should be useful for composition determination of deboned and further processed poultry meats.
REFERENCES Association of Official Analytical Chemists, 1980. Official Methods of Analysis. Assoc. Offic. Anal. Chem., 13th ed., Arlington, VA. Bjarno, O., 1981. Multicomponent analysis of meat products. J. Assoc. Off. Anal. Chem. 6 4 : 1 3 9 2 1396. Bornstein, S., I. Plavnik, and Y. Lev, 1984. Body weight and/or fatness as potential determinants of the onset of egg production in broiler breeder hens. Br. Poult. Sci. 2 5 : 3 2 3 - 3 4 1 . Eisen, E. J., T. R. Bandy, W. F. McClure, and G. Horstgen-Schwark, 1984. Estimating body composition in mice by near-infrared spectrophotometry. J. Anim. Sci. 58:1181-1190. Hocking, P. M., J. S. Gavora, J. R. Chambers, and A. Fortin, 1985. Genetic variation in body size, composition, temperature, and feed intake in mature chickens. Poultry Sci. 64:6—28. Kruggell, W. C , R. A. Field, M. L. Riley, H. D. Radloff, and K. M. Horton, 1981. Near-infrared reflectance determination of fat, protein, and moisture in fresh meat. J. Assoc. Off. Anal. Chem. 64:692-696. Lanza, E., 1983. Determination of moisture, protein, fat, and calories in raw pork and beef by near infrared spectroscopy. J. Food Sci. 48:471-474. Morrison, W. D., and S. Leeson, 1978. Relationship of feed efficiency to carcass composition and metabolic rate in laying birds. Poultry Sci. 57:735-739. SAS Institute Inc., SAS User's Guide: Statistics. 1982 ed. Cary, NC. Whetsel, K. B., 1968. Near-infrared spectrophotometry. Appl. Spectrosc. 2:1—67.
Downloaded from http://ps.oxfordjournals.org/ at Kungl Tekniska Hogskolan Biblioteket on July 19, 2015
calibration equation for percent fat, and six wavelengths (1.45, 1.680, 1.715, 1.722, 1.940, and 2.100 ;um) were used for percent moisture (Table 2). The standard errors of the partial regression coefficient estimates were small and significant for both fat and moisture. Coefficients of determination were high for the fat and moisture calibration equations (.92 and .94, respectively), and the standard errors of the estimate and coefficients of variation were small (Table 3). This indicates that there were good fits between the chemical values for fat and moisture and reflectance readings from the NIR spectrophotometer.
1986 Poultry Science 65:1539-1541 INTRODUCTION
Carcass compostion, particularly fat, is an important criterion in genetic (Hocking et al., 1985), nutritional (Morrison and Leeson, 1978), and physiological (Bornstein et al, 1984) studies of poultry. Standard methods for determination of total body fat and moisture are time consuming, and rapid procedures would be very desirable. Near-infrared (NIR) reflectance (R) spectrophotometric techniques have been developed to measure oil and moisture content in grains, forages, and meats such as beef, pork, and lamb (reviewed by Lanza, 1983). The objective of this study was to determine the accuracy of NIR techniques for measuring total fat and moisture in chicken carcasses. MATERIALS AND METHODS
Sexually mature hens from dwarf Single Comb White Leghorn stocks were maintained in individual cages and received feed (16% protein and 2816 kcal/kg metabolizable energy) and water ad libitum. Ninety-nine birds were randomly selected for percent fat and moisture 1
Dept. Poultry Science. Dept. of Research Data Analysis. 'Alabama Agricultural Experiment Station No. 12-85910. 4 Hobart Manufacturing Co., Troy, OH. s Dickey-John Corp., Auburn, IL. 6 Ivan Sorvall, Inc., Norwalk, CT. 2
carcass evaluation. The birds were killed by cervical dislocation, scalded (63 C), and defeathered. Carcasses including viscera, were individually stored in airtight containers at —15 C for no more than 5 months. The frozen carcasses were cut into smaller sections with a Hobart 1 band saw and ground sequentially through plate openings of 6.4 and 3.2 mm (Hobart 4 Model 4046). Two representative samples (100 g each)/bird were placed in individual airtight containers at —15 C for later analysis. The grinder was steam cleaned between carcasses. One sample from each bird was analyzed in duplicate for percent fat and moisture (wetweight basis) according to standard Association of Official Analytical Chemists techniques (1980). The other sample from each bird was prepared for scanning in a NIR R spectrophotometer (Dickey-John 5 Instalab Model 810) by the following procedures. Frozen samples were allowed to thaw at room temperature (22.2 C) and were homogenized in a Sorvall6 Omni-Mixer. The homogenized samples were then refrigerated in airtight containers at 1.1 C for approximately 18 hr. Five to 10 g of the refrigerated sample were placed into a DickeyJohn open sample cup, and readings (expressed as log 1/R) were taken in the NIR spectrophotometer at 10 wavelengths (1.445, 1.680, 1.715, 1.940, 2.100, 2.139, 2.180, 2.230, and 2.310 /um) within 10 sec. The cup was cleaned and dried, and a duplicate sample was measured.
1539
Downloaded from http://ps.oxfordjournals.org/ at Kungl Tekniska Hogskolan Biblioteket on July 19, 2015
ABSTRACT The accuracy of near-infrared (NIR) reflectance (R) spectroscopy for predicting whole-carcass percent fat and moisture in mature dwarf hens was established. Fat and moisture contents of ground samples from 99 hens were determined by standard fat extraction and oven-drying procedures. Homogenized samples from the same birds were also scanned in a NIR spectrophotometer (1.445 to 2.310 Mm), and the log 1/R was recorded. Calibration equations were generated with 49 randomly selected samples, and the coefficients of determination between chemical values for fat and moisture and reflectance readings were .92 and .94, respectively. The caliration equations were then used to predict fat and moisture values in the remaining 50 samples. Coefficients of determination between the predicted fat and moisture values and actual chemical values were .96 and .95, respectively. Near-infrared reflectance spectroscopy was found to be a rapid and effective method for determining percent body fat and moisture. (Key words: near-infrared spectroscopy, body composition, chickens)
RENDEN ET AL.
1540
TABLE 1. Percent body fat and moisture of dwarf hens determined by standard chemical analysis Fat
Moisture
Group
X1
SD
N
r'
X
SD
N
Calibration Prediction
17.17 16.17
4.58 4.40
49 50
.995 .998
62.78 63.92
4.09 4.03
49 50
1
i
r
.996 .998
X = Mean, SD = standard deviation, N = sample size, r' = repeatability.
with the SAS Varcomp procedure. Validations (R 2 , SE, and CV) of the calibration equations were performed by the Reg procedure of SAS within the prediction group by regressing chemically determined fat and moisture values on fat and moisture estimates predicted by the calibration equations.
RESULTS AND DISCUSSION
Percent body fat and moisture levels of dwarf hens in the calibration and prediction groups are shown in Table 1. Repeatability values were greater than .99 for both composition parameters and for both the calibration and prediction groups. Mean percent fat and moisture were very similar for the two groups. Five wavelengths (1.445, 1.680, 1.722, 2.110, and 2.139 M m ) w e r e used to generate a
TABLE 2. Partial regression coefficient estimates and standard errors of estimates (SE) of calibration equations for percent fat and moisture Moisture
Fat X1
Estimate
SE
1.445 1.680 1.715 1.722 1.940 2.100 2.139 Intercept
127 273
.038** .034***
410
r\-t H * * *
276 306 23 898
074*** .068 4.371
Estimate
SE
(%)
'Wavelengths (Mm) at which reflectance was measured. *P<.05. **P<.01. ***P<.001.
.165 .099 -.129 -.205 -.075 .127 55.147
.033*** .041* .052* .035*** .014*** .033*** 3.721
Downloaded from http://ps.oxfordjournals.org/ at Kungl Tekniska Hogskolan Biblioteket on July 19, 2015
The 99 samples were randomly assigned to either a calibration (N = 49) or prediction group (N = 50). Samples in the calibration group were used to generate multiple-linearregression (calibration) equations for predicting percent fat and moisture. Samples in the prediction group were used to verify the calibration equations. Optimum wavelengths and calibration equations were selected by regressing chemically determined percent fat or moisture values on log 1/R readings with the stepwise regression procedure of SAS (SAS, 1982). Partial regression coefficient estimates, standard error of the estimates (SE), coefficients of determination (R 2 ), and coefficients of variation (CV) for the calibrated equations were obtained with the General Linear Models procedure of SAS. Variance components for repeatability estimates (r ) were calculated
FAT AND MOISTURE IN DWARF HENS
1541
TABLE 3. Coefficients of determination (R'1), standard errors of the estimate (SE), and coefficients of variation (CV) for calibration and prediction equations1 Fat
Moisture
Group
R2
SE
CV
Calibration Prediction
.92 .96
1.29 .92
7.52 5.69
R2
SE
CV
.94 .95
1.01 .95
1.61 1.48
(%)
1
All R 2 values were significant at P<,0001.
The calibration equations accurately predicted percent fat and moisture values in the prediction group. Regression of the predicted composition values on the actual composition values exhibited high coefficients of determination for fat and moisture (.96 and .95, respectively) and low standard errors and coefficients of variation for both composition variables (Table 3). High coefficients of determination and small standard errors for predicted and actual chemical fat and moisture values have been found in ground beef, pork, and lamb (Bjarno, 1981; Kruggell et al, 1981; Lanza, 1983) and for body composition in mice (Eisenef «/., 1984). The results of this study demonstrate that NIR spectroscopy is an effective alternative to standard chemical analyses of whole body fat and moisture content in chickens. Expensive and hazardous chemicals and procedural difficulties associated with fat extraction are eliminated with the NIR technique. It is important, however, to note that the calibration equations are dependent upon sample compositions, wavelengths, and instrument type. Both
temperature and particle size of the sample can influence the accuracy of the calibration equations (Whetsel, 1968). The predictive value of NIR spectroscopy on homogenized meat is greater than on ground meat (Kruggell et al., 1981). Near-infrared analysis should be useful for composition determination of deboned and further processed poultry meats.
REFERENCES Association of Official Analytical Chemists, 1980. Official Methods of Analysis. Assoc. Offic. Anal. Chem., 13th ed., Arlington, VA. Bjarno, O., 1981. Multicomponent analysis of meat products. J. Assoc. Off. Anal. Chem. 6 4 : 1 3 9 2 1396. Bornstein, S., I. Plavnik, and Y. Lev, 1984. Body weight and/or fatness as potential determinants of the onset of egg production in broiler breeder hens. Br. Poult. Sci. 2 5 : 3 2 3 - 3 4 1 . Eisen, E. J., T. R. Bandy, W. F. McClure, and G. Horstgen-Schwark, 1984. Estimating body composition in mice by near-infrared spectrophotometry. J. Anim. Sci. 58:1181-1190. Hocking, P. M., J. S. Gavora, J. R. Chambers, and A. Fortin, 1985. Genetic variation in body size, composition, temperature, and feed intake in mature chickens. Poultry Sci. 64:6—28. Kruggell, W. C , R. A. Field, M. L. Riley, H. D. Radloff, and K. M. Horton, 1981. Near-infrared reflectance determination of fat, protein, and moisture in fresh meat. J. Assoc. Off. Anal. Chem. 64:692-696. Lanza, E., 1983. Determination of moisture, protein, fat, and calories in raw pork and beef by near infrared spectroscopy. J. Food Sci. 48:471-474. Morrison, W. D., and S. Leeson, 1978. Relationship of feed efficiency to carcass composition and metabolic rate in laying birds. Poultry Sci. 57:735-739. SAS Institute Inc., SAS User's Guide: Statistics. 1982 ed. Cary, NC. Whetsel, K. B., 1968. Near-infrared spectrophotometry. Appl. Spectrosc. 2:1—67.
Downloaded from http://ps.oxfordjournals.org/ at Kungl Tekniska Hogskolan Biblioteket on July 19, 2015
calibration equation for percent fat, and six wavelengths (1.45, 1.680, 1.715, 1.722, 1.940, and 2.100 ;um) were used for percent moisture (Table 2). The standard errors of the partial regression coefficient estimates were small and significant for both fat and moisture. Coefficients of determination were high for the fat and moisture calibration equations (.92 and .94, respectively), and the standard errors of the estimate and coefficients of variation were small (Table 3). This indicates that there were good fits between the chemical values for fat and moisture and reflectance readings from the NIR spectrophotometer.